Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-supervised structural damage detection method based on pretext tasks learning feature representations

Machine learning has become an influential and effective tool in numerous civil engineering applications, especially in the field of structural health monitoring (SHM). Recently, the emergence of self-supervised learning has led to the development of many industries, and its accuracy and stability are superior to previous methods. Self-supervised learning learns generalizable information representation from unlabeled mixed data by solving pretext tasks, and this feature is exactly in line with the mixed and unlabeled data in the SHM field. This is of great significance to the improvement of detection accuracy in SHM practical applications. Therefore, this paper proposes a new self-supervised method for structural damage detection. The key to this method is that we use two self-supervised pretext tasks to learn the latent feature representation of the data, and we introduce homoscedastic uncertainty for automatically assigning weights to the two pretext tasks. The relative confidence between tasks is captured, the impact of noise on tasks is reduced, the pretext tasks can better learn data feature representation, and the purpose of improving the accuracy of damage detection is achieved.
A semantic segmentation method for power transmission line point clouds based on PointNet++

We present a 3D point cloud semantic segmentation method for power transmission line based on PointNet++. The method aims to identify pylons, ground, vegetation, insulators, earth wires, conductors, bypass jumpers, etc. We created a dataset of 54,733,560 points, collected from real power transmission lines by airborne lidars. We modified the feature extraction network of PointNet++, the radius, and the number of samplings to fit the complicated structures of the power transmission system components. The experimental results showed accuracy improvement in all 8 categories, the highest improvement is with the insulators, by 2.73%, and the overall improvement is by 0.72%, compared to the original model.
New Perspectives on Observed and Simulated Antarctic Sea Ice Extent Trends Using Optimal Fingerprinting Techniques

AbstractUsing optimal fingerprinting techniques, a detection analysis is performed to determine whether observed trends in Southern Ocean sea ice extent since 1979 are outside the expected range of natural variability. Consistent with previous studies, it is found that for the seasons of maximum sea ice cover (i.e., winter and early spring), the observed trends are not outside the range of natural variability and in some West Antarctic sectors they may be partially due to tropical variability. However, when information about the spatial pattern of trends is included in the analysis, the summer and autumn trends fall outside the range of internal variability. The detectable signal is dominated by strong and opposing trends in the Ross Sea and the Amundsen–Bellingshausen Sea regions. In contrast to the observed pattern, an ensemble of 20 CMIP5 coupled climate models shows that a decrease in Ross Sea ice cover would be expected in response to external forcings. The simulated decreases in the Ross, Bellingsha.
Passively mode-locked ytterbium-doped fiber laser based on MnPSxSey saturable absorber

Passively mode-locked fiber laser based on two-dimensional material MnPSxSey saturable absorber is studyed. By changing the doping ratio of S and Se, three magnetic materials, MnPS2.9Se0. 1, MnPS2.8Se0. 2 and MnPS2.7Se0. 3, are obtained. The three materials were fabricated into sandwich absorbers, and stable mode-locked pulse was obtained in ytterbium-doped fiber lasers. The total length of the fiber laser is 88 m, and the corresponding repetition rate is 2.27 MHz. The signal-to-noise ratio of the three materials is all above 60 dB, indicating the stability of the mode-locking pulse. The experimental results show that MnPSxSey has good nonlinear optical modulation characteristics and optical switching ability, which has potential application value.
Infrared and color image fusion method based on guided filtering

In this paper, an infrared and color image fusion algorithm based on guided filtering was proposed . The intensity and chromaticity component of the color image in a color space, e.g., HSV, was extracted first. Secondly, in order to avoid edge blurring and reduce computing costs, guided filtering was used to decompose the intensity component of the infrared image and color image to obtain their base layer and detail layer, respectively. Then, the two base and two detail layers were fused using the proposed method separately. The clear and supplementary areas were distinguished by the sum of gradients, the initial base layer was obtained by preliminary fusion, and the intensity level of the fused base layer was adjusted similarly to that of the color image. The two detail layers were fused simply by selecting their maximum absolute value of the gradient map in each pixel, and the intensity component of the fused image was obtained by inverse transformation. Finally, the fused color image was reconstructed by merging the fused intensity component and original chromaticity components. Multiple sets of images were selected for testing the proposed algorithm, and some state-of-art algorithms in the experiment, and the results show that the proposed algorithm had good image visibility, stable color, and fast fusion speed.
HPC: hierarchical point cloud completion method based on multi-scale feature points and cross attention

3D Point cloud, which is considered as the simplest and most efficient shape representation for 3D objects, has been widely used in various real-world applications such as virtual reality, autonomous driving and digital twin. Point cloud completion aims to predict the complete shape structure and recover faithful details given partial observation of an incomplete input. Unlike previous completion methods based on linear architectures, this paper presents a novel hierarchical architecture for point cloud completion which divides the completion process into several levels in a coarse-to-fine manner and significantly improves the network capacity for recovering local details. First, we exploit feature connections between encoded partial inputs and decoded recovery results at the same resolution by extracting multi-scale feature points, which can provide rich information for the following generation process. Second, in order to exploit the local geometric information and interpolate the extracted features points, we introduce cross-attention based generators into the decoding phase. The cross-attention based generator preserves produced structures from previous levels and incorporate the extracted feature points into each step of a progressive generation. Extensive experiments show that our method outperforms state-of-the-art completion approaches on popular PCN and ShapeNet55 datasets.
Method for solving ordinary differential equations of a neural network combined with the ReLU function

Neural networks, as powerful tools for information processing, have been widely applied in various engineering fields. In this paper, we propose a neural network-based solution method that combines the ReLU function for ordinary differential equation (ODE) problems in scientific and engineering applications. The fundamental idea is to approximate the analytical solution of the initial value problem using the output of the neural network model proposed in this study. Through different examples, we demonstrate the accuracy and stability of this algorithm.
Clinical use of an emergency manual by resuscitation teams and impact on performance in the emergency department: a prospective mixed-methods study protocol

Introduction Simulation-based studies indicate that crisis checklist use improves management of patients with critical conditions in the emergency department (ED). An interview-based study suggests that use of an emergency manual (EM)—a collection of crisis checklists—improves management of clinical perioperative crises. There is a need for in-depth prospective studies of EM use during clinical practice, evaluating when and how EMs are used and impact on patient management. Methods and analysis This 6-month long study prospectively evaluates a digital EM during management of priority 1 patients in the Skåne University Hospital at Lund’s ED. Resuscitation teams are encouraged to use the EM after a management plan has been derived (‘Do-Confirm’). The documenting nurse activates and reads from the EM, and checklists are displayed on a large screen visible to all team members. Whether the EM is activated, and which sections are displayed, are automatically recorded. Interventions performed thanks to Do-Confirm EM use are registered by the nurse. Fifty cases featuring such interventions are reviewed by specialists in emergency medicine blinded to whether the interventions were performed prior to or after EM use. All interventions are graded as indicated, of neutral relevance or not indicated. The primary outcome measures are the proportions of interventions performed thanks to Do-Confirm EM use graded as indicated, of neutral relevance, and not indicated. A secondary outcome measure is the team’s subjective evaluation of the EM’s value on a Likert scale of 1–6. Team members can report events related to EM use, and information from these events is extracted through structured interviews. Ethics and dissemination The study is approved by the Swedish Ethical Review Authority (Dnr 2022-01896-01). Results will be published in a peer-reviewed journal and abstracts submitted to national and international conferences to disseminate our findings. Trial registration number NCT05649891.
Locally advanced solitary fibrous tumour of the prostate

Solitary fibrous tumours (SFTs) are rare mesenchymal neoplasms composed of spindle cells, most often occurring in the pleura. SFTs arising from the prostate are exceptionally rare, with only around 40 cases reported in literature to date. We report a man in his 60s who was referred to our clinic for elevated prostate-specific antigen and presented with mild obstructive lower urinary tract and defecatory symptoms. Prostate needle-core biopsy revealed neoplastic spindle cells that strongly expressed CD34. Cross-sectional imaging demonstrated a 12 cm locally advanced heterogeneous prostate mass with intravesical extension and mass effect on the anterior rectum. Radical cystoprostatectomy with orthotopic neobladder reconstruction was performed, and the diagnosis of primary prostatic SFT was made based on histological characteristics and immunophenotyping. We present diagnostic, clinical management and prognostic considerations in patients with primary prostatic SFT.
Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: P-CARDIAC model

This study aimed to develop and validate a cardiovascular diseases (CVD) risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events using Machine-Learning technique. Three cohorts of Chinese patients with established CVD in Hong Kong were included; Hong Kong Island cohort as the derivation cohort, whilst the Kowloon and New Territories cohorts were validation cohorts. The 10-year CVD outcome was a composite of diagnostic or procedure codes for coronary heart disease, ischaemic or haemorrhagic stroke, peripheral artery disease, and revascularization. We estimated incidence of recurrent CVD events for each cohort with reference to the total person-years of each cohort. Multivariate imputation with chained equations (MICE) and XGBoost were applied for the model development. The comparison with TRS-Secondary-P and SMART2 used the validation cohorts with 1000 bootstrap replicates. A total 48,799, 119,672 and 140,533 patients were included in the derivation and validation cohorts, respectively. A list of 125 risk variables were used to make predictions on CVD risk, of which, eight classes of medications were considered interactive drug use. Model performance in the derivation cohort showed satisfying discrimination and calibration with a C-statistic of 0.69. Internal validation showed good discrimination and calibration performance with C-statistic over 0.6. P-CARDIAC also showed better performance than TRS-Secondary-P and SMART2. Compared to other risk scores, P-CARDIAC enables to identify unique patterns of Chinese patients with established CVD. We anticipate that P-CARDIAC can be applied in various settings to prevent recurrent CVD events, thus reducing the related healthcare burden.
Classification of Chest X-ray Images(Pneumonia) Based on ResNet and Grad-CAM

Accurate image classification and an intuitive evaluation framework are crucial for the design and analysis of an automatic medical imaging diagnosis system, however traditional image classification methods lack the demonstration of feature weights focused. To this end, this paper proposes a chest X-ray image classification system, named deep model attention mechanism, based on ResNet and Grad-CAM. The system is designed to classify X-ray images of pneumonia and normal with large feature differences, as well as viral pneumonia and bacterial pneumonia with small feature differences. The attention mechanism of the model is displayed via the class activation map. Experimental results demonstrate that the proposed system achieves high accuracy in distinguishing normal and pneumonia X-ray images (97.27%) as well as discriminating between viral pneumonia and bacterial pneumonia X-ray images (80.28%). Besides, the class activation map vividly presents its feature weights. These results suggest that the proposed system has the potential to be a valuable tool for automatic medical imaging diagnosis.
Learning Aligned Audiovisual Representations for Multimodal Sentiment Analysis

In this paper, we present the solutions to the MER-SEMI subchallenge of Multimodal Emotion Recognition Challenge (MER 2023). This subchallenge focuses on predicting discrete emotions for a small subset of unlabeled videos within the context of semi-supervised learning. Participants are provided with a combination of labeled and large amounts of unlabeled videos. Our preliminary experiments on labeled videos demonstrate that this task is primarily driven by the video and audio modalities, while the text modality plays a relatively weaker role in emotion prediction. To address this challenge, we propose the Video-Audio Transformer (VAT), which takes raw signals as inputs and extracts multimodal representations. VAT comprises a video encoder, an audio encoder, and a cross-modal encoder. To leverage the vast amount of unlabeled data, we introduce a contrastive loss to align the image and audio representations before fusing them through cross-modal attention. Additionally, to enhance the model's ability to learn from noisy video data, we apply momentum distillation, a self-training method that learns from pseudo-targets generated by a momentum model. Furthermore, we fine-tune VAT on annotated video data specifically for emotion recognition. Experimental results on the MER-SEMI task have shown the effectiveness of the proposed VAT model. Notably, our model ranks first (0.891) on the leaderboard. Our project is publicly available at https://github.com/dingchaoyue/Multimodal-Emotion-Recognition-MER-and-MuSe-2023-Challenges.
CLIP-based Model for Effective and Explainable Apparent Personality Perception

In the field of Apparent Personality Perception (APP), a central challenge involves drawing robust inferences from observable behaviour and appearance. Various existing methods attempt to accomplish this by deploying diverse neural network architectures, all aimed at extracting and integrating features from multimodal data sources such as text, audio, and video. Notwithstanding, these methods grapple with issues related to generalisability and explainability, which hamper their applicability and trustworthiness in real-world situations. Responding to this challenge, our paper presents a novel approach to APP that capitalizes on the unique strengths of CLIP (Contrastive Language-Image Pre-Training), a large-scale multimodal pre-training method. CLIP learns image and text representations via natural language supervision and demonstrates a remarkable ability to handle a range of vision-related tasks using natural language queries and prompts. In the context of APP, we harness CLIP as a feature extractor for both personality traits and visual cues. These features are then incorporated into a regression model trained to predict apparent personality traits of a target person from their image or video. A unique strength of our model lies in its inherent multimodal nature (using image and text) giving it the ability to perform competently using only visual data, as opposed to most other models that require both image and audio features. This highlights our method's versatility in handling varied multimodal inputs. We put our method to the test using the ChaLearn First Impressions V2 (CVPR'17) challenge dataset, comparing our results with several state-of-the-art baselines that employ different modalities and architectures for APP. We found that our method either matches or outperforms the competition, and notably, provides intuitive and interpretable explanations for its predictions. By relying on natural language supervision and queries, our approach significantly enhances the generalisability and explainability of APP models. Beyond this, our work underscores the prospective role that large-scale transformers can play in better comprehending psychological phenomena. As a result, our method contributes positively to the broader domain of Multimodal and Responsible Affective Computing.
Bibliographic Reference Classification in Historiographic Documents using Supervised Machine Learning and Grammatical Features

Bibliographic references in scholarly documents are integral to discourse in humanities disciplines. While prior work has focused on reference extraction and parsing from these documents, little research has investigated the classification of footnotes containing bibliographic citations and author commentary using supervised machine learning methodologies. Using an historiographic dataset drawn from the JSTOR humanities archive, we train and compare the performance of a suite of single and hybrid machine learning classifiers on a novel, previously unexplored reference classification task in archival document analysis. Moreover, as a part of this analysis, we investigate the feasibility of using the grammar of these scholarly footnotes as training features for our machine learning models. In our work we compare the performance of traditional features previously used in reference mining and these novel, grammatical features inspired by natural language processing techniques. Our work demonstrates the superiority of hybrid models for classification of scholarly footnotes containing historiographic bibliographic references, the transferability of features from reference extraction to this research problem, and the viability of training machine learning models for this task utilizing novel, grammatical feature sets.
Optimization of Heterogeneous Data in Sensor Networks to Industrial Internet

The growth of applications in the context of the Industrial Internet (IIoT) has resulted in new requirements to meet the heterogeneity of sensors used in monitoring industrial assets. Data transmission is a fundamental activity for IIoT applications. However, an efficient network transmission must care about consumption of available resources. These are the aims of the Approach for Heterogeneous Data Reduction in Wireless Sensors Networks (Δ HEAR), presented in this paper. Applying Dispersion Analysis, Data Aggregation and Lossless Compression models to reduce heterogeneous data transmitted in sensor networks in a distributed way. Performance evaluation results on the Cooja simulator confirm the effectiveness of approach in reduces sensor data and energy consumption.
Summary of the 2nd Natural Language-based Software Engineering Workshop (NLBSE 2023)

Natural language processing (NLP) involves the automated anal- ysis and manipulation of human language. This includes algo- rithms that can analyze text created by humans and algorithms that can generate text that appears natural. Nowadays, NLP methods are becoming increasingly prevalent to enhance various aspects of software development. Indeed, throughout the software development lifecycle, numerous natural language artifacts are produced. Therefore, the existence of NLP-based approaches and tools has opened up possibilities for improving the e ectiveness and productivity of software engineers, processes, and products. The second edition of the Natural Language-Based Software Engi- neering Workshop (NLBSE) took place in 2023 alongside the 45th International Conference on Software Engineering (ICSE 2023), where the research community engaged in discussions about these approaches. This event brought together researchers and practi- tioners from the elds of NLP and software engineering to ex- change experiences, establish future research directions, and pro- mote the adoption of NLP techniques and tools in tackling chal- lenges speci c to software engineering. In this paper, we present a summary of the 2nd edition of the workshop, which comprised three full papers, four short/position papers, ve tool competi- tion/demonstration papers, two keynote talks (\Automated Bug Management: Re ections & the Road Ahead" by David Lo and \Trends and Opportunities in the Application of Large Language Models: the Quest for Maximum E ect" by Albert Ziegler), fol- lowed by extensive discussion among NLBSE participants. More details can be found at https://nlbse2023.github. io/index. html
Machine Learning Techniques for Escaped Defect Analysis in Software Testing

Software testing is crucial to ensure the quality of a software under development. Once a potential bug is identified, a Bug Report (BR) is opened with information to describe and reproduce the found issue. Usually in big companies, hundreds of BRs are opened weekly by different testing teams, which have to be inspected and fixed adequately. This paper is focused on the use of Machine Learning (ML) techniques to automate the Escaped Defect Analysis (EDA), which is an important (but expensive) task to improve the effectiveness of the testing teams. In our work, Escaped Defects (EDs) are bugs or issues that should have been opened by a specific team, but which was accidentally found by another team. The occurrence of EDs is risky, as it is usually related to failures in the testing activities. EDA is usually performed manually by software engineers, who read each BR’s textual content to judge whether it is an ED or not. This is challenging and time-consuming. In our solution, the BR’s content is preprocessed by textual operations and then a feature representation is adopted by a ML classifier to return the probability of EDA labels. Experiments were performed in a dataset of 3767 BRs provided by the Motorola Mobility Comércio de Produtos Eletrônicos Ltda. Different ML algorithms were adopted to build classifiers, obtaining high AUC values (usually higher than 0.8), in a cross-validation experiment. This result indicates a good trade-off between the number of EDs correctly identified and the number of BRs that have to be actually inspected in the EDA process. This paper presents a ML based approach to classify escaped defects described in bug reports. EDs are bugs missed by the QA team in charge and happened to be uncovered by a different team. To automate the identification of EDs (a costly and error-prone task), a dataset of a partner company is leveraged, text processing operators are adopted for feature engineering and 6 classical ML algorithms are applied. The results show satisfactory accuracy and AUC and the experiments indicate a good trade-off between the number of EDs correctly identified and the number of BRs that have to be inspected in the EDA.