High-quality Datasets Research Articles

MaDroid: A maliciousness-aware multifeatured dataset for detecting android malware

System call sequences representing the runtime behavior of an application is particularly useful for anomaly detection in mobile applications. However, one of the main obstacles in this area is the lack of publicly available high-quality datasets. Because of the low computational power and storage constraints of mobile application platforms, a single mobile device cannot accomplish the task of massively installing applications and extracting interaction details with the operating system, making it extremely challenging to build large-scale fine-grained system call datasets. In this paper, we present the MaDroid dataset. It is the first comprehensive dataset and benchmark for anomaly detection in mobile applications using high-dimensional feature sequence data and maliciousness, and the first to incorporate virus total rating (VT) values into dataset features. It is constructed based on an automated collection framework that collects system call sequences from simulation environments at a fine-grained level for both normal and malicious mobile applications. The dataset is 457 GB in size and consists of 50,429 labeled system call sequences. The dataset covers mobile applications released at different times over the past 14 years, and the selected applications span across 10 major mainstream APP markets. We extract different feature subsets from the dataset and perform evaluations using RF, MLP, and GBDT to show the effectiveness and accuracy in detecting malicious mobile APPs. Our dataset can be a useful resource for security and machine learning community.

AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry

This paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction datasets in the realm of machine learning applications in organic chemistry. Recent advances in artificial intelligence have expanded the application of machine learning in chemistry, particularly in yield prediction, retrosynthesis, and reaction condition prediction. However, the effectiveness of these models hinges on the integrity of chemical reaction datasets, which are often plagued by inconsistencies like missing reactants, incorrect atom mappings, and outright erroneous reactions. AutoTemplate introduces a two-stage approach to refine these datasets. The first stage involves extracting meaningful reaction transformation rules and formulating generic reaction templates using a simplified SMARTS representation. This simplification broadens the applicability of templates across various chemical reactions. The second stage is template-guided reaction curation, where these templates are systematically applied to validate and correct the reaction data. This process effectively amends missing reactant information, rectifies atom-mapping errors, and eliminates incorrect data entries. A standout feature of AutoTemplate is its capability to concurrently identify and correct false chemical reactions. It operates on the premise that most reactions in datasets are accurate, using these as templates to guide the correction of flawed entries. The protocol demonstrates its efficacy across a range of chemical reactions, significantly enhancing dataset quality. This advancement provides a more robust foundation for developing reliable machine learning models in chemistry, thereby improving the accuracy of forward and retrosynthetic predictions. AutoTemplate marks a significant progression in the preprocessing of chemical reaction datasets, bridging a vital gap and facilitating more precise and efficient machine learning applications in organic synthesis.Scientific contributionThe proposed automated preprocessing tool for chemical reaction data aims to identify errors within chemical databases. Specifically, if the errors involve atom mapping or the absence of reactant types, corrections can be systematically applied using reaction templates, ultimately elevating the overall quality of the database.Graphical

Accurate prediction of CDR-H3 loop structures of antibodies with deep learning

Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model and provides a 2.24 Å average RMSDCα between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody–antigen interactions. This structural prediction tool can be used to optimize antibody–antigen binding and engineer therapeutic antibodies with biophysical properties for specialized drug administration route.

Accurate prediction of CDR-H3 loop structures of antibodies with deep learning.

Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model and provides a 2.24 Å average RMSDCα between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody-antigen interactions. This structural prediction tool can be used to optimize antibody-antigen binding and engineer therapeutic antibodies with biophysical properties for specialized drug administration route.

Misogynistic attitude detection in YouTube comments and replies: A high-quality dataset and algorithmic models

Social media platforms are now not only a medium for expressing users views, feelings, emotions and sentiments but are also being abused by people to propagate unpleasant and hateful content. Consequently, research efforts have been made to develop techniques and models for automatically detecting and identifying hateful, abusive, vulgar, and offensive content on different platforms. Although significant progress has been made on the task, the research on design of methods to detect misogynistic attitude of people in non-English and code-mixed languages is not very well-developed. Non-availability of suitable datasets and resources is one main reason for this. Therefore, this paper attempts to bridge this research gap by presenting a high-quality curated dataset in the Hindi-English code-mixed language. The dataset includes 12,698 YouTube comments and replies, with each comment annotated under two-level categories, first as optimistic and pessimistic, and then into different types at second level based on the content. The inter-annotator agreement in the dataset is found to be 0.84 for the first subtask, and 0.79 for the second subtask, indicating the reasonably high quality of annotations. Different algorithmic models are explored for the task of automatic detection of the misogynistic attitude expressed in the comments, with the mBERT model giving best performance on both subtasks (reported macro average F1 scores of 0.59 and 0.52, and weighted average F1 scores of 0.66 and 0.65, respectively). The analysis and results suggest that the dataset can be used for further research on the topic and that the developed algorithmic models can be applied for automatic detection of misogynistic attitude in social media conversations and posts.

Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients

BackgroundLeft ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strategy to identify LVE in HF patients by means of pulse wave signals.MethodWe constructed two high-quality pulse wave datasets comprising a non-LVE group and an LVE group based on the 264 HF patients. Fourier series calculations were employed to determine if significant frequency differences existed between the two datasets, thereby ensuring their validity. Then, the ML-based identification was undertaken by means of classification and regression models: a weighted random forest model was employed for binary classification of the datasets, and a densely connected convolutional network was utilized to directly estimate the left ventricular diastolic diameter index (LVDdI) through regression. Finally, the accuracy of the two models was validated by comparing their results with clinical measurements, using accuracy and the area under the receiver operating characteristic curve (AUC-ROC) to assess their capability for identifying LVE patients.ResultsThe classification model exhibited superior performance with an accuracy of 0.91 and an AUC-ROC of 0.93. The regression model achieved an accuracy of 0.88 and an AUC-ROC of 0.89, indicating that both models can quickly and accurately identify LVE in HF patients.ConclusionThe proposed ML methods are verified to achieve effective classification and regression with good performance for identifying LVE in HF patients based on pulse wave signals. This study thus demonstrates the feasibility and potential of the ML-based strategy for clinical practice while offering an effective and robust tool for diagnosing and intervening ventricular remodeling.

Broiler health monitoring technology based on sound features and random forest

The existing broiler health monitoring technology has problems such as low automation, unstable monitoring results, and low practical value, making it difficult to provide timely and reliable broiler health monitoring results. The broiler sound signal can provide feedback on their health. A widely validated and correct experience is to analyze the frequency of coughs in a segment of broiler sound signal to determine the health of the broiler group. Based on this, in this paper, the authors proposed a new broiler health monitoring technology based on sound detection. The broiler health monitoring problem is cleverly transformed into a multi-classification problem, which can be solved by identifying the sound types in broiler sound signals. Specifically, the audio signal collection system was designed to complete signal collection and preliminary signal filtering. Wiener filtering was used for deep signal filtering. The 60-dimensional sound features with good performance from three aspects, time-frequency domain, Mel-Frequency Cepstral Coefficients, and sparse representation were extracted, and a preliminary data set was created. Min-max normalization was used to align the numerical distribution of the data set, and a high-quality data set was created. Multi-classification models based on different classification algorithms and neural networks were trained, and the best-performing Random Forest was obtained, thus parameter optimization was carried out, and the optimal multi-classification model was obtained, achieving a classification accuracy of 91.14%. The visualization platform was built to process the classification results of the multi-classification model, completing majority voting processing and cough rate calculation, thereby achieving broiler health monitoring. In addition, the definitions of cough rate and prediction accuracy were newly proposed. A large number of experiments have verified the feasibility of the broiler health monitoring technology proposed in this paper, with an average prediction accuracy of 98.97% achieved.

Effect of Using Numerical Data Scaling on Supervised Machine Learning Performance

Before building machine learning models, the dataset should be prepared to be a high quality dataset, we should give the model the best possible representation of the data. Different attributes may have different scales which possibly will increase the difficulty of the problem that is modeled. A model with varying scale values may suffers from poor performance during learning. Our study explores the usage of Numerical Data Scaling as a data pre-processing step with the purpose of how effectively these methods can be used to improve the accuracy of learning algorithms. In particular, three numerical data Scaling methods with four machine learning classifiers to predict disease severity were compared. The experiments were built on Coronavirus 2 (SARS-CoV-2) datasets which included 1206 patients who were admitted during the period between June 2020 and April 2021. The diagnosis of all cases was confirmed with RT-PCR. Basic demographic data and medical characteristics of all participants was collected. The reported results indicate that all techniques are performing well with Numerical Data Scaling and there are significant improvement in the models for unseen data. lastly, we can conclude that there are increase in the classifier performance while using scaling techniques. However, these methods help the algorithms to better understand learn the patterns in the dataset which help making accurate models

BCN20000: Dermoscopic Lesions in the Wild

Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a collection of 18,946 dermoscopic images spanning from 2010 to 2016, collated at the Hospital Clínic in Barcelona, Spain. The BCN20000 dataset aims to address the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions in hard-to-diagnose locations such as those found in nails and mucosa, large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. Our dataset covers eight key diagnostic categories in dermoscopy, providing a diverse range of lesions for artificial intelligence model training. Furthermore, a ninth out-of-distribution (OOD) class is also present on the test set, comprised of lesions which could not be distinctively classified as any of the others. By providing a comprehensive collection of varied images, BCN20000 helps bridge the gap between the training data for machine learning models and the day-to-day practice of medical practitioners. Additionally, we present a set of baseline classifiers based on state-of-the-art neural networks, which can be extended by other researchers for further experimentation.

Digital-twin-driven intelligent tracking error compensation of ultra-precision machining

In ultra-precision machining (UPM), linear axis tracking affects contour accuracy and final machining quality. Traditional error modeling is complicated by the identification of numerous unknown parameters linked to nonlinear characteristics in the linear feed axes. To fill this gap, this study proposed a digital-twin-driven framework integrating the developed G-code interpreter and the deep learning model to achieve real-time tracking error compensation for UPM. To enhance the prediction accuracy of the tracking error of each axis of UPM machines, Bayesian hyperparameter optimization and feature importance analysis were conducted in the proposed TCN-BiLSTM model using high-quality training datasets from well-designed experiments. Ultimately, validation of the proposed system on a three-axis ultra-precision milling machine demonstrated its excellent performance. The experimental results showed that the optimized TCN-BiLSTM model exhibited an excellent capacity to predict the tracking error of the X-axis and Y-axis with minimal mean absolute error values of 0.000009 and 0.000023, respectively. Implementing the customized application reduced X-axis and Y-axis tracking errors by approximately 45–75% and 40–70%, respectively. This study first validates the feasibility of deep learning to improve accuracy in the UPM field, which will provide significant insight into speeding up the digitalization and intellectualization of the UPM scenario.

A high-quality dataset featuring classified and annotated cervical spine X-ray atlas

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image recognition in the medical field, which requires large-scale and high-quality training datasets consisting of raw images and annotated images. However, suitable experimental datasets for cervical spine X-ray are scarce. We fill the gap by providing an open-access Cervical Spine X-ray Atlas (CSXA), which includes 4963 raw PNG images and 4963 annotated images with JSON format (JavaScript Object Notation). Every image in the CSXA is enriched with gender, age, pixel equivalent, asymptomatic and symptomatic classifications, cervical curvature categorization and 118 quantitative parameters. Subsequently, an efficient algorithm has developed to transform 23 keypoints in images into 77 quantitative parameters for cervical spine disease diagnosis and treatment. The algorithm’s development is intended to assist future researchers in repurposing annotated images for the advancement of machine learning techniques across various image recognition tasks. The CSXA and algorithm are open-access with the intention of aiding the research communities in experiment replication and advancing the field of medical imaging in cervical spine.

De novo transcriptomes of cave and surface isopod crustaceans: insights from 11 species across three suborders

Isopods are a diverse group of crustaceans, that inhabit various environments, including terrestrial, freshwater, and marine, both on the surface and in the underground. The biological mechanisms underlying their wide range of adaptations to diverse ecological niches remain elusive. In order to unravel the molecular basis of their adaptability, we generated a comprehensive RNAseq dataset comprising 11 isopod species belonging to the three different suborders: freshwater Asellota, marine, brackish and freshwater Sphaeromatidea, and terrestrial Oniscidea, with representatives from families Asellidae, Sphaeromatidae, and Trichoniscidae, respectively. Representatives of each family were collected from both cave and surface environments, representing at least three independent cave colonization events. Three biological replicates were sequenced from each species to ensure data robustness. The 11 high-quality RNAseq datasets will serve as a valuable resource for understanding cave-specific adaptations, comparative and functional genomics, ecological annotation as well as aid in conservation efforts of these non-model organisms. Importantly, transcriptomes of eight featured species have been made publicly accessible for the first time.

Revisiting Bundle Recommendation for Intent-aware Product Bundling

Product bundling represents a prevalent marketing strategy in both offline stores and e-commerce systems. Despite its widespread use, previous studies on bundle recommendation face two significant limitations. Firstly, they rely on noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session. Secondly, they target specific tasks by holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. This paper proposes to take a step back and considers the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich metadata, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion and ranking, to explanation and auto-naming, whereby 19 research questions are raised correspondingly to guide the analysis. Finally, we conduct extensive experiments and analyses with representative recommendation models and large language models (LLMs), demonstrating the challenges and opportunities, especially with the emergence of LLMs. To summarize, our study contributes by introducing novel data sources, paving the way for new research avenues, and offering insights to guide product bundling in real e-commerce platforms.

Meta-Fed IDS: Meta-learning and Federated learning based fog-cloud approach to detect known and zero-day cyber attacks in IoMT networks

The Internet of Medical Things (IoMT) is a transformative fusion of medical sensors, equipment, and the Internet of Things, positioned to transform healthcare. However, security and privacy concerns hinder widespread IoMT adoption, intensified by the scarcity of high-quality datasets for developing effective security solutions. Addressing these challenges, we propose a novel framework for cyberattack detection in dynamic IoMT networks. This framework integrates Federated Learning with Meta-learning, employing a multi-phase architecture for identifying known attacks, and incorporates advanced clustering and biased classifiers to address zero-day attacks. The framework's deployment is adaptable to dynamic and diverse environments, utilizing an Infrastructure-as-a-Service (IaaS) model on the cloud and a Software-as-a-Service (SaaS) model on the fog end. To reflect real-world scenarios, we introduce a specialized IoMT dataset. Our experimental results indicate high accuracy and low misclassification rates, demonstrating the framework's capability in detecting cyber threats in complex IoMT environments. This approach shows significant promise in bolstering cybersecurity in advanced healthcare technologies.

Benchmarking compound activity prediction for real-world drug discovery applications

Identifying active compounds for target proteins is fundamental in early drug discovery. Recently, data-driven computational methods have demonstrated promising potential in predicting compound activities. However, there lacks a well-designed benchmark to comprehensively evaluate these methods from a practical perspective. To fill this gap, we propose a Compound Activity benchmark for Real-world Applications (CARA). Through carefully distinguishing assay types, designing train-test splitting schemes and selecting evaluation metrics, CARA can consider the biased distribution of current real-world compound activity data and avoid overestimation of model performances. We observed that although current models can make successful predictions for certain proportions of assays, their performances varied across different assays. In addition, evaluation of several few-shot training strategies demonstrated different performances related to task types. Overall, we provide a high-quality dataset for developing and evaluating compound activity prediction models, and the analyses in this work may inspire better applications of data-driven models in drug discovery.

Prescriptive procedure for manual code smell annotation

– Code smells are structures in code that present potential software maintainability issues. Manually constructing high-quality datasets to train ML models for code smell detection is challenging. Inconsistent annotations, small size, non-realistic smell-to-non-smell ratio, and poor smell coverage hinder the dataset quality. These issues arise mainly due to the time-consuming nature of manual annotation and annotators’ disagreements caused by ambiguous and vague smell definitions.To address challenges related to building high-quality datasets suitable for training ML models for smell detection, we designed a prescriptive procedure for manual code smell annotation. The proposed procedure represents an extension of our previous work, aiming to support the annotation of any smell defined by Fowler. We validated the procedure by employing three annotators to annotate smells following the proposed annotation procedure.The main contribution of this paper is a prescriptive annotation procedure that benefits the following stakeholders: annotators building high-quality smell datasets that can be used to train ML models, ML researchers building ML models for smell detection, and software engineers employing ML models to enhance the software maintainability. Secondary contributions are the code smell dataset containing Data Class, Feature Envy, and Refused Bequest, and DataSet Explorer tool which supports annotators during the annotation procedure.

Feature Selection Techniques in Intrusion Detection: A Comprehensive Review

This investigation aims to explore previous research on the implementation of feature selection in intrusion detection. Feature selection has demonstrated its ability to enhance or sustain comparable classification accuracy levels for intrusion detection systems, while simultaneously improving classification efficiency. The evaluation includes an assessment of filter-based, wrapper-based, and hybrid feature selection techniques. Given that Big Data challenges can affect intrusion detection, feature selection’s classification efficiency can aid in lowering computing requirements. Older KDD intrusion detection datasets have received considerable attention in previous feature selection research. Consequently, researchers need more high-quality datasets that are available to the general public.

Harnessing AI for solar energy: Emergence of transformer models

This review emphasizes the critical need for accurate integration of solar energy into power grids. It meticulously examines the advancements in transformer models for solar forecasting, representing a confluence of renewable energy research and cutting-edge machine learning. It evaluates the effectiveness of various transformer architectures, including single, hybrid, and specialized models, across different forecasting horizons, from short to medium term. This review unveils substantial improvements in forecasting accuracy and computational efficiency, highlighting the models' proficiency in handling complex and diverse solar data. A key contribution is the emphasis on the crucial role of hyperparameters in refining model performance, balancing precision against computational demands. Importantly, the research also identifies critical challenges, such as the significant computational resources required and the need for expansive, high-quality datasets, which limit the broader application of these models. In response, this review advocates for future research directions focused on standardizing model configurations, venturing into longer-term forecasting, and fostering innovations to enhance computational economy. These proposed pathways aim to surmount current challenges, steering the domain towards more accurate, adaptable, and sustainable solar forecasting solutions that can contribute to achieving global renewable energy and climate objectives. This review not only maps the present landscape of transformer models in solar energy forecasting but also charts a trajectory for future advancements. It serves as a pivotal guide for researchers and practitioners, delineating the current advancements and future directions in navigating the complexities of solar data interpretation and forecasting, thereby significantly contributing to the development of reliable and efficient renewable energy systems.

ParisLuco3D: A High-Quality Target Dataset for Domain Generalization of LiDAR Perception

ParisLuco3D: A High-Quality Target Dataset for Domain Generalization of LiDAR Perception

AI-generated synthetic clinical-genomic data for precision oncology research: Validation using a case study on lung adenocarcinoma.

e13627 Background: The analysis of genomic variants is crucial in precision oncology research, offering insights into cancer risks and progression, especially in diverse types such as lung adenocarcinoma (LUAD). However, such research often grapples with balancing patient privacy with the need for comprehensive, high-quality genomic datasets. Our project addresses this by creating synthetic clinical-genomic data, which maintains patient confidentiality and provides a rich resource for genomic cancer research. Methods: Leveraging the GuardantINFORM database, which includes anonymized genomic data and structured payer claims, we focused on generating synthetic data for LUAD patient cohorts. This approach involves processing real patient data into a format compatible with Medisyn’s generative AI models, ensuring the synthetic data retains the original's statistical properties, and processing the output back into the original database structure and format. This method plays a crucial role in maintaining patient privacy and serves as a valuable tool for research by enabling the generation of realistic patients with desired properties on demand. Results: Our synthetic data closely mirrors real-world genomic and claims variable distributions, evidenced by a 0.994 R2 correlation between real and synthetic data along with comparable Oncoprints. Importantly, privacy tests show that patient confidentiality is effectively maintained despite this effective performance. The synthetic data's utility was then demonstrated in a study replicating real-world findings: LUAD patients with KRAS G12C in combination with STK11 mutations showed a significantly higher risk of early mortality. This underscores the potential of synthetic data in advancing cancer research. Conclusions: This research offers a promising avenue for the cancer research community. By providing a method to share privatized, synthetic genomic data, which can be combined and generated on demand, we enable broader, more responsible data sharing. This approach protects patient privacy and offers a rich dataset for groundbreaking research, potentially accelerating advances in cancer diagnosis and treatment. [Table: see text]