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国际计算机前沿大会会议论文集

国际计算机前沿大会于2015年首次举办，是由计算机领域专家学者发起的一个面向前沿技术领域的国际性学术会议，大会主要宗旨是为来自学术界和工业界的研究者、开发者、教育工作者提供一个展示成果和共享经验的交流平台，每年确定一个当前研究和工业界的热点作为会议主题，并在全球不同的国家和地区召开。

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主管单位：国际计算机前沿大会学术委员会

主办单位：中科国鼎数据科学研究院(北京)有限公司

国际计算机前沿大会会议论文集-联系信息

主编：王宏志;景维鹏
电话：010-53947856
邮箱：zeguanglu@icpcsee.org
地址：北京市海淀区西三旗昌临801号
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国际计算机前沿大会会议论文集 >2020年第2期

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1.Recognizing Visual Attention Allocation Patterns Using Gaze Behaviors
- Cheng Wu1;Feng Xie1;Changsheng Yan1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Human Attention Allocation Strategy (HAAS) is related closely to operating performance when he/she is interacting a machine through a human-machine interface. Gaze behaviors, which is acquisited by eye tracking technology, can be used to observe attention allocation. But the performance-sensitive attention allocation strategy is still hard to measure using gaze cue. In this paper, we attempt to understand visual attention allocation behavior and reveal the relationship between attention allocation strategy and interactive performance in a quantitative manner. By using a novel Multiple-Level Clustering approach, we give some results on probabilistic analysis about interactive performance of HAAS patterns in a simulation platform of thermal-hydraulic process plant. It can be observed that these patterns are sensitive to interactive performance. We conclude that our Multiple-Level Clustering approach can extract efficiently human attention allocation patterns and evaluate interactive performance using gaze movements.
2.The Framework Design of Intelligent Checkers
- Biao Wang1;Lijuan Jia2;He Quan1;Changsong Zheng1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：As one of the research direction in the field of artificial intelligence, the computer game has a great practical significance. Checkers become a represent of complete knowledge game that because of it has few chess pieces, and moves easily, So it can reflect the thought of the computer game better. First, the recursive algorithm of artificial intelligence can achieve the independent moves and checkmates in draughts game program. Second, the optimization of evaluation function makes the program choose beneficial move ways. Third, the game tree can improve the level of computer in-depth thinking, simplify the search scope, and save computer thinking time by pruning, which improve the level of chess program. In this paper, overall framework of intelligent checkers is built from board representation, game tree, valuation optimization. Based on the accumulation of experience, algorithm upgrade, depth analysis of existing framework,then we give a more optimized system framework and then strengthen the computer game level.
3.App Store Analysis: Using Regression Model for App Downloads Prediction
- Shanshan Wang1;Wenjun Wu1;Xuan Zhou1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：App store provides rich information for software vendors and customers to understand the market of mobile applications. However, app store analysis don’t consider some vital factors such as version number, app description and app name currently. In this paper we propose an approach that App Store Analysis can be used to predict app downloads. We use data mining to extract app name and description and app rank information etc. from the Wandoujia App Store and AppCha App Store. We use questionnaire and sentimentanalysis to quantify some app nonnumeric information. We revealed strong correlations app name score, app rank, app rating with app downloads by Spearman’s rank correlation analysis respectively. Finally, we establish a multiple nonlinear regression model which app downloads defined as dependent variable and three relevant attributes defined as independent variable. On average, 59.28 % of apps in Wandoujia App Store and 66.68 % of apps in AppCha App Store can be predicted accurately within threshold which error rate is 25 %. One can observe the more detailed classification of app store, the more accurate for regression modeling to predict app downloads. Our approach can help app developers to notice and optimize the vital factors which influence app downloads.
4.Design and Implementation of Chinese Historical Text Mining System Based on Culturomics
- Lin Tang12;Chonghui Guo1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Culturomics and Chinese text mining methods are of great significance for analyzing the development and evolution of Chinese history and culture. To help researchers analyze a large number of Chinese historical text data, a Chinese historical text mining system based on cultruomics is designed, which includes text data processing and analyzing subsystem, text data visualizing subsystem, and text data clustering and retrieval subsystem. First of all, our system preprocesses the text data, then visualizes the text data with the frequency of words line chart and word cloud, at last selects the text data through clustering and retrieval methods. It further supports researchers to discover knowledge from a large number of historical text data. We demonstrate its general performance on text data of Canton Customs into our system. The result shows that our system is feasible and effective.
5.Determining Web Data Currency Based on Markov Logic Network
- Yan Zhang1;Rui Zhang2;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：This paper proposes a method based on Markov Logic Network (MLN) to determine the time order of entity attribute values. We use the characteristics of web sources’ currency, web sources inter-dependency and attribute data currency in a certain web source as predicates in MLN. We define five rules (new rules can be added) to infer the currency of different values provided by different sources. On one hand, this method considers currency problem based on entity attribute instead of the entire entity, which is critical to improve the qualityof data provided by Web Integration Systems; on the other hand, this method summarizes characteristics of web sources and web data based on carefully analysis. It is noteworthy that it is not complicate for the MLN model to incorporate new rules, which shows that the proposed method is extensible.
6.Identifying Transportation Modes from Raw GPS Data
- Qiuhui Zhu1;Min Zhu1;Mingzhao Li2;Min Fu1;Zhibiao Huang3;Qihong Gan4;Zhenghao Zhou5;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Raw Global Positioning System (GPS) data can provide rich context information for behaviour understanding and transport planning. However, they are not yet fully understood, and fine-grained identification of transportation mode is required. In this paper, we present a robust framework without geographic information, which can effectively and automatically identify transportation modes including car, bus, bike and walk. Firstly, a trajectory segmentation algorithm is designed to divide raw GPS trajectory into single mode segments. Secondly, several modern features are proposed which are more discriminating than traditional features. At last, an additional postprocessing procedure is adopted with considering the wholeness of trajectory. Based on Random Forest classifier, our framework can achieve a promising accuracy by distance of 82.85% for identifying transportation modes and especially 91.44% for car mode.
7.A Strategy for Small Files Processing in HDFS
- Zhenshan Bao1;Shikun Xu1;Wenbo Zhang1;Juncheng Chen1;Jianli Liu1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Hadoop distributed file system (HDFS) as a popular cloud storage platform, benefiting from its scalable, reliable and low-cost storage capability.However it is mainly designed for batch processing of large files, it’s mean that small files cannot be efficiently handled by HDFS. In this paper, we propose a mechanism to store small files in HDFS. In our approach, file size need to be judged before uploading to HDFS. If the file size is less than the size of the block, all correlated small files will be merged into one single file and we will build index for each small file. Furthermore, prefetching and caching mechanism are used to improve the reading efficiency of small files. Meanwhile, for the new small files, we can execute appending operation on the basis of merged file. Contrasting to original HDFS, experimental results show that the storage efficiency of small files is improved.
8.CoGrec: A Community-Oriented Group Recommendation Framework
- Yu Liu1;Bai Wang1;Bin Wu1;Xuelin Zeng1;Jing Shi1;Yunlei Zhang1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Recently, group recommendation becomes substantially significant when it frequently happens that a group of users need to determine which item (e.g. movie, music, restaurant, etc.) to choose. In this paper we employ the information of friend network to propose a Community-Oriented Group Recommendation framework (CoGrec) consisting of non-negative matrix factorization based user profile generation, community detection based group identification, and overlapping community membership based group decision. Along with four inherent aggregation and allocation strategies, our proposed framework is evaluated through extensive experiments on real-world datasets. The experimental results show that the proposed framework is promising and more accurate when the given friend network is much denser, which is suitable for modern review and rating systems.
9.Teaching Reformation and Practice of “.NET Program Design” Course Oriented CDIO Model
- RuiGai Li1;AChuan Wang1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：“.NET Program Design” is a very important professional elective course in the Department of Computer Science and Technology. It is a practical, comprehensive curriculum with highly specialized applications. This paper analyses the disadvantages of traditional teaching model in the .NET Program Design, and discusses the methods and practices for teaching reform such as introducing CDIO engineering education model to classroom teaching with project cases, arranging teaching content properly, choosing modern teaching methods and reform on the curriculum assessment system. The practice has proved that this model with highly effective in teaching process can promote the training of students abilities in all aspects.
10.Image Segmentation: A Novel Cluster Ensemble Algorithm
- Lei Wang1;Guoyin Zhang1;Chen Liu1;Wei Gao1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Cluster ensemble has testified to be a good choice for addressing cluster analysis issues, which is composed of two processes: creating a group of clustering results from a same data set and then combining these results into a final clustering results. How to integrate these results to produce a final one is a significant issue for cluster ensemble. This combination process aims to improve the quality of individual data clustering results. A novel image segmentation algorithm using the Binary k-means and the Adaptive Affinity Propagation clustering (CEBAAP) is designed in this paper. It uses a Binary k-means method to generate a set of clustering results and develops an Adaptive Affinity Propagation clustering to combine these results. The experiments results show that CEBAAP has good image partition effect.
11.Link Mining in Online Social Networks with Directed Negative Relationships
- Baofang Hu12;Hong Wang2;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：One of the most important work to analyse online social networks is link mining. A new type of social networks with positive and negative relationships are burgeoning. We present a link mining method based on random walk theory to mine the unknown relationships in directed social networks which have negative relationships. Firstly, we define an extended Laplacian matrix based on this type of social networks.Then, we prove the matrix can be used to compute the similarities of the node pairs. Finally, we propose a link mining method based on collaboration recommendation method. We apply our method in two real social networks. Experimental results show that our method do better in terms of sign accuracy and AUC for mining unknown links in the two real datasets.
12.Multi-GPU Based Recurrent Neural Network Language Model Training
- Xiaoci Zhang1;Naijie Gu1;Hong Ye1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Recurrent neural network language models (RNNLMs) have been applied in a wide range of research fields, including nature language processing and speech recognition. One challenge in training RNNLMs is the heavy computational cost of the crucial back-propagation (BP) algorithm. This paper presents an effective approach to train recurrent neural network on multiple GPUs, where parallelized stochastic gradient descent (SGD) is applied. Results on text-based experiments show that the proposed approach achieves 3.4× speedup on 4 GPUs than the single one, without any performance loss in language model perplexity.
- Recurrent neural network language models (RNNLMs);
13.Research of Constructing 3D Digital River Based on ArcEngine and SketchUp
- Xueyan Tang1;Yuansheng Lou1;Feng Ye1;Xiaorong Yan1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：With the development of the 3D GIS and the digital basin, it is important for us to deal with the rivers from the 3D space. Compared to traditional forms of 2D GIS, the 3D GIS system brings better user experience for us. After introducing the current development status of the 3D digital river, the paper proposes a novel method for constructing 3D digital river system. It explores the methods in 3D digital river modeling by SketchUp, driving 3D scene by ArcScene and secondary developing by ArcGIS Engine. It discusses the specific issues of 3D river modeling with a river in Nanjing, and sums up a complete 3D modeling of operational process. Finally, that proves the feasibility of the method.
14.Research of the DBN Algorithm Based on Multi-innovation Theory and Application of Social Computing
- Pinle Qin1;Meng Li1;Qiguang Miao2;Chuanpeng Li1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Aimed at the problems of small gradient, low learning rate, slow convergence error when the DBN using back-propagation process to fix the network connection weight and bias, proposing a new algorithm that combines with multi-innovation theory to improve standard DBN algorithm, that is the multi-innovation DBN(MI-DBN). It sets up a new model of back-propagation process in DBN algorithm, making the use of single innovation in previous algorithm extend to the use of innovation of the preceding multiple period, thus increasing convergence rate of error largely. To study the application of the algorithm in the social computing, and recognize the meaningful information about the handwritten numbers in social networking images. This paper compares MI-DBN algorithm with other representative classifiers through experiments. The result shows that MI-DBN algorithm, comparing with other representative classifiers, has a faster convergence rate and a smaller error for MNIST dataset recognition. And handwritten numbers on the image also have a precise degree of recognition.
15.The Software Behavior Trend Prediction Based on HMM-ACO
- Ziying Zhang1;Dong Xu1;Xin Liu2;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：For the HMM exists defects in application in the aspect of software behavior prediction, namely, HMM could trap into local optimization because of the problem of B-parameter, which results in the decrease of HMM’s precision. This paper builds a new model HMM-ACO through combining Ant Colony Optimization (ACO) algorithm with HMM, with system calls as the data source, improving the prediction accuracy rate of HMM. In order to eliminate the HMM’s reflection on observations characteristics, this paper puts forward a new approach to recognize software behavior with hidden states.
16.Application Progress of Signal Clustering Algorithm
- Chujie Deng1;Jing Qi1;Mei Li1;Xuanchicheng Luo1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Clustering algorithm, which is a statistical analysis method for research in classifications, plays an important role in data mining algorithm. Clustering algorithm based on similarity, and is easy to combine with other methods in optimization. In this review, signal clustering algorithm is introduced by discussing of the clustering parametric in different signal clustering algorithms. In order to develop traditional algorithm, we introduce a series of improvement, development and application of the methods in recent years. Finally, we make an outlook of the future direction and content of the research in this field.
17.Detection of Copy-Scale-Move Forgery in Digital Images Using SFOP and MROGH
- Mahmoud Emam12;Qi Han1;Hongli Zhang1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Social network platforms such as Twitter, Instagram and Facebook are one of the fastest and most convenient means for sharing digital images. Digital images are generally accepted as credible news but, it may undergo some manipulations before being shared without leaving any obvious traces of tampering; due to existence of the powerful image editing softwares. Copy-move forgery technique is a very simple and common type of image forgery, where a part of the image is copied and then pasted in the same image to replicate or hide some parts from the image. In this paper, we proposed a copy-scale-move forgery detection method based on Scale Invariant Feature Operator (SFOP) detector. The keypoints are then described using MROGH descriptor. Experimental results show that the proposed method is able to locate and detect the forgery even if under some geometric transformations such as scaling.
18.Efficient File Accessing Techniques on Hadoop Distributed File Systems
- Wei Qu1;Siyao Cheng1;Hongzhi Wang1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Hadoop framework emerged at the right moment when traditional tools were powerless in terms of handling big data. Hadoop Distributed File System (HDFS) which serves as a highly fault-tolerance distributed file system in Hadoop, can improve the throughput of data access effectively. It is very suitable for the application of handling large amounts of datasets. However, Hadoop has the disadvantage that the memory usage rate in NameNode is so high when processing large amounts of small files that it has become the limit of the whole system. In this paper, we propose an approach to optimize the performance of HDFS with small files. The basic idea is to merge small files into a large one whose size is suitable for a block. Furthermore, indexes are built to meet the requirements for fast access to all files in HDFS. Preliminary experiment results show that our approach achieves better performance.
- HDFS;
- Hadoop;
- Index;
- Small files;
19.A Novel Filtering Method for Infrared Image
- Jian Kang1;Chunxiao Zhou1;Weiqiang Xia1;Chaopeng Shen1;Guanglu Sun2;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：The image filtering technology is widely used in many fields, such as environmental monitoring and assessment, space remote sensing, recognition and tracking of infrared target. In this paper, aiming at the problem of poor generalization capability and over-fitting with artificial neutral network for infrared image filtering, a new filtering method is presented. The structure elements are used to set up the training samples. And then, based on support vector machine theory, it builds the learning ma-chine with proper model and trains the samples. The result can be used to suppress the background SNR of following image. The experimental result with infrared image shows that the method can obtain higher SNR than conventional neutral network and fixed Top-Hat operator method, especially in low SNR.
20.A SVM-Based Feature Extraction for Face Recognition
- Peng Cui1;Tian-tian Yan1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Social computing, a cross science of computational science and social science, is affecting people’s learning, work and life recently. Face recognition is going deep into every field of social life, and the feature extraction is particularly important. Linear Discriminant Analysis (LDA) is an effective feature extraction method. However, the traditional LDA cannot solve the nonlinear problem and small sample problem existing in high dimensional space. In this paper, the method of the Support Vector-based Direct Discriminant Analysis (SVDDA) is proposed. It incorporates SVM algorithm into LDA, extends SVM to nonlinear eigenspace, and optimizes eigenvalue to improve performance. Moreover, this paper combines SVDDA with the social computing theory. The experiments were tested on different face datasets. Compared with other existing methods, SVDDA has higher robustness and optimal performance.
21.A Transductive Support Vector Machine Algorithm Based on Ant Colony Optimization
- Xu Yu1;Chun-nian Ren1;Yan-ping Zhou1;Yong Wang2;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Transductive support vector machine optimization problem is a NP problem, in the case of larger number of labeled samples, it is often difficult to obtain a global optimal solution, thereby the good generalization ability of transductive learning has been affected. Previous methods can not give consideration to both running efficiency and classification precision. In this paper, a transductive support vector machine algorithm based on ant colony optimization is proposed to overcome the drawbacks of the previous methods. The proposed algorithm approaches the approximate optimal solution of Transductive support vector machine optimization problem by ant colony optimization algorithm, and the advantage of transductive learning can be fully demonstrated. Experiments on several UCI standard datasets and the newsgroups 20 dataset showed that, with respect to running time and classification precision, the proposed algorithm has obvious advantage over the previous algorithms.
22.An Approach for Automatically Generating R2RML-Based Direct Mapping from Relational Databases
- Mohamed A.G. Hazber1;Ruixuan Li1;Guandong Xu2;Khaled M. Alalayah34;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：For integrating relational databases (RDBs) into semantic web applications, the W3C RDB2RDF Working Group recommended two approaches, Direct Mapping (DM) and R2RML. The DM provides a set of mapping rules according to RDB schema, while the R2RML allows users to manually define mappings according to existing target ontology. The major problem to use R2RML is the effort for creating R2RML mapping documents manually. This may lead to appearance of many mistakes in the R2RML documents and requires domain experts. In this paper, we propose and implement an approach to generate an R2RML mapping documents automatically from RDB schema. The R2RML mapping reflects the behavior of the DM specification and allows any R2RML parser to generate a set of RDF triples from relational data.The input of generating approach is DBsInfo class that automatically generated from relational schema. An experimental prototype is developed and shows the effectiveness of our approach algorithms.
23.A Context-Aware Model Using Distributed Representations for Chinese Zero Pronoun Resolution
- Bingbing Wu1;Tiejun Zhao1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Previous approaches to Chinese zero pronoun resolution mainly use syntactic information and probabilistic methods, but the context information is ignored. To make full use of the context and semantic information, we build a context-aware model. We propose a key words extraction strategy and design a classification model by using distributed representations as context feature. To our best knowledge, this is the first work using distributed representations in Chinese zero pronoun resolution. Experimental results show that our approach achieves a better performance than previous supervised methods.
24.Lossless and High Robust Watermarking of Electronic Chart for Copyright Protection
- Jianguo Sun12;Junyu Lin1;Liguo Zhang1;Shouzheng Liu1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：With the progression of sea exploration and offshore engineering, electronic charts have come to see widespread use in many intelligent applications. Like other digital products, electronic charts are easy to duplicate and distribute. Some watermarking solutions have proven defective to prevent copying of electronic charts because it’s as easy to forge as it is to redistribute. If the problems of copyright infringement cannot be solved, the creation of these electronic charts will be limited. The most important characteristic of electronic charts is the topological relationships among vertices, but few algorithms can control this feature. A new watermarking algorithm is here proposed as a means of copyright protection, in which the watermarks will be hosted in the electronic chart by taking into account the preservation of the topology. Sometimes, additional vertices are inserted into the middle of two adjacent vertices, sometimes not, which are governed by the value of the watermark. Experiments show that the improved algorithm is better than similar algorithms; it was found to resist geometric attacks and format exchange attacks.
25.Negation Scope Detection with Recurrent Neural Networks Models in Review Texts
- Lydia Lazib1;Yanyan Zhao1;Bing Qin1;Ting Liu1;
- 《国际计算机前沿大会会议论文集》 | 2016年第1期
摘要：Identifying negation scopes in a text is an important subtask of information extraction, that can benefit other natural language processing tasks, like relation extraction, question answering and sentiment analysis. And serves the task of social media text understanding. The task of negation scope detection can be regarded as a token-level sequence labeling problem. In this paper, we propose different models based on recurrent neural networks (RNNs) and word embedding that can be successfully applied to such tasks without any task-specific feature engineering efforts. Our experimental results show that RNNs, without using any hand-crafted features, outperform feature-rich CRF-based model.
26.Research and Practice on College Students’Innovation and Entrepreneurship Education
- Hui Gao1;Zhaowen Qiu2;Zhengyu Liu1;Lei Huang1;Ying San3;
- 《国际计算机前沿大会会议论文集》 | 2016年第2期
摘要：Strengthening the education of innovation and entrepreneurship is one of the important tasks of China’s higher education reform and development.Entrepreneurship Education should focus on setting pioneering genetic code for future generations.Essentially,it is an education innovation-oriented entrepreneurial revolution of human resource development.Technological innovation is strategic support to improve social productivity and comprehensive national strength,which should be placed at the core of national overall development.For China’s higher education,it proposes more new requirements.Serious discussion on the innovation education of college students is needed.Through practice,improvements in the quality of innovation and entrepreneurship education will be achieved.
27.Daily ETC Traffic Flow Time Series Prediction Based on k-NN and BP Neural Network
- Yanjing Chen1;Yawei Zhao1;Peng Yan2;
- 《国际计算机前沿大会会议论文集》 | 2016年第2期
摘要：Daily Electronic Toll Collection(ETC)traffic flow prediction is one of the fundamental processes in ETC management.The precise prediction of traffic flow provides instructions for transportation hub management solution planning and ETC lane construction.At present,some of studies are proposed in forecasting traffic flow.However,most studies of model presentation are in the form of mathematical expressions,and it is difficult to describe the trend accurately.Therefore,an ETC traffic flow prediction model based on k nearest neighbor searching(k-NN)and Back Propagation(BP)neural network is proposed,which takes the effect of external factors like holiday,the free of highway and weather etc.into consideration.The traffic flow data of highway ETC lane somewhere is used for prediction.The prediction results indicate that the total average absolute relative error is 5.01%.The accuracy suggests its advantage in traffic flow prediction and on site application.
28.Sliding Window Network Coding for Free Viewpoint Multimedia Streaming in MANETs
- Chao Gui1;Chengli Huang2;Baolin Sun1;Xiaoyan Zhu3;
- 《国际计算机前沿大会会议论文集》 | 2016年第2期
摘要：Network coding(NC)can prove to be particularly useful in multimedia streaming applications,where it could help to increase the network throughput and data persistence in Mobile Ad hoc NETwork(MANET).Slidingwindow Network Coding is a variation of NC that is an addition to multimedia streaming and improves the data delay on MANETs.In this paper,we propose a Sliding Window Network Coding for free Viewpoint Multimedia streaming in MANETs(SWNC-VM).SWNC-VM preserves the degree distribution of the encoded packets through the recombination at the nodes.SWNC-VM enables to control the decoding complexity of each sliding-window independently from the packets received and recover the original data.The performance of the SWNCVM is studied using NS2 and evaluated in terms of the encoding overhead,decoding delay,packet loss probability and throughput when a packet is transmitted.The simulations result shows that the SWNC-VM with our proposition can significantly improve the network throughput and encoding efficiency.
29.Research on the Localization of High-Quality Teaching Resources
- Deng Hong1;Haoliang Qi1;Leilei Kong1;Changwei Wu1;An Bo1;
- 《国际计算机前沿大会会议论文集》 | 2016年第2期
摘要：The emergence of the MOOC leads to the revolution of education.All people can be taught by excellent teachers.But the interest of most of the MOOC learners only last less than two weeks.The main reasons may be the courses too difficult to study and without the restraint mechanism.This paper tries to solve the problem that learners cannot last in a MOOC.We put forward that it is feasible to bring the MOOC of elite courses to general university classroom after the localization of high quality teaching resources.We imported Information and Entropy of MIT to the curriculum of Heilongjiang Institute of Technology.First,we combine with own school characteristics in Information and Entropy.Second,we brought part of Information and Entropy in our class and edit of the original video in some part.Third,we have a test in the end of the course.At last all of students of class finished the instructional video and got good grades in test.The method of bringing the MOOC of elite courses to the curriculum of general university outperforms that of traditional teaching meth.
30.Thread Structure Prediction for MOOC Discussion Forum
- Chengjie Sun1;Shang-wen Li2;Lei Lin1;
- 《国际计算机前沿大会会议论文集》 | 2016年第2期
摘要：Discussion forums are an indispensable interactive component for Massive Open Online Courses(MOOC).However,the organization of current discussion forums is not well-designed.Trouble-shooting threads are valuable for both learners and instructors,but they are drowned out in the forums with huge amounts of threads.This work first built a labeled data set for trouble-shooting thread structure prediction by crowdsourcing and then proposed methods for trouble-shooting thread detection and thread structure prediction on the data set.The output of this work can be used to spot trouble-shooting threads and show them along with structure tags in MOOC discussion forums.

国际计算机前沿大会会议论文集的期刊信息

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