Reviewerly/exHarmony huggingface dataset:
An Efficient Particle YOLO Detector for Urine Sediment Detection

Urine sediment detection is an essential aid in assessing kidney health. Traditional machine learning approaches treat urine sediment particle detection as an image classification task, segmenting particles for detection based on information such as edges or thresholds. However, the segmentation of sediment particles is complex due to the low contrast and weak edge characteristics of urine sediment images. In this paper, we consider urine sediment particle detection as a object detection task and propose the YOLOv5s-CBL, a detector dedicated to particle detection. Specifically, to mitigate the impact of background noise on detection accuracy, we inherit CBAM on the YOLOv5s model to help the network filter useless noise information and find regions of interest to extract target features. Then, we expand the original three-scale feature layer to improve the sensitivity of the model to larger-scale target sediment particles. Finally, we use the BiFPN structure instead of the original PANet combined with the FPN structure, which can more effectively fuse multiple different scales of information to improve the detection performance of the model. We compared state-of-the-art methods on two real-world datasets, and the experimental results demonstrated the effectiveness of YOLOv5s-CBL.
Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing

Current advances in machine (deep) learning and the exponential growth of data collected by and shared between smart manufacturing processes give a unique opportunity to get extra value from that data. The use of public machine learning services actualizes the issue of data privacy. Ordinary encryption protects the data but could make it useless for the machine learning objectives. Therefore, "privacy of data vs. value from data" is the major dilemma within the privacy preserving machine learning activity. Special encryption techniques or synthetic data generation are being in focus to address the issue. In this paper, we discuss a complex hybrid protection algorithm, which assumes sequential use of two components: homeomorphic data space transformation and synthetic data generation. Special attention is given to the privacy of image data. Specifics of image representation require special approaches towards encryption and synthetic image generation. We suggest use of (convolutional, variational) autoencoders and pre-trained feature extractors to enable applying privacy protection algorithms on top of the latent feature vectors captured from the images, and we updated the hybrid algorithms composed of homeomorphic transformation-as-encryption plus synthetic image generation accordingly. We show that an encrypted image can be reconstructed (by the pre-trained Decoder component of the convolutional variational autoencoder) into a secured representation from the extracted (by either the Encoder or a feature extractor) and encrypted (homeomorphic transformation of the latent space) feature vector.
Comparative Classification Performance Evaluation of Various Deep Learning Techniques for Sentiment Analysis

In the discipline of text mining, the current subject of study is sentiment analysis. It detects textual subjectivity, feelings, and views. It is the use of text analytics and natural language processing tools to discover and extract subjective information from commonly utilized sources such as the web and microblogs. The prime aim of sentiment analysis is to study product and service evaluations and calculate sentiment ratings. The main issue is that the reviews are mainly unstructured, necessitating categorization or grouping to offer useful data for future use. Sentiment Analysis (SA) problem of Natural Language Processing (NLP) on text has been addressed in this research. With the help of neural networks trained on the “Movie Review Database,” the job of sentiment analysis from movie reviews has been completed. The ultimate accuracy of the trained network was 91 percent. The research adds to the body of knowledge by comparing the classification performance of several deep learning algorithms on a labeled corpus. This is intended to assist sentiment analysis specialists.
Detecting Anomalous Multivariate Time-Series via Hybrid Machine Learning

This article investigates the use of hybrid machine learning (HML) for the detection of anomalous multivariate time-series (MVTS). Focusing on a specific industrial use-case from geotechnical engineering, where hundreds of MVTS need to be analyzed and classified, has permitted extensive testing of the proposed methods with real measurement data. The novel hybrid anomaly detector combines two means for detection, creating redundancy and reducing the risk of missing defective elements in a safety relevant application. The two parts are: 1) anomaly detection based on approximately 50 physics-motivated key performance indicators (KPIs) and 2) an unsupervised variational autoencoder (VAE) with long short-term memory layers. The KPI captures expert knowledge on the properties of the data that infer the quality of produced elements; these are used as a type of auto-labeling. The goal of the extension using machine learning (ML) is to detect anomalies that the experts may not have foreseen. In contrast to anomaly detection in streaming data, where the goal is to locate an anomaly, each MVTS is complete in itself at the time of evaluation and is categorized as anomalous or nonanomalous. The article compares the performance of different VAE architectures [e.g., long short-term memory (LSTM-VAE) and bidirectional LSTM (BiLSTM-VAE)]. The results of using a genetic algorithm to optimize the hyperparameters of the different architectures are also presented. It is shown that modeling the industrial process as an assemblage of subprocesses yields a better discriminating power and permits the identification of interdependencies between the subprocesses. Interestingly, different autoencoder architectures may be optimal for different subprocesses; here two different architectures are combined to achieve superior performance. Extensive results are presented based on a very large set of real-time measurement data.
Forecasting Solar Cycle 25 with Physical Model-Validated Recurrent Neural Networks

Abstract The Sun’s activity, which is associated with the solar magnetic cycle, creates a dynamic environment in space known as space weather. Severe space weather can disrupt space-based and Earth-based technologies. Slow decadal-scale variations on solar-cycle timescales are important for radiative forcing of the Earth’s atmosphere and impact satellite lifetimes and atmospheric dynamics. Predicting the solar magnetic cycle is therefore of critical importance for humanity. In this context, a novel development is the application of machine-learning algorithms for solar-cycle forecasting. Diverse approaches have been developed for this purpose; however, with no consensus across different techniques and physics-based approaches. Here, we first explore the performance of four different machine-learning algorithms – all of them belonging to a class called Recurrent Neural Networks (RNNs) – in predicting simulated sunspot cycles based on a widely studied, stochastically forced, nonlinear time-delay solar dynamo model. We conclude that the algorithm Echo State Network (ESN) performs the best, but predictability is limited to only one future sunspot cycle, in agreement with recent physical insights. Subsequently, we train the ESN algorithm and a modified version of it (MESN) with solar-cycle observations to forecast Cycles 22 – 25. We obtain accurate hindcasts for Solar Cycles 22 – 24. For Solar Cycle 25 the ESN algorithm forecasts a peak amplitude of 131 ± 14 sunspots around July 2024 and indicates a cycle length of approximately 10 years. The MESN forecasts a peak of 137 ± 2 sunspots around April 2024, with the same cycle length. Qualitatively, both forecasts indicate that Cycle 25 will be slightly stronger than Cycle 24 but weaker than Cycle 23. Our novel approach bridges physical model-based forecasts with machine-learning-based approaches, achieving consistency across these diverse techniques.
Marine predator algorithm with elite strategies for engineering design problems

Summary Marine predator algorithm (MPA) is a powerful metaheuristic optimization algorithm that shows effective convergence ability on complex benchmark functions. The combination of Brownian and Levy flight distributions directly affects the convergence strategy of MPA. Although MPA has good convergence performance, it is open to improvement as it falls to a local optimum and cannot comprehensively scan the search area during the exploration phase. In this study, MPA has been improved by integrating elite natural evolution and elite random mutation strategies. In addition, these two strategies are combined with Gaussian mutation. The proposed method in this study which is named as elite evolution strategy MPA (EEMPA) has achieved comprehensive scanning of the solution space and considerably reduced the risk of falling into the local optimum trap, with elite strategies. The effect of EEMPA has been tested with the CEC2017 and CEC2019 benchmark functions. EEMPA has been compared with some metaheuristic algorithms frequently used in the literature and gives promising results among the considered optimization methods. Furthermore, EEMPA has been examined for seven well‐known real world engineering problems. When the results are compared with both classical MPA and enhanced MPA methods, EEMPA converges to better than the other methods.
Symmetric pyramid network for medical image inverse consistent diffeomorphic registration

Over the past few years, deep learning-based image registration methods have achieved remarkable performance in medical image analysis. However, many existing methods struggle to ensure accurate registration while preserving the desired diffeomorphic properties and inverse consistency of the final deformation field. To address the problem, this paper presents a novel symmetric pyramid network for medical image inverse consistent diffeomorphic registration. Specifically, we first encode the multi-scale images to the feature pyramids via a shared-weights encoder network and then progressively conduct the feature-level diffeomorphic registration. The feature-level registration is implemented symmetrically to ensure inverse consistency. We independently carry out the forward and backward feature-level registration and average the estimated bidirectional velocity fields for more robust estimation. Finally, we employ symmetric multi-scale similarity loss to train the network. Experimental results on three public datasets, including Mindboggle101, CANDI, and OAI, show that our method significantly outperforms others, demonstrating that the proposed network can achieve accurate alignment and generate the deformation fields with expected properties. Our code will be available at https://github.com/zhangliutong/SPnet.
Multi-objective distributed Web service composition—A link-dominance driven evolutionary approach

Service-oriented architecture (SOA) encourages the creation of modular applications involving Web services as the reusable components. Data-intensive Web services manipulate and deal with the massive data that emerged by technological advances and their various applications. Distributed Data-intensive Web Service Composition (DWSC) includes the selection of data-intensive Web services from diverse locations on the network and composing them into a service to accomplish a complex task. Optimising Quality of Service (QoS) while satisfying the functional requirements is a fundamental challenge for service developers. The multi-objective and distributed nature of the DWSC problem demands satisfying algorithms to automatically produce the Pareto optimal set of composite services. In this paper, we propose a new Evolutionary Computation (EC) approach based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) and a novel local search technique to effectively solve the multi-objective distributed DWSC problems. Our local search technique employs an innovative concept, communication link dominance, to incorporate domain-specific knowledge. The performance of our proposed link-dominance driven EC approach is evaluated on benchmark datasets. The results show that our proposed method has the highest quality with acceptable execution time for most of the composition tasks among competing algorithms.
A Novel Interval Iterative Multi-Thresholding Algorithm Based on Hybrid Spatial Filter and Region Growing for Medical Brain MR Images

Medical image segmentation is widely used in clinical medicine, and the accuracy of the segmentation algorithm will affect the diagnosis results and treatment plans. However, manual segmentation of medical images requires extensive experience and knowledge, and it is both time-consuming and labor-intensive. To overcome the problems above, we propose a novel interval iterative multi-thresholding segmentation algorithm based on hybrid spatial filter and region growing for medical brain MR images. First, a hybrid spatial filter is designed to perform on the original image, which can make full use of the spatial information while denoising. Second, the interval iterative Otsu method based on region growing is proposed to segment the original image and its filtering layer. The initial thresholds can be quickly obtained by region growing algorithm, which can reduce the time complexity. The interval iterative algorithm is used to optimize the thresholds. Finally, a weighted strategy is used to refine the segmentation results. The segmentation results of our proposed algorithm outperform other comparison algorithms in both subjective and objective evaluations. Subjectively, the obtained segmentation results have clear edges, complete and consistent regions. We use the uniformity measure (U) for objective evaluation, and the U value is significantly higher than other comparison algorithms. The proposed algorithm achieved an average U value of 0.9854 across all test images. The proposed algorithm can segment medical images well and expand the doctor’s ability to utilize medical images.
Homomorphic cryptosystem-based secure data processing model for edge-assisted IoT healthcare systems

The substantial acceptance of Internet-of-Things (IoT) in digital healthcare systems necessitates secure data processing before storing it on a cloud server. Since resource-limited IoT devices engender complexity in secure processing, edge computing technology is adopted. Edge-assisted IoT locates edge nodes in proximity to user devices enabling computation offloading and seamless interaction between IoT applications and cloud servers. Integration of these entities results in severe security and privacy issues due to large amount of sensitive information transmitted over vulnerable communication channels. As such, information protection and data integrity in edge-assisted IoT have become critical factors for improvement. Several data processing models have been proposed regarding the above-stated concerns. However, under a powerful adversarial scenario, traditional solutions lack in preserving robust security and privacy. This motivates us to address critical challenges of communication security and privacy-preserving computation offloading in edge-assisted IoT systems. In this article, we design a homomorphic cryptosystem-based secure data processing model (HC-SDPM), enabling secure data collection and aggregation offloading on edge nodes for our hypothesized edge-assisted IoT healthcare system. Paillier homomorphic encryption and certificateless linear homomorphic ID-based signatures are utilized to achieve end-to-end confidentiality and data integrity during transmission and aggregation. Moreover, HC-SDPM involves a semi-trusted authority to enable key-escrow resilience. The security analysis of HC-SDPM assures signatures unforgeability in the random oracle model under computational Diffie–Hellman assumptions. Compared with similar modeled schemes, HC-SDPM demonstrates a relative advantage in computational overhead, communication overhead, and storage complexity.
Evaluating state-of-the-art # SAT solvers on industrial configuration spaces

Abstract Product lines are widely used to manage families of products that share a common base of features. Typically, not every combination (configuration) of features is valid. Feature models are a de facto standard to specify valid configurations and allow standardized analyses on the variability of the underlying system. A large variety of such analyses depends on computing the number of valid configurations. To analyze feature models, they are typically translated to propositional logic. This allows to employ SAT solvers that compute the number of satisfying assignments of the propositional formula translated from a feature model. However, the SAT problem is generally assumed to be even harder than SAT and its scalability when applied to feature models has only been explored sparsely. Our main contribution is an investigation of the performance of off-the-shelf SAT solvers on computing the number of valid configurations for industrial feature models. We empirically evaluate 21 publicly available SAT solvers on 130 feature models from 15 subject systems. Our results indicate that current solvers master a majority of the evaluated systems (13/15) with the fastest solvers requiring less than one second for each successfully evaluated feature model. However, there are two complex systems for which none of the evaluated solvers scales. For the given experiment design, the solvers that consumed the least runtime are (2.5 seconds in sum for the 13 systems) and (3.5 seconds).
CAMR: cross-aligned multimodal representation learning for cancer survival prediction

Accurately predicting cancer survival is crucial for helping clinicians to plan appropriate treatments, which largely improves the life quality of cancer patients and spares the related medical costs. Recent advances in survival prediction methods suggest that integrating complementary information from different modalities, e.g. histopathological images and genomic data, plays a key role in enhancing predictive performance. Despite promising results obtained by existing multimodal methods, the disparate and heterogeneous characteristics of multimodal data cause the so-called modality gap problem, which brings in dramatically diverse modality representations in feature space. Consequently, detrimental modality gaps make it difficult for comprehensive integration of multimodal information via representation learning and therefore pose a great challenge to further improvements of cancer survival prediction.To solve the above problems, we propose a novel method called cross-aligned multimodal representation learning (CAMR), which generates both modality-invariant and -specific representations for more accurate cancer survival prediction. Specifically, a cross-modality representation alignment learning network is introduced to reduce modality gaps by effectively learning modality-invariant representations in a common subspace, which is achieved by aligning the distributions of different modality representations through adversarial training. Besides, we adopt a cross-modality fusion module to fuse modality-invariant representations into a unified cross-modality representation for each patient. Meanwhile, CAMR learns modality-specific representations which complement modality-invariant representations and therefore provides a holistic view of the multimodal data for cancer survival prediction. Comprehensive experiment results demonstrate that CAMR can successfully narrow modality gaps and consistently yields better performance than other survival prediction methods using multimodal data.CAMR is freely available at https://github.com/wxq-ustc/CAMR.Supplementary data are available at Bioinformatics online.
Investigation of Netizen Sentiment Analysis Toward The Controversy of Information and Electronic Transaction Law

The society has important roles for ITE implementation that were regulated in several articles. With the explosion of social media network enabling several governments across the world become closer to their citizen to achieve transparency and engagement. This opportunity has been changing the way of government in order to understand about facts and realities that evolving in the society, particularly involving public policy implementation. Moreover, in the world of big data, the feedback from social media users can be profitably used to extract useful information to support actions that policymakers take throughout the policy cycle. This study work used text mining to explore the twitter data to help the government see what urgent issues, challenges, and benefits arise from the implementation of policies, especially the implementation of ITE which is attracting the attention of “netizens” today. The overall process of the method used are text extracting, text cleaning, and text representation. This study resulted that it is clearly stated that the users talked about the elastic interpretation of UU ITE article which can potentially be abused by the certain person. The original tweets attracted user's attention to re-post that message repeatedly
Adversarial Unsupervised Domain Adaptation for Hand Gesture Recognition Using Thermal Images

Hand gesture recognition has a wide range of applications, including in the automotive and industrial sectors, health assistive systems, authentication, and so on. Thermal images are more resistant to environmental changes than red–green–blue (RGB) images for hand gesture recognition. However, one disadvantage of using thermal images for the aforementioned task is the scarcity of labeled thermal datasets. To tackle this problem, we propose a method that combines unsupervised domain adaptation (UDA) techniques with deep-learning (DL) technology to remove the need for labeled data in the learning process. There are several types and methods for implementing UDA, with adversarial UDA being one of the most common. In this article, the first time in this field, we propose a novel adversarial UDA model that uses channel attention and bottleneck layers to learn domain-invariant features across RGB and thermal domains. Thus, the proposed model leverages the information from the labeled RGB data to solve the hand gesture recognition task using thermal images. We evaluate the proposed model on two hand gesture datasets, namely, Sign Digit Classification and Alphabet Gesture Classification, and compare it to other benchmark models in terms of accuracy, model size, and model parameters. Our model outperforms the other state-of-the-art methods on the Sign Digit Classification and Alphabet Gesture Classification datasets and achieves 91.32% and 80.91% target test accuracy, respectively.