Search

Biomedical data exhibit high-dimensional complexity in its internal structure and are susceptible to noise interference, making classification tasks in biomedical data highly challenging. Twin support vector machine (TSVM) is a machine learning algorithm that can effectively solve pattern recognition problems. To mitigate the negative impact of noise, researchers have combined fuzzy set theory with TSVM and use fuzzy membership to describe the influence of different samples on constructing the optimal hyperplane, thus, extending TSVM to fuzzy twin support vector machines (FTSVM). In this paper, the dissimilarity measure based on data distribution is innovatively introduced into the fuzzy membership assignment process, and a novel fuzzy membership assignment strategy is designed to effectively reduce the negative impact of noise in biomedical data. Rather than rely on geometric distance, this strategy takes data distribution as the primary factor in measuring dissimilarity between samples and then constructs a heuristic function to assign fuzzy membership to different samples. Combining this strategy with TSVM, this paper proposed a fuzzy twin support vector machine based on dissimilarity measure (DFTSVM), which could effectively solve the classification problem with noise and shows excellent generalization performance in biomedical data. Moreover, DFTSVM employs a coordinate descent strategy with shrinking by active set to reduce computational complexity, which significantly improves the training speed of the model. Experiments are conducted on 14 biomedical datasets to compare the performance of DFTSVM with 10 heterogeneous machine learning classification algorithms and four homology algorithms. The results demonstrate that DFTSVM outperforms other algorithms in terms of classification performance on biomedical data. It exhibits excellent generalization performance in noisy environments, and its advantages in terms of generalization performance and noise robustness become more prominent as the noise rate increases.

In the field of autonomous driving, trajectory prediction of traffic agents is an important and challenging problem. Fully capturing the complex spatio-temporal features in trajectory data is crucial for accurate trajectory prediction. This paper proposes a trajectory prediction model called multi-dimensional spatio-temporal feature fusion (MDSTF), which integrates multi-dimensional spatio-temporal features to model the trajectory information of traffic agents. In the spatial dimension, we employ graph convolutional networks (GCN) to capture the local spatial features of traffic agents, spatial attention mechanism to capture the global spatial features, and LSTM combined with spatial attention to capture the full-process spatial features of traffic agents. Subsequently, these three spatial features are fused using a gate fusion mechanism. Moreover, during the modeling of the full-process spatial features, LSTM is capable of capturing short-term temporal dependencies in the trajectory information of traffic agents. In the temporal dimension, we utilize a Transformer-based encoder to extract long-term temporal dependencies in the trajectory information of traffic agents, which are then fused with the short-term temporal dependencies captured by LSTM. Finally, we employ two temporal convolutional networks (TCN) to predict trajectories based on the fused spatio-temporal features. Experimental results on the ApolloScape trajectory dataset demonstrate that our proposed method outperforms state-of-the-art methods in terms of weighted sum of average displacement error (WSADE) and weighted sum of final displacement error (WSFDE) metrics. Compared to the best baseline model (S2TNet), our method achieves reductions of 4.37% and 6.23% respectively in these metrics.

Intelligent Transportation Systems (ITSs) are vital for alleviating traffic congestion and improving traffic efficiency. Due to the delay of network transmission and failure of detectors, massive missing traffic data are often produced in ITSs, which evidently decreases the accuracy of decision-making in road traffic management. Hence, how establishing a precise and efficient estimation of missing traffic data becomes a hot yet thorny issue. Low-rank matrix completion (LR-MC) model has proven to be highly effective to address this issue owing to its fine representativeness of such high-dimensional and incomplete data. However, the existing LR-MC models mostly fail to model the inherently temporal and spatial correlations hidden in traffic network structure, resulting in low estimation accuracy. To improve it, this paper proposes a Hessian regularization spatio-temporal low rank (HRST-LR) algorithm with three main-fold ideas: a) imposing low-rank property into the global features of a traffic matrix for precisely learning its structure, b) capturing the temporal evolvement via a second-order difference of time-series constraint, and c) modeling the similar space of road segments through a Hessian regularization spatial constraint, thus exploring the local correlation between road segments for representing the spatial patterns in the traffic data. Experimental results on four traffic data sets prove that HRST-LR outperforms several state-of-the-art methods in the missing traffic data estimation with the root mean squared error improvements often higher than 14% when the missing rate is 90%. Hence, the HRST-LR algorithm is highly valuable for traffic data imputation with the need of performing spatio-temporal low-rank analysis.

Because of its outstanding performance, NAND flash memory in the form of embedded flash drives like Universal Flash Storage (UFS) has been widely used in the modern smartphones. As the core software in UFS, the flash translation layer (FTL) is essential to the performance improvement of UFS. To consider all workloads, a balanced and fixed mapping cache allocation scheme is used in the existing FTLs. Confronted with frequent changes of I/O request patterns of applications in smartphones, the existing FTLs with the fixed mapping cache allocation scheme could not perform well. To consider the I/O characteristics of smartphones, a novel cache reallocation-based page-level flash translation layer, called CRFTL, is proposed in this paper. Through analyzing the life cycle of I/O requests of applications in smartphones, CRFTL stores the mapping entries of I/O requests with different life cycles in different mapping cache structures. CRFTL can be aware of the inner states of NAND flash memory and I/O request patterns, and adaptively adjusts the cache space allocation of the mapping cache structures by applying the approach combining the heuristics and reinforcement learning algorithms. The experimental results show that the performance of our proposed CRFTL is better than that of existing FTLs.

Dear Editor, This letter puts forward a novel scalable temporal dimension preserved tensor completion model based on orthogonal initialization for missing traffic data (MTD) imputation. The MTD imputation acts directly on accessing the traffic state, and affects the traffic management. However, it still faces the following challenges: 1) The MTD imputation is usually formulated as matrix completion or tensor completion, which ignores the information across different dimensions; 2) Most of the existing models cannot generalize to traffic datasets of different scales or different missing rates; and 3) The MTD imputation models based on Gaussian random initialization easily leads to gradient explosion or vanishing, so that the training accuracy is not effectively improved. Inspired by these findings, the proposed scalable temporal dimension preserved tensor completion (ST-DPTC) model creatively establishes the following three-fold ideas: a) Incorporating the dimension preserved tensor completion (DPTC) to extract more distinctive traffic structure changes from the low-rank latent factor tensors; b) Adopting a scalable temporal (ST) regularization with first-order difference and second-order difference operators to adapt to different scales of traffic data; and c) Embedding ST regularization into DPTC with orthogonal initialization to perform low-rank latent factor tensor extraction and MTD imputation. Results on real-world traffic datasets with different scales show that our proposed model exceeds the state-of-the-art models in terms of the imputation accuracy.

This study proposes a primary node election method based on probabilistic linguistic term set (PLTS) for the practical Byzantine fault tolerance (PBFT) consensus mechanism to effectively enhance the efficiency of reaching consensus. Specifically, a novel concept of the probabilistic linguistic term set with a confidence interval (PLTS-CI) is presented to express the uncertain complex voting information of nodes during primary node election. Then, a novel score function based on the exponential semantic value and confidence approximation value for the PLTS-CI, called Score-ESCA, is used to solve the problems of comparing different nodes with various voting attitudes. This method helps select the node with the highest score by utilizing complex decision attitudes, making it an accurate primary node election solution. Furthermore, the feasibility of our proposed method is proved by both theoretical analysis and experimental evaluations.

This paper proposes a novel fuzzy multi-criteria decision-making method based on an improved score function of connection numbers and Choquet integral under interval-valued Pythagorean fuzzy environment. To do so, we first introduce a method to convert interval-valued Pythagorean fuzzy numbers into connection numbers based on the set pair analysis theory. Then an improved score function of connection numbers is proposed to make the ranking order of connection numbers more in line with reality in multi-criteria decision-making process. In addition, some properties of the proposed score function of connection numbers and some examples have been given to illustrate the advantages of conversion method proposed in the paper. Then, considering interactions among different criteria, we propose a fuzzy multi-criteria decision-making approach based on set pair analysis and Choquet integral under interval-valued Pythagorean fuzzy environment. Finally, a case of online learning satisfaction survey and a brief comparative analysis with other existing approaches are studied to show that the proposed method is simple,convenient and easy to implement. Comparing with previous studies, the method in this paper, from a new perspective, effectively deals with multi-criteria decision-making problems that the alternatives cannot be reasonably ranked in the decision-making process under interval-valued Pythagorean fuzzy environment.

Autonomous underwater vehicles (AUVs) play an increasingly important role in underwater missions. However, traditional visual localization methods struggle to meet the accuracy requirements for AUV terminal docking in strong-illumination or turbid water conditions. This work proposes a universal navigation system using electromagnetic (EM) and light beacons for nontorpedo AUVs to be recovered using a planar docking station. The major challenge in designing this system is to deal with the incomplete sensing of the light and EM beacons. To solve this problem, a two-step fusion method of EM localization information is given first. The kinematic model of the AUV and visual information are also utilized to improve the robustness of the navigation system. Based on the localization result, a comprehensive docking strategy is proposed to address the time-varying sensing conditions. Localization and docking experiments were conducted in a pool. The test results show that the maximum EM localization error is less than 20 cm in both static and dynamic tests. The AUV completed all six docking attempts.

Information leakage has become an urgent problem in multiple Online Social Networks (OSNs). The interactive communication of users has raised several privacy concerns. However, the current related work on privacy measurement only considers the privacy disclosure of user profile settings, ignoring the importance of profile attributes. To solve the efficient measurement problem, we consider the influence of attribute weight on privacy disclosure scores and propose a privacy measurement method by quantifying users’ privacy disclosure scores in social networks. Through introducing Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), we propose a Privacy Scores calculation model based on Fuzzy TOPSIS decision method (PSFT), that is more accurate calculate users’ privacy disclosure scores and that can improve users’ privacy awareness in multiple OSNs. Users can reasonably set the attribute file configuration based on privacy scores and attribute weight. We conduct extensive experiments on synthetic data set and real data set. The results of the experiments demonstrate the effectiveness of our model.

In recent years, artificial intelligence methods have been widely applied to solve issues related to renewable energy because of their ability to solve nonlinear and complex data structures. In this paper, we provide a comprehensive bibliometric analysis to better understand the evolution of Artificial Intelligence in Renewable Energy (AI&RE) research from 2006 to 2022. This study is performed based on the Web of Science Core Collection Database, and a dataset of 469 publications have been retrieved. This paper uses VOS viewer, CiteSpace, and Bibliometrix to perform bibliometric analysis and science mapping. The analysis results show that China is the most productive and influential country/region, with the widest range of collaborative partners. The study reveals that AI-related technologies can effectively solve issues related to integrating renewable energy with power system, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. In addition, future research trends are discussed. This paper helps scholars to understand the evolution of AI&RE research from a bibliometric perspective and inspires them to think about the field through multiple aspects.

Histopathological image recognition of breast cancer is an onerous task. Although many deep learning models have achieved good classification results on histopathological image classification tasks, these models do not take full advantage of the staining properties of histopathological images. In this paper, we propose a novel Deconv-Transformer (DecT) network model, which incorporates the color deconvolution in the form of convolution layers. This model uses a self-attention mechanism to match the independent properties of the HED channel information obtained by the color deconvolution. It also uses a method similar to the residual connection to fuse the information of both RGB and HED color space images, which can compensate for the information loss in the process of transferring RGB images to HED images. The training process of the DecT model is divided into two stages so that the parameters of the deconvolution layer can be better adapted to different types of histopathological images. We use the color jitter in the image data augmentation process to reduce the overfitting in the model training process. The DecT model achieves an average accuracy of 93.02% and F1-score of 0.9389 on BreakHis dataset, and an average accuracy of 79.06% and 81.36% on BACH and UC datasets. [ABSTRACT FROM AUTHOR]

Chemotherapy continues to be the most commonly applied strategy for cancer. Despite the impressive clinical success obtained with several drugs, increasing numbers of (multi)drug-resistant tumors are reported. To overcome this shortcoming, novel drug candidates and delivery systems are urgently needed. Herein, a therapeutic copper polypyridine complex encapsulated in natural nanocarrier apoferritin is reported. The generated nanoparticles showed higher cytotoxicity toward various (drug-resistant) cancer cell lines than noncancerous cells. The study of the mechanism revealed that the compound triggers cell autophagy-dependent apoptosis. Promisingly, upon injection of the nanodrug conjugate into the bloodstream of a mouse model bearing a multidrug-resistant colon tumor, a strong tumor growth inhibition effect was observed. To date, this is the first study describing the encapsulation of a copper complex in apoferritin that acts by autophagy-dependent apoptosis.

• A novel definition of concentration degree for probabilistic linguistic term sets. • A novel score function for probabilistic linguistic term sets. • Two decision-making methods are proposed for probabilistic linguistic term sets. • The effectiveness and robustness of decision-making methods are tested. Probabilistic linguistic term sets (PLTSs) can express the qualitative information of decision makers more accurately in the complicated linguistic setting. However, the existing comparison methods for PLTSs cannot compare some special PLTSs. To tackle this problem, a novel score function based on the concentration degree of an PLTS, called ScoreC-PLTS, is proposed. Additionally, the existing distance measures may distort the original information and lead to unreasonable results. Therefore, a novel probability splitting algorithm is proposed to preprocess PLTSs, based on which, a novel generalized hybrid distance is proposed for PLTSs. Moreover, a novel multiplicative analytic hierarchy process (MAHP) based on ScoreC-PLTS is proposed to determine the weight vector of attributes. Based on the generalized hybrid distance and MAHP, two novel TOPSIS-ScoreC-PLTS and VIKOR-ScoreC-PLTS methods are put forward to handle multi-attribute decision-making problems with PLTSs. Afterwards, an illustrative example concerning the selection of children English educational organization is solved using the proposed TOPSIS-ScoreC-PLTS and VIKOR-ScoreC-PLTS methods. In this example, four indicators are developed and the superiority of our studies is verified by comparing with the previous TOPSIS and VIKOR methods. [ABSTRACT FROM AUTHOR]

To guarantee high performance and reliability, storage systems require better devices and data redundancy schemes, e.g., SSD-based RAID5. However, failures in the large-scale storage systems are common. In order to serve requests on a failed node, the SSD-based RAID5 causes additional disk I/Os to trigger degraded reads. Existing coding methods are not suitable for the SSD-based RAID5. How to maintain the same storage cost as the traditional RAID5 does while obtaining less disk I/Os for degraded reads is an interesting problem. In this paper, we first come up with an alternate coding scheme (ACS) by using the characteristics of SSD to reduce disk I/Os for boosting degraded reads. To work for realistic workloads, we further propose ACS-W and ACS-DR approaches for write and degraded requests. Our evaluations based on the trace-driven simulator with real-world workloads show that ACS indeed reduces disk I/Os and improves the degraded read performance.

AbstractSome subjective methods should divide input values into local clusters before determining the ordered weighted averaging (OWA) operator weights based on the data distribution characteristics of input values. However, the process of clustering input values is complex. In this paper, a novel probability density based OWA (PDOWA) operator is put forward based on the data distribution characteristics of input values. To capture the local cluster structures of input values, the kernel density estimation (KDE) is used to estimate the probability density function (PDF), which fits to the input values. The derived PDF contains the density information of input values, which reflects the importance of input values. Therefore, the input values with high probability densities (PDs) should be assigned with large weights, while the ones with low PDs should be assigned with small weights. Afterwards, the desirable properties of the proposed PDOWA operator are investigated. Finally, the proposed PDOWA operator is applied to handle the multicriteria decision making problem concerning the evaluation of smart phones and it is compared with some existing OWA operators. The comparative analysis shows that the proposed PDOWA operator is simpler and more efficient than the existing OWA operators.

Missing traffic data imputation is an important step in the intelligent transportation systems. Low rank approximation is an important method for the missing traffic data imputation, especially the low rank matrix/tensor completion. However, the low-rank matrix completion has a huge time cost and the low-rank tensor completion cannot fully extract the information and maintain non-negativity. Therefore, we propose a novel non-negative temporal dimension preserved tensor completion (NT-DPTC) model with ideas: a) proposing a novel dimension preserved (DP) tensor decomposition method, which decomposes a tensor into three latent factor tensors to fully extract the intrinsic feature, b) using a Sigmoid mapper for releasing non-negative constraint from the training process and increasing flexibility, c) exploiting temporal constrains and AdamW schemes for obtaining a higher accuracy and faster convergence. Experiments on four industrial application scenarios highlight the superiority of our proposed model when compared with the existing state-of-the-art models. [ABSTRACT FROM AUTHOR]

Multimodal sentiment analysis is an actively growing research area that utilizes language, acoustic and visual signals to predict sentiment inclination. Compared to language, acoustic and visual features carry a more evident personal style which may degrade the model generalization capability. The issue will be exacerbated in a speaker-independent setting, where the model will encounter samples from unseen speakers during the testing stage. To mitigate personal style's impact, we propose a framework named SIMR for learning speaker-independent multimodal representation. This framework separates the nonverbal inputs into style encoding and content representation with the aid of informative cross-modal correlations. Besides, in terms of integrating cross-modal complementary information, the classical transformer-based approaches are inherently inclined to discover compatible cross-modal interactions but ignore incompatible ones. In contrast, we suggest simultaneously locating both through an enhanced cross-modal transformer module. Experimental results show that the proposed model achieves state-of-the-art performance on several datasets. [ABSTRACT FROM AUTHOR]

The p75 neurotrophin receptor p75NTR is an essential component of neurotrophin system, and has been implicated in the pathogenesis of major depressive disorder MDD and in the mechanism of antidepressant action. This study aimed to delineate the association between phenotype MDD susceptibility and antidepressant response and genotype p75NTRcommon genetic variants in a Chinese population. A total of 228 MDD patients and 402 unrelated controls were recruited. Subjects took selective serotonin reuptake inhibitors SSRIs and responders were defined as those with at least a 50 decrease in score of the Hamilton Rating Scale for Depression HAMD from baseline. Five p75NTRpolymorphisms were genotyped and their association with MDD or treatment response was assessed by haplotype and single marker analysis. No significant association with MDD was discovered in single locus or haplotype analyses. With regard to the therapeutic outcome, however, one missense polymorphism S250L showed association in both genotype distribution P  0.039 and allele frequency P  0.012. Haplotype analysis also revealed that p75NTRTCT carriers had a more unfavorable response to therapy P  0.010. Our exploratory study has demonstrated the association between p75NTRand SSRI response for the first time, which may assist in individualized therapy for MDD patients in the future. © 2007 WileyLiss, Inc.

Covering about three quarters of the surface area of the earth, the ocean is a critical source of sustenance, medicine, and commerce. However, such vast expanse in both surface area and depth, presents myriad observing challenges for researchers, such as corrosion, attenuation of electromagnetic waves, and high pressure. Ocean observation technologies are progressing from the conventional single node, static and short-term modalities to multiple nodes, dynamic and long-term modalities, to increase the density of both temporal and spatial samplings. Although people’s knowledge of the oceans has been still quite limited, the contributions of many nations cooperating to develop the Global Ocean Observing System (GOOS) have remarkably promoted the development of ocean observing technologies. This paper reviews the typical observing technologies deployed from the sea surface to the seafloor, and discusses the future trend of the ocean observation systems with the docking technology and sustained ocean energy.