共查询到20条相似文献，搜索用时 46 毫秒
1.
《Information processing & management》2020,57(6):102288
Deep hashing has been an important research topic for using deep learning to boost performance of hash learning. Most existing deep supervised hashing methods mainly focus on how to effectively preserve the similarity in hash coding solely depending on pairwise supervision. However, such pairwise similarity-preserving strategy cannot fully explore the semantic information in most cases, which results in information loss. To address this problem, this paper proposes a discriminative dual-stream deep hashing (DDDH) method, which integrates the pairwise similarity loss and the classification loss into a unified framework to take full advantage of label information. Specifically, the pairwise similarity loss aims to preserve the similarity and structural information of high-dimensional original data. Meanwhile, the designed classification loss can enlarge the margin between different classes which improves the discrimination of learned binary codes. Moreover, an effective optimization algorithm is employed to train the hash code learning framework in an end-to-end manner. The results of extensive experiments on three image datasets demonstrate that our method is superior to several state-of-the-art deep and non-deep hashing methods. Ablation studies and analysis further show the effectiveness of introducing the classification loss in the overall hash learning framework. 相似文献
2.
《Information processing & management》2022,59(2):102844
Previous studies have adopted unsupervised machine learning with dimension reduction functions for cyberattack detection, which are limited to performing robust anomaly detection with high-dimensional and sparse data. Most of them usually assume homogeneous parameters with a specific Gaussian distribution for each domain, ignoring the robust testing of data skewness. This paper proposes to use unsupervised ensemble autoencoders connected to the Gaussian mixture model (GMM) to adapt to multiple domains regardless of the skewness of each domain. In the hidden space of the ensemble autoencoder, the attention-based latent representation and reconstructed features of the minimum error are utilized. The expectation maximization (EM) algorithm is used to estimate the sample density in the GMM. When the estimated sample density exceeds the learning threshold obtained in the training phase, the sample is identified as an outlier related to an attack anomaly. Finally, the ensemble autoencoder and the GMM are jointly optimized, which transforms the optimization of objective function into a Lagrangian dual problem. Experiments conducted on three public data sets validate that the performance of the proposed model is significantly competitive with the selected anomaly detection baselines. 相似文献
3.
文章基于粒子群BP神经网络,同时结合水利水电工程等级划分标准建立大型水电工程投资估算模型。并利用MATLAB软件实现了模型的训练与测试。最后对已建水电工程进行了预测。结果表明模型改进了现有水电工程投资估算方法,在水电工程投资估算中具有较高的精度与泛化能力。 相似文献
4.
《Information processing & management》2023,60(3):103289
Anomalous event recognition requires an instant response to reduce the loss of human life and property; however, existing automated systems show limited performance due to considerations related to the temporal domain of the videos and ignore the significant role of spatial information. Furthermore, although current surveillance systems can detect anomalous events, they require human intervention to recognise their nature and to select appropriate countermeasures, as there are no fully automatic surveillance techniques that can simultaneously detect and interpret anomalous events. Therefore, we present a framework called Vision Transformer Anomaly Recognition (ViT-ARN) that can detect and interpret anomalies in smart city surveillance videos. The framework consists of two stages: the first involves online anomaly detection, for which a customised, lightweight, one-class deep neural network is developed to detect anomalies in a surveillance environment, while in the second stage, the detected anomaly is further classified into the corresponding class. The size of our anomaly detection model is compressed using a filter pruning strategy based on a geometric median, with the aim of easy adaptability for resource-constrained devices. Anomaly classification is based on vision transformer features and is followed by a bottleneck attention mechanism to enhance the representation. The refined features are passed to a multi-reservoir echo state network for a detailed analysis of real-world anomalies such as vandalism and road accidents. A total of 858 and 1600 videos from two datasets are used to train the proposed model, and extensive experiments on the LAD-2000 and UCF-Crime datasets comprising 290 and 400 testing videos reveal that our framework can recognise anomalies more effectively, outperforming other state-of-the-art approaches with increases in accuracy of 10.14% and 3% on the LAD-2000 and UCF-Crime datasets, respectively. 相似文献
5.
本文首先介绍了PF(Particle Filter)和UPF(Unscented Particle Filter)的基本原理,然后针对无线传感器网络(WSN)目标跟踪这一应用方向,采用等级网络结构,参考分布式粒子滤波算法,将UPF应用于WSN单目标跟踪以提高网络跟踪精度,仿真证明UPF较PF在跟踪精度上确实有明显的提高。 相似文献
6.
爬虫是搜索引擎的重要组成部分,它决定了搜索引擎的性能,而Larbin正是一种高效的网络爬虫。首先分析了Larbin的设计结构,再由对其核心的算法Bloom Filter进行了研究,并对其提出了改进。最后是关于Larbin优化的实现。 相似文献
7.
《Information processing & management》2022,59(3):102919
Similarity search with hashing has become one of the fundamental research topics in computer vision and multimedia. The current researches on semantic-preserving hashing mainly focus on exploring the semantic similarities between pointwise or pairwise samples in the visual space to generate discriminative hash codes. However, such learning schemes fail to explore the intrinsic latent features embedded in the high-dimensional feature space and they are difficult to capture the underlying topological structure of data, yielding low-quality hash codes for image retrieval. In this paper, we propose an ordinal-preserving latent graph hashing (OLGH) method, which derives the objective hash codes from the latent space and preserves the high-order locally topological structure of data into the learned hash codes. Specifically, we conceive a triplet constrained topology-preserving loss to uncover the ordinal-inferred local features in binary representation learning. By virtue of this, the learning system can implicitly capture the high-order similarities among samples during the feature learning process. Moreover, the well-designed latent subspace learning is built to acquire the noise-free latent features based on the sparse constrained supervised learning. As such, the latent under-explored characteristics of data are fully employed in subspace construction. Furthermore, the latent ordinal graph hashing is formulated by jointly exploiting latent space construction and ordinal graph learning. An efficient optimization algorithm is developed to solve the resulting problem to achieve the optimal solution. Extensive experiments conducted on diverse datasets show the effectiveness and superiority of the proposed method when compared to some advanced learning to hash algorithms for fast image retrieval. The source codes of this paper are available at https://github.com/DarrenZZhang/OLGH . 相似文献
8.
《Information processing & management》2023,60(3):103282
Detecting collusive spammers who collaboratively post fake reviews is extremely important to guarantee the reliability of review information on e-commerce platforms. In this research, we formulate the collusive spammer detection as an anomaly detection problem and propose a novel detection approach based on heterogeneous graph attention network. First, we analyze the review dataset from different perspectives and use the statistical distribution to model each user's review behavior. By introducing the Bhattacharyya distance, we calculate the user-user and product-product correlation degrees to construct a multi-relation heterogeneous graph. Second, we combine the biased random walk strategy and multi-head self-attention mechanism to propose a model of heterogeneous graph attention network to learn the node embeddings from the multi-relation heterogeneous graph. Finally, we propose an improved community detection algorithm to acquire candidate spamming groups and employ an anomaly detection model based on the autoencoder to identify collusive spammers. Experiments show that the average improvements of precision@k and recall@k of the proposed approach over the best baseline method on the Amazon, Yelp_Miami, Yelp_New York, Yelp_San Francisco, and YelpChi datasets are [13%, 3%], [32%, 12%], [37%, 7%], [42%, 10%], and [18%, 1%], respectively. 相似文献
9.
Generic, radical technology is of interest because of its potential for value creation across a broad range of industries and applications. Advanced materials ventures are attracted by this opportunity yet face distinctive challenges in commercializing such technology. We explore an anomaly in common assumptions about the commercialization of generic technology. We build on Freeman's concept of technological innovation as a technological and market matching process, on existing literature and on prior experience to build, inductively, a model of the variables influencing value creation by advanced materials ventures. We then test the model on the basis of detailed observation and analysis of two case studies, which have successfully created value through commercialization of advanced materials technology. Extending this theory, we offer managerial and policy recommendations to support value creation by advanced materials ventures. 相似文献
10.
11.
《Information processing & management》2022,59(1):102743
Multi-modal hashing can encode the large-scale social geo-media multimedia data from multiple sources into a common discrete hash space, in which the heterogeneous correlations from multiple modalities could be well explored and preserved into the objective semantic-consistent hash codes. The current researches on multi-modal hashing mainly focus on performing common data reconstruction, but they fail to effectively distill the intrinsic and consensus structures of multi-modal data and fully exploit the inherent semantic knowledge to capture semantic-consistent information across multiple modalities, leading to unsatisfactory retrieval performance. To facilitate this problem and develop an efficient multi-modal geographical retrieval method, in this article, we propose a discriminative multi-modal hashing framework named Cognitive Multi-modal Consistent Hashing (CMCH), which can progressively pursue the structure consensus over heterogeneous multi-modal data and simultaneously explore the informative transformed semantics. Specifically, we construct a parameter-free collaborative multi-modal fusion module to incorporate and excavate the underlying common components from multi-source data. Particularly, our formulation seeks for a joint multi-modal compatibility among multiple modalities under a self-adaptive weighting manner, which can take full advantages of their complementary properties. Moreover, a cognitive self-paced learning policy is further leveraged to conduct progressive feature aggregation, which can coalesce multi-modal data onto the established common latent space in a curriculum learning mode. Furthermore, deep semantic transform learning is elaborated to generate flexible semantics for interactively guiding collaborative hash codes learning. An efficient discrete learning algorithm is devised to address the resulting optimization problem, which obtains stable solutions when dealing with large-scale multi-modal retrieval tasks. Sufficient experiments performed on four large-scale multi-modal datasets demonstrate the encouraging performance of the proposed CMCH method in comparison with the state-of-the-arts over multi-modal information retrieval and computational efficiency. The source codes of this work could be available at https://github.com/JunfengAn1998a/CMCH . 相似文献
12.
Interactivity, which is a key characteristic of the live streaming commerce environment, fosters users’ active attitudes and behaviors in communications and transactions. However, the literature on live streaming commerce, is scarce, and few studies examine how interactivity influences customers’ non-transactional behaviors from a dynamic perspective. In this setting, based on the stimulus-organism-response (S-O-R) framework, we developed a research model using real-time data to investigate the dynamic effect of interactivity on customer engagement behavior through tie strength in live streaming commerce, which is a relatively new derivative of social commerce. This study developed a text mining method to quantify constructs using a large-scale sample of 3,500,445 online review texts. Our empirical study found that interactivity has a curvilinear relationship with customer engagement behavior. Besides, tie strength plays an intermediary role between interactivity and customer engagement behavior. It was further observed that both tenure of membership and popularity have an important moderating relationship between interactivity and tie strength. The study enriches the relationship marketing theory and live streaming commerce literature. Moreover, this study is one of the first studies to use real-time online data for live streaming commerce research. 相似文献
13.
Iti Chaturvedi Edoardo Ragusa Paolo Gastaldo Rodolfo Zunino Erik Cambria 《Journal of The Franklin Institute》2018,355(4):1780-1797
Subjectivity detection is a task of natural language processing that aims to remove ‘factual’ or ‘neutral’ content, i.e., objective text that does not contain any opinion, from online product reviews. Such a pre-processing step is crucial to increase the accuracy of sentiment analysis systems, as these are usually optimized for the binary classification task of distinguishing between positive and negative content. In this paper, we extend the extreme learning machine (ELM) paradigm to a novel framework that exploits the features of both Bayesian networks and fuzzy recurrent neural networks to perform subjectivity detection. In particular, Bayesian networks are used to build a network of connections among the hidden neurons of the conventional ELM configuration in order to capture dependencies in high-dimensional data. Next, a fuzzy recurrent neural network inherits the overall structure generated by the Bayesian networks to model temporal features in the predictor. Experimental results confirmed the ability of the proposed framework to deal with standard subjectivity detection problems and also proved its capacity to address portability across languages in translation tasks. 相似文献
14.
Yingchun Wang Longfei Zheng Huaguang Zhang Xiangpeng Xie 《Journal of The Franklin Institute》2018,355(17):8392-8411
This paper investigates the problem of event-triggered fault detection filter design for nonlinear networked control systems with both sensor faults and process faults. First, Takagi–Sugeno (T–S) fuzzy model is utilized to represent the nonlinear systems with faults and disturbances. Second, a discrete event-triggered communication scheme is proposed to reduce the utilization of limited network bandwidth between filter and original system. At the same time, considering network-induced delays and event-triggered scheme, a novel T–S fuzzy fault detection filter is constructed to generate a residual signal, which has nonsynchronous premise variables with the original T–S fuzzy system. Then, the fuzzy Lyapunov functional based approach and the reciprocally convex approach are developed such that the obtained sufficient conditions ensure that the fuzzy fault detection system is asymptotically stable with H∞ performance and is less conservative. All the conditions are given in terms of linear matrix inequalities (LMIs), which can be solved by LMI tools in MATLAB environment. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed results. 相似文献
15.
This paper is concerned with the event-based fault detection for the networked systems with communication delay and nonlinear perturbation. We propose an event-triggered scheme, which has some advantages over existing ones. The sensor data is transmitted only when the specified event condition involving the sampled measurements of the plant is violated. An event-based fault detection model is firstly constructed by taking the effect of event-triggered scheme and the network transmission delay into consideration. The main purpose of this paper is to design an event-based fault detection filter such that, for all unknown input, communication delay and nonlinear perturbation, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions for the existence of the desired fault detection filter are established in terms of linear matrix inequalities. Based on these conditions, the explicit expression is given for the designed fault detection filter parameters. A numerical example is employed to illustrate the advantage of the introduced event-triggered scheme and the effectiveness of the proposed method. 相似文献
16.
企业家诚信是市场机制良性运行的重要因素。如何构建一个测度企业家诚信程度的评价体系,来引导企业家进行诚信修炼和自律,进而评价企业家及企业的诚信程度,迄今为止国内外相关研究尚不系统。本文在已有研究的基础上,尝试构建了企业家诚信评价指标的框架,然后借助spss软件对指标进行筛选和分析,从而确定了企业家诚信评价的指标体系。 相似文献
17.
本文从流媒体的特点及相关技术入手，对流媒体技术在网络视频新闻传播中的应用现状进行了研究；并通过分析其网络传播中存在的难点及对策，初步探讨了流媒体新闻的发展前景。 相似文献
18.
滤波器是一个频率选择部件,它可以通过某些频率而抑制或衰减另外一些频率的信号,该文介绍了模拟滤波器的发展及类型,通过对模拟滤波器的设计,对低通有源滤波器采用的阻容元件随温度变化特性进行分析及模拟仿真,提出如何解决阻容元件温漂对频率响应及产品灵敏度精度的影响。 相似文献
19.
大型企业原始创新系统动力学模型的构建研究 总被引：4，自引：0，他引：4
大型企业是原始创新的主体,增强大型企业原始创新能力是提升一个国家综合竞争力的必由之路.在分析原始创新和大型企业各自特点的基础上,应用系统动力学相关理论,构建大型企业原始创新的系统动力学模型.通过对模型主回路的分析,得出优化大型企业内部各部门之间的投入比例可以提升大型企业原始创新能力,从系统学的角度研究了大型企业原始创新能力问题. 相似文献
20.
【目的/意义】信息技术与互联网技术的飞速发展让教育行业进入了大数据时代。由于学习者在整个学习
过程中的学习行为数据能够被记录下来,这种多源过程性数据为基于大数据的教育评价提供了新的可能。【方法/
过程】本文从学习者行为特征视角出发,设计了多源过程性数据驱动的学习者综合评价模型。该模型利用流数据
聚类算法对不断涌入的学习者数据进行处理,及时生成或更新学习者画像,然后基于学习者画像对学习者学习行
为进行分析,构建学习者综合评价模型,以实现对学习者学习表现的实时反馈。【结果/结论】该模型可以对学习者
的学习过程进行综合评价,及时的反馈有助于教学评价的开展,同时丰富现有的教学评价体系,为实现教学评价与
优化教学的良性循环提供依据和参考方向。【创新/局限】本文提出了过程性数据驱动的对学习者动态综合评价模
型,后续将基于研究模型开展实际应用研究。 相似文献