Redundant Information Research Articles

Unveiling redundancy in environmental, social, and governance reporting framework in India: an exploratory study

Purpose This study aims to investigate redundant information in mandatory non-financial reports (NFRs) demanded by regulators, focusing primarily on overlapping disclosures in a new Indian sustainability reporting (SR) framework. Design/methodology/approach The study sample comprised NIFTY100 listed entities that published SR voluntarily during 2021–2022. The authors used content analysis and cosine similarity techniques to conceptually compare redundancy in SR disclosures with non-financial disclosures. Findings The findings reveal an information overlap in SR disclosure with other NFRs disclosures. The disclosures of Directors’ Report have higher cosine similarity scores at the firm level with SR, followed by the Management Discussion and Analysis report, Corporate Governance report and Corporate Social Responsibility report. The additional analysis reveals that qualitative disclosures and disclosures comprising governance factors overlap more in SR. Practical implications Policymakers should look to establish relevant disclosure guidelines in the SR system, and thereby, shed light on fundamental issues to enhance future SR framework reforms. Social implications The study highlight the need for integration and amendment in the disclosure guidelines of NFRs to improve the overall transparency of the reports. Originality/value Previous studies have examined the redundancy in annual reports and SRs from the point of view of overlapping information. To the best author’s knowledge, this is possibly among the first studies to offer insights into the repetition of disclosures required by regulators in statutory NFRs based on environmental, social, and governance factors through the lenses of the institutional theory.

Vehicle identification and analysis based on lightweight yolov5 on edge computing platform

Abstract With the accelerating process of urbanization, the role of intelligent transportation systems in urban traffic management has become more crucial. Nevertheless, traditional surveillance cameras only possess the ability to capture videos and lack the capacity for vehicle detection. Consequently, in order to enhance the intelligent capabilities of front-end surveillance cameras and achieve lightweight model deployment, this research has designed a lightweight detection model that can be deployed on embedded devices. Deploying detection models on embedded devices requires a high level of storage and system computing power, and there exist issues such as model speed, model accuracy, model size, and energy efficiency that are difficult to address in existing studies.Specifically, based on YOLOv5, we first use lightweight structures to replace the C3 module and introduce GSConv and Slim Neck structures to replace the Neck of YOLOv5 in order to reduce the model's parameters and computational complexity. After replacing the model structure, we use global channel pruning to remove redundant information, and use asymmetric quantization to convert model parameters from floating point numbers to fixed point numbers, significantly reducing the model's size and load time, and improving the inference speed. For the target tracking part, we combine the ByteTrack target tracking algorithm with the proposed model, effectively reducing the impact of target occlusion on detection and improving the correlation between video frames. In terms of data recording, detection information such as vehicle color, license plate number, and road blockage level can be uploaded to the database server, and the model's detection results can be encoded using the H.264 video encoding format and uploaded to the streaming media server through streaming media protocols. In the experimental part, we selected the embedded Rk3399pro as the deployment object and achieved 97.8% accuracy in the vehicle counting task, meeting the real-time performance requirements of 31FPS.

A Fault Diagnosis Method for Turnout Switch Machines Based on Sound Signals

The turnout switch machine, a vital outdoor component of railway signaling, controls train steering amidst complex operations and high frequencies. Its malfunction significantly disrupts train operations, potentially causing derailments. This paper proposes a sound-based fault diagnosis method, termed ERS (a method combining EMD, ReliefF, and SVM), for effective monitoring and detection of turnout switch machines. The method employs Eigenmode Decomposition (EMD) to smooth the sound signal, reduce noise, and extract key statistical parameters of both the time and frequency domains. To address redundant information in high-dimensional features, the ReliefF algorithm is utilized for feature selection, dimension reduction, and fault classification based on weighted parameters. Subsequently, the selected feature parameters are used to train the Support Vector Machine (SVM). A comparison with results obtained without ReliefF feature selection demonstrates the necessity of this step. The results show that the fault diagnosis accuracy reaches 98% in the positioning work mode and 95.67% in the reversing work mode, verifying the method’s effectiveness and feasibility.

Data-driven approach for the classification of gas turbine faults

Gas turbines (GTs) play a crucial role in the production of electricity. Extreme working conditions can lead to deterioration in GTs' performance, resulting in the occurrence of various issues. This study proposes an approach to deal with this issue by combining a layered recurrent neural network (LRNN) with principal component analysis (PCA). This approach aims to reduce the dimensionality of data and computational complexity effectively while enhancing the accuracy of gas turbine fault classification. The methodology outlined consists of two steps. The first step is to apply PCA to the dataset that was collected from the gas turbine. By transforming the data into a lower-dimensional space, PCA helps to eliminate redundant information and improve computational efficiency. Next, LRNN is employed to detect and classify faults in the gas turbine. The LRNN’s structure enables it to capture complex patterns and relationships in the data, which enhances the accuracy of fault classification. This study is based on historical data collected from a gas turbine power station, consisting of 8200 samples of 34 measured variables. The operating parameters contain data such as temperature and pressure. Each data point's relationship to a specific turbine scenario reveals if it is healthy or one of the four faulty scenarios. The results showed that by combining the LRNN with PCA, the gas turbine fault classification achieved good performance in terms of accuracy, precision and neural network model performances, while also showcasing the faster convergence speed of the LRNN when trained on PCA instead of the original dataset.

NeutronSketch: An in-depth exploration of redundancy in large-scale graph neural network training

Graph Neural Networks (GNNs) have achieved notable success in various applications. However, the increasing scale of real-world graphs poses a challenge for efficient GNN training. Recent works propose reducing computations by reducing the scale of the input graph through graph compression or sparsification. However, these methods introduce deep learning models requiring multiple training iterations, which leads to significant additional time overhead. Meanwhile, they fail to consider redundant information that is unhelpful or even harmful for model training in the input graphs. In this work, we propose a universal, one-time redundancy removal method called NeutronSketch to remove the redundant information from the input graph. This method can improve training efficiency while maintaining the model accuracy. In the experiments, we compare NeutronSketch with graph compression, sparsification, and coarsening methods. The results show that NeutronSketch has a faster execution speed and better model accuracy. Additionally, we apply NeutronSketch to the sample-based GNN models. The results show that NeutronSketch reduces input graph scale by an average of 25% compared to the original graph, reducing training time by 10%–90% while maintaining the model accuracy.

Applying the consensus reporting items for studies in primary care checklist to improve reporting quality in primary care research in China

In 2023, the Consensus Reporting Items for Studies in Primary Care (CRISP) Working Group introduced a consensus checklist aimed at enhancing the quality, practicality, and dissemination of research reports in general practice and primary care. The editorial team of the Chinese General Practice and Chinese General Practice Journal translated the CRISP Checklist into Chinese and conducted a review of the current status of primary care research in China.The review identifies major problems in the field, including six common problems: “author-centered orientation,” “overemphasis on authority,” “uncritically adopting international experience,” “lack of rigor in reporting methodologies,” “inclusion of excessive and redundant information,” and “lack of transparency” Finally, the editorial team encourages authors, editors, and reviewers in China to adopt the CRISP Checklist to improve the quality of primary care research reports. Additionally, the review provides specific recommendations for applying the CRISP Checklist in various subfields, such as discipline development research, clinical research, health services research, health policy research, medical education research, and community epidemiological research.

Tensorized and Compressed Multi-View Subspace Clustering via Structured Constraint.

Multi-view learning has raised more and more attention in recent years. However, traditional approaches only focus on the difference while ignoring the consistency among views. It may make some views, with the situation of data abnormality or noise, ineffective in the progress of view learning. Besides, the current datasets have become high-dimensional and large-scale gradually. Therefore, this paper proposes a novel multi-view compressed subspace learning method via low-rank tensor constraint, which incorporates the clustering progress and multi-view learning into a unified framework. First, for each view, we take the partial samples to build a small-size dictionary, which can reduce the effect of both redundancy information and computation cost greatly. Then, to find the consistency and difference among views, we impose a low-rank tensor constraint on these representations and further design an auto-weighted mechanism to learn the optimal representation. Last, due to the non-square of the learned representation, the bipartite graph has been introduced, and under the structured constraint, the clustering results can be obtained directly from this graph without any post-processing. Extensive experiments on synthetic and real-world benchmark datasets demonstrate the efficacy and efficiency of our method, especially for the views with noise or outliers.

HPD-Depth: High performance decoding network for self-supervised monocular depth estimation

Self-supervised monocular depth estimation methods have shown promising results by leveraging geometric relationships among image sequences for network supervision. However, existing methods often face challenges such as blurry depth edges, high computational overhead, and information redundancy. This paper analyzes and investigates technologies related to deep feature encoding, decoding, and regression, and proposes a novel depth estimation network termed HPD-Depth, optimized by three strategies: utilizing the Residual Channel Attention Transition (RCAT) module to bridge the semantic gap between encoding and decoding features while highlighting important features; adopting the Sub-pixel Refinement Upsampling (SPRU) module to obtain high-resolution feature maps with detailed features; and introducing the Adaptive Hybrid Convolutional Attention (AHCA) module to address issues of local depth confusion and depth boundary blurriness. HPD-Depth excels at extracting clear scene structures and capturing detailed local information while maintaining an effective balance between accuracy and parameter count. Comprehensive experiments demonstrate that HPD-Depth performs comparably to state-of-the-art algorithms on the KITTI benchmarks and exhibits significant potential when trained with high-resolution data. Compared with the baseline model, the average relative error and squared relative error are reduced by 6.09% and 12.62% in low-resolution experiments, respectively, and by 11.3% and 18.5% in high-resolution experiments, respectively. Moreover, HPD-Depth demonstrates excellent generalization performance on the Make3D dataset.

A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant information in measurement data, present challenges to forecasting methods. The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced data-driven forecasting models. Focus on the seasonal variation characteristics of wind energy, a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed. The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

Automated multi-document summarization using extractive-abstractive approaches

This study presents a multi-document text summarizing system that employs a hybrid approach, including both extractive and abstractive methods. The goal of document summarizing is to create a coherent and comprehensive summary that captures the essential information contained in the document. The difficulty in multi-document text summarization lies in the lengthy nature of the input material and the potential for redundant information. This study utilises a combination of methods to address this issue. This study uses the TextRank algorithm as an extractor for each document to condense the input sequence. This extractor is designed to retrieve crucial sentences from each document, which are then aggregated and utilised as input for the abstractor. This study uses bidirectional and auto-regressive transformers (BART) as an abstractor. This abstractor serves to condense the primary sentences in each document into a more cohesive summary. The evaluation of this text summarizing system was conducted using the ROUGE measure. The research yields ROUGE R1 and R2 scores of 41.95 and 14.81, respectively.

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.

Wavelet-Entropy Enhanced Clustering: A Comprehensive Analysis of Drought Patterns in the Southern Plains, United States

Abstract Droughts may exhibit spatiotemporal heterogeneity at a regional scale. Effective drought assessment and management necessitates identifying homogeneous areas. However, previous studies often simplified clustering analysis by focusing only on a single variable. In this study, we present a novel drought clustering map for the southern plains (SP) region of the United States by integrating the wavelet-entropy approach with k-means clustering algorithm to capture spatiotemporal patterns of drought-related variables across various resolutions while eliminating redundant information. We considered multiple drought indicators and indices including gridded precipitation (P), potential evapotranspiration (PET), normalized difference vegetation index (NDVI), and standardized precipitation evapotranspiration index (SPEI) as well as geographical coordinates and topography map. Through evaluating five different combinations of input datasets, we selected the one demonstrating optimal results based on the Davies–Bouldin and Calinski–Harabasz criteria. In addition to P, PET, and NDVI, including the coordinates and elevation as secondary variables significantly enhanced the clustering performance. Using these variables, the region was subdivided into 21 clusters. Pearson’s correlation coefficients for the SPEI between centroid members and corresponding cells within clusters averaged between 0.84 and 0.94. Comparison with an existing cluster map [Drought Risk Atlas (DRA)] for the region revealed that our proposed cluster map showed higher variability between clusters for P, PET, and NDVI, confirming the robustness of the clustering results for drought conditions in the SP. The new clustering framework is expected to provide valuable insights for understanding and addressing drought dynamics in the SP region.

HMS-TENet: A hierarchical multi-scale topological enhanced network based on EEG and EOG for driver vigilance estimation

Driving vigilance estimation is an important task for traffic safety. Nowadays, electroencephalography (EEG) and electrooculography (EOG) have made some achievements in vigilance estimation, but there are still some challenges: 1) The traditional approachs with direct multimodal fusion may face the problems of information redundancy and data dimensionality mismatch; 2) Capture key discriminative features during multimodal fusion without losing specific patterns to each modality. In order to solve the above problems, this paper proposes a approach with fusion of EEG and EOG features in split bands, which not only preserves the information about brain activities in different bands of EEG, but also effectively integrates the relevant information of EOG. On this basis, we further propose a hierarchical multi-scale topological enhanced network (HMS-TENet). This network first introduces a pyramid pooling structure (PPS) to capture contextual relationships from different discriminative perspectives. And then we design a selective convolutional structure (SCS) for adaptive sense-field selection, which enables us to mine the desired discriminative information in small-size features. In addition, we design a topology self-aware attention to enhance the learning of representations of complex topological relationships among EEG channels. Finally, the output of the model can be selected for both regression and classification tasks, providing higher flexibility and adaptability. We demonstrate the robustness, generalizability, and utility of the proposed method based on intra-subject and cross-subject experiments on the SEED-VIG public dataset. Codes are available at https://github.com/tangmeng28/HMS-TENet.

Remaining useful life prediction of rolling bearings based on parallel feature extraction

Purpose This study aims to introduce a method for predicting the remaining useful life (RUL) of bearings based on parallel feature extraction. The proposed model provides prior knowledge and removes redundant handcrafted feature information, additionally, which focuses on the important features at different time scales. Design/methodology/approach Distinct from traditional parallel feature extraction methods, which can lead to information redundancy, a one-dimensional convolutional autoencoder is introduced to process selected indicators to remove redundancy and retain useful feature information. To fully capture the important degradation information within different stages in the feature sequences, a novel multi-scale attention feature fusion module is proposed to extract degradation features at different time scales. Considering the impact of degradation modes on RUL prediction, a dual-task prediction module based on no degradation mode labels is designed to obtain accurate RUL. Findings Comparative experiments and ablation studies on the PHM2012 bearing dataset verified the effectiveness of the proposed method. Furthermore, the rationality of the selected parameters is confirmed through model parameter analysis. Originality/value The novelty of the proposed method is that it not only provides prior knowledge but also further removes redundant information from prior knowledge. In addition, the distribution differences between the original features and their multi-scale convolution results are measured through Kullback–Leibler divergence as the attention scores, which allows the proposed method to focus on important information at different time scales.

A Multispectral Feature Selection Method Based on a Dual-Attention Network for the Accurate Estimation of Fractional Vegetation Cover in Winter Wheat

Spectral information plays a crucial role in fractional vegetation cover (FVC) estimation, and selecting the appropriate spectral information is essential for improving the accuracy of FVC estimation. Traditionally, spectral feature selection is primarily guided by physical mechanisms or empirical statistical models. This has led to the use of multispectral and hyperspectral images, which often result in missing or redundant information, thereby decreasing the efficiency and accuracy of FVC estimation. This study proposes a novel dual-attention network to select the feature bands of Sentinel-2 multispectral images for the accurate FVC estimation of winter wheat. In the first step, the importance of hyperspectral band reflectances was determined using simulated data from the PROSAIL model, by combining the dual-attention mechanism with the convolutional neural network (DAM-CNN). In the second step, the importance of Sentinel-2 multispectral bands was converted from the hyperspectral band importance identified in the previous stage, and subsequently ranked accordingly. Based on the feature ranking results, multispectral simulated data translated from hyperspectral simulated data were used for CNN training, and multispectral feature selection was conducted based on FVC accuracy. Finally, the selected features were assessed based on their performance in FVC estimation using a CNN model with real data. The experimental results indicate that during the key growth period of winter wheat, the combination of red, green, and red-edge bands significantly influences the FVC estimation accuracy. Band 3 (Green), band 4 (Red), band 5 (Red-edge 1), and band 6 (Red-edge 2) of Sentinel-2 satellite images contribute most significantly to winter wheat FVC estimation, achieving an accuracy comparable to that obtained using all bands, while reducing the training time by 19.1%, as confirmed by field survey data.

Normative or Confirmative: Effects of Information Quality and Redundancy in Decision Support Systems.

The study investigates users' tendency to access decision support (DS) systems as a function of the correlation between the DS information and the information users already have, the ongoing interaction with such systems, and the effect of correlated information on subjective trust. Previous research has shown inconclusive findings regarding whether people prefer information that correlates with information they already have. Some studies conclude that individuals recognize the value of noncorrelated information, given its unique content, while others suggest that users favor correlated information as it aligns with existing evidence. The impact of the level of correlation on performance, subjective trust, and the decision to use DS remains unclear. In an experiment (N = 481), participants made classification decisions based on available information. They could also purchase additional DS with different degrees of correlation with the available information. Participants tended to purchase information more often when the DS was not correlated with the available information. Correlated information reduced performance, and the effect of correlation on subjective trust and performance depended on DS sensitivity. Additional information may not improve performance when it is correlated with available information (i.e., it is redundant). Hence, the benefits of additional information and DS depend on the information the system and the operator use. It is essential to analyze the correlations between information sources and design the available information to allow optimal task performance and possibly minimize redundancy (e.g., by locating sensors in different positions to capture independent data).

Cooperative SLAM Algorithm for Multi-AUV Underwater Exploration and Mapping

SLAM (Simultaneous Localisation and Mapping) is very important in the task of mapping unknown deep-sea environments. This paper proposes an AUV cluster SLAM algorithm to improve the efficiency of SLAM mapping and navigation. The algorithm includes three main parts: (1) multi-beam sonar image processing algorithm, which detects and eliminates dynamic points while removing redundant information. (2) Combining DVL (Doppler Velocity Log), IMU (Inertial Measurement Unit) and DM (Depth Meter) data, SLAM is performed based on a Rao-Blackwellised particle filter (RBPF ). (3) The innovative iUSBL (inverted ultra-short baseline) system is used to realise the cooperative positioning between the master and slave AUVs. The multi-AUV underwater detection and mapping collaborative SLAM algorithm proposed in this paper not only significantly improves the mapping efficiency in unknown deep-sea environments but also effectively suppresses the errors introduced by dynamic points and ensures stable SLAM performance. Compared with a single AUV, the efficiency of mapping is significantly improved.

A stacked graph neural network with self-exciting process for robotic cognitive strategy reasoning in proactive human-robot collaborative assembly

Proactive human-robot collaborative assembly, a cognitively driven human-robot collaboration, requires research into robot cognitive strategy reasoning to ensure that the robot actively collaborates with the operator in task completion. However, current methods primarily focus on the pairwise relationships of assembly components in discrete snapshots. They could fail to represent the interconnected status of dynamic assembly, leading to inaccurate task allocation, thereby affecting robotic cognitive strategy. To address this problem, we propose a stacked graph neural network (GNN) with self-exciting process to capture the correlation and triggering mechanisms between time-varying tasks. Firstly, a temporal hypergraph with assembly knowledge is constructed to represent the non-pairwise relationships among assembly components in time-varying tasks, aiming to reduce the redundant information brought by pairwise relationships. Then, considering the characteristic of mutual influence between assembly events, a Hawkes process is introduced into the stacked GNN architecture to learn the event correlation representation in the temporal hypergraph. This point process models the self-exciting process of assembly events for simultaneously capturing the individual and collective features of events, thereby revealing the triggering mechanisms of the dynamic events. Finally, the effectiveness of proposed method is demonstrated by comparative experiments and the results of robotic cognitive strategy reasoning on dynamic assembly.

ІНТЕРПРЕТАЦІЙНА МОДЕЛЬ ДЕТЕКТИВУ(ЗА ПРОГРАМОЮ З ЛІТЕРАТУРИ НОВОЇ УКРАЇНСЬКОЇ ШКОЛИ)

The interpretive model of HalynaVdovychenko’s detective story «36 and 6 detective cats» (studied according to the program of Ukrainian literature by M. Chumarnaia, N. Pastushenko in the 5th grade of NUS) is based on the definition of the genre-creating features of the detective story, novel, fable, fairy tale; combination of theoretical conclusions and the practice of literary analysis. A new work of art in the program is interpreted with the involvement of current knowledge, skills, attitudes that belong to a whole range of educational subjects (Ukrainian language, Foreign literature, Biology and Ecology, Economics, Financial literacy, History), students acquire the ability to argue reasoning about what they have read, citing examples from various specialties, spheres of life. It is substantiated that reading and interpreting detective stories not only contributes to the awareness of universal values on which social laws and norms are built, it is important for the understanding and application of typical formal-logical elements (hypotheses, versions, proofs); is an opportunity to work on the comments published in the National PISA-2022 results report on reading about the under development of our schoolchildren’s abilities to analyze, systematize facts, and form logical conclusions. Does not contribute to the students’ understanding of the concept of the detective genre: the proposal of the program compilers to read the work abbreviated, problems of accessibility for reading in full, appropriateness of the age of fifth-graders; information redundancy due to the same works (excerpts) in the programs; conceptual inconsistency in the content of terms, ill-conceived system of questions and tasks in the school textbook; critical comments on the aesthetic quality of H. Vdovychenko’s detective story «36 and 6 detective cats». To a certain extent, the analysts of current model programs on Ukrainian literature are right about the formal nature of the reform. Practical application of the content of the article: the development of innovative educational, methodological and literary support for the systematization of the study of Ukrainian literature, the organization aimed at achieving the specified educational results of the activity of students, in particular, in the current State Standard of basic secondary education of general skills: «perceive, analyze, interpret, critically evaluate information in artistic texts of various genres», including an explanation of experts' comments regarding non-compliance with the criteria of artistic and aesthetic perfection.

Multi-branch spatial pyramid dynamic graph convolutional neural networks for solar defect detection

The imperative for automating solar panel monitoring techniques has become increasingly apparent with the global expansion of photovoltaic usage and the continuous installation of large-scale photovoltaic systems. Manual or visual inspection, limited in its applicability, is insufficient to manage this growing demand. To address this, we propose a novel Multi-Branch Spatial Pyramid Dynamic Graph Convolutional Neural Network (MB SPDG-CNN) for automatic fault detection in solar photovoltaic panels. The proposed architecture utilizes two separate input branches for thermal and RGB images, effectively leveraging complementary information from both image types. This multi-branch design enables the model to extract multi-stage features through a spatial pyramid pooling layer, enhancing feature fusion and improving classification accuracy. Additionally, compared to single-branch systems, our approach prevents feature redundancy and loss of important contextual information by fusing features from different layers in a unified end-to-end manner. Extensive experiments show that the proposed MB SPDG-CNN significantly outperforms single-branch architectures and other existing methods, achieving a precision of 99.78 %, recall of 98.91 %, and F1-score of 99.78 %. The integration of both RGB and thermal features within a multi-branch setup resulted in a 10 % improvement in detection rates compared to single-branch models, demonstrating the effectiveness of our architecture in achieving robust and accurate defect detection.