Reviewerly/exHarmony huggingface dataset:
Trajectory optimization of wall-building robots using response surface and non-dominated sorting genetic algorithm III

Traditional wall-building robots regard brick masonry as a simple assembly process, ignoring the viscoelastic effect of cement mortar, which leads to poor masonry quality. Therefore, this paper proposes a many-objective trajectory optimization method based on response surface methodology (RSM) and non-dominated sorting genetic algorithm III (NSGA-III). Firstly, a substitution model between the objective functions and the design variables is established using RSM, which solves the problem of difficult construction of cement mortar viscoelastic model. Then, a trajectory optimization model is constructed based on seven times non-uniform B-spline, and it is solved using NSGA-III. Finally, the optimal solution is obtained from the Pareto solution set, and it is compared and analyzed with the standard gate-type masonry solution. The results show that after the trajectory optimization, the masonry error is reduced from 2.62 mm to 0.11 mm, which significantly improves the masonry quality, and other performance indicators are also improved. These results contribute to the construction of walls and promote the development of the smart construction industry.
Rule-Based Architectural Design Pattern Recognition with GPT Models

Architectural design patterns are essential in software development because they offer proven solutions to large-scale structural problems in software systems and enable developers to create software that is more maintainable, scalable, and comprehensible. Model-View-Whatever (MVW) design patterns are prevalent in many areas of software development, but their use in Web development is on the rise. There are numerous subtypes of MVW design patterns applicable to Web systems, but there is no exhaustive listing of them. Additionally, it is unclear how these subtypes can be utilized in contemporary Web development, as their usage is typically unconscious. Here, we discuss and define the most prevalent MVW design patterns used in Web development, as well as provide Angular framework examples and guidance on when to employ a particular design pattern. On the premise of the primary characteristics of design patterns, we created a rule system that large language models (LLMs) can comprehend without doubt. Here, we demonstrate how effectively Generative Pre-trained Transformer (GPT) models can identify various design patterns based on our principles and verify the quality of our recommendations. Together, our solution and GPT models constitute an effective natural language processing (NLP) solution capable of detecting MVW design patterns in Angular projects with an average accuracy of 90%.
A self-explanatory contrastive logical knowledge learning method for sentiment analysis

Although interpretable methods for deep learning models have become popular in sentiment analysis domains in recent years, existing methods still face the challenge of providing predictions with both high accuracy and user-friendly explanations. To address this problem, we propose a novel framework called Contrasting Logical Knowledge Learning (CLK) that utilizes contrastive learning, label knowledge, and logical rule learning. Logical rule learning is used to provide human-understandable explanations while label knowledge and contrastive learning are used to achieve high performance on both pre-trained models and ordinary DNNs. To ensure model interpretability, we design a novel knowledge reasoning strategy based on learned logical rules and trained models. Empirical results from binary sentiment analysis tasks and fine-grained sentiment analysis tasks show that CLK can effectively balance accuracy and interpretability. Additionally, we conduct two case studies to demonstrate the process of explanation generation and knowledge reasoning, which shows that our method's explanations are causally consistent with the implicit model decision logic.
Multi-level autonomous integrity monitoring method for multi-source PNT resilient fusion navigation

Abstract For the integrity monitoring of a multi-source PNT (Positioning, Navigation, and Timing) resilient fusion navigation system, a theoretical framework of multi-level autonomous integrity monitoring is proposed. According to the mode of multi-source fusion navigation, the framework adopts the top-down logic structure and establishes the navigation source fault detection model based on the multi-combination separation residual method to detect and isolate the fault source at the system level and subsystem level. For isolated non-redundant navigation sources, the system level recovery verification model is used. For the isolated multi-redundant navigation sources, the sensor fault detection model optimized with the dimension-expanding matrix is used to detect and isolate the fault sensors, and the isolated fault sensors are verified in real-time. Finally, according to the fault detection and verification results at each level, the observed information in the fusion navigation solution is dynamically adjusted. On this basis, the integrity risk dynamic monitoring tree is established to calculate the Protection Level (PL) and evaluate the integrity of the multi-source integrated navigation system. The autonomous integrity monitoring method proposed in this paper is tested using a multi-source navigation system integrated with Inertial Navigation System (INS), Global Navigation Satellite System (GNSS), Long Baseline Location (LBL), and Ultra Short Baseline Location (USBL). The test results show that the proposed method can effectively isolate the fault source within 5 s, and can quickly detect multiple faulty sensors, ensuring that the positioning accuracy of the fusion navigation system is within 5 m, effectively improving the resilience and reliability of the multi-source fusion navigation system.
Corner guided instance segmentation network for power lines and transmission towers detection

Power lines (PLs) and transmission towers (TTs) detection from aerial images is the prerequisite step for unmanned aerial vehicles (UAVs) navigation, path planning, and obstacle avoidance. Unlike the previous works which only focus on PLs detection or TTs detection, this work aims to detect PLs and TTs together based on the instance segmentation algorithm. However, PLs and TTs in aerial images present numerous challenges to object detection and segmentation researchers—the slender characteristic of PLs, the foreground–background class imbalance, and the foreground–foreground class imbalance. To cope with the above challenges, this work proposes a corner-guided instance segmentation network (CGISnet) for detecting PLs and TTs together from aerial images. Specifically, an attention-based multiscale feature extraction module facilitates the extraction of the basic spatial feature information in the case of serious background noise interference. To accumulate more feature information about the objects, especially the slender objects, a corner-guided region of interest (ROI) align module detects the object as a pair of corners to obtain the corresponding ROI information for further segmentation. In addition, a weighted cross entropy loss function is utilized to resolve the foreground–foreground class imbalance issue. Extensive experiments were conducted on self-built and two public datasets to demonstrate the effectiveness and robustness of the proposed CGISnet. The results demonstrate that our approach not only detects PLs and TTs with high accuracy, but also achieves competitive performance in detecting objects with different shapes and sizes.
Deep learning algorithms for the early detection of breast cancer: A comparative study with traditional machine learning

Deep learning has been widely applied in breast cancer screening to analyze images obtained from X-rays, ultrasound, magnetic resonances, and biopsies. This study suggests that deep learning can also be used to prescreen for cancer by analyzing heterogeneous data obtained from demographic and anthropometric information from patients, biological markers from routine blood samples, and relative risks from meta-analysis and international databases. In this document, feature selection is applied to a database of 64 women diagnosed with breast cancer and a counterfactual group of 52 healthy women, to identify the best predictors of cancer prescreening. The best predictors are used in k-fold Monte Carlo cross-validation experiments that compare deep learning against machine learning. The results indicate that a deep learning architecture that is fine-tuned using feature selection has the lowest false negative rate (i.e., the lowest Type II errors) and can effectively distinguish between patients with and without cancer. Consequently, deep learning—compared to traditional machine learning—promotes a more accurate detection of malignancies, hence reducing the risk of increased tumor size and cancer spreading to nearby or distant lymph nodes, tissues, or organs, due to late detection. Additionally, compared to machine learning, deep learning has the lowest uncertainty in its predictions, as indicated by the lowest standard deviation of its performance metrics. These findings indicate that deep learning algorithms applied to cancer prescreening offer a radiation-free, non-invasive, and an affordable complement to screening methods based on imagery. The implementation of deep learning algorithms in cancer prescreening helps to identify individuals who may require imaging-based screening, can encourage self-examination, and decreases the psychological drawbacks associated with false positives in cancer screening, ultimately leading to earlier detection of malignancy and reducing the healthcare and societal burden associated with cancer treatment.
An extended taxonomy of advanced information visualization and interaction in conceptual modeling

Conceptual modeling is integral to computer science research and is widely adopted in industrial practices, e.g., business process and enterprise architecture management. Providing adequate and usable modeling tools is necessary to adopt modeling languages efficiently. Meta-modeling platforms provide a rich and mature set of functionalities for realizing state-of-the-art modeling tools. These tools, albeit their stability and rich set of features, often lack a modern look and feel considering (i) how they visualize the models, and (ii) how modelers interact with the models. Current web technologies enable much richer, advanced opportunities for visualizing and interacting with conceptual models. However, a structured and comprehensive overview of possible information visualization and interaction techniques linked to conceptual models and modeling tools must be established. This paper aims to fill this gap by presenting an extended taxonomy of advanced information visualization and interaction in conceptual modeling. We present a generic taxonomy that is afterward contextualized within the specific domain of conceptual modeling. The taxonomy serves orientation in the vast developing field of information visualization and interaction and hopefully sparks innovation if future modeling tool development.
Toward a lightweight ASR solution for atypical speech on the edge

In this article, to the purpose of simplifying challenges in designing automatic speech recognition (ASR) systems working on disordered speech, we focus on an isolated word recognition solution based on a convolutional neural network architecture to predict the presence of precise voice commands within atypical utterances. Italian speech recognizers have trained from scratch on custom datasets by following a speaker dependent approach, then their performances (in terms of word error rate) have been investigated with the collaboration of 16 Italian speakers with motor and speech impairments. Our ASR system works on Mel-frequency Cepstral Coefficients features extraction method, and thanks to its inner structure, the trained ASR model can be deployed on edge computing nodes where the local inference task requires limited computational resources. We acknowledge that it may have significant implications in the field of assistive technology. Then, in the end of the paper, we present a prototype of an edge computing device aimed at supporting the voice interaction between its user with speech disorders and his/her surrounding smart environment through a custom interaction with virtual assistants' services.
Question-conditioned debiasing with focal visual context fusion for visual question answering

Existing Visual Question Answering models suffer from the language prior, where the answers provided by the models overly rely on the correlations between questions and answers, ignoring the exact visual information, resulting in a significant drop in the out-of-distribution datasets. To eliminate such language bias, prevalent approaches mainly focus on weakening the language prior with one auxiliary question-only branch while focusing on the statistical question type–answer pairs’ distribution prior rather than that of question–answer pairs. Besides, most models provide the answer with improper visual groundings. This paper proposes a model-agnostic framework to address the above drawbacks by question-conditioned debiasing with focal visual context fusion. To begin with, instead of the question type-conditioned correlations, we overcome the language distribution shortcut from the aspect of question-conditioned correlations by removing the shortcut between questions and the most occurring answer. Additionally, we utilize the deviation of the predicted answer distribution and ground truth as the pseudo target to avoid the model falling into other frequent answers’ distribution bias. Further, we stress the imbalance of the number of images and questions that post higher requirements of a proper visual context. We improve the correct visual utilization ability based on contrastive sampling and design a focal visual context fusion module that incorporates the critical object word extracted from the question after the Part-Of-Speech tagging into the visual features to augment the salient visual information without human annotations. Extensive experiments on the three public benchmark datasets, i.e., VQA v2, VQA-CP v2, and VQA-CP v1, demonstrate the effectiveness of our model.
Towards developing a segmentation method for cerebral aneurysm using a statistical multiresolution approach

Abstract The computer aided diagnosis (CAD) algorithms are considered crucial during the treatment planning of cerebral aneurysms (CA), where segmentation is the first and foremost step. This paper presents a segmentation algorithm in two-dimensional domain combining a multiresolution and a statistical approach. Precisely, Contourlet transform (CT) extracts the image features, while Hidden Markov Random Field with Expectation Maximization (HMRF-EM) segments the image, based on the spatial contextual constraints. The proposed algorithm is tested on Three-Dimensional Rotational Angiography (3DRA) datasets; the average values of accuracy, DSC, FPR, FNR, specificity, and sensitivity, are found to be 99 . 64%, 92 . 44%, 0 . 09%, 5 . 81%, 99 . 84%, and 93 . 22%, respectively. Both qualitative and quantitative results obtained show the potential of the proposed method.
A cascaded differential evolution optimization framework with adaptive population allocation and reduction

Differential Evolution (DE) -type algorithms perform efficiently on optimization problems, but for some problems, single DE-type algorithms still cannot achieve good results. Integrating multiple algorithms or strategies is an effective way to improve the performance of a single algorithm. This paper proposes a cascaded DE-type algorithm optimization framework with population adaptive allocation and reduction (cDE-APAR), which improves the performance of a single algorithm by cascading two DE-type algorithms and combining related strategies. The first stage of the algorithm runs a single algorithm. The second stage implements the other algorithm in parallel with a local search strategy and a global search strategy, and allocates resources reasonably among the single algorithm and related strategies through adaptive population allocation. Finally, individuals with the worst performance are eliminated through the population reduction strategy to improve the population's quality. In order to verify the performance of the algorithm, extensive simulations were performed using four test suites of CEC2005, CEC2013, CEC2014, and CEC2017. The results show that cDE-APAR significantly improves the performance of a single algorithm. Compared with nine state-of-the-art algorithms, cDE-APAR is significantly better than the comparison algorithms according to the Wilcoxon rank sum test and has significantly improved the accuracy and stability of the solution.
A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications

Substance use, including the non-medical use of prescription medications, is a global health problem resulting in hundreds of thousands of overdose deaths and other health problems. Social media has emerged as a potent source of information for studying substance use-related behaviours and their consequences. Mining large-scale social media data on the topic requires the development of natural language processing (NLP) and machine learning frameworks customized for this problem. Our objective in this research is to develop a framework for conducting a content analysis of Twitter chatter about the non-medical use of a set of prescription medications.We collected Twitter data for four medications-fentanyl and morphine (opioids), alprazolam (benzodiazepine), and Adderall® (stimulant), and identified posts that indicated non-medical use using an automatic machine learning classifier. In our NLP framework, we applied supervised named entity recognition (NER) to identify other substances mentioned, symptoms, and adverse events. We applied unsupervised topic modelling to identify latent topics associated with the chatter for each medication.The quantitative analysis demonstrated the performance of the proposed NER approach in identifying substance-related entities from data with a high degree of accuracy compared to the baseline methods. The performance evaluation of the topic modelling was also notable. The qualitative analysis revealed knowledge about the use, non-medical use, and side effects of these medications in individuals and communities.NLP-based analyses of Twitter chatter associated with prescription medications belonging to different categories provide multi-faceted insights about their use and consequences. Our developed framework can be applied to chatter about other substances. Further research can validate the predictive value of this information on the prevention, assessment, and management of these disorders.
Lite-FENet: Lightweight multi-scale feature enrichment network for few-shot segmentation

Current methods for few-shot segmentation focus on extracting information from support and query targets, however, most of these methods not only suffer from high model complexity but also fail to capture long-range interactions between query features and support features. To efficiently reduce the information loss due to the appearance inconsistency in spatial between support and query objects and make full use of the limited features extracted from support and query samples, we present a lightweight multi-scale feature enrichment network named Lite-FENet that enhances multi-scale feature interactions and ensures the discrimination with the final features under low computational cost. Specifically, we design a lightweight and efficient spatial interaction module for the Lite-FENet to achieve the contextual information transfer between different spatial scales over long distances. The repeated interactions generate a descriptive feature, and show superior robustness for pixel-level prediction, especially when there is a gap between different instances. Experimental results show that the proposed method achieves 63.5% and 64.7% MIoU on the PASCAL-5i dataset, and 39.5% and 43.1% MIoU on the COCO-20i datasets under 1-shot and 5-shot settings with only 6.7M learnable parameters, respectively, which significantly exceeds the results of current methods, while ensuring a far smaller model size. Code is available at: https://github.com/Sunbaoquan/Lite-FENet.
Total variation regularized multi-matrices weighted Schatten p-norm minimization for image denoising

Motivated by the superior performance of nonconvex nonsmooth Lp (0<p<1) norm, this paper introduces a novel method that combines the weighted Schatten p-norm, Lp-norm, and total variation regularization based on the multiple matrices denoising framework. The weighted Schatten p-norm encodes the global low-rank to multiple matrices data, while the Lp-norm provides noise robustness. Total variation regularization is incorporated to promote structural smoothness and edge preservation in the image. To solve the nonconvex and nonsmooth model, an efficient alternating direction method of multipliers (ADMM) is designed. In addition, we discuss how the value of p in the three items affects the denoising performance in simulated images. Extensive experiments on face datasets, videos, and real-world noisy images demonstrate that the proposed method significantly improves denoising performance, particularly for removing large sparse noise.
Healthcare Transformation Using Blockchain Technology in the Era of Society 5.0

Prior to the advancement of technology as we know it today, many hospitals stored maintenance and patient medical records using paper-based record systems. However, this is changing as the digital era is tremendously advancing. The shortcomings of paper-based record systems include data loss, manipulation, and access constraints. In some healthcare facilities, patient medical records are kept only for a certain period of time. Thus, patients are unable to retrieve their medical records after a certain period of time. Although many facilities store patients’ medical records in electronic computer systems, these data can still be accessed and manipulated without the knowledge of the patient. However, this downside can be solved by the use of Blockchain technology. Blockchain can be described as a structure that stores records of transactions into blocks in cyberspace. This technology prevents records from being manipulated or corrupted without a common consensus. In essence, Blockchain is a “digital ledger” whereby every transaction is monitored and permitted by the owner using a digital signature, thus making this technology secure and reliable. With regard to data management and maintenance, the introduction of Blockchain technology will make a significant difference to the healthcare ecosystem in Society 5.0. Blockchain technology, although it is not a panacea, provides fertile ground for investment and experimentation as well as proof-of-concept testing. Blockchain is still in developmental stages and as such, it faces several challenges such as latency, security, privacy, and usability. Thus, legal and technological experts need to collectively revise feasible solutions that are in line with human rights so that this technology can thrive and succeed.