Problem Of Information Redundancy Research Articles

ОСОБЛИВОСТІ СТРУКТУРИ БАЗИ ДАНИХ ДЛЯ ЗБЕРЕЖЕННЯ РІЗНОТИПНОЇ ІНФОРМАЦІЇ В СИСТЕМАХ КОМПЛЕКСНОГО МОНІТОРИНГУ

Modern integrated monitoring systems of territories and objects, such as countries, regions, cities, districts, enterprises, or even natural resources, deal with a vast amount of diverse information. Indicators characterizing the state of these objects may differ fundamentally and have distinct components, specifics of data processing, and storage. This diversity poses a challenge for standardizing data under a single template, as it requires accommodating the peculiarities of each indicator. The creation of a unified database structure for such systems is a complex and extremely important aspect that can significantly enhance monitoring efficiency and productivity. This paper discusses the peculiarities of developing a database structure for complex monitoring systems of ecological, economic, energy, and social parameters of territories, where comprehensive analysis requires consideration of data from different fields, emphasizing the importance of creating a flexible database structure that can be easily modified. A vertical data storage principle is proposed for preserving heterogeneous information, which eliminates the problem of information redundancy and facilitates the development of a universal database structure. The proposed structure remains unchanged when adding new parameters, allowing various experts to work with the system and analyze the environmental state using different indicators, without requiring database restructuring or changes to the software modules utilizing it. A database structure for storing calculations and formulas employing the vertical data storage principle is developed and presented, aiming to increase data processing speed and reduce time spent on programming new mathematical tools and analysis. The described database structure is successfully utilized in a comprehensive eco-energetic-economic monitoring system.

Document-Level Relation Extraction with Deep Gated Graph Reasoning

Extracting the relations of two entities on the sentence-level has drawn increasing attention in recent years but remains facing great challenges on document-level, due to the inherent difficulty in recognizing the relations of two entities across multiple sentences. Previous works show that employing the graph convolutional neural network can help the model capture unstructured dependent information of entities. However, they usually employed the non-adaptive weight edges to build the correlation weight matrix which suffered from the problem of information redundancy and gradient disappearance. To solve this problem, we propose a deep gated graph reasoning model for document-level relation extraction, namely, BERT-GGNNs, which employ an improved gated graph neural network with a learnable correlation weight matrix to establish multiple deep gated graph reason layers. The proposed deep gated graph reasoning layers make the model easier to reasoning the relations between entities hidden in the document. Experiments show that the proposed model outperforms most of strong baseline models, and our proposed model is 0.3% and 0.3% higher than the famous LSR-BERT model on the F1 and Ing F1, respectively.

A Deep Learning Method for Building Extraction from Remote Sensing Images by Fuzing Local and Global Features

As important disaster-bearing bodies, buildings are the focus of attention in seismic disaster risk assessment and emergency rescue. It is of great practical significance to extract buildings quickly and accurately with complex textures and variable scales and shapes from high-resolution remote sensing images. We proposed an improved TransUnet model based on multiscale grouped convolution and attention named MATUnet to retain more local detail features and enhance the representation ability of global features, while reducing the network parameters. We designed the multiscale grouped convolutional feature extraction module with attention (GAM) to enhance the representation of detailed features. The convolutional positional encoding module (PEG) was added to redetermine the number of transformer, it solved the problem of local feature information loss and the difficulty of convergence of the network. The channel attention module (CAM) of the decoder enhanced the salient information of the features and solved the problem of information redundancy after feature fusion. We experimented through MATUnet on the WHU building dataset and Massachusetts dataset. MATUnet achieved the best IOU results of 92.14% and 83.22%, respectively, and achieved better than the other generalized and state-of-the-art networks under the same conditions. We also have achieved good segmentation results on the GF2 Xichang building dataset.

UnifiedTT: Visual tracking with unified transformer

Target tracking is an important research task in computer vision. Existing tracking algorithms based on Siamese networks often suffer from the problem of information redundancy between adjacent frames and a lack of ability to capture global dependencies. When similar backgrounds appear around the target, the tracking performance usually significantly decreases. Although target tracking algorithms based on deep convolution and the Transformer have partially addressed these issues, achieving a good balance between the two remains a challenge. In this work, we propose a unified convolution and self-attention Siamese network for target tracking. By utilizing a feature extraction backbone network based on integrated convolution and self-attention styles, we are able to capture globally important regions and key frames while greatly reducing local redundant computations, thereby improving tracking performance. We apply this approach to the task of target tracking and enhance the feature extraction capabilities of both the target template and search template. Experimental results show that our proposed tracking algorithm outperforms some recent classical tracking algorithms, especially achieving improvements of 10.7 % on the high-diversity dataset GOT-10K and 24.7 % on the large-scale and high-quality LaSOT dataset.

Artificial intelligence can dynamically adjust strategies for auxiliary diagnosing respiratory diseases and analyzing potential pathological relationships

Respiratory diseases are one of the leading causes of human death and exacerbate the global burden of non-communicable diseases. Finding a method to assist clinicians pre-diagnose these diseases is an urgent task. Existing artificial intelligence-based methods can improve the clinical diagnosis efficiency, but still face challenges. For example, the lack of interpretability, the problem of information redundancy or missing caused by only using static data, the difficulty of model to learn the interdependence between features, and the performance of model is limited by sparse datasets, etc. To alleviate these problems, we propose a novel RQPA-Net. It consists of Q&A diagnosis module (QAD) and pathological inference module (PI). The QAD is responsible for interacting with patients, adjusting inquiry strategies dynamically and collecting effective information for disease diagnosis. The designed multi-subspace network can alleviate the problem that classical method is difficult to understand the interdependence between features. The deep reinforcement learning designed also can alleviate the problem of classical methods lack of interpretability. The PI is responsible for reasoning potential pathological relationships between diseases or symptoms based on existing knowledge. Through integrating the advantages of deep learning and reinforcement learning techniques, PI can handle sparse datasets. Finally, for auxiliary diagnosis, the model achieves 0.9780 ± 0.0002 Recall, 0.9778 ± 0.0003 Acc, 0.9779 ± 0.0003 Precision and 0.9780 ± 0.0003 F1-score on the test set. In terms of assisting pathological analysis, compared with the end-to-end model, our model achieves higher comprehensive performance on different tasks and datasets with different degrees of sparsity. Even in sparse datasets, it can effectively infer potential associations between diseases or symptoms, and has higher potential clinical application. In this paper, we propose a novel network structure, which can not only assist doctors in diagnosing diseases, but also contribute to explore the potential disease mechanisms. It provides a new perspective for integrating AI technology and clinical practice.

Gesture image recognition method based on DC-Res2Net and a feature fusion attention module

To extract decisive features from gesture images and solve the problem of information redundancy in the existing gesture recognition methods, we propose a new multi-scale feature extraction module named densely connected Res2Net (DC-Res2Net) and design a feature fusion attention module (FFA). Firstly, based on the new dimension residual network (Res2Net), the DC-Res2Net uses channel grouping to extract fine-grained multi-scale features, and dense connection has been adopted to extract stronger features of different scales. Then, we apply a selective kernel network (SK-Net) to enhance the representation of effective features. Afterwards, the FFA has been designed to remove redundant information in features by fusing low-level location features with high-level semantic features. Finally, experiments have been conducted to validate our method on the OUHANDS, ASL, and NUS-II datasets. The results demonstrate the superiority of DC-Res2Net and FFA, which can extract more decisive features and remove redundant information while ensuring high recognition accuracy and low computational complexity.

C3DBed: Facial micro-expression recognition with three-dimensional convolutional neural network embedding in transformer model

Facial micro-expression is often used for emotional recognition of people in a high-risk or pressure scene, which may reflect genuine emotions due to the low intensity of facial action units. Current methods focus on locating regions with emotional changes and cropping these regions for local feature extraction. However, these methods may lead to the problem of information redundancy caused by overlapping cropped regions. This paper proposes a novel three-dimensional convolutional neural network embedding in the transformer model (C3DBed). This model learns the attention weight of each local region of the micro-expression image, thereby perceiving the detail changes of the facial image and extracting robust local detail features. Solve the problem of model complexity and information redundancy caused by low-intensity local area positioning of facial muscle movement. The experiment results demonstrated that the proposed C3DBed model achieved competitive performance with accuracy rates of 78.04%, 77.64%, and 75.73% on SMIC, CASME II, and SAMM datasets, respectively.

Hybrid partial-constrained learning with orthogonality regularization for unsupervised person re-identification

Person re-identification (re-ID) aims at determining whether there is a specific person in image sets or videos via computer vision technology. State-of-the-art unsupervised re-ID methods extract image features through CNNs-based networks and store these extracted features in memory for identity matching. However, extracted global features of these methods ignore the problem of information redundancy and the influence of the constraints between the internal features. To overcome these problems, a Hybrid Partial-constrained Learning (HPcL) network with orthogonality regularization is proposed to learn a discriminative visual representation by generating hybrid features. Specifically, the hybrid features are generated by our designed Dynamic Fusion Module (DFM) to initialize the memory dictionary and match the identity, which can constrain each part of the features extracted by our proposed Multi-Scale (M-S) module and learn robust visual representations. In addition, a new orthogonal regularization method is introduced to constrain orthogonality of the kernel weights and features, which reduces the correlations among features. Extensive experimental results on Market-1501, DukeMTMC-reID, PersonX, and MSMT17 datasets demonstrate that our method is effective and superior to the state-of-the-art methods.

Self-Supervised Fusion for Multi-Modal Medical Images via Contrastive Auto-Encoding and Convolutional Information Exchange

This paper proposes a self-supervised framework based on a contrastive auto-encoding and convolutional information exchange for multi-modal medical fusion tasks. It is well known that multi-modal medical images have the same and unique features, and information redundancy is easily led when source image features are extracted in pairs. Inspired by contrastive learning, this article constructs positive and negative results pairs and proposes a novel contrastive loss in an auto-encoder. The paired source images are considered as positive and negative results used to reconstruct the source images to avoid the information redundancy problem. This article proposes preserving both the global and local features based on prior knowledge by combining transformer and convolution neural networks in parallel as an auto-encoder. A contribution estimation model is adopted to fuse multi-modal medical images. In the contribution estimation stage, an information exchange block is designed to exchange the feature maps of source images in multi-kernel convolutions, and then the multi-convolutional features are utilized to estimate the best fusion contribution of the paired source images. Experiments demonstrate that our method gives the best results than other state-of-the-art fusion approaches.

Bayesian Self-Attentive Speaker Embeddings for Text-Independent Speaker Verification

Learning effective and discriminative speaker embeddings is a crucial task in speaker verification. Usually, speaker embeddings are extracted from a speaker-classification DNN that averages the hidden vectors over all the spoken frames of a speaker; the hidden vectors produced from all the frames are assumed to be equally important. In our previous work, we relaxed this assumption and computed the speaker embedding as a weighted average of a speaker's frame-level hidden vectors, and their weights were automatically determined by a self-attention mechanism. The effect of multiple attention heads have also been investigated to capture different aspects of a speaker's input speech. One challenge for multi-head attention is the information redundancy problem. If there is no constraint during the training of multi-head attention, different heads may extract similar attentive features, leading to the attention redundancy problem. In this paper, we generalize the deterministic multi-head attention to a Bayesian attention framework, and provide a new understanding of multi-head attention from a Bayesian perspective. Under the Bayesian framework, we adopt the recently developed sampling method in optimization, which explicitly enforces the repulsiveness among the multiple heads. Systematic evaluation of the proposed Bayesian self-attentive speaker embeddings is performed on VoxCeleb and SITW evaluation sets. Significant and consistent improvements over other multi-head attention systems are achieved on all the evaluation datasets. The best Bayesian system with eight heads improves the EER by around 26% on VoxCeleb and 9% on SITW over the single-head baseline.

RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion Recognition

Emotion recognition in conversation (ERC) has received increasing attention from researchers due to its wide range of applications. As conversation has a natural graph structure, numerous approaches used to model ERC based on graph convolutional networks (GCNs) have yielded significant results. However, the aggregation approach of traditional GCNs suffers from the node information redundancy problem, leading to node discriminant information loss. Additionally, single-layer GCNs lack the capacity to capture long-range contextual information from the graph. Furthermore, the majority of approaches are based on textual modality or stitching together different modalities, resulting in a weak ability to capture interactions between modalities. To address these problems, we present the relational bilevel aggregation graph convolutional network (RBA-GCN), which consists of three modules: the graph generation module (GGM), similarity-based cluster building module (SCBM) and bilevel aggregation module (BiAM). First, GGM constructs a novel graph to reduce the redundancy of target node information. Then, SCBM calculates the node similarity in the target node and its structural neighborhood, where noisy information with low similarity is filtered out to preserve the discriminant information of the node. Meanwhile, BiAM is a novel aggregation method that can preserve the information of nodes during the aggregation process. This module can construct the interaction between different modalities and capture long-range contextual information based on similarity clusters. On both the IEMOCAP and MELD datasets, the weighted average F1 score of RBA-GCN has a 2.17 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 5.21% improvement over that of the most advanced method.

Adaptive Broad Echo State Network for Nonstationary Time Series Forecasting

Time series forecasting provides a vital basis for the control and management of various systems. The time series data in the real world are usually strongly nonstationary and nonlinear, which increases the difficulty of reliable forecasting. To fully utilize the learning capability of machine learning in time series forecasting, an adaptive broad echo state network (ABESN) is proposed in this paper. Firstly, the broad learning system (BLS) is used as a framework, and the reservoir pools in the echo state network (ESN) are introduced to form the broad echo state network (BESN). Secondly, for the problem of information redundancy in the reservoir structure in BESN, an adaptive optimization algorithm for the BESN structure based on the pruning algorithm is proposed. Thirdly, an adaptive optimization algorithm of hyperparameters based on the nonstationary test index is proposed. In brief, the structure and hyperparameter optimization algorithms are studied to form the ABESN based on the proposed BESN model in this paper. The ABESN is applied to the data forecasting of air humidity and electric load. The experiments show that the proposed ABESN has a better learning ability for nonstationary time series data and can achieve higher forecasting accuracy.

Aggregate interactive learning for RGB-D salient object detection

The task of salient object detection is to find the most noticeable areas in the image. On the one hand, most of the existing RGB-D saliency object detection requires additional networks to process depth features, and the sub-networks that process depth features rely too much on the RGB network, resulting in higher computational costs. On the other hand, when dealing with multi-scale features, most models tend to produce information loss, the semantic information representation ability is weak, and good detection results cannot be achieved, which limits its practical application. Firstly, this paper proposes a strategy of aggregation and interaction to extract edge features, depth features and salient features, while maintaining local details, fully extracting global information. Secondly, in the learning process of high-level features, depth features and salient features are extracted at the same time, which reduces the complexity of the network and does not require the additional sub-networks. On the other hand, the deformable convolution network is used to solve the multi-scale problem to ensure that more detailed feature information is extracted. Finally, taking advantage of the complementarity between features, using the one-to-one feature fusion module, the problem of information redundancy in the feature fusion process is solved, and the fused features can accurately locate the salient results with clear details. The experimental results on six datasets show that compared with other state-of-the-art algorithms, the algorithm in this paper has excellent performance.

Rethinking the Value of Just-in-Time Learning in the Era of Industrial Big Data

Just-in-time learning (JITL) has become a widely used industrial process modeling tool. With the advent of the industrial big data era, rich data information has brought new opportunities to JITL. Specifically, the completeness of data samples in the era of big data provides an important premise and support for the JITL method, prompting us to rethink the application value of JITL in the context of industrial big data. At the same time, the huge amount of data causes certain difficulties in data searching, which is a key issue for JITL. In this article, a parallel computing strategy is adopted to divide the entire computational searching task into several subtasks, and assign the subtasks to parallel computing nodes to complete parallel searching. In this way, not only the full advantage of big data information is effectively utilized, but also the search capability and efficiency under big data are improved. In addition, in order to improve the real-time nature of JITL, a model library management (MLM) strategy is adopted, and the query similar samples are used to operate with existing similar models. And by selectively adding new data, the database management (DBM) strategy is also developed, which not only alleviates the problem of information redundancy, but also reduces the search pressure caused by the increasingly large database. Obviously, MLM and DBM are particularly important as JITL auxiliary tools under industrial big data. Combining parallel computing, a parallel JITL (P-JITL) framework is proposed. As an example, the variational Bayesian factor regression model is transformed into the parallel Bayesian-JITL method for big process data modeling, which is further extended to a nonlinear form. To evaluate the feasibility and efficiency of the developed methods, a real industrial case is demonstrated.

Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception

Sensor data sharing enables vehicles to exchange locally perceived sensor data among each other and with the roadside infrastructure to increase their environmental awareness. It is commonly regarded as a next-generation vehicular communication service beyond the exchange of highly aggregated messages in the first generation. The approach is being considered in the European standardization process, where it relies on the exchange of locally detected objects representing anything safety-relevant, such as other vehicles or pedestrians, in periodically broadcasted messages to vehicles in direct communication range. Objects filtering methods for inclusion in a message are necessary to avoid overloading a channel and provoking unnecessary data processing. Initial studies provided in a pre-standardization report about sensor data sharing elaborated a first set of rules to filter objects based on their characteristics, such as their dynamics or type. However, these rules still lack the consideration of information received by other stations to operate. Specifically, to address the problem of information redundancy, several rules have been proposed, but their performance has not been evaluated yet comprehensively. In the present work, the rules are further analyzed, assessed, and compared. Functional and operational requirements are investigated. A performance evaluation is realized by discrete-event simulations in a scenario for a representative city with realistic vehicle densities and mobility patterns. A score and other redundancy-level metrics are elaborated to ease the evaluation and comparison of the filtering rules. Finally, improvements and future works to the filtering methods are proposed.

Computer Network Security Defense Model

With the rapid development of the Internet industry, hundreds of millions of online resources are also booming. In the information space with huge and complex resources, it is necessary to quickly help users find the resources they are interested in and save users time. At this stage, the content industry’s application of the recommendation model in the content distribution process has become the mainstream. The content recommendation model provides users with a highly efficient and highly satisfying reading experience, and solves the problem of information redundancy to a certain extent. Knowledge tag personalized dynamic recommendation technology is currently widely used in the field of e-commerce. The purpose of this article is to study the optimization of the knowledge tag personalized dynamic recommendation system based on artificial intelligence algorithms. This article first proposes a hybrid recommendation algorithm based on the comparison between content-based filtering and collaborative filtering algorithms. It mainly introduces user browsing behavior analysis and design, KNN-based item similarity algorithm design, and hybrid recommendation algorithm implementation. Finally, through algorithm simulation experiments, the effectiveness of the algorithm in this paper is verified, and the accuracy of the recommendation has been improved.

Automated detection of premature ventricular contraction based on the improved gated recurrent unit network

Background and Objective: Premature ventricular contraction (PVC) is the common arrhythmia disease, affecting thousands of individuals worldwide. However, the traditional PVC detection is cumbersome by visually inspecting electrocardiogram (ECG) signals.Methods: In this work, we specially propose an improved gated recurrent unit (IGRU) by setting a scale parameter into existing bidirectional GRU (BGRU) model for PVC signals recognition, which is used to alleviate the problem of information redundancy in BGRU. To verify the effectiveness, IGRU model will be embedded into a convolutional network frame and existing GRU and BGRU models are employed as control groups for a fair comparison.Results: The results exhibit that the model attains better model performance than control groups and several state-of-the-art algorithms with the accuracy of 98.3% and 97.9% with the MIT-BIH arrhythmia database and China Physiological Signal Challenge 2018 database. Besides, motivated from the waveform characteristics of ECG signals in PVC, the proposed model can provide certain physiological interpretability for physicians and researchers.Conclusions: To our knowledge, this is the first attempt to re-design the existing GRU network for ECG signals classification, thus exhibiting great application potentials especially in lightweight equipment such as mobile phone and camera.

An intelligent computer-aided approach for atrial fibrillation and atrial flutter signals classification using modified bidirectional LSTM network

Atrial fibrillation (AF) and atrial flutter (AFL) are the most common arrhythmias. Due to the similar clinical symptoms, both are one of the main causes of misdiagnosis for physicians. The visual inspection of electrocardiogram (ECG) signals is the most traditional detection strategy, however, it is often laborious and time-consuming. Thus, we specially propose a modified bidirectional long short-term memory (MB-LSTM) network for AF and AFL signals recognition. In this network, inspired by illustrious Squeeze-and-Excitation network, a feature recalibration strategy is performed on existing B-LSTM network so that this can enable the model to adaptively reallocate feature representation and thus alleviate the problem of information redundancy in B-LSTM to a certain extent. Further, we embed the proposed network into a convolutional network frame with attention mechanism and use existing LSTM, B-LSTM networks as control groups to evaluate its effectiveness with a subject-independent validation strategy on the two publicly available databases. The result shows that the model yields superior classification performance with an accuracy of 99.1% and 98.4% than several state-of-the-art methods while confirming the effectiveness of MB-LSTM. Particularly, the qualitative analysis is provided to elaborate on the mechanism of performance improvement, showing promising model practicability as an intelligent and efficient tool to assist physicians.

Research on Multimodal Emotion Analysis Algorithm Based on Deep Learning

In recent years, more and more people are keen to express their feelings and opinions in the form of pictures and texts on social media at the same time, which makes the multimodal data with pictures and texts as the main content growing. Compared with monomodal data, multimodal data contains more information and can reveal the real feelings of users. The analysis of the emotion of these massive multimodal data is helpful to better understand people’s attitudes and viewpoints and has a wide range of application scenarios. In order to solve the problem of information redundancy in multimodal emotion classification tasks, based on the tensor fusion scheme, a multimodal emotion analysis method based on attention neural network is proposed. This method constructs a text feature extraction model and an image feature extraction model based on attention neural network, which highlights the key areas of image emotional information and the words containing emotional information, which makes the expression of single-modal features more concise and accurate. The tensor product of each mode is regarded as the joint feature expression of multimodal data, and the redundant information of joint features is eliminated by principal component analysis, and then the emotion category of multimodal data is obtained by support vector machine. The proposed model is evaluated on two real Twitter image data sets. The experimental results show that, compared with other emotion classification models, this method has a great improvement in classification accuracy, recall rate, F1 index and accuracy.

A Novel Multiscale Deep Health Indicator with Bidirectional LSTM Network for Bearing Performance Degradation Trend Prognosis

As rolling bearings are the key components in rotating machinery, bearing performance degradation directly affects machine running status. A tendency prognosis for bearing performance degradation is thus required to ensure the stability of operation. This paper proposes a novel strategy for bearing performance degradation trend prognosis, including health indicator construction techniques and a performance degradation trend prediction method. To more accurately represent the degradation trend, the multiscale deep bottleneck health indicator is proposed as a new synthesized health indicator to remove high-frequency detail signals from features, which can reduce possible fluctuations in conventional synthetic health indicators. A suitable method for selecting the statistical characteristics required for fusion is also presented to solve the problem of information redundancy that affects trend representation. In addition, a stacked autoencoder network is used for deep feature extraction of selected statistical features. A bidirectional long short-term memory network prediction model is also proposed for the prediction of degradation trend, which can make full use of historical and future information to improve prediction accuracy. Finally, experiments are carried out to verify the effectiveness of the proposed method.