检索结果-内蒙古大学图书馆

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Chniter, Malak Abid, Abir Kallel, Ilhem Kanoun, Slim Mir@cl Lab: Multimedia InfoRmation Systems and Advanced Computing Laboratory University of Sfax ReGIM-Lab: Research Groups in Intelligent Machines Tunisia

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The task of assessing, grouping and arranging data into meaningful groups or clusters based on their similarities/dissimilarities measures known as cluster analysis. Thereby, there are numerous clustering algorithms: hierarchical and partitional. In the last decade, clustering using bio-inspired algorithms received more attention, specifically the ant clustering algorithms. Regardless, they have required a lot of processing power due to the massive amount of data that has been generated during the last years. As a consequence, determining the computational cost of these algorithms is one of the most interesting tasks in the quest for optimal clustering solutions in a real-time system. This study presents a research guide for the researchers working in the same field. A series of experiments are elaborated to investigate the computational complexity of the most promising algorithms applied to students grouping problem. The results indicate two challenges that arise when using ant clustering algorithms: the difficulty in adjusting parameters and extended computation time. © 2022 IEEE.

关键词： Computational complexity

来源：评论

学校读者我要写书评

暂无评论

Explore Contextual Information for 3D Scene Graph Generation

arXiv

引用

arXiv 2022年

作者： Liu, Yuanyuan Long, Chengjiang Zhang, Zhaoxuan Liu, Bokai Zhang, Qiang Yin, Baocai Yang, Xin The Department of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Meta Reality Labs BurlingameCA94010 United States The Key Lab of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China The Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology Beijing University of Technology Beijing100124 China

3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing works is still far from being perfect for fine-grained and multi-label situations. In this paper, we propose a framework fully exploring contextual information for the 3D SGG task, which attempts to satisfy the requirements of fine-grained entity class, multiple relation labels, and high accuracy simultaneously. Our proposed approach is composed of a Graph Feature Extraction module and a Graph Contextual Reasoning module, achieving appropriate information-redundancy feature extraction, structured organization, and hierarchical inferring. Our approach achieves superior or competitive performance over previous methods on the 3DSSG dataset, especially on the relationship prediction sub-task. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Combining Deep Embeddings of Acoustic and Articulatory Features for Speaker Identification

Combining Deep Embeddings of Acoustic and Articulatory Featu...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Qian-Bei Hong Chung-Hsien Wu Hsin-Min Wang Chien-Lin Huang Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Tainan Taiwan PingAn AI Lab Palo Alto CA USA

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

In this study, deep embedding of acoustic and articulatory features are combined for speaker identification. First, a convolutional neural network (CNN)-based universal background model (UBM) is constructed to generate acoustic feature (AC) embedding. In addition, as the articulatory features (AFs) represent some important phonological properties during speech production, a multilayer perceptron (MLP)-based AF embedding extraction model is also constructed for AF embedding extraction. The extracted AC and AF embeddings are concatenated as a combined feature vector for speaker identification using a fully-connected neural network. This proposed system was evaluated by three corpora consisting of King-ASR, LibriSpeech and SITW, and the experiments were conducted according to the properties of the datasets. We adopted all three corpora to evaluate the effect of AF embedding, and the results showed that combining AF embedding into the input feature vector improved the performance of speaker identification. The LibriSpeech corpus was used to evaluate the effect of the number of enrolled speakers. The proposed system achieved an EER of 7.80% outperforming the method based on x-vector with PLDA (8.25%). And we further evaluated the effect of signal mismatch using the SITW corpus. The proposed system achieved an EER of 25.19%, which outperformed the other baseline methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Statistics Pooling Time Delay Neural Network Based on X-Vector for Speaker Verification

Statistics Pooling Time Delay Neural Network Based on X-Vect...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

This paper aims to improve speaker embedding representation based on x-vector for extracting more detailed information for speaker verification. We propose a statistics pooling time delay neural network (TDNN), in which the TDNN structure integrates statistics pooling for each layer, to consider the variation of temporal context in frame-level transformation. The proposed feature vector, named as statsvector, are compared with the baseline x-vector features on the VoxCeleb dataset and the Speakers in the Wild (SITW) dataset for speaker verification. The experimental results showed that the proposed stats-vector with score fusion achieved the best performance on VoxCeleb1 dataset. Furthermore, considering the interference from other speakers in the recordings, we found that the proposed statsvector efficiently reduced the interference and improved the speaker verification performance on the SITW dataset.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Emotion Regulation in intelligent Tutoring Systems: A Systematic Literature Review

Emotion Regulation in Intelligent Tutoring Systems: A System...

引用

IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Rihab Khadimallah Mouna Abdelkefi Ilhem Kallel REGIM-Lab: Research Groups in Intelligent Machines ISIMS: Higher Institute of Computer Science and Multimedia University of Sfax Tunisia MIRACL: Multimedia InfoRmation Systems and Advanced Computing Laboratory ENIS: National School of Engineers Route Soukra Km 3.5 BP 1173 3038 University of Sfax Tunisia

ISBN: (数字)9781728169422

ISBN: (纸本)9781728169439

Emotion regulation (ER) is a process responsible for altering emotions' reactions and expressions as well as situations that simulate such feelings to better respond to environmental ***, ER is actually a mere mental and behavioral process that many of us already perform consciously and unconsciously. In this issue, with the imperious role of emotions in learning process which they crucially influence student's cognitive performances and academic outcomes, several researchers proposed different methods and tools for regulating learners' negative emotions while learning in online environment. In the present paper, we discuss the current and original research works that apply different strategies and methods for emotion regulation in different smart environments (i.e., intelligent Tutoring Systems) by adopting a Systematic Literature Review (SLR) approach.

关键词： Systematics Bibliographies Education Machine learning Tools Regulation Erbium

来源：评论

学校读者我要写书评

暂无评论

Bilingual Word Embedding with Sentence Combination CNN for 1-to-N Sentence Alignment 20

Bilingual Word Embedding with Sentence Combination CNN for 1...

引用

Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval

作者： Xinyuan Ren Xiangling Fu Xuesi Zhou Chunsheng Liu Songfeng Gao Lei Peng School of Software Engineering Beijing University of Posts and Telecommunications Beijing China and Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Hai Dian Beijing China and Multimedia Signal and Intelligent Information Processing Lab Tsinghua University Hai Dian Beijing China BUPT and Huarong Joint lab of Smart Finance Beijing P. R. China and HuaRong RongTong (Beijing) Technology Co. Ltd Beijing P. R. China

ISBN: (纸本)9781450377607

Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly proposed, the other, which are adopted later, is based on the Neural Network method. Presently, under the limitation that the existing mainstream data corpora are mostly in the form of 1-to-1, the alignment models with relatively good performance mainly apply to the cases of 1-to-1 sentence alignment. However, under the circumstance that a sentence contains too much information, 1-to-N sentence alignment can actually have a better effect on sentence translation tasks, compared with the 1-to-1 form, since it is more flexible and can reduce the complexity of the original sentence. As a result, we attempt to exploit neural networks with relatively good performance in the cases of 1-to-1 to fit in the cases of 1-to-N. In this paper, a novel 1-N Bilingual word Embedding with Sentence Combination CNN Improved Framework (1-NBESCC) is proposed in order to align 1-to-N sentences more precisely. Experiments show that our proposed model performs as good as the traditional methods such as BLEUALIGN in 1-to-1 situation, but much better in 1-to-N situation.

关键词： Convolutional Neural Network

来源：评论

学校读者我要写书评

暂无评论

MFPR: A Personalized Ranking Recommendation with Multiple Feedback

引用

ACM Transactions on Social computing 2018年第2期1卷 1–22页

作者： Shi, Chuan Liu, Jian Zhang, Yiding Hu, Binbin Liu, Shenghua Yu, Philip S. Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications No. 10 Xi Tu Cheng Road Beijing China Cas Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Zhongguancun Beijing China Department of Computer Science University of Illinois at Chicago 851 South Morgan Street Chicago United States

Recently, recommender systems have played an important role in improving web user experiences and increasing profits. Recommender systems exploit users' behavioral history (i.e., feedback on items) to build models. The feedback usually includes explicit feedback (e.g., ratings) and implicit feedback (e.g., browsing history, click logs), which are both useful for improving recommendations. However, as far as we are concerned, no existing works have integrated both explicit and multiple implicit feedback simultaneously. Therefore, we propose a unified and flexible model, named Multiple Feedback-based Personalized Ranking (MFPR), to make full use of multiple feedback, which uses a personalized ranking framework. To train model MFPR, we design an algorithm to generate ordered item pairs as labeled data, with consideration of both rating scores and multiple implicit feedback. Extensive experiments on two real-world datasets validate the effectiveness of the MFPR model. With the integration of multiple feedback, MFPR significantly improves recommendation performance. © 2018 ACM.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Recommendation in heterogeneous information network via dual similarity regularization

引用

International Journal of Data Science and Analytics 2017年第1期3卷 35-48页

作者： Zheng, Jing Liu, Jian Shi, Chuan Zhuang, Fuzhen Li, Jingzhi Wu, Bin Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Southern University of Science and Technology Shenzhen China

Recommender system has caught much attention from multiple disciplines, and many techniques are proposed to build it. Recently, social recommendation becomes a hot research direction. The social recommendation methods tend to leverage social relations among users obtained from social network to alleviate data sparsity and cold-start problems in recommender systems. It employs simple similarity information of users as social regularization on users. Unfortunately, the widely used social regularization suffers from several aspects: (1) The similarity information of users only stems from social relations of related users;(2) it only has constraint on users without considering the impact of items for recommendation;(3) it may not work well for dissimilar users. To overcome the shortcomings of social regularization, we design a novel dual similarity regularization to impose the constraint on users and items with high and low similarities simultaneously. With the dual similarity regularization, we further propose an optimization function to integrate the similarity information of users and items under different semantic meta-paths, and a gradient descend solution is derived to optimize the objective function. Experiments with different meta-paths validate the superiority of integrating much available information, and the experiments conducted on three real data sets validate the effectiveness of the proposed solution. © 2016, Springer International Publishing Switzerland.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Aspect mining with rating bias 15th

引用

15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016

作者： Li, Yitong Shi, Chuan Zhao, Huidong Zhuang, Fuzhen Wu, Bin Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Institute of Computing Technology CAS Beijing China

ISBN: (纸本)9783319462264

Due to the personalized needs for specific aspect evaluation on product quality, these years have witnessed a boom of researches on aspect rating prediction, whose goal is to extract ad hoc aspects from online reviews and predict rating or opinion on each aspect. Most of the existing works on aspect rating prediction have a basic assumption that the overall rating is the average score of aspect ratings or the overall rating is very close to aspect ratings. However, after analyzing real datasets, we have an insightful observation: there is an obvious rating bias between overall rating and aspect ratings. Motivated by this observation, we study the problem of aspect mining with rating bias, and design a novel RAting-center model with BIas (RABI). Different from the widely used review-center models, RABI adopts the overall rating as the center of the probabilistic model, which generates reviews and topics. In addition, a novel aspect rating variable in RABI is designed to effectively integrate the rating bias priori information. Experiments on two real datasets (Dianping and TripAdvisor) validate that RABI significantly improves the prediction accuracy over existing state-of-the-art methods. © Springer International Publishing AG 2016.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

X-Prcaf : Xposed Based Protecting Cache File from Leaks in Android Social Applications

X-Prcaf : Xposed Based Protecting Cache File from Leaks in A...

引用

International Conference on Trustworthy Systems and their Applications (TSA)

作者： Qi Tang Wen Zhang Xiaoyong Li Bin Wang Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing P.R. China Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing P.R. China School of Computer Science Beijing University of Posts and Telecommunications Beijing P.R. China

ISBN: (纸本)9781509035403

With the high frequent use of social applications on Android platform, the cache file privacy disclosure issues have become increasingly serious. To our best knowledge, there is no effective privacy protection solution for social applications cache files. In this paper, we analyze the present situation of social applications cache file leaks on Android platform, and provide a privacy disclosure assessment criterion based on file storage directories and security state machines. And a cache file privacy protection framework, X-Prcaf (Xposed-based-Protecting-Cache-File), is proposed, which can make social applications avoid privacy data leaks in running process. This framework mainly uses taint tracking technology, operating system hook technology, and cryptographic technology. It aims to protect the entire life cycle of the social applications cache files, by strategy pre-generation, real-time monitoring and security reinforcement. Experiments demonstrate that X-Prcaf has a good effect on the cache file leaks of social software.

关键词： Data privacy Software Cryptography Smart phones Privacy Telecommunications

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：