The Minimum Dominating Set problem (MDS) is a challenging NP-Hard problem with many practical applications. In this paper, we focus on its generalization, the Minimum Weight Dominating Set problem (MWDS). We first propose a novel lower bound for MWDS and prove a condition in which the computed lower bound is tight, then present a new local search approach, called Dual Bounds Search (DBS), which searches for a lower bound and an upper bound simultaneously in an alternate and collaborative way. We implement the lower bound algorithm and integrate two state-of-the-art local search algorithms into our DBS approach to obtain two new DBS algorithms for MWDS. Extensive experiments show that DBS approach can improve the local search performance for MWDS significantly. Thanks to the lower bound, new DBS algorithms can also provide a quality measure of solutions, which is practical in applications and is a clear difference from existing local searches for MWDS.

With the vigorous development of Internet technology, the scale of systems in the network has increased sharply, which provides a great opportunity for potential attacks, especially the Distributed Denial of Service (DDoS) attack. In this case, detecting DDoS attacks is critical to system security. However, current detection methods exhibit limitations, leading to compromises in accuracy and efficiency. To cope with it, three key strategies are implemented in this paper: (i) Using tensors to model large-scale and heterogeneous data in complex networks; (ii) Proposing a denoising algorithm based on the improved and distributed tensor train (IDTT) decomposition, which optimizes the tensor train(TT) decomposition in terms of parallel computation and low-rank estimation; (iii) Combining (i), (ii) and Light Gradient Boosting Machine (LightGBM) classification model, an efficient DDoS attack detection framework is proposed. Datasets CIC-DDoS2019 and NSL-KDD are used to evaluate the framework, and results demonstrate that accuracy can reach 99.19% while having the characteristics of low storage consumption and well speedup ratio.

Advances in facial manipulation techniques have resulted in the increasing trend of realistic and indistinguishable identity swap media, which mislead the viewers and accompanied by severe security concerns. While current deepfake detectors demonstrate strong performance under high-quality conditions, they still face notable limitations. This article proposes a novel framework mining high frequency and degraded texture features for locating manipulated traces and improving the generalization ability. To improve the universality of the proposed detector, we design the Multi-feature Mining Stream for capturing the global and subtle discrepancies of undegraded images. Moreover, the Encoder-Decoder Structure is introduced for gaining high localization accuracy and full resolution manipulated regions. This work attempts to solve the tampered region localization issue and achieve face forgery image detection at the meantime. This contributes to help the model perform a more effective differentiation between real and fake content when confronted with high- or low-quality compressed images. Comprehensive experiments on the popular FaceForensics++, Celeb-DF, and DFDC datasets demonstrate the superior performance and robustness of our proposed framework, in particular, achieving performance improvements ranging from 1% to 10% in comparison with the most recent related work.

With the rapid advancement of 5G communication technology in the era of big data, massive terminal devices connected to the Internet have dramatically increased the scale of network, generating a large amount of high-dimensional and heterogeneous information. This not only enhances the difficulty of information processing in the network, but also poses a severe challenge to data storage and calculation, which has become a big data problem to be solved urgently. To cope with it, this paper proposes an efficient information processing framework and applies it to Distributed Denial of Service (DDoS) attack detection. Overall, three major highlights are made: (i) Tensor is used to represent multi-modal information in large-scale networks; (ii) A novel denoising algorithm based on tensor train(TT) decomposition is proposed, focused on optimizing both computation and correlation; (iii) A big data-driven information processing framework is developed, which includes information preprocessing, denoising and classification. Results in case study indicate that the framework can achieve an accuracy of 99.19%, all while maintaining the great storage advantage, well speedup ratio and strong computing capabilities under the same computational complexity. It can also be generalized to other network data processing scenarios.

As an emerging brain-inspired computing method, hyperdimensional computing (HDC) has attracted increasing attention. HDC encodes data into a high-dimensional hypervector and makes predictions based on the similarity comparison of the hypervector. However, the computational overhead of an HDC model increases, and the accuracy decreases as the number of predicted classes increases. Branching has been proposed to address this issue in HDC models, but it also introduces a new challenge: increased memory overhead for storing new branching hypervectors. In this paper, we aim to address this issue by integrating branching with the pruning method in HDC models. To this end, we propose a simple yet effective two-level branching structure that can reduce the memory overhead required by branching hypervectors. In addition, we propose a novel variable-position pruning method capable of removing redundant dimensions from hypervectors at various positions. This can best identify the important dimensions of a hypervector and further mitigate the memory issue caused by the branching structure. Experimental results demonstrate that our two-level branching structure can slightly increase the accuracy across various datasets. With only a 0.5 % accuracy loss, our pruning method can remove more than half of the dimensions, thereby improving the HDC model's inference latency and energy consumption.

Social media offers a promising way to analyze online public opinion, which has drawn extensive attention from various sectors. In academia, most studies focus on predicting public opinion using unimodal time series methods, paying little attention to multimodal approaches. However, public opinion may be affected by various complex social factors, so it is necessary to explore multimodal elements. Based on the N-Beats model, we propose a novel model, HFN-BeatsConv, which employs a powerful modal alignment strategy, 3D-TCN. Most fuses multimodal data for public opinion prediction. The model employs a component, 3D-TCN, for modal alignment, differs from other research in that it focuses on the time at which the text appears. Subsequently, the model, HFN-BeatsConv, the N-Beats model is enhanced through the utilization of 3D-TCN, which enables the processing of multivariate time series data and the reduction of multimodal time series forecast error. To increase the usability and sustainability of research, this study provides a valuable social media dataset, as a supplementary feature of time series prediction. Through extensive experiments, the proposed method outperforms the existing methods.

Seasonal time series contain rich long-term dependencies. How to make good use of the seasonal information to predict the future is still a challenging problem. In this paper, we propose a neural network model called Multilayer Seasonal Perception Network (MSPNet) to predict seasonal time series. Firstly, we propose the idea of seasonal alignment, which converts univariate time series into multivariate time series, in order to capture seasonal features more effectively. Secondly, we extract the seasonal features and historical dependencies, using the Multi-layer Seasonal Perception Attention. Finally, we combine the obtained nonlinear features with linear features to conduct the final prediction. Experimental verification shows that the proposed MSPNet model is significantly superior to the baseline methods on multiple public datasets. The source code and datasets are available at https://github.com/MasterofEating/MSPNet

Predefined pattern detection from time series is an interesting and challenging task. In order to reduce its computational cost and increase effectiveness, a number of time series representation methods and similarity measures have been proposed. Most of the existing methods focus on full sequence matching, that is, sequences with clearly defined beginnings and endings, where all data points contribute to the match. These methods, however, do not account for temporal and magnitude deformations in the data and result to be ineffective on several real-world scenarios where noise and external phenomena introduce diversity in the class of patterns to be matched. In this paper, we present a novel pattern detection method, which is based on the notions of templates , landmarks, constraints and trust regions. We employ the Minimum Description Length (MDL) principle for time series preprocessing step , which helps to preserve all the prominent features and prevents the template from overfitting. Templates are provided by common users or domain experts, and represent interesting patterns we want to detect from time series. Instead of utilising templates to match all the potential subsequences in the time series, we translate the time series and templates into landmark sequences, and detect patterns from landmark sequence of the time series. Through defining constraints within the template landmark sequence, we effectively extract all the landmark subsequences from the time series landmark sequence, and obtain a number of landmark segments (time series subsequences or instances). We model each landmark segment through scaling the template in both temporal and magnitude dimensions. To suppress the influence of noise, we introduce the concept of trust region , which not only helps to achieve an improved instance model, but also helps to catch the accurate boundaries of instances of the given template. Based on the similarities derived from instance models, we introduce the probability density function to calculate a similarity threshold. The threshold can be used to judge if a landmark segment is a true instance of the given template or not. To evaluate the effectiveness and efficiency of the proposed method, we apply it to two real-world datasets. The results show that our method is capable of detecting patterns of temporal and magnitude deformations with competitive performance.