Data Representation Research Articles

Using word cloud as a formative assessment classroom technique in physiology.

Formative assessment is a valuable tool in medical education as it allows students to reflect on their own learning and make necessary corrections. The use of innovative techniques in formative assessment can make it more engaging and effective for students, particularly utilizing electronic tools. A word cloud is a visual representation of data that is created using text inputs from a group of individuals. The resulting image shows the most frequently used words in a larger font, allowing quick understanding of the common themes and concepts in the group's responses. Mentimeter is an online platform for interactive presentations where students can provide their responses to a question, which can then be visualized as a word cloud. The study aims to explore the benefits and drawbacks of using word cloud formation as a formative assessment technique in the classroom, utilizing the online platform Mentimeter.NEW & NOTEWORTHY The current study was conducted to present a novel formative assessment classroom technique using Mentimeter. The benefit of this novel technique is that it is easy to implement, quick, anonymous, and strengthens student's conceptual learning. It also gives the teacher an opportunity to give feedback and stress commonly committed mistakes.

Typed Unknown Values: A Step towards Solving the Problem of Missing Data Representation in Relational Databases

Typed Unknown Values: A Step towards Solving the Problem of Missing Data Representation in Relational Databases

Beyond Vision Impairments: Redefining the Scope of Accessible Data Representations.

The increasing ubiquity of data in everyday life has elevated the importance of data literacy and accessible data representations, particularly for individuals with disabilities. While prior research predominantly focuses on the needs of the visually impaired, our survey aims to broaden this scope by investigating accessible data representations across a more inclusive spectrum of disabilities. After conducting a systematic review of 152 accessible data representation papers from ACM and IEEE databases, we found that roughly 78% of existing articles center on vision impairments. In this article, we conduct a comprehensive review of the remaining 22% of papers focused on underrepresented disability communities. We developed categorical dimensions based on accessibility, visualization, and human-computer interaction to classify the papers. These dimensions include the community of focus, issues addressed, contribution type, study methods, participants involved, data type, visualization type, and data domain. Our work redefines accessible data representations by illustrating their application for disabilities beyond those related to vision. Building on our literature review, we identify and discuss opportunities for future research in accessible data representations.

A novel deep anomaly detection approach for intrusion detection in futurisitic network

<span lang="EN-US">In an era where networks are increasingly heterogeneous and multi-domain, establishing robust security models to protect data and network infrastructure is becoming ever more complex. Traditional intrusion detection systems (IDS) often struggle with novel or variant attacks that fall outside predefined rule sets, resulting in significant detection challenges. This paper proposes a methodologically refined approach leveraging data-driven insights and statistically robust feature selection to enhance the training dataset. The study presents a long short-term memory-autoencoder (LSTM-AE) based learning model designed for multi-class anomaly detection. The model's novelty lies in its application of distance metrics to define distinct thresholds for varied attack classifications, a strategy that significantly amplifies detection precision. Experimental results validate the superior performance of the proposed system, achieving 94.82% accuracy rate, outperforming similar existing works. The study also proactively addresses common issues of class imbalance and skewed data representation in benchmark datasets by strategically training the model on normal traffic, enhancing its capability to generalize and identify anomalies effectively.</span>

QKSAN: A Quantum Kernel Self-Attention Network.

The Self-Attention Mechanism (SAM) excels at distilling important information from the interior of data to improve the computational efficiency of models. Nevertheless, many Quantum Machine Learning (QML) models lack the ability to distinguish the intrinsic connections of information like SAM, which limits their effectiveness on massive high-dimensional quantum data. To tackle the above issue, a Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM. Further, a Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques to release half of quantum resources by mid-circuit measurement, thereby bolstering both feasibility and adaptability. Simultaneously, the Quantum Kernel Self-Attention Score (QKSAS) with an exponentially large characterization space is spawned to accommodate more information and determine the measurement conditions. Eventually, four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST, where the QKSAS tests and correlation assessments between noise immunity and learning ability are executed on the best-performing sub-model. The paramount experimental finding is that the QKSAN subclasses possess the potential learning advantage of acquiring impressive accuracies exceeding 98.05% with far fewer parameters than classical machine learning models. Predictably, QKSAN lays the foundation for future quantum computers to perform machine learning on massive amounts of data while driving advances in areas such as quantum computer vision.

Gaussian process regression as a powerful tool for analysing time series in environmental geochemistry

Monitoring programs require more advanced data management for the registered time series. Classical temporal series decomposition cannot fulfil current needs regarding adequate data representation, optimization of the spatial-temporal sampling resolution and predictive power. In the manuscript at hand, we will demonstrate that Gaussian process regression (GPR) models are a vital machine-learning tool to interpret temporal series, improving understanding of geochemical cycles, providing input data for geochemical models and acting as a guide for future decisions in environmental monitoring. Firstly, we explore the impacts of sampling frequency in the GPR performance for temporal series with variable lengths and sampled frequencies of water discharges. On a second approach, we present the strengths and weaknesses between classical decomposition of temporal series and GPR results for a case study: a 14-year record of water discharge, suspended particulate matter and antimony concentrations in the Garonne River. Our results suggest that (i) even short temporal series with low sampling resolution can be accurately characterized by GPR when presenting well defined seasonal patterns, and (ii) GPR provides more detailed and robust support than classical statistics to identify processes responsible for multi-scale geochemical signals. This work provides a reference for researchers, engineers, and stakeholders for more reliable monitoring, understanding, and managing aquatic ecosystems.

Enhancing performance of end-to-end communication system using Attention Mechanism-based Sparse Autoencoder over Rayleigh fading channel

Deep learning has revolutionized communication systems by introducing innovative approaches to address channel impairments through end-to-end models. Autoencoders, a type of deep learning architecture, are adept at learning compact data representations. However, conventional autoencoders in end-to-end models can suffer from overfitting, which limits their effectiveness in noisy communication environments. To address this issue, we propose a Sparse Autoencoder-based (SAE) model that enforces sparsity and promotes the extraction of robust features. Despite its effectiveness, the SAE model may still lack the ability to focus on the most relevant features of the input data. To overcome this limitation, we further introduce an Attention Mechanism-based Sparse Autoencoder (ASA) model. This model integrates the feature extraction capabilities of a sparse autoencoder with an attention mechanism that selectively highlights informative features of the signal. Through simulations, we demonstrate that both proposed models significantly improve M-PSK and M-QAM communication system performance. When trained at 7 dB, both proposed models exhibit significant performance improvements at higher testing average SNRs. Our results show that the SAE model outperforms the conventional Maximum Likelihood Detection (MLD) model and baseline autoencoder systems but suffers from error floor issues. The SAE model suffers from an error floor at average SNRs beyond 16 dB for BPSK and 14 dB for higher-order modulation schemes. As the value of M increases, the performance gap between the MLD and the proposed SAE model narrows. The ASA model, however, effectively mitigates the error floor observed in the SAE model for all values of M and across all modulation schemes. This research highlights the benefits of integrating an attention mechanism with SAE, resulting in enhanced robustness and reliability in communication systems characterized by improved accuracy and reduced error rates.

Shift-and-Safe: Addressing permanent faults in aggressively undervolted CNN accelerators

Underscaling the supply voltage (Vdd) to ultra-low levels below the safe-operation threshold voltage (Vmin) holds promise for substantial power savings in digital CMOS circuits. However, these benefits come with pronounced challenges due to the heightened risk of bitcell permanent faults stemming from process variations in current technology node sizes.This work delves into the repercussions of such faults on the accuracy of a 16-bit fixed-point Convolutional Neural Network (CNN) inference accelerator powering on-chip activation memories at ultra-low Vdd voltages. Through an in-depth examination of fault patterns, memory usage, and statistical analysis of activation values, this paper introduces Shift-and-Safe: two novel and cost-effective microarchitectural techniques exploiting the presence of outlier activation values and the underutilization of activation memories. Particularly, activation outliers enable a shift-based data representation that reduces the impact of faults on the activation values, whereas the memory underutilization is exploited to maintain a safe replica of affected activations in idle memory regions. Remarkably, these mechanisms do not add any burden to the programmer and are independent of application characteristics, rendering them easily deployable across real-world CNN accelerators.Experimental results show that Shift-and-Safe maintains the CNN accuracy even in the presence of almost a quarter of the total activations with faults. In addition, average energy savings are by 5% and 11% compared to the state-of-the-art approach and a conventional accelerator supplied at Vmin, respectively.

3D human model guided pose transfer via progressive flow prediction network

Human pose transfer is to transfer a conditional person image to a new target pose. The difficulty lies in modeling the large-scale spatial deformation from the conditional pose to the target one. However, the commonly used 2D data representations and one-step flow prediction scheme lead to unreliable deformation prediction because of the lack of 3D information guidance and the great changes in the pose transfer. Therefore, to bring the original 3D motion information into human pose transfer, we propose to simulate the generation process of real person image. We drive the 3D human model reconstructed from the conditional person image with the target pose and project it to the 2D plane. The 2D projection thereby inherits the 3D information of the poses which can guide the flow prediction. Furthermore, we propose a progressive flow prediction network consisting of two streams. One stream is to predict the flow by decomposing the complex pose transformation into multiple sub-transformations. The other is to generate the features of the target image according to the predicted flow. Besides, to enhance the reliability of the generated invisible regions, we use the target pose information which contains structural information from the flow prediction stream as the supplementary information to the feature generation. The synthesized images with accurate depth information and sharp details demonstrate the effectiveness of the proposed method.

Latent Semantic and Disentangled Attention.

Sequential learning using transformer has achieved state-of-the-art performance in natural language tasks and many others. The key to this success is the multi-head self attention which encodes and gathers the features from individual tokens of an input sequence. The mapping or decoding is performed to produce an output sequence via cross attention. There are threefold weaknesses by using such an attention framework. First, since the attention would mix up the features of different tokens in input and output sequences, it is likely that redundant information exists in sequence data representation. Second, the patterns of attention weights among different heads tend to be similar. The model capacity is bounded. Third, the robustness in an encoder-decoder network against the model uncertainty is disregarded. To handle these weaknesses, this paper presents a Bayesian semantic and disentangled mask attention to learn latent disentanglement in multi-head attention where the redundant features in transformer are compensated with the latent topic information. The attention weights are filtered by a mask which is optimized through semantic clustering. This attention mechanism is implemented according to Bayesian learning for clustered disentanglement. The experiments on machine translation and speech recognition show the merit of Bayesian clustered disentanglement for mask attention.

Multimodal transformer for early alarm prediction

Alarms are an essential part of distributed control systems designed to help plant operators keep the processes stable and safe. In reality, however, alarms are often noisy and thus can be easily overlooked. Early alarm prediction can give the operator more time to assess the situation and introduce corrective actions to avoid downtime and negative impact on human safety and environment. Existing studies on alarm prediction typically rely on signals directly coupled with these alarms. However, using more sources of information could benefit early prediction by letting the model learn characteristic patterns in the interactions of signals and events. Meanwhile, multimodal deep learning has recently seen impressive developments. Combination (or fusion) of modalities has been shown to be a key success factor, yet choosing the best fusion method for a given task introduces a new degree of complexity, in addition to existing architectural choices and hyperparameter tuning. This is one of the reasons why real-world problems are still typically tackled with unimodal approaches. To bridge this gap, we introduce a multimodal Transformer model for early alarm prediction based on a combination of recent events and signal data. The model learns the optimal representation of data from multiple fusion strategies automatically. The model is validated on real-world industrial data. We show that our model is capable of predicting alarms with the given horizon and that the proposed multimodal fusion method yields state-of-the-art predictive performance while eliminating the need to choose among conventional fusion techniques, thus reducing tuning costs and training time.

A comparative analysis of fire-weather indices for enhanced fire activity prediction with probabilistic approaches

BackgroundWeather conditions play a crucial role in driving fire activity in Mediterranean France. Previous research has demonstrated the influence of these conditions on the likelihood of large fire events over the world. However, certain limitations persist regarding the representation of fire weather in probabilistic models. AimThe objective of this paper is to develop an efficient method to rate fire danger by identifying the best representation of weather data for fire activity prediction in Mediterranean France. MethodsWe evaluated the performance of meteorological variables and the most common fire-weather indices (FWIs) worldwide as predictors of fire occurrence and size using the Firelihood framework, a probabilistic Bayesian model of fire activity. These models were compared to a fire activity baseline model incorporating only spatial and temporal effects but no explicit fire-weather information to allow for an in-depth study of information not captured by fire-weather indices. Key resultsThe results indicate that relying solely on fire-weather indices is insufficient for efficient rating of fire activities. The inclusion of spatial and seasonal effects in the models is crucial for improving the indices' performance. While the Canadian FWI remains the most skillful indicator among the tested indices, using new combinations of several of its subcomponents further increases accuracy. Various performance analyses, including threshold selections, were carried out to comparatively assess those improvements. ConclusionsThe approach shows that probabilistic models informed with appropriately constructed fire-weather indices substantially improve various aspects of fire activity predictions in the Mediterranean area.

Seamless Science: Lifting Experimental Mechanical Testing Lab Data to an Interoperable Semantic Representation

The scientific landscape is undergoing rapid transformations with the advent of the digital age which revolutionizes research methodologies. In materials science and engineering, an adoption of modern data management techniques is desirable to maximize the efficiency and accessibility of research efforts. Traditional practices in testing laboratories are usually inadequate for efficient data acquisition and utilization as they lead to local storage and difficulty in publication and correlation with other results. Electronic laboratory notebooks (ELNs) are promising prospects in this respect. Semantic concepts and ontologies enhance interoperability by standardizing experimental data representation. An in‐laboratory pipeline seamlessly integrating an ELN with transformation scripts to convert experimental into interoperable data in a machine‐actionable format is created in this study as a proof of concept. Tensile test results and the corresponding tensile test ontology are used exemplary. Linking ELN data to semantic concepts enriches the stored information while improving interpretability and reusability. Involving undergraduate students builds a bridge between theory and practice during their training and promotes their digital skills. This study underscores the potential of ELNs and knowledge representations as beneficial means toward improved data management practices that enhance collaborative research and education while ensuring compatibility with evolving standards and technologies.

UNIVERSAL BINARY FORMAT FOR FAST DATA TRANSMISSION OVER THE NETWORK

In the context of modern approaches to application development, it is increasingly necessary to use distributed data processing systems, for example, in a microservice architecture. Here, the issue of fast data exchange between parts of the same application becomes relevant. The article discusses a new format for binary data encoding of the non-fixed, supported by the cross-platform approach and with the ability to implement serialization and deserialization operations in different languages using a universal data representation method. The efficiency degree of the new algorithms is deter-mined by comparing them with the results for another formats and frameworks that are widely used at the moment. The analysis shows that, taking into account the limitations imposed by the problem being solved, the new data presentation format provides a higher exchange rate due to optimization of serialization-deserialization algorithms.

Drivers of global irrigation expansion: the role of discrete global grid choice

Abstract. Global statistical irrigation modeling relies on geospatial data and traditionally adopts a discrete global grid based on longitude–latitude reference. However, this system introduces area distortion, which may lead to biased results. We propose using the ISEA3H geodesic grid based on hexagonal cells, enabling efficient and distortion-free representation of spherical data. To understand the impact of discrete global grid choice, we employ a non-parametric statistical framework, utilizing random forest methods, to identify the main drivers of historical global irrigation expansion using, among other data, outputs from the global dynamic vegetation model Lund-Potsdam-Jena managed Land (LPJml). Irrigation is critical for food security amidst growing populations, changing consumption patterns, and climate change. It significantly boosts crop yields but also alters the water cycle and global water resources. Understanding past irrigation expansion and its drivers is vital for global change research, resource assessment, and the prediction of future trends. We compare predictive accuracy, simulated irrigation patterns, and identification of irrigation drivers between the two grid systems. Using the ISEA3H geodesic grid system increases the predictive accuracy by up to 28 % compared to the longitude–latitude grid. The model identifies population density, potential productivity increase, evaporation, precipitation, and water discharge as key drivers of historical global irrigation expansion. Gross domestic product (GDP) per capita also shows some influence. We conclude that the geodesic discrete global grid system significantly affects predicted irrigation patterns and identification of drivers and thus has the potential to enhance statistical modeling, which warrants further exploration in future research across related fields. This analysis lays the foundation for comprehending historical global irrigation expansion.

TorchSISSO: A PyTorch-based implementation of the sure independence screening and sparsifying operator for efficient and interpretable model discovery

Symbolic regression (SR) is a powerful machine learning approach that searches for both the structure and parameters of algebraic models, offering interpretable and compact representations of complex data. Unlike traditional regression methods, SR explores progressively complex feature spaces, which can uncover simple models that generalize well, even from small datasets. Among SR algorithms, the Sure Independence Screening and Sparsifying Operator (SISSO) has proven particularly effective in the natural sciences, helping to rediscover fundamental physical laws as well as discover new interpretable equations for materials property modeling. However, its widespread adoption has been limited by performance inefficiencies and the challenges posed by its FORTRAN-based implementation, especially in modern computing environments. In this work, we introduce TorchSISSO, a native Python implementation built in the PyTorch framework. TorchSISSO leverages GPU acceleration, easy integration, and extensibility, offering a significant speed-up and improved accuracy over the original. We demonstrate that TorchSISSO matches or exceeds the performance of the original SISSO across a range of tasks, while dramatically reducing computational time and improving accessibility for broader scientific applications.

GATv2EPI: Predicting Enhancer–Promoter Interactions with a Dynamic Graph Attention Network

Background: The enhancer–promoter interaction (EPI) is a critical component of gene regulatory networks, playing a significant role in understanding the complexity of gene expression. Traditional EPI prediction methods focus on one-to-one interactions, neglecting more complex one-to-many and many-to-many patterns. To address this gap, we utilize graph neural networks to comprehensively explore all interaction patterns between enhancers and promoters, capturing complex regulatory relationships for more accurate predictions. Methods: In this study, we introduce a novel EPI prediction framework, GATv2EPI, based on dynamic graph attention neural networks. GATv2EPI leverages epigenetic information from enhancers, promoters, and their surrounding regions and organizes interactions into a network to comprehensively explore complex EPI regulatory patterns, including one-to-one, one-to-many, and many-to-many relationships. To avoid overfitting and ensure diverse data representation, we implemented a connectivity-based sampling method for dataset partitioning, which constructs graphs for each chromosome and assigns entire connected subgraphs to training or test sets, thereby preventing information leakage and ensuring comprehensive chromosomal representation. Results: In experiments conducted on four cell lines—NHEK, IMR90, HMEC, and K562—GATv2EPI demonstrated superior EPI recognition accuracy compared to existing similar methods, with a training time improvement of 95.29% over TransEPI. Conclusions: GATv2EPI enhances EPI prediction accuracy by capturing complex topological structure information from gene regulatory networks through graph neural networks. Additionally, our results emphasize the importance of epigenetic features surrounding enhancers and promoters in EPI prediction.

SC-VAE: Sparse coding-based variational autoencoder with learned ISTA

Learning rich data representations from unlabeled data is a key challenge towards applying deep learning algorithms in downstream tasks. Several variants of variational autoencoders (VAEs) have been proposed to learn compact data representations by encoding high-dimensional data in a lower dimensional space. Two main classes of VAEs methods may be distinguished depending on the characteristics of the meta-priors that are enforced in the representation learning step. The first class of methods derives a continuous encoding by assuming a static prior distribution in the latent space. The second class of methods learns instead a discrete latent representation using vector quantization (VQ) along with a codebook. However, both classes of methods suffer from certain challenges, which may lead to suboptimal image reconstruction results. The first class suffers from posterior collapse, whereas the second class suffers from codebook collapse. To address these challenges, we introduce a new VAE variant, termed sparse coding-based VAE with learned ISTA (SC-VAE), which integrates sparse coding within variational autoencoder framework. The proposed method learns sparse data representations that consist of a linear combination of a small number of predetermined orthogonal atoms. The sparse coding problem is solved using a learnable version of the iterative shrinkage thresholding algorithm (ISTA). Experiments on two image datasets demonstrate that our model achieves improved image reconstruction results compared to state-of-the-art methods. Moreover, we demonstrate that the use of learned sparse code vectors allows us to perform downstream tasks like image generation and unsupervised image segmentation through clustering image patches. The code is available at https://github.com/sotiraslab/SC-VAE.

Multi-view Traffic Flow Prediction Model Based on Spatiotemporal Convolution

Predicting traffic flow has always been a significant task in intelligent transportation systems. Due to the substantial temporal and spatial dependencies of traffic flow sequences, accurately predicting traffic flow poses a considerable challenge. Many existing works primarily rely on recurrent neural networks, graph neural networks, and Transformer models to establish traffic flow prediction models. To better extract features and enhance efficiency, a traffic flow prediction model based on multi-view spatiotemporal convolution (MVSC) is proposed. This model learns the representation of sequence data at the input encoding layer and incorporates location and time information. In the spatiotemporal feature representation learning layer, considering the diverse periodic patterns in sequences, several representation learning modules are designed, conducting local spatiotemporal feature exploration through one-dimensional convolution and then accomplishing global spatiotemporal feature mining based on causal convolution. To further enhance the model's utilization of spatiotemporal features, a channel attention mechanism is introduced at the prediction layer. The forecasting method employed in the study is direct multistep, and subsequent experiments conducted on two real datasets demonstrate that the MVSC model exhibits a certain degree of superiority in MAE, RMSE, and MAPE for both short-term and long-term predictions compared to existing models. And through the latest experiments and investigations, it has been found that MVSC has improved MAPE performance by about 1.2% compared to recent models such as RTGCN and STRGCN, achieving the intended outcomes.

C3E: A framework for chart classification and content extraction

Incorporating charts into technical documents enhances richness by simplifying complex data representation and improving comprehension. However, automated chart content extraction (CCE) presents a significant challenge within the domain of document analysis and understanding. The CCE problem can be viewed through a series of six sub-tasks: chart classification (CC), text detection and recognition (TDR), text role classification (TRC), axis analysis, legend analysis, and data extraction. Improving these sub-tasks is important for enhancing the effectiveness of CCE. This paper introduces the chart classification and content extraction (C3E) framework, with a primary focus on the first three sub-tasks of CCE: CC, TDR, and TRC. We propose a ChartVision model for the CC, an EfficientNet-based model coupled with a dual-branch architecture incorporating a novel hybrid convolutional and dilated attention module. For text detection and TRC, we introduce a novel CCE method based on YOLOv5, CCE-YOLO, designed for localizing and classifying textual components of varying sizes. Further, for text recognition, we employ a convolutional recurrent neural network with connectionist temporal classification loss. We conducted experimental analysis on benchmark datasets to assess the effectiveness of our approach across each sub-task. Specifically, we evaluated CC, TDR, and TRC methods using the UB-PMC 2020 and UB-PMC 2022 datasets from the ICPR2020 and ICPR2022 CHART-Infographics competitions. The C3E framework achieved notable F1-scores of 94.26%, 92.44%, and 80.64% for CC, TDR, and TRC, respectively on the UB-PMC 2020 dataset and 94.0%, 91.98%, and 84.48% for CC, TDR, and TRC, respectively on the UB-PMC 2022 dataset.