2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)最新文献

A Recognition Algorithm for Signal Modulation Modes of Tactical Data Link 一种战术数据链信号调制模式识别算法

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00047

Qi Liu, Yuwen Wang, Juan Guo, Xintong Yao, Shuo Jin, Yujie Chen

引用次数: 2

Community Detection Algorithm for Heterogeneous Networks Based on Central Node and Seed Community Extension 基于中心节点和种子社区扩展的异构网络社区检测算法

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00040

Qichao Peng, Kebin Chen, Qi Liu, Yaofeng Su, Yunjun Lu

引用次数: 0

Product click-through rate prediction model integrating self-attention mechanism 整合自关注机制的产品点击率预测模型

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00056

Tong Zhu, Shuqin Li, Chunquan Liang, B. Liu, Xiaopeng Li

{"title":"Product click-through rate prediction model integrating self-attention mechanism","authors":"Tong Zhu, Shuqin Li, Chunquan Liang, B. Liu, Xiaopeng Li","doi":"10.1109/CTISC52352.2021.00056","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00056","url":null,"abstract":"In the commodity click-through rate prediction task, existing deep learning models implicitly construct combinatorial features and cannot know the optimal order of the combinatorial features that can be learned; at the same time, the intrinsic correlation between features is ignored, and invalid feature combinations will bring unnecessary noise to the model. To address these problems, a product click-through prediction model (ACDeepFM) incorporating a self-attentive mechanism is proposed, which first uses the self-attentive mechanism to mine the intrinsic connections among input features and adaptively models the weights of input features. Then a compressed interaction network is added to precisely mine the effect of different order combinations of features on the model prediction results. Then deep neural networks are added to fit complex interaction scenarios between users and items. Finally, the information extracted from the self-attentive mechanism module, the deep neural network module and the compressed interaction network module are fed into the subsequent multilayer perceptron layer to further learn meaningful combinatorial features. Experimental results on two publicly available datasets show that the proposed model achieves higher AUC values and lower Logloss values relative to FM, DNN, DeepFM and xDeepFM models, validating the effectiveness of the ACDeepFM model.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129762942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Shannon Capacity under Relative Motion 相对运动下的香农容量

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00014

Xu Tang, Nanzhou Hu, Hongzhi Zhao

引用次数: 0

Application of Structured-light 3D Reconstruction in Virtual Assembly 结构光三维重建在虚拟装配中的应用

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00077

Youteng Wan, Ang Cai, Qi Wei, Jing Liu, Xiangjia Chen, Jiaming Feng, Zhan Song, Juan Zhao

引用次数: 1

Prosody-Enhanced Mandarin Text-to-Speech System 韵律增强的普通话文本转语音系统

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00020

Fangfang Niu, Wushour Silamu

{"title":"Prosody-Enhanced Mandarin Text-to-Speech System","authors":"Fangfang Niu, Wushour Silamu","doi":"10.1109/CTISC52352.2021.00020","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00020","url":null,"abstract":"The end-to-end Text-to-Speech (TTS), which can generate speech directly from a given sequence of graphemes or phonemes, has shown superior performance over the conventional TTS. It has been able to generate high-quality speech, but it is still unable to control the local prosody such as word-level emphasis. Although the prominence of synthesized speech can be adjusted by explicit prosody tags, the acquisition of such tags is often time-consuming and laborious. This paper focuses on a deep neural prominence prediction module, using Continuous Wavelet Transform (CWT) to analyze the prosodic signal of input data, get the corresponding continuous prominence values of Chinese characters in the text to guide the training of a prominence prediction network, so that it can realize the mapping from the input text to the corresponding prominence value of each Chinese character in the text. The proposed method does not need to label the training data manually, so a fully automatic prosody control system is realized. Experiments show that the proposed system can generate more natural and expressive speech.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121998102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conditional GAN with Effective Attention for SAR-to-Optical Image Translation 有效关注sar到光学图像转换的条件GAN

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00009

Tianzhu Yu, Jiexin Zhang, Jianjiang Zhou

引用次数: 3

Algorithm of Handwritten String Segmentation Based on Recursive Rraining in Background Domain 基于背景域递归训练的手写字符串分割算法

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00026

Jia Luo, Kai-lin He, Xiaojing Ding

{"title":"Algorithm of Handwritten String Segmentation Based on Recursive Rraining in Background Domain","authors":"Jia Luo, Kai-lin He, Xiaojing Ding","doi":"10.1109/CTISC52352.2021.00026","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00026","url":null,"abstract":"How to accurately cut the handwritten string especially the sticky string, has become a key part of recognizing handwritten strings. Aiming at the traditional segmentation algorithm has some problems, such as more complex､the segmentation effect is not good and so on, this paper proposes a segmentation algorithm based on background domain recursive training. This algorithm is a common algorithm for handwritten digit string segmentation and English string segmentation. The principle is the use of handwritten string at the adhesion of the background domain features and recursive neural network RNN fusion of special mechanisms. It completes by modeling, training and implementation of three steps. RNN modeling is the core of the algorithm, it contain two important parts: ①Assignment for the background domain, extracting the eigenvector value of the depression area by the principle of adjacent matching, the connection weights of the RNN input layer are calculated.② Using the minimum area selection principle to modify eigenvector values in the RNN's acceptance layer, the connection weight of the layer are calculated agin. After modeling completion, RNN is training samples､studying and remembering. Finally, use the knowledge that RNN has learned to complete real segmentation, the effect is satisfactory.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"2010 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133829595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hyperspectral and panchromatic image fusion based on CNMF 基于CNMF的高光谱与全色图像融合

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00060

Tianyu Mu, Rencan Nie, Chaozhen Ma, Jie Liu

引用次数: 1

Searching for similar binary code based on the influence of basic block vertex 基于基本块顶点影响的相似二进制码搜索

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00011

Xiaodong Zhu, Liehui Jiang, Zeng Chen, Lin Yan

{"title":"Searching for similar binary code based on the influence of basic block vertex","authors":"Xiaodong Zhu, Liehui Jiang, Zeng Chen, Lin Yan","doi":"10.1109/CTISC52352.2021.00011","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00011","url":null,"abstract":"Binary code similarity detection (BCSD) has many security applications and has attracted a lot of focus. But existing solutions for BCSD problems mainly focus on comparing the similarity between two binary functions and what need to be compared is not always a pair of equal-size binary programs. To solve the problem of searching similar binary code for a small size program in a large size program, in this manuscript, we propose a searching method based on the influence of basic block vertex. Firstly, we formalize the similar code searching problem as a subgraph matching problem and extract the attributed control flow graphs (ACFGs) of the binary programs. Then, we propose a novel metrics to measure the influence of each basic block vertex in query ACFGs and choose the vertex with the largest influence value as the central node. Next, we take the matching nodes of the central node in target ACFG as seed nodes and extending each candidate sub-area from the seed nodes. At last, these candidate sub-areas are further filtered and similar subgraphs are verified in these sub-areas. Experimental results indicate that the proposed method can find similar binary code efficiently.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124454422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0