Recently, sequence anomaly detection has been widely used in many fields. Sequence data in these fields are usually multi-dimensional over the data stream. It is a challenge to design an anomaly detection method for a...
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Recently, sequence anomaly detection has been widely used in many fields. Sequence data in these fields are usually multi-dimensional over the data stream. It is a challenge to design an anomaly detection method for a multi-dimensional sequence over the data stream to satisfy the requirements of accuracy and high speed. It is because:(1) Redundant dimensions in sequence data and large state space lead to a poor ability for sequence modeling;(2) Anomaly detection cannot adapt to the high-speed nature of the data stream, especially when concept drift occurs, and it will reduce the detection rate. On one hand, most existing methods of sequence anomaly detection focus on the single-dimension sequence. On the other hand, some studies concerning multi-dimensional sequence concentrate mainly on the static database rather than the data stream. To improve the performance of anomaly detection for a multi-dimensional sequence over the data stream, we propose a novel unsupervised fast and accurate anomaly detection(FAAD) method which includes three algorithms. First, a method called "information calculation and minimum spanning tree cluster" is adopted to reduce redundant dimensions. Second, to speed up model construction and ensure the detection rate for the sequence over the data stream, we propose a method called"random sampling and subsequence partitioning based on the index probabilistic suffix tree." Last, the method called "anomaly buffer based on model dynamic adjustment" dramatically reduces the effects of concept drift in the data stream. FAAD is implemented on the streaming platform Storm to detect multi-dimensional log audit *** with the existing anomaly detection methods, FAAD has a good performance in detection rate and speed without being affected by concept drift.
Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new ...
ISBN:
(纸本)9798331314385
Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples; (ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC.
OpenFlow switches in SDN use Multiple Flow Tables (MFTs) for fine-grained flow control. Commodity switches integrate hardware storage resources such as SRAM and TCAM to store flow tables to achieve high-speed lookups....
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With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support th...
With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t
The deep image hashing aims to map the input image into simply binary hash codes via deep neural networks. Motivated by the recent advancements of Vision Transformers (ViT), many deep hashing methods based on ViT have...
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The deep image hashing aims to map the input image into simply binary hash codes via deep neural networks. Motivated by the recent advancements of Vision Transformers (ViT), many deep hashing methods based on ViT have been proposed. Nevertheless, the ViT has enormous number of model parameters and high computational complexity. Moreover, the last layer of the ViT outputs only the classification tokens as image feature vectors, while the rest of the vectors are discarded. This results in the inefficiency of the model computation and the neglect of useful image information. Therefore, this paper proposes a Transformer-based deep hashing method for multi-scale feature fusion (TDH). Specifically, we use a hierarchical Transformer backbone to capture both global and local features of images. The hierarchical Transformer utilizes a local self-attention mechanism to process image blocks in parallel, which reduces computational complexity and promotes computational efficiency. Multi-scale feature fusion module captures all image feature vectors of the hierarchical Transformer output to obtain more enriched image feature information. We perform comprehensive experiments on three widely-studied datasets: CIFAR-10, NUS-WIDE and IMAGENET. The experimental results demonstrate that the proposed method in this paper indicates superior results compared to the existing state-of-the-art work. Source code is available https://***/shuaichaochao/TDH.
Robotic manipulation requires accurate perception of the environment, which poses a significant challenge due to its inherent complexity and constantly changing nature. In this context, RGB image and point-cloud obser...
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Class incremental learning is widely applied in the classification scenarios as the number of classes is usually dynamically changing. However, the existing algorithms increase computational cost to implement class in...
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ISBN:
(纸本)9781728165981
Class incremental learning is widely applied in the classification scenarios as the number of classes is usually dynamically changing. However, the existing algorithms increase computational cost to implement class incremental learning in order to increase classification quality. In this paper, we propose a nested hierarchy algorithm based on OCSVM for class incremental learning, called NH-CIL. We reuse support vectors to eliminate redundant instances and catch the key ones to replace the whole model because of the generalization ability of OCSVM. When a new class arrives, NH-CIL adopts OCSVM on the new class and the old classes respectively to get the corresponding sketching supports vectors. Then NH-CIL reuses these two kinds of sketching support vectors to build a binary sub-classifier. These two steps are repeatedly nested to form a hierarchy classification model in a bottom-up manner while the number of classes increases. On the contrary, the testing phase is in a top-down manner. NH-CIL can be used as a flexible approach in the classification scenarios during the collaborative information processing. We conduct the experiments on 8 real-world benchmark datasets to compare NH-CIL with some other class incremental learning algorithms, e.g. SD-CIL, HS-CIL and OP-CIL. The experiment results show that NH-CIL averagely achieves more than 5.1%, 8.6% and 11.6% accuracy improvement and 39.8%, 24.7% and 12.6% efficiency improvement over SD-CIL, HS-CIL and OP-CIL, respectively.
China is a big agricultural county with more than 500 million rural population. In China, farmers usually loan from rural commercial banks or rural credit cooperatives. It is crucial for the national economic developm...
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ISBN:
(纸本)9781665418164
China is a big agricultural county with more than 500 million rural population. In China, farmers usually loan from rural commercial banks or rural credit cooperatives. It is crucial for the national economic development and the improvement of people's standard of living that how to reasonably use funds to subsidize the agricultural population and reduce the risk of rural loans. At present, credit risk prediction of farmers mainly depends on the experience of experts in the business field, and there is little published research on using artificial intelligence methods to solve this problem. This paper presents a complete set of methods, including data collection, feature selection, etc. We propose a novel deep neural network model named DNN-CRP for credit risk prediction of Chinese framers. Experiments on an actual credit loan dataset of Chinese farmers are presented, and experimental results show that the comprehensive performance of the DNN-CRP model is better than current state-of-the-art models. It is believed that the DNN-CRP model proposed in this paper can help banks improve the efficiency of the credit loan business of farmers and reduce credit risks.
Convolutional Neural Networks (CNNs), one of the most representative algorithms of deep learning, are widely used in various artificial intelligence applications. Convolution operations often take most of the computat...
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Convolutional Neural Networks (CNNs), one of the most representative algorithms of deep learning, are widely used in various artificial intelligence applications. Convolution operations often take most of the computational overhead of CNNs. The FFT-based algorithm can improve the efficiency of convolution by reducing its algorithm complexity, there are a lot of works about the high-performance implementation of FFT-based convolution on many-core CPUs. However, there is no optimization for the non-uniform memory access (NUMA) characteristics in many-core CPUs. In this paper, we present a NUMA-aware FFT-based convolution implementation on ARMv8 many-core CPUs with NUMA architectures. The implementation can reduce a number of remote memory access through the data reordering of FFT transformations and the three-level parallelization of the complex matrix multiplication. The experiment results on a ARMv8 many-core CPU with NUMA architectures demonstrate that our NUMA-aware implementation has much better performance than the state-of-the-art work in most cases.
Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been onl...
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