Short Data Sets Research Articles

A Mixed Neuro Graph Approach with Gradient Boosting to Hybrid Job-Shop Scheduling to Minimize a Regular Function of Job Completion Times and Numbers of Used Machines

The paper considers a multi-stage processing system including sets of identical (parallel) machines and a set of dedicated machines processing different operations of the given jobs in any sectors of economy. Based on the weighted Mixed Neuro graph model, the paper proposes adaptive algorithms for solving this problem via appropriate Mixed Neuro graph transformations. The main novelty is (1) low demands on the source data-unlike classical machine learning algorithms, the approach can offer stable interpretable results even with a short dataset size; (2) the number of new matrix multiplication operations that make up the main load when training models increases linearly with the number of new data from 0 to 999 time periods; (3) the results of the model are repeatable due to the stability of the coefficients of the model. These algorithms are able to solve (exactly or heuristically) the tested instances with N jobs and W types of parallel identical machines within on the personal computer. The gradient boosting result is in interval 5.9677410-3.4982093.

Haplotype-resolved genome and population genomics of the threatened garden dormouse in Europe.

Genomic resources are important for evaluating genetic diversity and supporting conservation efforts. The garden dormouse (Eliomys quercinus) is a small rodent that has experienced one of the most severe modern population declines in Europe. We present a high-quality haplotype-resolved reference genome for the garden dormouse, and combine comprehensive short and long-read transcriptomics data sets with homology-based methods to generate a highly complete gene annotation. Demographic history analysis of the genome reveal a sharp population decline since the last interglacial, indicating an association between colder climates and population declines before anthropogenic influence. Using our genome and genetic data from 100 individuals, largely sampled in a citizen-science project across the contemporary range, we conduct the first population genomic analysis for this species. We find clear evidence for population structure across the species' core Central European range. Notably, our data show that the Alpine population, characterized by strong differentiation and reduced genetic diversity, is reproductively isolated from other regions and likely represents a differentiated evolutionary significant unit (ESU). The predominantly declining Eastern European populations also show signs of recent isolation, a pattern consistent with a range expansion from Western to Eastern Europe during the Holocene, leaving relict populations now facing local extinction. Overall, our findings suggest that garden dormouse conservation may be enhanced in Europe through the designation of ESUs.

Glaucoma detection using hybrid machine learning techniques

An eye disease called glaucoma can cause irreversible blindness if left undetected and not properly managed. The major challenge is that glaucoma often has no symptoms in its early stages, making it hard to detect using traditional testing methods like eye pressure measurements and eye exams. Several techniques for Glaucoma detection encountered difficulties due to a short training dataset, resulting in overfitting and under fitting problems. A hybrid machine learning approach based on CNN-SVM for detection of Glaucoma was proposed. Initially images taken from glaucoma dataset were preprocessed using standard scalar, then the preprocessed images are fed into CNN for transformation into high level features, the extracted features are subsequently passed onto an SVM classifier to distinguish between normal and glaucomatous conditions. Experimental results for the proposed CNN-SVM offers an accuracy, precision, recall and F1-score of 100% demonstrating its superiority over other existing techniques such as SVM which has accuracy, precision, recall and F1-score of 93%, 92%, 90% and 94% respectively and CNN with the accuracy, precision, recall and F1- score of 95%, 99%, 88% and 90% respectively. The integration of CNN and SVM presents a promising framework for automated Glaucoma detection, offering significant potential for real-world clinical applications.

MMCL: Meta-mutual contrastive learning for multi-modal medical image fusion

The number of datasets and computational efficiency are always hindrances in the multi-modal medical image fusion (MMIF) research. To address these challenges, we propose a contrastive learning framework inspired meta-mutual, which divides the medical image fusion task into subtasks and pre-trains an optimal meta-representation suitable for all subtasks. We then fine-tune our proposed network using this optimal meta-representation as initialization, achieving the best model with only a few short datasets. Additionally, extracting source image features in pairs can lead to redundant information due to the invariant and unique features of multi-modal images. Therefore, we introduce novelty mutual contrastive coupled pairs to extract both invariant and unique features from source images. Experimental results demonstrate that our method outperforms other state-of-the-art fusion methods.

Comprehensive Assessment of BERT-Based Methods for Predicting Antimicrobial Peptides.

In recent years, the prediction of antimicrobial peptides (AMPs) has gained prominence due to their high antibacterial activity and reduced susceptibility to drug resistance, making them potential antibiotic substitutes. To advance the field of AMP recognition, an increasing number of natural language processing methods are being applied. These methods exhibit diversity in terms of pretraining models, pretraining data sets, word vector embeddings, feature encoding methods, and downstream classification models. Here, we provide a comprehensive survey of current BERT-based methods for AMP prediction. An independent benchmark test data set is constructed to evaluate the predictive capabilities of the surveyed tools. Furthermore, we compared the predictive performance of these computational methods based on six different AMP public databases. LM_pred (BFD) outperformed all other surveyed tools due to abundant pretraining data set and the unique vector embedding approach. To avoid the impact of varying training data sets used by different methods on prediction performance, we performed the 5-fold cross-validation experiments using the same data set, involving retraining. Additionally, to explore the applicability and generalization ability of the models, we constructed a short peptide data set and an external data set to test the retrained models. Although these prediction methods based on BERT can achieve good prediction performance, there is still room for improvement in recognition accuracy. With the continuous enhancement of protein language model, we proposed an AMP prediction method based on the ESM-2 pretrained model called iAMP-bert. Experimental results demonstrate that iAMP-bert outperforms other approaches. iAMP-bert is freely accessible to the public at http://iamp.aielab.cc/.

DMSeqNet-mBART: A state-of-the-art Adaptive-DropMessage enhanced mBART architecture for superior Chinese short news text summarization

Mandarin Chinese, a globally prevalent language, boasts an abundance of regularly refreshed short news texts accessible online. Consequently, devising concise summaries of these texts has emerged as a pivotal challenge for enhancing information dissemination and comprehension efficiency. To tackle this issue, we introduce DMSeqNet-mBART, an innovative model grounded in the mBART framework, positioning it as a state-of-the-art solution for Chinese short news summarization. DMSeqNet-mBART incorporates the Adaptive-DropMessage technique, an innovative methodology that intelligently discards or retains information contingent upon the attention mechanism’s output. Furthermore, the model integrates several enhanced technologies, such as dynamic convolutional layers, gated residual connections, customized feed-forward networks enhanced with batch normalization, self-attention, and cross-attention, all aimed at bolstering the performance and robustness of Chinese short news summarization. Rigorous comparative experiments, conducted across six recognized Chinese short news summary datasets, demonstrate that DMSeqNet-mBART significantly surpasses industry-leading models like T5, MLC, PLCC, and GPT-4 in terms of fluency, completeness, robustness, and accuracy. These results, as validated by benchmarks including BERTScore, BLEU, and ROUGE metrics, underscore the model’s superiority across diverse evaluation standards.

Deep Learning of a 200-Member Ensemble with a Limited Historical Training to Improve the Prediction of Extreme Precipitation Events

Abstract This study introduces a deep learning (DL) scheme to generate reliable and skillful probabilistic quantitative precipitation forecasts (PQPFs) in a postprocessing framework. Enhanced machine learning model architecture and training mechanisms are proposed to improve the reliability and skill of PQPFs while permitting computationally efficient model fitting using a short training dataset. The methodology is applied to postprocessing of 24-h accumulated PQPFs from an ensemble forecast system recently introduced by the Center for Western Weather and Water Extremes (CW3E) and for lead times from 1 to 6 days. The ensemble system was designed based on a high-resolution version of the Weather Research and Forecasting (WRF) Model, named West-WRF, to produce a 200-member ensemble in near–real time (NRT) over the western United States during the boreal cool seasons to support Forecast-Informdayed Reservoir Operations (FIRO) and studies of prediction of heavy-to-extreme events. Postprocessed PQPFs are compared with those from the raw West-WRF ensemble, the operational Global Ensemble Forecast System version 12 (GEFSv12), and the ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF). As an additional baseline, we provide PQPF verification metrics from a recently developed neural network postprocessing scheme. The results demonstrate that the skill of postprocessed forecasts significantly outperforms PQPFs and deterministic forecasts from raw ensembles and the recently developed algorithm. The resulting PQPFs broadly improve upon the reliability and skill of baselines in predicting heavy-to-extreme precipitation (e.g., >75 mm) across all lead times while maintaining the spatial structure of the high-resolution raw ensemble.

Clustering Integrated Fusion Model Based on the Maximum Matching Problem

The maximum-weight perfect matching inverse issue in graph theory and text clustering are the two primary topics of this study. We suggest a novel approach to text clustering that makes use of self-encoders and BERT embeddings for feature extraction in order to increase clustering accuracy. According to experimental results, our technique enhances the clustering results greatly and performs well on numerous short text datasets. In the context of graph theory, we examine the unit paradigm inverse issue of maximum-weight perfect matching with value constraints and provide a robust polynomial-time method for its solution. In addition to effectively solving the maximum-weight perfect matching inverse issue, our technique can also produce the best weight vector configuration scheme for real-world uses. In conclusion, our work has advanced the domains of text clustering and graph theory significantly, offering fresh approaches and theoretical underpinnings for future investigations.

A data-driven short video international communication model based on indicator system communication network and attention BiLSTM neural network

As one of the most popular ways to disseminate Internet content in the current era of media convergence, short videos have increasingly become part of people’s daily lives. This paper takes TikTok as the research object, focusing on the study of short video communication process. The research objective of this paper is to improve the international communication effect of short videos through data-driven analysis and strategy research on the law of international communication of short videos. Firstly, the key objective indicator system about international communication of short videos is constructed, the definition, selection and design logic of key objective indicators are introduced, the construction idea of the corresponding indicator system is proposed, and then the key objective indicator system about international communication of short videos and related data set are constructed. Secondly, an international communication network model for short videos is built. The construction of this network model is divided into three steps, the construction of the short video communication network KOIS-PN based on the key objective indicators system, the construction of AT-BiLSTM neural network model to adapt to the research scenario of short video international communication, the construction of an international communication network model of short videos combining KOIS-PN and AT-BiLSTM. The international short video communication network is constructed according to the key objective index system of international short video, and the key objective index system is used as nodes in the communication network, and the short video communication network is constructed by hierarchical relationship. Thirdly, the performance of the core theoretical model proposed in this paper is verified. The experimental results show that, the KOISPN-ATBiLSTM international communication network model of short videos has certain validity and advancement.

FPGA-enhanced system-on-chip for finger vein-based biometric system using novel DL model

In an era dominated by technology, the imperative for robust personal authentication in electronic information systems becomes increasingly evident. A secure and dependable solution to address this need is biometric authentication. Due to their intrinsic features of being universal, unique, and fraud-resistant, finger vein-based recognition systems have gained importance. Veins provide an efficient barrier against misleading methods since they are buried under the skin and undetectable to human sight. While many researchers focus on advanced technology for finger-vein-based authentication systems, existing research has often overlooked significant challenges, such as short datasets, high computational complexity, and a lack of efficient and lightweight feature descriptors. This paper proposes a unique method for automated Finger Vein Recognition (FVR) based on a fusion model known as “CNN-ViT” for FVR. Transfer learning-based Convolutional Neural Network (CNN) models, such as Inception-V3 and ResNet-50, compute the correlation of adjacent pixels to process texture-based features. Furthermore, shape-based features are processed using the vision transformer (ViT) model to determine the relationship between distant pixels. The combination of these three models enables the learning of textural features based on forms, contributing to more effective finger vein identification. In addition to our databases, we utilize two benchmark databases, FV-USM and SDUMLA-HMT, to validate our experiments. Our proposed approach achieves outstanding accuracy values of 99.95 %, 98.9 %, and 97.78 % on both the benchmark and our datasets. When compared to previous methods, the proposed Deep Learning (DL) model outperforms state-of-the-art models, demonstrating higher recognition rates and accuracy. To prototype the proposed FVR system, a Zynq XCZU4EV UltraScale + Multiprocessor System-On-Chip (MPSoC) was employed. The proposed model exhibits high throughput and competitive power efficiency, making it an excellent choice for scenarios where computing performance is critical, albeit utilizing more power and resources. This was established through a comprehensive examination of FPGA resource utilization and performance metrics.

An efficient multivariate approach to dictionary learning for portfolio selection

Financial market noise greatly limits portfolio optimization by concealing key patterns and resulting in inaccurate asset selection. In this paper, we propose a novel approach that leverages dictionary learning, specifically the modified K-SVD algorithm, for denoising financial time series in the context of multivariate portfolio selection. Our method, which considers highly correlated short datasets, trains the dictionary simultaneously for multiple assets, resulting in more robust and adaptive denoising. We next use a vector auto-regressive process on the denoised data to estimate covariance matrices and build optimal portfolios using the minimum variance approach. Extensive computer simulations are conducted to assess the impact of our denoising method on portfolio performance in terms of several metrics, such as cumulative returns, Sharpe ratio, and model accuracy. The findings indicate how dictionary learning can improve the robustness of investment portfolios in the face of market noise and volatility.

Illumina short read-based shotgun metagenomic data sets of simulated bacterial communities derived from fresh spinach and surface water.

Paired-end short reads of Illumina HiSeq, MiSeq, and NovaSeq of simulated bacterial communities from fresh spinach and surface water were generated in silico at various sequencing depths. Multidrug-resistant Salmonella enterica serotype Indiana was included in the spinach community, while the water community contained multidrug-resistant Pseudomonas aeruginosa.

Knockout of the longevity gene Klotho perturbs aging and Alzheimer’s disease-linked brain microRNAs and tRNA fragments

Overexpression of the longevity gene Klotho prolongs lifespan, while its knockout shortens lifespan and impairs cognition via perturbation of myelination and synapse formation. However, comprehensive analysis of Klotho knockout effects on mammalian brain transcriptomics is lacking. Here, we report that Klotho knockout alters the levels of aging- and cognition related mRNAs, long non-coding RNAs, microRNAs and tRNA fragments. These include altered neuronal and glial regulators in murine models of aging and Alzheimer’s disease and in human Alzheimer’s disease post-mortem brains. We further demonstrate interaction of the knockout-elevated tRNA fragments with the spliceosome, possibly affecting RNA processing. Last, we present cell type-specific short RNA-seq datasets from FACS-sorted neurons and microglia of live human brain tissue demonstrating in-depth cell-type association of Klotho knockout-perturbed microRNAs. Together, our findings reveal multiple RNA transcripts in both neurons and glia from murine and human brain that are perturbed in Klotho deficiency and are aging- and neurodegeneration-related.

SPRAG: building and benchmarking a Short Programming-Related Answer Grading dataset

SPRAG: building and benchmarking a Short Programming-Related Answer Grading dataset

Urban flood prediction using ensemble artificial neural network: an investigation on improving model uncertainty

Reducing the impact of artificial neural networks (ANN) affected by sources of uncertainty is crucial to improving the reliability of the flood prediction model. This study proposes an ensemble artificial neural network (EANN) model to predict the degree of flooding in coastal cities. Combined methods are used to reduce the model’s uncertainty, heuristic neural pathway strength feature selection is used to select inputs, the coupling method is used to optimize network architecture and parameters, and the integration method which paralleling three ANN models with different predicted lead periods ensemble together is used to capture output uncertainty. The EANN model has successfully predicted flooding in the Chinese coastal city of Macao during a typhoon, with convincing accuracy. The study also analyzed the impacts of both long and short training datasets with appropriate time intervals on ANN modeling performance. It was found that the performance of short training datasets, with appropriate time intervals, was similar to or better than models with long training datasets.

Effect of Corporate Environmental Performance on Banks’ Loan Pricing

Effect of Corporate Environmental Performance on Banks’ Loan Pricing

Short text classification using semantically enriched topic model

Modelling short text is challenging due to the small number of word co-occurrence and insufficient semantic information that affects downstream Natural Language Processing (NLP) tasks, for example, text classification. Gathering information from external sources is expensive and may increase noise. For efficient short text classification without depending on external knowledge sources, we propose Expressive Short text Classification (EStC). EStC consists of a novel document context-aware semantically enriched topic model called the Short text Topic Model (StTM) that captures words, topics and documents semantics in a joint learning framework. In StTM, the probability of predicting a context word involves the topic distribution of word embeddings and the document vector as the global context, which obtains by weighted averaging of word embeddings on the fly simultaneously with the topic distribution of words without requiring an additional inference method for the document embedding. EStC represents documents in an expressive (number of topics × number of word embedding features) embedding space and uses a linear support vector machine (SVM) classifier for their classification. Experimental results demonstrate that EStC outperforms many state-of-the-art language models in short text classification using several publicly available short text data sets.

Complexity from ordinal pattern positioned slopes (COPPS)

Measuring complexity allows to characterize complex systems. Existing techniques are limited to simultaneously measure complexity from short length data sets, detect transitions and periodic dynamics. This paper presents an approach based on ordinal pattern positioned slopes (OPPS). It considers exclusively OPPS group occurrences to compute the complexity from OPPS (COPPS) as the average number of patterns and applies to short data series. The COPPS measure was successfully applied to simulation data for measuring complexity, detecting transition phases and regular dynamics, distinguishing between chaotic and stochastic dynamics; and to real-world data for detecting arrhythmia ECG beats.

ALGORITHM FOR DETECTION OF ABUSIVE CONTENT IN AUDIO CONTENT FOR IMPLEMENTATION IN OBJECT-ORIENTED INFORMATION SYSTEM

The paper proposes the basic principles of developing an object-oriented information system for detecting abusive content in Ukrainian-language audio content based on a new algorithm that uses statistical and neural network approaches to detect abusive content. Detecting offensive content in text and audio content is an urgent task, as it helps to create a safe and healthy environment for communication, especially in online platforms. Offensive content can harm the people who hear or read it and violate their rights. It can also have a negative impact on society, contributing to the spread of hatred and violence. To detect abusive speech in audio content, the proposed approach uses two key components: the use of dictionary methods and the analysis of the emotional tonality of utterances. A set of reviews was used as a dataset to determine the abusive component of the content, which was expanded by the authors by adding words of abuse. An object-oriented information system architecture written in the Python programming language in the PyCharm programming environment is proposed. The information system consists of a software module for training recurrent neural network models and further saving trained instances, and a software module for detecting abusive content in Ukrainian-language audio content using trained RNN models. Since the recurrent neural network is trained on a short text data set, the system is less efficient at identifying texts that have a larger number of words. In the example of the proposed approach, the accuracy of detecting offensive content is more than 90%. This means that the algorithm works correctly in the absence of offending highlights in the test data set. The results of the analysis of the effectiveness of the proposed approach show that in the vast majority of cases the conclusions regarding the acceptability of audio content based on the level of abuse are correct.

Referenceless Nyquist ghost correction outperforms standard navigator-based method and improves efficiency of in vivo diffusion tensor cardiovascular magnetic resonance.

The study aims to assess the potential of referenceless methods of EPI ghost correction to accelerate the acquisition of in vivo diffusion tensor cardiovascular magnetic resonance (DT-CMR) data using both computational simulations and data from in vivo experiments. Three referenceless EPI ghost correction methods were evaluated on mid-ventricular short axis DT-CMR spin echo and STEAM datasets from 20 healthy subjects at 3T. The reduced field of view excitation technique was used to automatically quantify the Nyquist ghosts, and DT-CMR images were fit to a linear ghost model for correction. Numerical simulation showed the singular value decomposition (SVD) method is the least sensitive to noise, followed by Ghost/Object method and entropy-based method. In vivo experiments showed significant ghost reduction for all correction methods, with referenceless methods outperforming navigator methods for both spin echo and STEAM sequences at b = 32, 150, 450, and 600 . It is worth noting that as the strength of the diffusion encoding increases, the performance gap between the referenceless method and the navigator-based method diminishes. Referenceless ghost correction effectively reduces Nyquist ghost in DT-CMR data, showing promise for enhancing the accuracy and efficiency of measurements in clinical practice without the need for any additional reference scans.