共查询到20条相似文献，搜索用时 31 毫秒
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《Information processing & management》2020,57(1):102141
Detecting sentiments in natural language is tricky even for humans, making its automated detection more complicated. This research proffers a hybrid deep learning model for fine-grained sentiment prediction in real-time multimodal data. It reinforces the strengths of deep learning nets in combination to machine learning to deal with two specific semiotic systems, namely the textual (written text) and visual (still images) and their combination within the online content using decision level multimodal fusion. The proposed contextual ConvNet-SVMBoVW model, has four modules, namely, the discretization, text analytics, image analytics, and decision module. The input to the model is multimodal text, m ε {text, image, info-graphic}. The discretization module uses Google Lens to separate the text from the image, which is then processed as discrete entities and sent to the respective text analytics and image analytics modules. Text analytics module determines the sentiment using a hybrid of a convolution neural network (ConvNet) enriched with the contextual semantics of SentiCircle. An aggregation scheme is introduced to compute the hybrid polarity. A support vector machine (SVM) classifier trained using bag-of-visual-words (BoVW) for predicting the visual content sentiment. A Boolean decision module with a logical OR operation is augmented to the architecture which validates and categorizes the output on the basis of five fine-grained sentiment categories (truth values), namely ‘highly positive,’ ‘positive,’ ‘neutral,’ ‘negative’ and ‘highly negative.’ The accuracy achieved by the proposed model is nearly 91% which is an improvement over the accuracy obtained by the text and image modules individually. 相似文献
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本文介绍了基于FFT的伪随机码快速捕获的原理,并且对快速捕获模块的核心单元,即采样率转换单元和FFT/IFFT计算单元的FPGA实现进行了详细论述。仿真和实现结果表明,此方法能快速地捕获扩频信号中的伪随机码相位。 相似文献
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基于FPGA的CCSDS图像数据压缩系统的设计 总被引：1，自引：0，他引：1
针对CCSDS图像数据压缩(IDC)标准,提出了一种基于FPGA的CCSDS IDC并行实现方案.该方案包括离散小波变换(DWT)、直流系数量化编码、位平面编码(BPE)、码字拼接等4个模块.位平面编码模块采用了并行扫描、并行编码的快速算法,以提高编码速度.仿真结果表明了本方案的可行性和有效性,处理时间比现有的CCSDS IDC串行编码改进方法减少了13.6%,适用于空间通信的图像数据压缩编码. 相似文献
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主要研究基于VHDL的可编程逻辑器件虚拟实验平台的设计和实现方案。平台的主体是VHDL程序翻译模块和模拟调度模块。翻译模块的核心是VHDL编译器,它将用户编写的VHDL源程序翻译转换为等价的C++语言描述,最终生成若干C++程序文件;模拟调度模块通过改进传统的事件驱动模拟算法,对翻译模块得到的C++文件进行编译、连接和动态调度,实现对所描述的静态电路的动态行为的模拟。通过典型实验样例对平台进行了验证,结果表明该系统中进行实验的过程和结果与真实实验一致,达到预定设计要求。 相似文献
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本文介绍一种适用于瓦片天线系统中使用的S波段小型化收发组件模块的设计思想、结构、工艺实现形式,并给出相关测试结果。该组件具有轻型化、小型化、低成本、高可靠等特点 相似文献
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随着数字信号处理器(DSP)的迅速发展,面向DSP设计可重定目标的C编译器成为必然。由GCC编译器移植面向媒体处理器的C编译器,利用指令模板匹配不能有效的生成并行指令。本文结合32位具有RISC-DSP结构的媒体处理器MD32特有的体系结构特点,提出C编译器支持的．在汇编级通过指令调度和调整指令操作数及其类型的代码优化方法,实现输出高效的并行指令．同时提高了代码的运行效率和代码的密度。统计数据表明：代码执行效率平均可以提高14％．而代码密度平均提高11．75％。这种方法比树模式匹配方法更容易实现,并适用于支持并行指令的其他DSP处理器。 相似文献
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食品安全问题是现代科技亟待解决的核心问题之一,需要从源头开始对有害食品的流通渠道进行监控。基于食品安全角度,分析了常规的流通环节、监管对象和监督机制,提出了基于物联网技术平台的食品流通监控系统,详细设计了源头追溯模块、报警模块和管理模块,以实现对食品生产、加X-、存储、物流和销售等各环节的监管。 相似文献
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布尔函数是否存在低次零化子,是代数攻击成功与否的关键。在定义了零化子相关度基础上,给出确定布尔函数存在低次零化子的算法。其核心思想是根据布尔函数与具有低次零化子函数之间的零化子相关度来判断布尔函数是否存在低次零化子。相对于直接计算布尔函数的低次零化子复杂度明显降低。 相似文献
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中文搜索引擎结构初探 总被引：4，自引：0，他引：4
随着Internet的进一步发展，网上中文信息的激增使中文搜索引擎日益受到人们的关注。本文对中文搜索引擎的结构做了初步分析，将其划分为四大模块：网页搜集模块、网页索引模块、查询模块和用户界面，并对各模块的工作原理、技术做了相应的说明。 相似文献
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本图书管理系统采用Java做前台界面,后台支持则采用数据库Access,本系统提供6个功能模块,分别是图书类别管理模块、图书信息管理模块、读者信息管理模块、新书订购管理模块、图书借阅模块以及系统模块。这6个模块里又有许多子模块,通过这些模块之间的相互连接与配合,完成操作员发出的各种指令。 相似文献
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PLC控制系统需要处理的逻辑运算指令为多操作位指令,根据每条指令所带的操作位个数不同,设计了一种能满足PLC逻辑运算的逻辑运算器。阐述了逻辑运算器的结构组成,并对各功能模块进行了说明,最后通过功能仿真与测试来验证逻辑运算器的功能,从测试结果分析,设计的逻辑运算器满足设计要求。设计的逻辑运算器可对保存的一段程序进行运算,并将运算结果按照要求保存至对应的存储器中,降低程序的执行时间,提高系统工作效率。 相似文献
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本文介绍了基于ARM7 LPC2119的灌区分布式水情测控系统设计方案,详细叙述了整个系统的功能及其构成模块,给出了各个模块的接口电路框图。通过实验室的测试,该系统能够完成相应的测控任务。 相似文献
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Zenun Kastrati Ali Shariq Imran Sule Yildirim Yayilgan 《Information processing & management》2019,56(5):1618-1632
This paper presents a semantically rich document representation model for automatically classifying financial documents into predefined categories utilizing deep learning. The model architecture consists of two main modules including document representation and document classification. In the first module, a document is enriched with semantics using background knowledge provided by an ontology and through the acquisition of its relevant terminology. Acquisition of terminology integrated to the ontology extends the capabilities of semantically rich document representations with an in depth-coverage of concepts, thereby capturing the whole conceptualization involved in documents. Semantically rich representations obtained from the first module will serve as input to the document classification module which aims at finding the most appropriate category for that document through deep learning. Three different deep learning networks each belonging to a different category of machine learning techniques for ontological document classification using a real-life ontology are used.Multiple simulations are carried out with various deep neural networks configurations, and our findings reveal that a three hidden layer feedforward network with 1024 neurons obtain the highest document classification performance on the INFUSE dataset. The performance in terms of F1 score is further increased by almost five percentage points to 78.10% for the same network configuration when the relevant terminology integrated to the ontology is applied to enrich document representation. Furthermore, we conducted a comparative performance evaluation using various state-of-the-art document representation approaches and classification techniques including shallow and conventional machine learning classifiers. 相似文献
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《Information processing & management》2023,60(3):103284
Deep multi-view clustering (MVC) is to mine and employ the complex relationships among views to learn the compact data clusters with deep neural networks in an unsupervised manner. The more recent deep contrastive learning (CL) methods have shown promising performance in MVC by learning cluster-oriented deep feature representations, which is realized by contrasting the positive and negative sample pairs. However, most existing deep contrastive MVC methods only focus on the one-side contrastive learning, such as feature-level or cluster-level contrast, failing to integrating the two sides together or bringing in more important aspects of contrast. Additionally, most of them work in a separate two-stage manner, i.e., first feature learning and then data clustering, failing to mutually benefit each other. To fix the above challenges, in this paper we propose a novel joint contrastive triple-learning framework to learn multi-view discriminative feature representation for deep clustering, which is threefold, i.e., feature-level alignment-oriented and commonality-oriented CL, and cluster-level consistency-oriented CL. The former two submodules aim to contrast the encoded feature representations of data samples in different feature levels, while the last contrasts the data samples in the cluster-level representations. Benefiting from the triple contrast, the more discriminative representations of views can be obtained. Meanwhile, a view weight learning module is designed to learn and exploit the quantitative complementary information across the learned discriminative features of each view. Thus, the contrastive triple-learning module, the view weight learning module and the data clustering module with these fused features are jointly performed, so that these modules are mutually beneficial. The extensive experiments on several challenging multi-view datasets show the superiority of the proposed method over many state-of-the-art methods, especially the large improvement of 15.5% and 8.1% on Caltech-4V and CCV in terms of accuracy. Due to the promising performance on visual datasets, the proposed method can be applied into many practical visual applications such as visual recognition and analysis. The source code of the proposed method is provided at https://github.com/ShizheHu/Joint-Contrastive-Triple-learning. 相似文献
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《Information processing & management》2023,60(4):103359
Large-scale search engines have become a fundamental tool to efficiently access information on the Web. Typically, users expect answers in sub-second time frames, which demands highly efficient algorithms to traverse the data structures to return the top-k results. Despite different top-k algorithms that avoid processing all postings for all query terms, finding one algorithm that performs the fastest on any query is not always possible. The fastest average algorithm does not necessarily perform the best on all queries when evaluated on a per-query basis. To overcome this challenge, we propose to combine different state-of-the-art disjunctive top-k query processing algorithms to minimize the execution time by selecting the most promising one for each query. We model the selection step as a classification problem in a machine-learning setup. We conduct extensive experimentation and compare the results against state-of-the-art baselines using standard document collections and query sets. On ClueWeb12, our proposal shows a speed-up of up to 1.20x for non-blocked index organizations and 1.19x for block-based ones. Moreover, tail latencies are reduced showing proportional improvements on average, but a resulting dramatic decrease in latency variance. Given these findings, the proposed approach can be easily applied to existing search infrastructures to speed up query processing and reduce resource consumption, positively impacting providers’ operative costs. 相似文献
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基于中国网络购物环境,以SOR理论为基础,研究网络退货政策各维度对消费者购买意愿的影响机理.研究结果表明:网络退货政策的三个构成维度,退货时限、退货操作和退货费用是通过影响感知质量和感知风险来进一步影响消费者购买意愿的.三者对消费者购买意愿的影响程度从大到小依次排列为退货操作、退货费用和退货时限. 相似文献
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为适应当前科研院所项目经费管理多元化和复杂化趋势,基于经费管理但并不局限于经费管理,设计研发一套以预算管理为目标、贯穿整个项目管理过程的科研经费管理系统,为科研院所的项目管理和经费管理实践提供方法参考。该系统包含业务模块、工作流引擎、定时任务、权限控制等功能模块。其中,可自定义工作流引擎作为整体系统的一部分,以状态控制流程、关联每个业务,具有根据业务数据模型建立审批模型并进行流程扩展、执行等系统功能。并以某研究院使用该系统作为案例分析,其通过系统的项目管理、预算控制、支出应用三大业务模块功能,大幅度提高科研项目审批效率,通过统一数据标准提高用户查询结果的准确率,并有助于多部门协同运作,使得项目进度得到实时监控,从而显著改善科研经费管理效果。 相似文献
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2M线路测试仪软件系统设计与实现 总被引：1，自引：0，他引：1
2M传输线路是我国电信传输网的基本单元,是整个传输网的基础。在2M线路相关标准和DSP技术理论的基础上,着重介绍了DSP嵌入式系统软件的设计思想,结合模块化编程的方法和DSP灵活的中断机制技术完成系统程序设计任务。针对每个模块的软件,进行了调试,调试结果表明该设计的完整和正确。 相似文献