Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition最新文献

Query Recommendation Using Topic Modeling and Word Embeddings 使用主题建模和词嵌入的查询推荐

Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2018-08-18 DOI: 10.1145/3268866.3268873

Jianyong Duan, Yadi Song, Yongmei Zhang, Mingli Wu, Hao Wang

{"title":"Query Recommendation Using Topic Modeling and Word Embeddings","authors":"Jianyong Duan, Yadi Song, Yongmei Zhang, Mingli Wu, Hao Wang","doi":"10.1145/3268866.3268873","DOIUrl":"https://doi.org/10.1145/3268866.3268873","url":null,"abstract":"Query recommendation plays an important role in improving users' search experience. Traditional ways most mine recommended words from log information. However, in user logs, sessions are difficult to divide. At the same time, click results are with bias and noise, and many queries lack clicks, it will make useful information be sparse. In this paper, we present a novel method based on local documents. Different from the traditional query recommendation, this method recommends related terminology according to the meaning of the query. We extract terminology documents from the pseudo-related feedback documents, then model topics of the terminology documents and use the inference strategies to infer the topic of the query to solve the problem of theme drift. In addition, to bring better recommendation results, we fuse supervised and unsupervised methods to mine semantic concept relations between query words and recommended words. Finally, the words with semantic concepts relation are recommended to the user. Experimental results show that our method can meet the user's search needs better. Compared with traditional query recommendation, users prefer the query recommendation way that we propose.","PeriodicalId":285628,"journal":{"name":"Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123035929","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: 1

Combinatorial Optimization Approach for Arabic Word Recognition 阿拉伯语词识别的组合优化方法

Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2018-08-18 DOI: 10.1145/3268866.3268884

Zouaoui Zeineb, Ben Chiekh Imen, Jemni Mohamed

引用次数: 0

Fast, Multi-Scale Image Processing on a Square Spiral Framework 基于方形螺旋框架的快速多尺度图像处理

Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2018-08-18 DOI: 10.1145/3268866.3268882

J. Fegan, S. Coleman, D. Kerr, B. Scotney

引用次数: 0

Application of Domain Adaptation Approach for Weather Data Mining 领域自适应方法在天气数据挖掘中的应用

Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2018-08-18 DOI: 10.1145/3268866.3268879

Yang Wang, Yuanzhe Shi

引用次数: 4

Fast, Biologically Inspired Corner Detection Using a Square Spiral Address Scheme and Artificial Eye Tremor 快速，生物启发角检测使用方形螺旋地址方案和人工眼震颤

Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2018-08-18 DOI: 10.1145/3268866.3268883

J. Fegan, S. Coleman, D. Kerr, B. Scotney

引用次数: 1

A Low Complexity FFT-based Algorithm for Channel Estimation of Ultra-Wideband Communication Systems 一种基于fft的低复杂度超宽带信道估计算法

Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2018-08-18 DOI: 10.1145/3268866.3268875

Zhixing Wang, Xinghua Ren, Z. Tan

引用次数: 0

Method of Kidney Image Segmentation Based on Improved C-V Model 基于改进C-V模型的肾脏图像分割方法

Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2018-08-18 DOI: 10.1145/3268866.3268867

Hui Yu, Jian Jiao, Yuzhen Cao

引用次数: 0

Research on the Application Feasibility of Data Mining Technology 数据挖掘技术应用可行性研究

Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2018-08-18 DOI: 10.1145/3268866.3268878

Zhang Cheng-zhao

引用次数: 0

Autonomous Indoor Robot Navigation via Siamese Deep Convolutional Neural Network 基于Siamese深度卷积神经网络的自主室内机器人导航

Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2018-08-18 DOI: 10.1145/3268866.3268886

Yao Yeboah, Cai Yanguang, W. Wu, Shuai He

{"title":"Autonomous Indoor Robot Navigation via Siamese Deep Convolutional Neural Network","authors":"Yao Yeboah, Cai Yanguang, W. Wu, Shuai He","doi":"10.1145/3268866.3268886","DOIUrl":"https://doi.org/10.1145/3268866.3268886","url":null,"abstract":"The vast majority of indoor navigation algorithms either rely on manual scene augmentation and labelling or exploit multi-sensor fusion techniques in achieving simultaneous localization and mapping (SLAM), leading to high computational costs, hardware complexities and robustness deficiencies. This paper proposes an efficient and robust deep learning-based indoor navigation framework for robots. Firstly, we put forward an end-to-end trainable siamese deep convolutional neural network (DCNN) which decomposes navigation into orientation and localization in one branch, while achieving semantic scene mapping in another. In mitigating the computational costs associated with DCNNs, the proposed model design shares a significant amount of convolutional operations between the two branches, streamlining the model and optimizing for efficiency in terms of memory and inference latency. Secondly, a transfer learning regime is explored in demonstrating how such siamese DCNNs can be efficiently trained for high convergence rates without extensive manual dataset labelling. The resulting siamese framework combines semantic scene understanding with orientation estimation towards predicting collision-free and optimal navigation paths. Experimental results demonstrate that the proposed framework achieves accurate and efficient navigation and outperforms existing \"navigation-by-classification\" variants.","PeriodicalId":285628,"journal":{"name":"Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition","volume":"224 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114096936","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: 4

Optimized CF Recommendation Algorithm Based on Users' Characteristics and Trust 基于用户特征和信任的CF推荐算法优化

Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2018-08-18 DOI: 10.1145/3268866.3268887

Q. Jin, Xia Song, Ming-Hua Yang, Wu Cai

{"title":"Optimized CF Recommendation Algorithm Based on Users' Characteristics and Trust","authors":"Q. Jin, Xia Song, Ming-Hua Yang, Wu Cai","doi":"10.1145/3268866.3268887","DOIUrl":"https://doi.org/10.1145/3268866.3268887","url":null,"abstract":"CF (Collaborative filtering) algorithm has the widest and most successful applications in personalized recommendations. However, due to its over-reliance on the users' historical data, it is difficult to avoid data sparseness and cold start issues. The data sparseness and cold start may cause poor recommendation accuracy of the collaborative filtering algorithm. A hybrid optimal collaborative filtering algorithm based on users' characteristics and trust is proposed in this paper. In the process of users' similarity calculation, the age and gender of users' characteristics are introduced to make the determination of nearest neighbor more accurate. Besides, in order to improve the recommendation accuracy of the traditional CF recommendation algorithm, the trust relationship is introduced into the prediction score by measuring the users' trust, and this improvement will be used in the recommendation of new items in order to improve the recommendation accuracy of the traditional CF recommendation algorithm. The experimental results of Movie lens data set show that the improved recommendation accuracy of the recommendation system can be achieved by the proposed algorithm. Also, the problems of cold start and sparse data can be solved effectively.","PeriodicalId":285628,"journal":{"name":"Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128190772","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: 3