Relevant Datasets Research Articles

Second-harmonic generation tensors from high-throughput density-functional perturbation theory

Optical materials play a key role in enabling modern optoelectronic technologies in a wide variety of domains such as the medical or the energy sector. Among them, nonlinear optical crystals are of primary importance to achieve a broader range of electromagnetic waves in the devices. However, numerous and contradicting requirements significantly limit the discovery of new potential candidates, which, in turn, hinders the technological development. In the present work, the static nonlinear susceptibility and dielectric tensor are computed via density-functional perturbation theory for a set of 579 inorganic semiconductors. The computational methodology is discussed and the provided database is described with respect to both its data distribution and its format. Several comparisons with both experimental and ab initio results from literature allow to confirm the reliability of our data. The aim of this work is to provide a relevant dataset to foster the identification of promising nonlinear optical crystals in order to motivate their subsequent experimental investigation.

Fusing linguistic and acoustic information for automated forensic speaker comparison

Verifying the speaker of a speech fragment can be crucial in attributing a crime to a suspect. The question can be addressed given disputed and reference speech material, adopting the recommended and scientifically accepted likelihood ratio framework for reporting evidential strength in court. In forensic practice, usually, auditory and acoustic analyses are performed to carry out such a verification task considering a diversity of features, such as language competence, pronunciation, or other linguistic features. Automated speaker comparison systems can also be used alongside those manual analyses. State-of-the-art automatic speaker comparison systems are based on deep neural networks that take acoustic features as input. Additional information, though, may be obtained from linguistic analysis. In this paper, we aim to answer if, when and how modern acoustic-based systems can be complemented by an authorship technique based on frequent words, within the likelihood ratio framework. We consider three different approaches to derive a combined likelihood ratio: using a support vector machine algorithm, fitting bivariate normal distributions, and passing the score of the acoustic system as additional input to the frequent-word analysis. We apply our method to the forensically relevant dataset FRIDA and the FISHER corpus, and we explore under which conditions fusion is valuable. We evaluate our results in terms of log likelihood ratio cost (Cllr) and equal error rate (EER). We show that fusion can be beneficial, especially in the case of intercepted phone calls with noise in the background.

Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis.

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.

Efficient Heuristic Management of Cloud Data Lakes for Enhanced Big Data Handling in Cloud Computing Environments

Efficient control of cloud data lakes is important for improving big data dealing in cloud computing settings. Cloud data lakes serve as stores for vast amounts of organized, semi-structured, and unstructured data from varied sources, offering scaling and freedom in data storage and processing. However, the sheer amount and range of data offer difficulties in improving storage, access, and analysis processes. This study suggests a heuristic method to handle these issues by improving the order and recovery of data within cloud data lakes. The heuristic method focuses on automatically handling data splitting, storage, and caching strategies based on data access trends and task factors. By leveraging heuristic principles, such as adaptable splitting and predictive caching, the suggested method aims to reduce delay and improve total query speed. Key components of the heuristic management include adaptable splitting methods that actively change data partition sizes based on access frequency and data distribution patterns. This flexible method ensures that frequently accessed data is easily available, while less accessed data is efficiently saved to improve storage usage. Furthermore, predictive storing methods expect future data access patterns using machine learning models or previous data trends. By strategically saving relevant datasets in memory or high-speed storage, the system reduces delay for repeated questions, thereby improving real-time data processing capabilities. The success of the suggested heuristic management method is tested through models and benchmarks using real big data tasks. Comparative research against standard static splitting and caching methods shows significant changes in query response times and resource utilization efficiency.

AENet: Image Retrieval of Kazakh Handwritten Documents Based on Attention Mechanism and Feature Aggregation

Kazakh is one of the multilingual languages of China and is widely spoken in some areas of Xinjiang, China. However, due to the fact that Kazakh is a language in which several characters are glued together to form a continuous word with a unique shape and complex structural combinations of relationships. This paper explores a solution for offline image retrieval of handwritten Kazakh words, which is a challenging task because, due to the lack of relevant datasets and the special writing morphology of the Kazakh language, traditional text image retrieval algorithms often struggle to achieve satisfactory results when dealing with writing styles that are varied and adherent to the language. Therefore, a dataset of offline Kazakh handwritten document images was created in this paper. The dataset contains 300 pages of document images with 20 500 words. Then, a new model called the ‘AENet’ is proposed. The model utilizes an attention mechanism to focus more finely on focal regions such as centers, inflection points, and contours of handwritten word images and to capture important local features from different scales. Fusion space pyramid pooling, feature aggregation, encoding operations, and feature downscaling and reconstruction are used to extract and reconstruct more representative feature representations from local to global to capture the overall information in the word images. Through experimental evaluation on Kazak‐80, Zilla‐64, and HWDB1.1‐375 datasets, it is verified that the method significantly improves the mAP for image retrieval of handwritten words, which is especially applicable to adhesive languages like Kazakh. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

A cross-temporal contrastive disentangled model for ancient Chinese understanding

Ancient Chinese is a crucial bridge for understanding Chinese history and culture. Most existing works utilize high-resource modern Chinese to understand low-resource ancient Chinese, but they fail to fully consider the semantic and syntactic gaps between them due to their changes over time, resulting in the misunderstanding of ancient Chinese. Hence, we propose a novel language pre-training framework for ancient Chinese understanding based on the Cross-temporal Contrastive Disentanglement Model (CCDM), which bridges the gap between modern and ancient Chinese with their parallel corpus. Specifically, we first explore a cross-temporal data augmentation method by disentangling and reconstructing the parallel ancient-modern corpus. It is noteworthy that the proposed decoupling strategy takes full account of the cross-temporal character between ancient and modern Chinese. Then, cross-temporal contrastive learning is exploited to train the model by fully leveraging the cross-temporal information. Finally, the trained language model is utilized for downstream tasks. We conduct extensive experiments on six ancient Chinese understanding tasks. Results demonstrate that our model outperforms the state-of-the-art baselines. Our framework also holds potential applicability to other languages that have undergone evolutionary changes, leading to shifts in syntax and semantics.11Our model is available at https://github.com/yuting-wei/CCDM.

Implementasi Machine Learning Untuk Prediksi Harga Laptop Menggunakan Algoritma Regresi Linear Berganda

This article discusses the implementation of machine learning using multiple linear regression algorithms to predict laptop prices. The main objective of this research is to design an appropriate predictive model based on various features such as technical specifications and laptop brands. The research stages include literature study, collection of relevant datasets, pre-processing or data cleaning, Exploratory Data Analysis, Feature Engineering, data splitting, model building, and website development. The results show that the proposed model is able to provide price predictions with a high level of accuracy, measured using evaluation metrics such as Mean Absolute Error (MAE) which produces a value of 0.18 and R-squared (R²) which has a value of 0.68. Implementation was done by integrating Jupyter Notebook, Visual Studio Code (VS Code) and Streamlit for interactive web application development. The conclusion of this research is that the multiple linear regression algorithm is effective in predicting laptop prices, and can be used as a tool in making business decisions and pricing strategies.

Current landscape and future trends in salivary gland oncology research-a bibliometric evaluation.

The salivary glands are susceptible to both endogenous and exogenous influences, potentially resulting in the development of oncology. With the wide application of various technologies, research in this area has experienced rapid growth. Therefore, researchers must identify and characterize the current research hot topics to grasp the forefront of developments in the dynamic field of salivary gland oncology. The objective of this study was to thoroughly assess the current status and identify potential future research directions in salivary gland oncology. The relevant salivary gland oncology dataset was obtained from the Web of Science Core Collection (WOSCC) database. Subsequently, VoSviewer and CiteSpace were employed for further evaluation. A total of 9,695 manuscripts were extracted and downloaded from the WOSCC database. Our findings revealed a substantial surge in research volume over the past 12 years. The researchers' analysis revealed that Abbas Agami showed unparalleled dedication, with over 180 publications, and that RH Spiro had the highest cocitation count, confirming its status as a key figure in the field. The detection of bursts in secretory carcinoma and the integration of artificial intelligence in salivary oncology have attracted increasing interest. Notably, there is a discernible trend towards increased research engagement in the study of salivary gland malignancies. This study not only evaluated the current research landscape in salivary gland oncology but also anticipates future trends. These insights could contribute to the advancement of knowledge and policymaking in salivary gland oncology.

YOLOv8n-DDA-SAM: Accurate Cutting-Point Estimation for Robotic Cherry-Tomato Harvesting

Accurately identifying cherry-tomato picking points and obtaining their coordinate locations is critical to the success of cherry-tomato picking robots. However, previous methods for semantic segmentation alone or combining object detection with traditional image processing have struggled to accurately determine the cherry-tomato picking point due to challenges such as leaves as well as targets that are too small. In this study, we propose a YOLOv8n-DDA-SAM model that adds a semantic segmentation branch to target detection to achieve the desired detection and compute the picking point. To be specific, YOLOv8n is used as the initial model, and a dynamic snake convolutional layer (DySnakeConv) that is more suitable for the detection of the stems of cherry-tomato is used in neck of the model. In addition, the dynamic large convolutional kernel attention mechanism adopted in backbone and the use of ADown convolution resulted in a better fusion of the stem features with the neck features and a certain decrease in the number of model parameters without loss of accuracy. Combined with semantic branch SAM, the mask of picking points is effectively obtained and then the accurate picking point is obtained by simple shape-centering calculation. As suggested by the experimental results, the proposed YOLOv8n-DDA-SAM model is significantly improved from previous models not only in detecting stems but also in obtaining stem’s masks. In the mAP@0.5 and F1-score, the YOLOv8n-DDA-SAM achieved 85.90% and 86.13% respectively. Compared with the original YOLOv8n, YOLOv7, RT-DETR-l and YOLOv9c, the mAP@0.5 has improved by 24.7%, 21.85%, 19.76%, 15.99% respectively. F1-score has increased by 16.34%, 12.11%, 10.09%, 8.07% respectively, and the number of parameters is only 6.37M. In the semantic segmentation branch, not only does it not need to produce relevant datasets, but also improved its mIOU by 11.43%, 6.94%, 5.53%, 4.22% and mAP@0.5 by 12.33%, 7.49%, 6.4%, 5.99% compared to Deeplabv3+, Mask2former, DDRNet and SAN respectively. In summary, the model can well satisfy the requirements of high-precision detection and provides a strategy for the detection system of the cherry-tomato.

Are your apps accessible? A GCN-based accessibility checker for low vision users

Context:Accessibility issues (e.g., small size and narrow interval) in mobile applications (apps) lead to obstacles for billions of low vision users in interacting with Graphical User Interfaces (GUIs). Although GUI accessibility scanning tools exist, most of them perform rule-based check relying on complex GUI hierarchies. This might make them detect invisible redundant information, cannot handle small deviations, omit similar components, and is hard to extend. Objective:In this paper, we propose a novel approach, named ALVIN (Accessibility Checker for Low Vision), which represents the GUI as a graph and adopts the Graph Convolutional Neural Networks (GCN) to label inaccessible components. Method:ALVIN removes invisible views to prevent detecting redundancy and uses annotations from low vision users to handle small deviations. Also, the GCN model could consider the relations between GUI components, connecting similar components and reducing the possibility of omission. ALVIN only requires users to annotate the relevant dataset when detecting new kinds of issues. Results:Our experiments on 48 apps demonstrate the effectiveness of ALVIN, with precision of 83.5%, recall of 78.9%, and F1-score of 81.2%, outperforming baseline methods. In RQ2, the usefulness is verified through 20 issues submitted to open-source apps. The RQ3 also illustrates the GCN model is better than other models. Conclusion:To summarize, our proposed approach can effectively detect accessibility issues in GUIs for low vision users, thereby guiding developers in fixing them efficiently.

Prognostic relevance and validation of ARPC1A in the progression of low-grade glioma.

Low-grade glioma (LGG) is a grade II-III glioma accompanied by distinct clinical and molecular characteristics and the studies related to its prognosis are still unclear. The objective of this study is to explore the involvement of mitochondrial-related genes SLBP, COMMD7, LSM4, TOMM34, RPP40, FKBP1A, ARPC1A, and TBCA for the prognosis of LGG. We detected differences in the expression of some of the genes by analyzing the bioinformatics dataset and combining it with RT-PCR experiments. Subsequently, a nomogram was constructed and validated for the clinical relevance of risk factors such as age, WHO grade, IDH mutation status, Ch.1p19q co-deletion status, and high and low expression of ARPC1A to predict the 1-, 3-, 5-year overall survival and prognostic relevance of ARPC1A. Gene set enrichment analysis was performed for the relevant datasets pertinent to the expression of ARPC1A to elucidate the cancer-promoting pathways involved in the LGG through KEGG and GO analysis. Transfection assays, CCK-8 assays, and flow cytometry were used to determine the proliferation rate, and apoptosis rate of the HS683 and SW1783 cell lines respectively. Western blotting was used to examine the involvement of the cancer-promoting activity of ARPC1A through MAPK signaling. In this study, the prognostic value of ARPC1A in LGG was found by bioinformatics analysis combined with experimental approach analysis and may be a significant independent risk factor. ARPC1A fosters a higher LGG proliferation rate that may control the MAP kinase signaling and could be a prominent biomarker for LGG. Future studies are warranted to explore its clinical implications.

ESG and financial performance via uncertain mining technology: do Multilatinas contribute to the sustainability of the region?

PurposeThis paper proposes a methodology based on an uncertain mining technology that identifies the linguistic relationships of ESG and its components with a financial performance metric to help the sustainability diagnosis of a region, specifically Latin America.Design/methodology/approachFirst, based on a relevant dataset of companies in a region, a procedure is formulated whereby an uncertain mining technology extracts the mathematically significant linguistic relationships of ESG and its components with a financial performance metric. Second, a knowledge management process is designed based on the linguistic summaries obtained from the mining process. As a final step and drawing upon the two preceding processes, a diagrammatic system of signals is proposed for diagnosing the sustainability of the region as contributed by its companies.FindingsAfter this methodology is instantiated on a group of Multilatinas, it is observed that their sustainability contributions to the region are limited and that none of the identified linguistic relationships between ESG and the financial performance metric are favorable for the region.Originality/valueThis is the first proposal of its kind and it can be applied to any region of the world to assess the financial performance of its companies regarding their ESG commitments. In addition, it enables the region to comprehensively monitor compliance with the 2030 SDG agenda.

A Review of Point Cloud Alignment Methods and Their Applications

Point cloud alignment is a key technique in the field of computer vision, which involves estimating the transformation between two-point clouds. With the development of optimization methods and deep learning techniques, the robustness and efficiency of point cloud alignment have been significantly improved. Recent studies have combined these two methods to further optimize the performance. Meanwhile, advances in 3D sensing and 3D reconstruction techniques have given rise to the new research field of cross-source point cloud alignment. This paper reviews recent advances in point cloud alignment, including both homologous and cross-source alignment techniques, and explores the interconnection of optimization and deep learning techniques. In addition, the paper reviews relevant benchmark datasets and explores their applications in different domains. Finally, future research directions for point cloud alignment are also described.

Detecting abnormal logins by discovering anomalous links via graph transformers

Anomalous authentications are a critical indicator of advanced persistent threats (APTs), in which adversaries exploit network vulnerabilities to gain unauthorized access and move stealthily between devices using stolen credentials. As the set of interactions between entities in a network essentially forms graph-structured data, state-of-the-art algorithms such as graph neural networks (GNNs) can be used to detect anomalous interactions that may indicate an ongoing attack. However, the success of detecting anomalous authentications using GNNs is conditioned on the representational power and performance of those models. A crucial problem is how to aggregate the node embeddings so that the GNN can better represent the network topology. Existing graph neural networks traditionally use simple functions (e.g., sum, max, mean) on the node embeddings to preserve permutation invariance and achieve consistent node representations. However, we argue that an effective aggregation of node features into a graph-level representation cannot be achieved through simple sum or mean operations. In this work, we propose a residual soft-attention scheme that facilitates the aggregation of node representations through a weighted sum, resulting in enhanced node representations and improved filtration of irrelevant information. Experimental results on three relevant datasets have shown the proposed method can detect abnormal authentications with lower false positives than competitors.

An agent‐based model of consumer demand

AbstractThis paper studies an agent‐based model of consumer demand. Agents are heterogeneous with respect to their preferences and incomes. There are two basic ingredients in the model. The first ingredient is a metric that captures the degree of heterogeneity between agents. The second ingredient is a serial computer algorithm that is used in order to compute a terminal consumption bundle at which income is exhausted and overall utility is maximized. Agents are clustered into heterogeneous groups based on their preferences and incomes. We extract information about the evolution of consumer expenditure under different price regimes and the buildup of optimal demand for varying levels of income and preference parameter values. These features cannot be obtained in the classical framework of static utility maximization. Our agent‐based data‐driven methodology can be applied to any relevant data set and so provide a reliable model for forecasting demand given some agent characteristics.

Health literacy and language teaching: data-based host language lexicons

Using a corpus-based approach, this paper presents methods and results for assessing, extracting, and describing the core vocabulary relevant to healthcare access among migrant populations. The aim is to bridge the gap between the basic information conveyed to people arriving in Portugal and the materials as well as other lexicographic resources used in language teaching. The work includes identifying available resources and/or sources for compiling the relevant dataset for healthcare access; selecting available tools for corpus inquiry; testing and comparing results from different functionalities and different lexical statistics measures available in the tools; manual filtering of the data; and analyzing the results and the extracted lexicon. The obtained results reflect the organization of the extracted lexicon in subdomains, the organization of the items within each subdomain, the relationship with common vocabulary, and the extraction of authentic examples from the corpus.

Deep Learning Hyperspectral Pansharpening on Large-Scale PRISMA Dataset

Hyperspectral pansharpening is crucial for the improvement of the usability of images in various applications. However, it remains underexplored due to a scarcity of data. The primary goal of pansharpening is to enhance the spatial resolution of hyperspectral images by reconstructing missing spectral information without compromising consistency with the original data. This paper addresses the data gap by presenting a new hyperspectral dataset specifically designed for pansharpening and the evaluation of several deep learning strategies using this dataset. The new dataset has two crucial features that make it invaluable for deep learning hyperspectral pansharpening research. (1) It presents the highest cardinality of images in the state of the art, making it the first statistically relevant dataset for hyperspectral pansharpening evaluation, and (2) it includes a wide variety of scenes, ensuring robust generalization capabilities for various approaches. The data, collected by the ASI PRISMA satellite, cover about 262,200 km2 and their heterogeneity is ensured by a random sampling of the Earth’s surface. The analysis of the deep learning methods consists in the adaptation of these approaches to the PRISMA hyperspectral data and the quantitative and qualitative evaluation of their performance in this new scenario. The investigation included two settings: Reduced Resolution (RR) to evaluate the techniques in a controlled environment and Full Resolution (FR) for a real-world evaluation. In addition, for the sake of completeness, we have also included machine-learning-free approaches in both scenarios. Our comprehensive analysis reveals that data-driven neural network methods significantly outperform traditional approaches, demonstrating a superior adaptability and performance in hyperspectral pansharpening under both RR and FR protocols.

Rotation invariance and equivariance in 3D deep learning: a survey

Deep neural networks (DNNs) in 3D scenes show a strong capability of extracting high-level semantic features and significantly promote research in the 3D field. 3D shapes and scenes often exhibit complicated transformation symmetries, where rotation is a challenging and necessary subject. To this end, many rotation invariant and equivariant methods have been proposed. In this survey, we systematically organize and comprehensively overview all methods. First, we rewrite the previous definition of rotation invariance and equivariance by classifying them into weak and strong categories. Second, we provide a unified theoretical framework to analyze these methods, especially weak rotation invariant and equivariant ones that are seldom analyzed theoretically. We then divide existing methods into two main categories, i.e., rotation invariant ones and rotation equivariant ones, which are further subclassified in terms of manipulating input ways and basic equivariant block structures, respectively. In each subcategory, their common essence is highlighted, a couple of representative methods are analyzed, and insightful comments on their pros and cons are given. Furthermore, we deliver a general overview of relevant applications and datasets for two popular tasks of 3D semantic understanding and molecule-related. Finally, we provide several open problems and future research directions based on challenges and difficulties in ongoing research.

BXCNN: A novel depression detection model

In response to the mounting societal pressures, an increasing number of individuals grappling with mental health challenges are turning to social media platforms to express their feelings. The utilization of deep learning models for analyzing social media data has become increasingly crucial in detecting early signs of depression. Early intervention through depression detection can significantly enhance patients quality of life and even save lives. However, many existing deep learning models suffer from low prediction accuracy, exacerbated by the imbalance between positive and negative samples in the collected data. To address these challenges, we propose a novel depression detection model integrated with BERT, XGBoost, and Convolutional Neural Networks (BXCNN). This model harnesses the advantages of ensemble learning and deep learning technologies by integrating XGBoost for feature extraction to alleviate data imbalance and CNN for classification. We transform depression-related textual data into sentence vectors using BERT to capture semantic information effectively. These features are then fed into a CNN classifier to accurately predict the likelihood of individuals exhibiting depressive symptoms. Through empirical evaluations on relevant datasets, our approach excels across various evaluation metrics, including accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC-ROC).

‘Matters-of-concern’ associated with discontinuation of long-acting injectable buprenorphine: Findings from a longitudinal qualitative study

BackgroundDiscontinuation of medications such as methadone and buprenorphine amongst patients receiving opioid agonist treatment (OAT) is an international phenomenon. Recent developments in OAT medication include depot-injections of buprenorphine. Circumstances underlying discontinuation of these new formulations of medication are not fully understood from a qualitative perspective. MethodsData derive from a longitudinal qualitative study of patients’ experience of long-acting injectable buprenorphine (LAIB), involving semi-structured telephone-interviews held at six-points in time. The relevant dataset for this article consists of 44 interview transcripts, generated from 8 participants who were each affected by discontinuation of LAIB prescriptions (during the first 12-months of treatment). Analyses sought to identify circumstances associated with LAIB discontinuation and data were further situated within a framework of ‘evidence making intervention’ and associated ‘matters-of-concern’. Matters-of-concern relate to the ways in which an intervention is ‘made’ and constructed through engagement and practice, from the perspective of the recipient. FindingsIn this study, participants experienced either ‘discontinuation of LAIB prescriptions by treatment services’ or patient-led ‘opt-out’ from treatment. Matters-of-concern underlying the former were associated with late attendance for scheduled appointments, non-prescribed substance use or receiving a custodial sentence. Matters-of-concern relating to patient-initiated discontinuation were associated with personal circumstances that affected treatment motivation, side-effects (of buprenorphine), a preference to resume heroin use, or because individual treatment goals had been achieved. ConclusionThe assorted matters-of-concern that influence discontinuation of LAIB demonstrate that such OAT is complex and multi-faceted, is neither fixed nor stable, and does not generate universally shared outcome. Experiences of LAIB discontinuation are shaped by a wide range of social, temporal and treatment-related effects that include disconnected therapeutic alliance between patient and treatment providers. In order to maximise the benefits of LAIB it is necessary to develop meaningful therapeutic alliances (notwithstanding policy boundaries) to enable exploration of matters-of-concern during treatment.