Reviewerly/exHarmony huggingface dataset:
Machine Learning-Based Relative Performance Analysis for Breast Cancer Prediction

The current high population growth in medical research has made early disease identification an urgent issue. The danger of dying from breast cancer is increasing exponentially along with the rapid population expansion. The second most serious cancer among those that have already been identified is breast cancer. Breast cancer poses a major hazard to women, as it is highly morbid and lethal. Doctors find it challenging to develop a treatment strategy that could increase patient survival time due to the lack of reliable prognostic models. As a result, developing a technique that results in the fewest errors while increasing accuracy takes time. A reliable, effective, and prompt response is offered by an automatic illness detection system, which also helps medical workers identify diseases and reduces the likelihood of fatalities. In this work, we investigate eight machine learning techniques, including GaussianNB, Decision Tree, K-Nearest Neighbor, Random Forest, support vector machine (SVM), XGBoost, LightGBM, AdaBoost. The Wisconsin Breast Cancer dataset was discovered in the UCI machine learning database, a well-known machine learning database. The performance of the study is assessed with relation to accuracy, precision, recall, F1 score, and ROC score. Among the eight-machine learning model, Random Forest and AdaBoost perform best which provides 99.20% accuracy and around 99% ROC curve score.
VLSI Design and FPGA Implementation of an NTT Hardware Accelerator for Homomorphic SEAL-Embedded Library

Homomorphic Encryption (HE) allows performing specific algebraic computations on encrypted data without the need for decryption. For this reason, HE is emerging as a strong privacy-preserving solution in cloud computing environments since it allows to keep data secure even in the case the cloud server is not trusted. HE libraries such as Microsoft SEAL have been recently released; however, such libraries are not specifically designed for resource-constrained platforms and they are often expensive in terms of computational resources and memory consumption, which limits their usage in edge devices. This limitation is contained by the SEAL-Embedded library, the first C-based HE library specifically designed for embedded platforms. In this article, we propose a hardware accelerator specifically designed for the SEAL-Embedded library and its implementation of the CKKS scheme: the proposed hardware presents a configurable Number Theoretic Transform (NTT) unit for all the polynomial degrees available on the SEAL-Embedded, a memory architecture able to reduce the I/O latency and a dedicated module for the generation of roots of unity. A complete system that includes a 32-bit RISC-V processor has been implemented on a Xilinx ZCU106 FPGA board to test the functionality of the hardware accelerator and to measure performance improvements. The results showed a speed-up of around x1000 with the hardware acceleration respect to the pure software implementation of the SEAL-Embedded library for the symmetric encryption function.
Improved Sine Cosine Algorithm for Optimization Problems Based on Self-Adaptive Weight and Social Strategy

The Sine Cosine Algorithm (SCA) is a well-known optimization technique that utilizes sine and cosine functions to identify optimal solutions. Despite its popularity, the SCA has limitations in terms of low diversity, stagnation in local optima, and difficulty in achieving global optimization, particularly in complex large-scale problems. In response, we propose a novel approach named the Improved Weight and Strategy Sine Cosine Algorithm (IWSCA). The IWSCA achieves this by introducing novel self-adaptive weight and social strategies that enable the algorithm to efficiently search for optimal solutions in complex large-scale problems. The performance of the IWSCA is evaluated with 23 benchmark test functions and the IEEE CEC 2019 benchmark suite, compare it to a state-of-the-art heuristic algorithm and two improved versions of the SCA. Our experimental results demonstrate that the IWSCA outperforms existing methods in terms of convergence precision and robustness.
Shadow Detection on High-Resolution Digital Orthophoto Map Using Semantic Matching

Shadow detection and compensation on high-resolution orthophoto is one of the most important tasks for ensuring high-quality of the radiometric balance of digital orthophoto map (DOM). This paper proposes a novel shadow detection method through semantic matching between the artificial “shadow” polygons (ASPs) and the real shadowed polygons (RSPs). The ASPs are created by digital building model (DBM), solar zenith and solar azimuth. A group of polygons semantic features, such as position similarity, area similarity, direction similarity and shape similarity are described and used as matching parameters, and then the real shadow regions (polygons) (RSPs) are detected using two-level matchings between the ASPs and the RSPs. The initial of matching aims at obtaining the initial probability and the candidates of the matching pair through determining the initial search circle cantered at the ASPs. The final of matching aims at finding the final match pairs through iteration of semantic matching, of which the maximum probability, which is calculated by the support coefficient of the adjacent match pair, is adopted as the criterion of the iteration. The experimental area located in Denver, Colorado, USA is used to validate the proposed algorithm. When compared with the dual-threshold, the Least-Squares Support Vector Machine (LS-SVM), and the UNet shadow detection method, the method proposed in this paper is able to increase the rate of shadow detection by 38.98%, 17.33%, and 13.14%, and decrease the fault detection rate by 19.43%, 10.01%, and 4.57%.
A New Parallel Cuckoo Flower Search Algorithm for Training Multi-Layer Perceptron

This paper introduces a parallel meta-heuristic algorithm called Cuckoo Flower Search (CFS). This algorithm combines the Flower Pollination Algorithm (FPA) and Cuckoo Search (CS) to train Multi-Layer Perceptron (MLP) models. The algorithm is evaluated on standard benchmark problems and its competitiveness is demonstrated against other state-of-the-art algorithms. Multiple datasets are utilized to assess the performance of CFS for MLP training. The experimental results are compared with various algorithms such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Evolutionary Search (ES), Ant Colony Optimization (ACO), and Population-based Incremental Learning (PBIL). Statistical tests are conducted to validate the superiority of the CFS algorithm in finding global optimum solutions. The results indicate that CFS achieves significantly better outcomes with a higher convergence rate when compared to the other algorithms tested. This highlights the effectiveness of CFS in solving MLP optimization problems and its potential as a competitive algorithm in the field.
The Role of Blockchain in Medical Data Sharing

As medical technology advances, there is an increasing need for healthcare providers all over the world to securely share a growing volume of data. Blockchain is a powerful technology that allows multiple parties to securely access and share data. Given the enormous challenge that healthcare systems face in digitizing and sharing health records, it is not unexpected that many are attempting to improve healthcare processes by utilizing blockchain technology. By systematically examining articles published from 2017 to 2022, this review addresses the existing gap by methodically discussing the state, research trends, and challenges of blockchain in medical data exchange. The number of articles on this issue has increased, reflecting the growing importance and interest in blockchain research for medical data exchange. Recent blockchain-based medical data sharing advances include safe healthcare management systems, health data architectures, smart contract frameworks, and encryption approaches. The evaluation examines medical data encryption, blockchain networks, and how the Internet of Things (IoT) improves hospital workflows. The findings show that blockchain can improve patient care and healthcare services by securely sharing data.
Haar wavelet downsampling: A simple but effective downsampling module for semantic segmentation

Downsampling operations such as max pooling or strided convolution are ubiquitously utilized in Convolutional Neural Networks (CNNs) to aggregate local features, enlarge receptive field, and minimize computational overhead. However, for a semantic segmentation task, pooling features over the local neighbourhood may result in the loss of important spatial information, which is conducive for pixel-wise predictions. To address this issue, we introduce a simple yet effective pooling operation called the Haar Wavelet-based Downsampling (HWD) module. This module can be easily integrated into CNNs to enhance the performance of semantic segmentation models. The core idea of HWD is to apply Haar wavelet transform for reducing the spatial resolution of feature maps while preserving as much information as possible. Furthermore, to investigate the benefits of HWD, we propose a novel metric, named as feature entropy index (FEI), which measures the degree of information uncertainty after downsampling in CNNs. Specifically, the FEI can be used to indicate the ability of downsampling methods to preserve essential information in semantic segmentation. Our comprehensive experiments demonstrate that the proposed HWD module could (1) effectively improve the segmentation performance across different modality image datasets with various CNN architectures, and (2) efficiently reduce information uncertainty compared to the conventional downsampling methods. Our implementation are available at https://github.com/apple1986/HWD.
Tensorized topological graph learning for generalized incomplete multi-view clustering

The success of the current multi-view clustering lies in the default assumption of completeness on each view, while it is hardly satisfied for real-world applications. Incomplete multi-view clustering (IMC) studies clustering multi-view instances that are partially observed due to data corruption or sensor failure. Although making some progress, the current research suffers from the following obstacles: (1) Existing works are always built on pairwise similarity measurement, which cannot fully capture the topological structure of incomplete multi-view samples for precise clustering; (2) Due to the absence of partially-observed samples across multiple views, how could we recover and enhance the high-order similarities across different views becomes one of the main bottlenecks of IMC; (3) Current research mainly aims to handle specific dual-view IMC, which greatly limits the deployment in real-world applications. In this paper, we propose a novel Tensorized Topological Graph Learning (TTGL) for the generalized incomplete multi-view clustering, which jointly considers the topological graph construction, missing feature completion, and high-order uncertain correlation enhancement. Specifically, instead of using pairwise similarity measurement, we formulate a consensus topological graph construction module, which can coalesce affinity matrices to analyze the topological structure of incomplete multi-view data. Moreover, we build an iterative missing similarity imputation scheme for feature completion, which ensures the intrinsic similarity preservation on observed instances as well as an imputation on unobserved ones. Furthermore, a low-rank tensor structure is imposed to capture the high-order similarities via both feature and similarity enhancement and completion across different views. Our method can cluster incomplete data with an arbitrary number of views and any missing statuses. Extensive experiments on several benchmark datasets validate the effectiveness of our method when compared with a series of state-of-the-art IMC clustering algorithms. The source code of our TTGL is available at: https://github.com/DarrenZZhang/TTGL.
Rank-Aware Negative Training for Semi-Supervised Text Classification

Abstract Semi-supervised text classification-based paradigms (SSTC) typically employ the spirit of self-training. The key idea is to train a deep classifier on limited labeled texts and then iteratively predict the unlabeled texts as their pseudo-labels for further training. However, the performance is largely affected by the accuracy of pseudo-labels, which may not be significant in real-world scenarios. This paper presents a Rank-aware Negative Training (RNT) framework to address SSTC in learning with noisy label settings. To alleviate the noisy information, we adapt a reasoning with uncertainty-based approach to rank the unlabeled texts based on the evidential support received from the labeled texts. Moreover, we propose the use of negative training to train RNT based on the concept that “the input instance does not belong to the complementary label”. A complementary label is randomly selected from all labels except the label on-target. Intuitively, the probability of a true label serving as a complementary label is low and thus provides less noisy information during the training, resulting in better performance on the test data. Finally, we evaluate the proposed solution on various text classification benchmark datasets. Our extensive experiments show that it consistently overcomes the state-of-the-art alternatives in most scenarios and achieves competitive performance in the others. The code of RNT is publicly available on GitHub.
YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System

This work is focused on the preliminary stage of the 3D drone tracking challenge, namely the precise detection of drones on images obtained from a synchronized multi-camera system. The YOLOv5 deep network with different input resolutions is trained and tested on the basis of real, multimodal data containing synchronized video sequences and precise motion capture data as a ground truth reference. The bounding boxes are determined based on the 3D position and orientation of an asymmetric cross attached to the top of the tracked object with known translation to the object's center. The arms of the cross are identified by the markers registered by motion capture acquisition. Besides the classical mean average precision (mAP), a measure more adequate in the evaluation of detection performance in 3D tracking is proposed, namely the average distance between the centroids of matched references and detected drones, including false positive and false negative ratios. Moreover, the videos generated in the AirSim simulation platform were taken into account in both the training and testing stages.
Fault Detection in Power Distribution Networks Based on Comprehensive-YOLOv5

Real-time fault detection in power distribution networks has become a popular issue in current power systems. However, the low power and computational capabilities of edge devices often fail to meet the requirements of real-time detection. To overcome these challenges, this paper proposes a lightweight algorithm, named Comprehensive-YOLOv5, for identifying defects in distribution networks. The proposed method focuses on achieving rapid localization and accurate identification of three common defects: insulator without loop, cable detachment from the insulator, and cable detachment from the spacer. Based on the You Only Look Once version 5 (YOLOv5) algorithm, this paper adopts GhostNet to reconstruct the original backbone of YOLOv5; introduces Bidirectional Feature Pyramid Network (BiFPN) structure to replace Path Aggregation Network (PANet) for feature fusion, which enhances the feature fusion ability; and replaces Generalized Intersection over Union GIOU with Focal Extended Intersection over Union (Focal-EIOU) to optimize the loss function, which improves the mean average precision and speed of the algorithm. The effectiveness of the improved Comprehensive-YOLOv5 algorithm is verified through a "morphological experiment", while an "algorithm comparison experiment" confirms its superiority over other algorithms. Compared with the original YOLOv5, the Comprehensive-YOLOv5 algorithm improves mean average precision (mAP) from 88.3% to 90.1% and increases Frames per second (FPS) from 20 to 52 frames. This improvement significantly reduces false positives and false negatives in defect detection. Consequently, the proposed algorithm enhances detection speed and improves inspection efficiency, providing a viable solution for real-time detection and deployment at the edge of power distribution networks.
Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion

Various deep neural network (DNN) coverage criteria have been proposed to assess DNN test inputs and steer input mutations. The coverage is characterized via neurons having certain outputs, or the discrepancy between neuron outputs. Nevertheless, recent research indicates that neuron coverage criteria show little correlation with test suite quality. In general, DNNs approximate distributions, by incorporating hierarchical layers, to make predictions for inputs. Thus, we champion to deduce DNN behaviors based on its approximated distributions from a layer perspective. A test suite should be assessed using its induced layer output distributions. Accordingly, to fully examine DNN behaviors, input mutation should be directed toward diversifying the approximated distributions. This paper summarizes eight design requirements for DNN coverage criteria, taking into account distribution properties and practical concerns. We then propose a new criterion, Neural Coverage (nlc),that satisfies all design requirements. NLC treats a single DNN layer as the basic computational unit (rather than a single neuron) and captures four critical properties of neuron output distributions. Thus, NL C accurately describes how DNNs comprehend inputs via approximated distributions. We demonstrate that NLC is significantly correlated with the diversity of a test suite across a number of tasks (classification and generation) and data formats (image and text). Its capacity to discover DNN prediction errors is promising. Test input mutation guided by NLC results in a greater quality and diversity of exposed erroneous behaviors.
Unified Instance and Knowledge Alignment Pretraining for Aspect-Based Sentiment Analysis

The goal of aspect-based sentiment analysis (ABSA) is to determine the sentiment polarity towards an aspect. Because of the expensive and limited amounts of labelled data, the pretraining strategy has become the de facto standard for ABSA. However, there always exists a severe domain shift between the pretraining and downstream ABSA datasets, which hinders effective knowledge transfer when directly fine-tuning, making the downstream task suboptimal. To mitigate this domain shift, we introduce a unified alignment pretraining framework into the vanilla pretrain-finetune pipeline, that has both instance- and knowledge-level alignments. Specifically, we first devise a novel coarse-to-fine retrieval sampling approach to select target domain-related instances from the large-scale pretraining dataset, thus aligning the instances between pretraining and the target domains ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First Stage</i> ). Then, we introduce a knowledge guidance-based strategy to further bridge the domain gap at the knowledge level. In practice, we formulate the model pretrained on the sampled instances into a knowledge guidance model and a learner model. On the target dataset, we design an on-the-fly teacher-student joint fine-tuning approach to progressively transfer the knowledge from the knowledge guidance model to the learner model ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Second Stage</i> ). Therefore, the learner model can maintain more domain-invariant knowledge when learning new knowledge from the target dataset. In the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Third Stage,</i> the learner model is finetuned to better adapt its learned knowledge to the target dataset. Extensive experiments and analyses on several ABSA benchmarks demonstrate the effectiveness and universality of our proposed pretraining framework.
EfficientFace: an efficient deep network with feature enhancement for accurate face detection

In recent years, deep convolutional neural networks (CNN) have significantly advanced face detection. In particular, lightweight CNN-based architectures have achieved great success due to their low-complexity structure facilitating real-time detection tasks. However, current lightweight CNN-based face detectors trading accuracy for efficiency have inadequate capability in handling insufficient feature representation, faces with unbalanced aspect ratios and occlusion. Consequently, they exhibit deteriorated performance far lagging behind the deep heavy detectors. To achieve efficient face detection without sacrificing accuracy, we design an efficient deep face detector termed EfficientFace in this study, which contains three modules for feature enhancement. To begin with, we design a novel cross-scale feature fusion strategy to facilitate bottom-up information propagation, such that fusing low-level and high-level features is further strengthened. Besides, this is conducive to estimating the locations of faces and enhancing the descriptive power of face features. Second, we introduce a Receptive Field Enhancement module to consider faces with various aspect ratios. Third, we add an Attention Mechanism module for improving the representational capability of occluded faces. We have evaluated EfficientFace on four public benchmarks and experimental results demonstrate the appealing performance of our method. In particular, our model respectively achieves 95.1% (Easy), 94.0% (Medium) and 90.1% (Hard) on a validation set of WIDER Face dataset, which is competitive with heavyweight models with only 1/15 computational costs of the state-of-the-art MogFace detector.
Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning

The use of reinforcement learning (RL) for dynamic obstacle avoidance (DOA) algorithms and path planning (PP) has become increasingly popular in recent years. Despite the importance of RL in this growing technological era, few studies have systematically reviewed this research concept. Therefore, this study provides a comprehensive review of the literature on dynamic reinforcement learning-based path planning and obstacle avoidance. Furthermore, this research reviews publications from the last 5 years (2018–2022) to include 34 studies to evaluate the latest trends in autonomous mobile robot development with RL. In the end, this review shed light on dynamic obstacle avoidance in reinforcement learning. Likewise, the propagation model and performance evaluation metrics and approaches that have been employed in previous research were synthesized by this study. Ultimately, this article’s major objective is to aid scholars in their understanding of the present and future applications of deep reinforcement learning for dynamic obstacle avoidance.