2017 IEEE International Conference on Computer and Information Technology (CIT)最新文献_第3页

Predicting Student Performance in an ITS Using Task-Driven Features 使用任务驱动特征预测ITS学生表现

2017 IEEE International Conference on Computer and Information Technology (CIT) Pub Date : 2017-08-01 DOI: 10.1109/CIT.2017.34

Ritu Chaturvedi, C. Ezeife

{"title":"Predicting Student Performance in an ITS Using Task-Driven Features","authors":"Ritu Chaturvedi, C. Ezeife","doi":"10.1109/CIT.2017.34","DOIUrl":"https://doi.org/10.1109/CIT.2017.34","url":null,"abstract":"Intelligent Tutoring Systems (ITS) are typically designed to offer one-on-one tutoring on a subject to students in an adaptive way so that students can learn the subject at their own pace. The ability to predict student performance enables an ITS to make informed decisions towards meeting the individual needs of students. It is also useful for ITS designers to validate if students are actually able to succeed in learning the subject. Predicting student performance is a function of two complex and dynamic factors: (f1) student learning behavior and (f2) their current knowledge in the subject. Learning behavior is captured from student interaction with the ITS (e.g. time spent on an assigned task) and is stored in the form of web logs. Student knowledge in the subject is represented by the marks they score in assigned tasks and is stored in a specific component of the ITS called student model. In order to build an accurate prediction model, this raw data from student model and web logs must be engineered carefully and transformed into meaningful features. Existing systems such as LON-CAPA predict students performance using their learning behavior alone, without considering their (current) knowledge on the subject. Lack of proper feature engineering is evident from the low values of accuracy of their prediction models. This research proposes a highly accurate model that predicts student success in assigned tasks with a 96% accuracy by using features that are better informed not only about students in terms of the two factors f1 and f2 mentioned above, but also on the assigned task itself (e.g. task's difficulty level). In order to accomplish this, an Example Recommendation System (ERS) is designed with a fine-grained student model (to represent student data) and a fine-grained domain model (to represent domain resources such as tasks).","PeriodicalId":378423,"journal":{"name":"2017 IEEE International Conference on Computer and Information Technology (CIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124391847","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: 9

The Enumeration of Flippable Edges in Maximal Planar Graphs 极大平面图中可翻转边的枚举

2017 IEEE International Conference on Computer and Information Technology (CIT) Pub Date : 2017-08-01 DOI: 10.1109/CIT.2017.44

Dongyan Zhao, Yangyang Zhou, Jin Xu

引用次数: 0

Changing for Win-Win: Change Management on Global Value Chain Optimization 变革共赢:全球价值链优化中的变革管理

2017 IEEE International Conference on Computer and Information Technology (CIT) Pub Date : 2017-08-01 DOI: 10.1109/CIT.2017.25

G. Xiong, T. Nyberg, Gang-Yu Xiong

{"title":"Changing for Win-Win: Change Management on Global Value Chain Optimization","authors":"G. Xiong, T. Nyberg, Gang-Yu Xiong","doi":"10.1109/CIT.2017.25","DOIUrl":"https://doi.org/10.1109/CIT.2017.25","url":null,"abstract":"A GVC (Global Value Chain) optimization requires appropriate approach to make it happen, however, a good approach is not enough when the optimization requires a big changing throughout the chain. It is very important to have a methodology of change management during change processes for GVC optimization. This paper describes the principle of change management, and illustrates a case study on how to apply concepts of change management to obtain successful change for GVC project's achievement. Firstly, a brief literature review relevant to change management in a business environment is introduced. Some general methodologies and tools to make a change effectively are explained. A general GVC approach is reviewed. Next a case study of change management based on a GVC project is introduced, which conducts for three partners along the chain. Then, several critical success factors for buy-in changing are identified, which support the relevant people or group to accept the changing solutions. Then, actual result shows a success of buy-in changing throughout the GVC from the upstream suppliers to downstream customers. Obviously, the successful change management helps the GVC optimization project to make a win-win scenarios for all partners. Finally, the case study's findings are in line with in the field, the method of change management on a GVC project is concluded.","PeriodicalId":378423,"journal":{"name":"2017 IEEE International Conference on Computer and Information Technology (CIT)","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133177392","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: 2

A Software-Hardware Co-designed Methodology for Efficient Thread Level Speculation 一种高效线程级推测的软硬件协同设计方法

2017 IEEE International Conference on Computer and Information Technology (CIT) Pub Date : 2017-08-01 DOI: 10.1109/CIT.2017.49

Qiong Wang, Jialong Wang, Li Shen, Zhiying Wang

引用次数: 3

A Sensitivity Approach to Energy-Efficient Mapping of Dependable Virtual Networks 可靠虚拟网络节能映射的灵敏度方法

2017 IEEE International Conference on Computer and Information Technology (CIT) Pub Date : 2017-08-01 DOI: 10.1109/CIT.2017.47

V. Cavalcanti, Eduado Antonio Guimaraes Tavares, Meuse Nogueira de O. Junior, Dennys Azevedo, Jonas Pontes

引用次数: 0

Sentiment Classification: Feature Selection Based Approaches Versus Deep Learning 情感分类:基于特征选择的方法与深度学习

2017 IEEE International Conference on Computer and Information Technology (CIT) Pub Date : 2017-08-01 DOI: 10.1109/CIT.2017.53

A. Uysal, Y. Murphey

{"title":"Sentiment Classification: Feature Selection Based Approaches Versus Deep Learning","authors":"A. Uysal, Y. Murphey","doi":"10.1109/CIT.2017.53","DOIUrl":"https://doi.org/10.1109/CIT.2017.53","url":null,"abstract":"Classification of text documents is commonly carried out using various models of bag-of-words that are generated using feature selection methods. In these models, selected features are used as input to well-known classifiers such as Support Vector Machines (SVM) and neural networks. In recent years, a technique called word embeddings has been developed for text mining and, deep learning models using word embeddings have become popular for sentiment classification. However, there is no extensive study has been conducted to compare these approaches for sentiment classification. In this paper, we present an in-depth comparative study on these two types of approaches, feature selection based approaches and and deep learning models for document-level sentiment classification. Experiments were conducted using four datasets with varying characteristics. In order to investigate the effectiveness of word embeddings features, feature sets including combination of selected bag-of-words features and averaged word embedding features were used in sentiment classification. For analyzing deep learning models, we implemented three different deep learning architecture, convolutional neural network, long short-term memory network, and long-term recurrent convolutional network. Our experimental results show that that deep learning models performed better on three out of the four datasets, a combination of selected bag-of-words features and averaged word embedding features gave the best performance on one dataset. In addition, we will show that a deep learning model initialized with either one-hot vectors or fine-tuned word embeddings performed better than the model initialized using than word embeddings without tuning.","PeriodicalId":378423,"journal":{"name":"2017 IEEE International Conference on Computer and Information Technology (CIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133095794","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: 36

Cooperative Multi-relay Assisted Multicast Beamforming in Wirelessly Powered Communications 无线供电通信中的协同多中继辅助多播波束形成

2017 IEEE International Conference on Computer and Information Technology (CIT) Pub Date : 2017-08-01 DOI: 10.1109/CIT.2017.62

Jiachen Li, Yue Mi, Shimin Gong, Jing Xu, Xiaoxia Huang, Yanyan Shen

{"title":"Cooperative Multi-relay Assisted Multicast Beamforming in Wirelessly Powered Communications","authors":"Jiachen Li, Yue Mi, Shimin Gong, Jing Xu, Xiaoxia Huang, Yanyan Shen","doi":"10.1109/CIT.2017.62","DOIUrl":"https://doi.org/10.1109/CIT.2017.62","url":null,"abstract":"This paper studied downlink multicasting in a wirelessly powered communication network, where multiple energy-constrained relay nodes collaboratively assist the transmissions from a multi-antenna hybrid access point (HAP) to multiple receivers in the downlink. Each relay is equipped with a single antenna and capable of energy harvesting (EH) from the HAP's RF signals in a power splitting protocol. Due to the confliction between EH and information transmission at the relays, they need to optimally control the power splitting ratio, according to the relays' channel conditions and energy status, with the aim of improving the signal-to-noise ratio (SNR) at all receivers. We propose a SNR maximization problem by jointly optimizing the HAP's signal beamforming and the relays' EH parameters. Though it is non-convex and difficult to solve optimally, we design an iterative algorithm by efficiently solving semi-definite programs successively. Simulation results show the efficacy of the proposed algorithm and validate our findings that relays will choose not to broadcast signal when its channel condition is poor.","PeriodicalId":378423,"journal":{"name":"2017 IEEE International Conference on Computer and Information Technology (CIT)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114161900","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

Indoor Localization Using Ambient FM Radio RSS Fingerprinting: A 9-Month Study 利用环境调频无线电RSS指纹进行室内定位:一项为期9个月的研究

2017 IEEE International Conference on Computer and Information Technology (CIT) Pub Date : 2017-08-01 DOI: 10.1109/CIT.2017.57

A. Popleteev

引用次数: 14

A Multi-objective Metaheuristic Approach to Search-Based Stress Testing 基于搜索的压力测试多目标元启发式方法

2017 IEEE International Conference on Computer and Information Technology (CIT) Pub Date : 2017-08-01 DOI: 10.1109/CIT.2017.19

N. Gois, Pedro Porfírio Muniz de Farias, André L. V. Coelho

引用次数: 0

Dynamic MAC Protocol for Tactical Data Links 战术数据链路的动态MAC协议

2017 IEEE International Conference on Computer and Information Technology (CIT) Pub Date : 2017-08-01 DOI: 10.1109/CIT.2017.43

Zhe Guo, Zheng Yan

引用次数: 2