Classical classification methods often fail in dynamic environments where data distributions shift over time, known as concept drift. Applications like flight delay prediction and weather forecasting require handling such dynamic data streams. Concept drift can be either virtual, affecting unconditional probability distributions, or real, affecting conditional distributions. While most research focuses on real drift, virtual drift and noise also degrade classifier performance. In this paper, we propose gaussian mixture models based on contrastive learning (GMMCL), a novel approach that integrates noise handling, contrastive learning, drift detection, and gaussian mixture models. Our method significantly enhances adaptability to noisy and drifting data streams, outperforming mainstream approaches across twelve synthetic and real-world datasets. This provides a robust solution for managing concept drift and noise in dynamic classification tasks.

Cross-domain sequential recommendation (CDSR) has emerged as an effective solution to address the challenges of data sparsity and cold-start issues in recommendation systems by leveraging shared knowledge across multiple domains. Nevertheless, existing methods face notable long-term challenges, such as ineffective knowledge transfer caused by distributional shifts and sparse overlapping users, insufficient modeling of temporal dynamics and intricate sequential patterns in user behavior, and suboptimal generalization across heterogeneous domains.To tackle these issues, we propose DiTAGInt, a novel diffusion-based generative network with augmented-graph embedding integration. DiTAGInt introduces a dynamic embedding fusion mechanism to harmonize domain-specific and shared-user representations, thereby enhancing generalization and alleviating rigid transfer constraints. Furthermore, it employs a diffusion-based generative module to effectively model temporal dynamics and capture complex sequential patterns in user behavior, facilitating precise user preference learning and significantly advancing recommendation accuracy. Extensive experiments conducted on three public datasets demonstrate the superiority of our method.

The highly dynamic nature of data streams makes streaming data prone to concept drift, making it difficult for traditional machine-learning models to maintain prediction accuracy. Existing drift detection methods usually rely on monitoring error rates or statistical changes, ignoring the impact of feature mining and feature fusion on drift detection. To address these limitations, we propose a concept drift detection method based on Difference Embedding and Drift Type Awareness called DEDT. DEDT enhances the feature representation of error rate differences through TabTransformer embedding and combines an auxiliary drift type classifier to improve detection accuracy. In addition, we propose a joint loss function to improve the generalization ability of the model in different drift scenarios. Compared with traditional methods, DEDT shows excellent detection ability and time efficiency, which has been verified in experimental evaluations on multiple drift datasets. Our scheme not only improves feature representation, detection accuracy, and generalization ability but also shows higher efficiency, making it capable of solving concept drift detection in complex data streams.

Identifying essential proteins is crucial for understanding disease mechanisms and developing therapeutic strategies. During the past several decades, numerous algorithms have been introduced to integrate topological and biological information to tackle the challenge of identifying essential proteins. However, existing methods still have some drawbacks: (1) the lack of rigorous mathematical interpretation for the parameters that determine their respective weights when integrating topological and biological information; (2) the lack of flexibility for adding or removing topological and biological information from integration process in real applications. To overcome these limitations, we propose a novel essential protein discovery method, called EPSIF, which assigns weights to PPI network interactions and integrates diverse topological and biological information via a statistical ensemble model. To assess the performance of EPSIF, we conduct experiments on real PPI networks and compare with eight state-of-the-art algorithms, which confirm the effectiveness and flexibility of EPSIF.

In the dynamic landscape of artificial intelligence and robotics, the pursuit of accurate positioning in mobile robots has intensified. This research addresses the limitations of single-sensor SLAM (Simultaneous Localization and Mapping) techniques in complex settings by harnessing the collective strengths of LiDAR (Light Detection And Ranging), Camera, IMU (Inertial Measurement Unit), and GNSS (Global Navigation Satellite System) sensors. The proposed multi-sensor tightly-coupled SLAM framework is an integration of point-line feature-based laser-visual-inertial odometry, visual-laser fusion loop closure detection, and factor graph-based back-end optimization. Within the visual-inertial subsystem, an advanced LSD (Line Segment Detector) feature extraction strategy is introduced, incorporating point-line fusion to enhance visual line features. Additionally, the laser point cloud is projected onto the camera coordinate system, establishing depth associations with visual attributes. Strengthening the robustness of the visual-inertial subsystem in low-texture environments, camera poses undergo optimization through a sliding-window bundle adjustment method. In the laser-inertial subsystem, IMU preintegration mitigates laser point cloud distortion. Extracting edge and plane features, coupled with frame-to-local-map matching, enhances matching efficiency while streamlining computational intricacies. This amalgamation forms the basis of the laser-visual-inertial odometry fusion system. To overcome the limitations of standalone visual and laser-based loop closure detection, a dual-loop closure method utilizing visual-laser fusion is proposed. Leveraging the DBoW2 bag-of-words model, complemented by temporal-spatial consistency checks, enhances detection efficiency and accuracy. The integration of GNSS factors imparts global constraints for expansive outdoor scenarios. Employing factor graph-based back-end optimization, the refinement of laser-visual-inertial odometry factors, visual-inertial odometry factors, IMU preintegration factors, loop closure factors, and GNSS factors culminates in precise global pose estimation and high-fidelity point cloud maps. Through rigorous evaluation of the M2DGR dataset and a mobile robot platform, the proposed methodology emerges as an exemplar of performance, showcasing superiority over the state-of-the-art LIO-SAM technique. Achieving a reduction of 2.86 m and 3.23 m in the root mean square error of absolute pose estimation across divergent environments, this approach exhibits remarkable efficacy in outdoor scenarios, thereby elevating the precision and resilience of SLAM algorithms for mobile robots.

As the scale of data on the Internet continues to increase, the management and monitoring of time series data are facing significant challenges. Efficient and stable time-series data anomaly detection methods are necessary for fields such as traffic detection, power grid operation and maintenance, financial stock market, and industry. However, there are fewer abnormal data labels in time series data, and the labeling cost is high. Traditional expert knowledge-based supervised methods have been difficult to adapt to large-scale data metric management and timely abnormal alarms. At the same time, the way based on the new neural network has an extensive time overhead when faced with massive data, and it isn’t easy to apply it in a real-time industrial environment. Therefore, we propose the TSFN model in this paper, an unsupervised method of a transformer-based self-feedback network. Which can capture timing dependencies, learn normal data distribution and improve the self-feedback ability for sensitive areas, and can be used to detect anomalies in multidimensional time series more quickly. Our experimental research on five public datasets shows that our method has fast training speed, good stability, excellent anomaly detection ability, and good generalization ability compared with the baseline method.

BACKGROUND: Identification of hot spots in protein-DNA binding interfaces is extremely important for understanding the underlying mechanisms of protein-DNA interactions and drug design. Since experimental methods for identifying hot spots are time-consuming and expensive, and most of the existing computational methods are based on traditional protein-DNA features to predict hot spots, unable to make full use of the effective information in the features. RESULTS: In this work, a method named WTL-PDH is proposed for hot spots prediction. To deal with the unbalanced dataset, we used the Synthetic Minority Over-sampling Technique to generate minority class samples to achieve the balance of dataset. First, we extracted the solvent accessible surface area features and structural features, and then processed the traditional features using discrete wavelet transform and wavelet packet transform to extract the wavelet energy information and wavelet entropy information, and obtained a total of 175 dimensional features. In order to obtain the best feature subset, we systematically evaluate these features in various feature selection strategies. Finally, light gradient boosting machine (LightGBM) was used to establish the model. CONCLUSIONS: Our method achieved good results on independent test set with AUC, MCC and F1 scores of 0.838, 0.533 and 0.750, respectively. WTL-PDH can achieve generally better performance in predicting hot spots when compared with state-of-the-art methods. The dataset and source code are available at https://github.com/chase2555/WTL-PDH .

Masquerade detection has always been a vital detection capacity in intrusion detection, and shell command detection plays an important role in masquerade detection. Shell command detection is used to detect the system commands and judge whether the commands are from the masquerader or not to protect the safety of the whole system. However, it is challenging to classify the masquerader commands because of the difference between common text classification and shell command detection. The paper presents a machine learning model to masquerade detection using a Temporal Convolutional Network, a deep learning neural network for temporal anomaly detection. We believe masquerader commands are highly related to the time series. We prove that our model has a better effect on the SEA dataset than the deep neural network, convolutional neural network, and LSTM in various model metrics.

Summary In this paper, a real‐time traffic index model of expressways is proposed by using a traffic index to evaluate the actual conditions of expressways. The model considers the actual situation of floating and nonfloating vehicles on expressways, in the context of massive floating car data. Included is the realization of the complete calculation model of real‐time traffic index estimation, including highway section division, spatial topology map matching, driving route calculation, and road congestion status judgment. For roads without floating car coverage, the weighted‐moving‐average time‐series prediction method is used to predict the traffic index, so that the running condition of all roads in the network can be analyzed completely. The simulation results show that the proposed highway traffic index can reflect not only the overall high‐speed operation but also real‐time congestion at specific high‐speed or specific highway intervals, providing an effective reference for travel.

We here analyzed the correlation between the genic GC content and the temperature range conditions of prokaryotes. To identify those genes whose surrounding GC levels exhibit patterns different for organisms under different temperature conditions but transcending phylogenetic boundaries, we first focused on the complete list of organisms, then partial lists of organisms with one phylum being excluded, and finally organisms of the same phylum but of different temperature conditions. To further validate the identified correlation relationships, we examined to what extent the temperature condition of an organism can be predicted based on the GC levels surrounding the selected genes. The overall prediction accuracy was 96.80% if based on the genes derived from the complete list of organisms, and 95.45% if based on the 17 phylum-independent genes. If the phylum-specific genes were used, the prediction accuracy was 90.00% and 95.63% for organisms of the Euryarchaeota and Firmicutes phyla, respectively. These results demonstrated the predictability of the temperature range conditions of prokaryotic organisms based on their genic GC levels surrounding certain genes, as well as the correlation between this particular duo of ecological and genomic traits.

BACKGROUND: The environment has been playing an instrumental role in shaping and maintaining the morphological, physiological and biochemical diversities of prokaryotes. It has been debatable whether the whole-genome Guanine-Cytosine (GC) content levels of prokaryotic organisms are correlated with their optimal growth temperatures. Since the GC content is variable within a genome, we here focus on the correlation between the genic GC content levels and the temperature range conditions of prokaryotic organisms. RESULTS: The GC content levels in the coding regions of four genes were consistently identified as correlated with the temperature range condition when the association analysis was applied to (i) the 722 mesophilic and 93 thermophilic/hyperthermophilic organisms regardless of their phylogeny, oxygen requirement, salinity, or habitat conditions, and (ii) partial lists of organisms when organisms with certain phylogeny, oxygen requirement, salinity or habitat conditions were excluded. These four genes are K01251 (adenosylhomocysteinase), K03724 (DNA repair and recombination proteins), K07588 (LAO/AO transport system kinase), and K09122 (hypothetical protein).To further validate the identified correlation relationships, we examined to what extent the temperature range condition of an organism can be predicted based on the GC content levels in the coding regions of the selected genes. The 84.52% accuracy for the complete genomes, the 84.09% accuracy for the in-progress genomes, and 82.70% accuracy for the metagenomes, especially when being compared to the 50% accuracy rendered by random guessing, suggested that the temperature range condition of a prokaryotic organism can generally be predicted based on the GC content levels of the selected genomic regions. CONCLUSIONS: The results rendered by various statistical tests and prediction tests indicated that the GC content levels of the coding/non-coding regions of certain genes are highly likely to be correlated with the temperature range conditions of prokaryotic organisms. Therefore, it is promising to carry out "reverse ecology" and to complete the ecological characterizations of prokaryotic organisms, i.e., to infer their temperature range conditions based on the GC content levels of certain genomic regions.

Metagenomics is to study microorganisms by directly extracting and cloning their DNAs from the environment without lab cultivation or isolation of individual genomes. Assembling of metagenomic DNA fragments is very much like the overlap-layout-consensus procedure for assembling isolated genomes, but is augmented by an additional binning step to differentiate scaffolds, contigs and unassembled reads into various taxonomic groups. In this paper, we employed oligonucleotide frequencies as the features and developed a hierarchical scheme for the challenging task of binning short metagenome fragments, in which principal component analysis (PCA) was implemented to reduce the high dimensionality of the feature space, and linear discriminant analysis (LDA) was used for the local classifier design. Simulation results and comparisons with a non-hierarchical classifier in silico were presented to demonstrate the effectiveness and performance of the proposed PCA and LDA-based hierarchical scheme. The HIER package for this study is available upon request.

Large sets of bioinformatical data provide a challenge in time consumption while solving the cluster identification problem, and that is why a parallel algorithm is so needed for identifying dense clusters in a noisy background. Our algorithm works on a graph representation of the data set to be analyzed. It identifies clusters through the identification of densely intraconnected subgraphs. We have employed a minimum spanning tree (MST) representation of the graph and solve the cluster identification problem using this representation. The computational bottleneck of our algorithm is the construction of an MST of a graph, for which a parallel algorithm is employed. Our high-level strategy for the parallel MST construction algorithm is to first partition the graph, then construct MSTs for the partitioned subgraphs and auxiliary bipartite graphs based on the subgraphs, and finally merge these MSTs to derive an MST of the original graph. The computational results indicate that when running on 150 CPUs, our algorithm can solve a cluster identification problem on a data set with 1,000,000 data points almost 100 times faster than on single CPU, indicating that this program is capable of handling very large data clustering problems in an efficient manner. We have implemented the clustering algorithm as the software CLUMP.

In this paper we have investigated linear combinations of oligonucleotide (k-mer) frequencies for binning the metagenomic DNA fragments of short-to-moderate lengths. The k-mer frequencies have been widely used for gene prediction, phylogenetic tree construction, and metagenomic binning. However, the k-mer frequencies will lead to a high dimensional feature space even for a modest value of k. Existing methods to reduce the dimensionality of the feature space focus on particular oligonucleotide patterns or rather small values of k. We have applied the principal component analysis (PCA) on the oligonucleotide frequencies, based on which we can not only achieve a reduction of the feature dimensionality at a ratio higher than five, but can also retain the most informative features. Our experiments on simulated metagenomic data sets with four types of classifiers have shown that (i) the PCA-based linear combinations of k-mer frequencies are capable of capturing the intrinsic characteristics of DNA fragments and can therefore adequately serve as the binning features; (ii) the PCA-based linear combinations of k-mer frequencies tend to be more effective and stable as the DNA fragment length increases; and (iii) the rather simple linear classifiers can achieve high accuracy for the metagenomic DNA fragment binning at various taxonomic levels, even at a level as specific as species.

In Part 1 of this two-part paper, we bounded the centroid of a symmetric interval type-2 fuzzy set (T2 FS), and consequently its uncertainty, using geometric properties of its footprint of uncertainty (FOU). We then used these bounds to solve forward problems, i.e., to go from parametric interval T2 FS models to data. The main purpose of the present paper is to formulate and solve inverse problems, i.e., to go from uncertain data to parametric interval T2 FS models, which we call type-2 fuzzistics. Given interval data collected from people about a phrase, and the inherent uncertainties associated with that data, which can be described statistically using the first- and second-order statistics about the end-point data, we establish parametric FOUs such that their uncertainty bounds are directly connected to statistical uncertainty bounds. These results should find applicability in computing with words

Interval type-2 fuzzy sets (IT2 FS) play a central role in fuzzy sets as models for words and in engineering applications of T2 FSs. These fuzzy sets are characterized by their footprints of uncertainty (FOU), which in turn are characterized by their boundaries-upper and lower membership functions (MF). The centroid of an IT2 FS, which is an IT1 FS, provides a measure of the uncertainty in the IT2 FS. The main purpose of this paper is to quantify the centroid of a non-symmetric IT2 FS with respect to geometric properties of its FOU. This is very important because interval data collected from subjects about words suggests that the FOUs of most words are non-symmetrical. Using the results in this paper, it is possible to formulate and solveforward problems, i.e., to go from parametric non-symmetric IT2 FS models to data with associated uncertainty bounds. We provide some solutions to such problems for non-symmetrical triangular, trapezoidal, Gaussian and shoulder FOUs.

In this paper, we demonstrate, through the multicategory classification of battlefield ground vehicles using acoustic features, how it is straightforward to directly exploit the information inherent in a problem to determine the number of rules, and subsequently the architecture, of fuzzy logic rule-based classifiers (FLRBC). We propose three FLRBC architectures, one non-hierarchical and two hierarchical (HFLRBC), conduct experiments to evaluate the performances of these architectures, and compare them to a Bayesian classifier. Our experimental results show that: 1) for each classifier the performance in the adaptive mode that uses simple majority voting is much better than in the non-adaptive mode; 2) all FLRBCs perform substantially better than the Bayesian classifier; 3) interval type-2 (T2) FLRBCs perform better than their competing type-1 (T1) FLRBCs, although sometimes not by much; 4) the interval T2 nonhierarchical and HFLRBC-series architectures perform the best; and 5) all FLRBCs achieve higher than the acceptable 80% classification accuracy

Interval type-2 fuzzy sets (T2 FS) play a central role in fuzzy sets as models for words and in engineering applications of T2 FSs. These fuzzy sets are characterized by their footprints of uncertainty (FOU), which in turn are characterized by their boundaries-upper and lower membership functions (MF). In this two-part paper, we focus on symmetric interval T2 FSs for which the centroid (which is an interval type-1 FS) provides a measure of its uncertainty. Intuitively, we anticipate that geometric properties about the FOU, such as its area and the center of gravities (centroids) of its upper and lower MFs, will be associated with the amount of uncertainty in such a T2 FS. The main purpose of this paper (Part 1) is to demonstrate that our intuition is correct and to quantify the centroid of a symmetric interval T2 FS, and consequently its uncertainty, with respect to such geometric properties. It is then possible, for the first time, to formulate and solve forward problems, i.e., to go from parametric interval T2 FS models to data with associated uncertainty bounds. We provide some solutions to such problems. These solutions are used in Part 2 to solve some inverse problems, i.e., to go from uncertain data to parametric interval T2 FS models (T2 fuzzistics)

The centroid of an interval type-2 fuzzy set (IT2 FS) provides a measure of the uncertainty of such a FS. Its calculation is very widely used in interval type-2 fuzzy logic systems. In this paper, we present properties about the centroid of an IT2 FS. We also illustrate many of the general results for a T2 fuzzy granule (FG) in order to develop some understanding about the uncertainty of the FG in terms of its vertical and horizontal dimensions. At present, the T2 FG is the only IT2 FS for which fit is possible to obtain closed-form formulas for the centroid, and those formulas are in this paper

Interval type-2 fuzzy sets (T2 FS) play a central role in fuzzy sets as models for words and in engineering applications of T2 FSs. These fuzzy sets are characterized by their footprints of uncertainty (FOU), which in turn are characterized by their boundaries-upper and lower membership functions (MF). The centroid of an interval T2 FS, which is an interval T1 FS, provides a measure of the uncertainty in the interval T2 FS. Intuitively, we anticipate that geometric properties about the FOU, such as its area and the center of gravities (centroids) of its upper and lower MFs, associated with the amount of uncertainty in an interval T2 FS. The main purpose of this paper is to demonstrate that our intuition is correct and to quantify the centroid of an interval T2 FS with respect to these geometric properties of its FOU. It is then possible to formulate and solve inverse problems, i.e., going from data to parametric T2 FS models.

We investigate ten compensatory operators and SOWA operators in the framework of Mamdani interval type-2 fuzzy logic systems (FLS) so that for the first time the uncertainties originating from descriptive words, connector words and data can be simultaneously modeled. Our investigations show that: 1) for a Mamdani singleton interval type-2 FLS all the ten operators can be implemented and optimized; and 2) for a Mamdani non-singleton interval type-2 FLS the multiplicative compensatory operator that uses the product t-norm and maximum t-conorm, /spl Phi//sub p//sup MCA/, can be implemented and optimized. We apply /spl Phi//sub p//sup MCA/ to chaotic time-series prediction where the observations are corrupted by non-stationary noise. Our experimental results show that by incorporating /spl Phi//sub p//sup MCA/ into a Mamdani interval type-2 FLS it may take less time to train an interval type-2 FLS to achieve a satisfactory performance, and the resulting system is more robust to noise.

We examine ten antecedent connector models in the framework of a singleton or nonsingleton fuzzy logic system (FLS), to establish which models can be used. In this work, a usable connector model must lead to a separable firing degree that is a closed-form and piecewise-differentiable function of the membership function parameters and also the parameter characterizing that connector model. Our analysis shows that: for a singleton FLS where the Mamdani-product or Mamdani-minimum implication method is used, all ten antecedent connector models are usable; for a nonsingleton FLS where the Mamdani-product implication method is used, only one antecedent connector model is usable; and for a nonsingleton FLS where the Mamdani-minimum implication method is used, none of the ten antecedent connector models is usable. We also show, by examples, that the parameter of the antecedent connector model provides additional freedom in adjusting a FLS, so that the FLS has the potential to achieve better performance than a FLS that uses the traditional product or minimum t-norm for the antecedent connections.

We examine ten antecedent connector models in the framework of a singleton or non-singleton fuzzy logic system (FLS) to establish which models can be used. In this work a usable connector model must lead to a separable firing degree that is a closed-form and piecewise-differentiable function of the membership function (MF) parameters and also the parameter characterizing that connector model. The. multiplicative compensatory and model that uses the product t-norm and maximum t-conorm, /spl Phi//sub p//sup MCA/, is shown to be usable for both singleton and non-singleton Mamdani-product FLSs. We also show, by examples, that the parameter of /spl Phi//sub p//sup MCA/ provides additional freedom in adjusting a FLS, so that the FLS has the potential to achieve better performance than a FLS that uses the traditional product or minimum t-norm for the antecedent connections.

We demonstrate that some of the so-called uncertainties that may be present in a rule-based fuzzy logic system (FLS) are not uncertainties, but are instead choices that must be made as a result of the rich variety of mathematical models associated with the various elements of a FLS. We have established a hierarchy for the uncertainties and the choices, one that will hopefully guide us in developing FLS models that can better account for all sources of uncertainties than do present models. It seems that, at the very least, we must account for the uncertainties present in all rule-words, including connector words. This can be accomplished by using type-2 fuzzy sets for antecedent and consequent words and parametric operators for connector words.

Topic distillation is the process of finding representative pages relevant to a given query. Well-known topic distillation approaches such as the HITS algorithm have shown to be useful for topic distillation. Many succeeding researchers focus on augmenting HITS with further content analysis to alleviate the steady deterioration of distillation quality suffered by HITS. In this paper, we attempt to revisit the behavior of HITS from a different point of view. Namely, a similarity-based analysis model is applied to observing the distillation procedure. By defining a generalized similarity, an algorithm is proposed, which can improve the quality of distillation only using the information of hyperlinks. The experimental results reveal that the new algorithm improves distillation quality without utilizing any content information of pages.

We derive inner- and outer-bound sets for the type-reduced set of an interval type-2 fuzzy logic system (FLS), based on a new mathematical interpretation of the Karnik-Mendel iterative procedure for computing the type-reduced set. The bound sets can not only provide estimates about the uncertainty contained in the output of an interval type-2 FLS, but can also be used to design an interval type-2 FLS. We demonstrate, by means of a simulation experiment, that the resulting system can operate without type-reduction and can achieve similar performance to one that uses type-reduction. Therefore, our new design method, based on the bound sets, can relieve the computation burden of an interval type-2 FLS during its operation, which makes an interval type-2 FLS useful for real-time applications.

In this paper, we derive inner- and outer-bound sets for the type-reduced set of an interval type-2 fuzzy logic system, based on a new mathematical interpretation of the Karnik-Mendel (2001) iterative procedure. The bound sets can not only provide estimates about the uncertainty contained in the output, but can also be used to design an interval type-2 fuzzy logic system. We demonstrate, by means of a simulation experiment, that the resulting system can operate without type reduction and that it can achieve similar performance to one that uses type reduction. Therefore, our new design method, based on the bound sets, can relieve the computational burden of an interval type-2 fuzzy logic system during its operation.