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检索条件"任意字段=9th International Conference on Predictive Models in Software Engineering, PROMISE 2013"

共 124 条记录，以下是41-50 订阅

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the Internal Fault Detection of Processor Based on Bayesian Network

The Internal Fault Detection of Processor Based on Bayesian ...

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Zongzhe Dai Yuzhuo Fu Ting Liu Hongjun You School of Electronic Information and Electrical Engineering Shanghai JiaoTong University Shanghai China Key Laboratory of Intelligent Computing Technology (SAST) Shanghai Aerospace Electronic Technology Institute Shanghai Shanghai China

ISBN: (纸本)9781538665664;9781538665657

After the fault is injected, different failure models are presented inside the processor. this paper proposes an automatic method that can inject fault in the processor and collect time-series data on the Simics simulation platform. At the same time, combined with relevant standards, each sample is labeled by data cleaning method. Finally, the Bayesian network structure and parameter learning are used to take a cascading analysis of the key registers inside of the processor, and detect the fault type at the real time.

关键词： Registers Benchmark testing Linux Bayes methods predictive models Timing Analytical models

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Enhancing Model Performance of Person Re-Indentification on Unknown Target Domain

Enhancing Model Performance of Person Re-Indentification on ...

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Rongsen Xu Yuzhuo Fu Ting Liu Suncheng Xiang School of Electronic Information and Electrical Engineering Shanghai JiaoTong University Shanghai China

ISBN: (纸本)9781538665664;9781538665657

Person re-identification(ReID) is the task that aims at retrieving the same person from the images taken across different cameras. Benefiting from the improvement of deep learning algorithms and the appearance of large datasets, the performance of ReID models has been greatly improved. However, most ReID models focus on a single dataset and their performance will drop dramatically when the train-set and test-set are from different datasets. To improve the generalization ability of the ReID model, this paper proposes a method that takes the advantage of triplet loss and multi-dataset training. And the experiment results show that this method can enhance the model performace in cross dataset usage.

关键词： Training Data models Measurement Task analysis predictive models Cameras Feature extraction

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Solar Power Generation Forecast Based on LSTM

Solar Power Generation Forecast Based on LSTM

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Jinxia Zhang Yuanying Chi Linpeng Xiao Beijing Advanced Innovation Center for Future Internet Technology Beijing University of Technology Beijing China School of Economics and Management Beijing University of Technology Beijing China Beijing Kedong Power Control System Co Ltd Beijing China

ISBN: (纸本)9781538665664;9781538665657

Photovoltaic power generation is an effective way to use solar energy, which is a recognized ideal renewable energy source. However, photovoltaic that is susceptible to weather conditions is unstable, and will adversely affect the power grid. therefore, it is necessary to improve the accuracy of solar power generation. this paper uses the LSTM model to predict solar power generation. At the same time, the data is reduced by using PCA to reduce the training duration of the model and improve the generalization ability of the model. Compared with other models, simulation experiment shows that the LSTM model is better.

关键词： predictive models Photovoltaic systems Data models Principal component analysis Artificial neural networks

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Learning Sentence Embeddings Based on Weighted Contexts from Unlabelled Data

Learning Sentence Embeddings Based on Weighted Contexts from...

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Yixin Ding Liutong Xu School of Computer Science Beijing University of Posts Telecommunications Beijing China

ISBN: (纸本)9781538665664;9781538665657

Supervised learning and unsupervised learning are mainstream methods to solve semantic textual similarity tasks. However, it is obvious that supervised learning needs substantial labeled data which is hard to obtain in reality. therefore, we turn our attention to construct sentence embeddings using unlabelled data due to lack of annotated data and success of unsupervised word embeddings in multiple tasks. We present a simple but efficient unsupervised learning method of sentence embeddings inspired by attention mechanism, in which weighted contexts are added to models to train distributed sentence representations inspired by word2vec. Our method outperforms state-of-the-art unsupervised models on semantic textual similarity tasks.

关键词： Semantics Training Task analysis Context modeling predictive models Unsupervised learning Computational modeling

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EKF-Based Position Prediction and Evaluation Toward Space-Based Moving Target Search

EKF-Based Position Prediction and Evaluation Toward Space-Ba...

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Hao Chen Peng Wu Juan Meng University of Chinese Academy of Sciences Beijing China Key Laboratory of Space Utilization Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538665664;9781538665657

Obtaining position prediction information is the precondition to launch space-based moving target search problem. In this paper, the EKF-based position prediction and accuracy evaluation method of moving target of sea surface is constructed to deal with the problem. the results show that the method is reliable and suitable, which lays the foundation for satellites to make search plans.

关键词： predictive models Covariance matrices Position measurement Satellites Jacobian matrices Sea measurements Measurement uncertainty

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A Neural Language Model with a Modified Attention Mechanism for software Code

A Neural Language Model with a Modified Attention Mechanism ...

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Xian Zhang Kerong Ben Department of Computer and Data Engineering Naval University of Engineering Wuhan Hubei Province China

ISBN: (纸本)9781538665664;9781538665657

the language model, which has its roots in statistical natural language processing, has been shown to successfully capture the predictable regularities of source code, and help with many software tasks, such as code suggestion, code porting, and bug detection. However, learning long-range dependencies is still a big challenge at present for modeling programming language. In this paper, we explore deep learning techniques to strengthen it, with a novel neural language model for software code (NLM4Code) proposed. this model is mainly constructed by a recurrent neural network and augmented with a modified key-value-predict attention mechanism, which can effectively facilitate learning context dependencies of source code in a simpler way. To evaluate the effectiveness, five language models are selected as the baselines on a dataset of 2.03M lines of Java code. the experimental results show NLM4Code is much better than traditional n-gram models and outperforms a state-of-the-art method by 3.12% in perplexity index.

关键词： software predictive models Computer architecture Training Task analysis Computer languages Semantics

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Microblog Emotional Analysis Based on TF-IWF Weighted Word2vec Model

Microblog Emotional Analysis Based on TF-IWF Weighted Word2v...

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Hao Tian Liuai Wu Department of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou Gansu Province China

ISBN: (纸本)9781538665664;9781538665657

In order to solve the problem of ignoring the importance of words and missing semantic relations between words in the emotional analysis of microblog, the TF-IWF weighted Word2vec model was proposed as a feature extraction method, and then use Support Vector Machine (SVM) to obtain more accurate results. Firstly, TF-IWF is used to calculate the word weight. then, Word2vec is used to calculate the words vector, and the weighted word vector is obtained by combining them. Finally, the data is trained and classified through SVM. Experimental results show that compared with the original TF-IWF classification method and Word2vec classification method, the precision and recall of this method are improved.

关键词： Analytical models Semantics Support vector machines Dictionaries Computers Context modeling predictive models

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Chinese Text Sentiment Analysis Based on Improved Convolutional Neural Networks

Chinese Text Sentiment Analysis Based on Improved Convolutio...

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Xing Lin Chunyan Han Software Engineering Northeastern University Shenyang Liaoning Province China

ISBN: (纸本)9781538665664;9781538665657

Aiming at the shortcomings of the existing depth learning methods in text sentiment classification task, a Chinese text emotion classification algorithm with three-channel convolution neural network is proposed. the textual Pinyin is proposed as the corpus of feature extraction for the first time. the PCCNN (Phoneticize Channel Convolutional Neural Network)model is trained by extracting Pinyin char features and traditional word and char features, which improves the model's ability to extract features and improves the classification performance of the model. Experiments show that the performance of the algorithm is not only easier to implement than the traditional algorithm, but also the classification performance is more accurate. At the same time, the sentiment of each category is strictly superior to the single-channel convolutional neural network algorithm.

关键词： Classification algorithms Support vector machines Feature extraction Sentiment analysis Training Convolution predictive models

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A Network Traffic Prediction Model Based on Wavelet Transformation and LSTM Network

A Network Traffic Prediction Model Based on Wavelet Transfor...

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Haipeng Lu Fan Yang Beijing Advanced Innovation Center for Future Internet Technology Beijing University of Technology Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781538665664;9781538665657

Network traffic has apparent characteristic of burst. the time series of it presents nonlinear. It is difficult for traditional linear method to predict it accurately. To solve the problem, this paper proposes to decompose the original traffic to an approximation sequence and several detail sequences with the method of wavelet transformation. On this basis, the change trend of traffic is learned by LSTM network and the burst information is extracted at multiscale to complete the prediction of future traffic. the experimental results show that for the prediction error, the model constructed with LSTM network is superior to the models constructed with LSSVM, BP neural network and Elman neural network. In addition, the model proposed in this paper performs better than the ordinary LSTM network model for predicting the burst of traffic.

关键词： predictive models Telecommunication traffic Wavelet transforms Logic gates Support vector machines Biological neural networks

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High-Confidence Discrimination via Maximum Response for Object Tracking

High-Confidence Discrimination via Maximum Response for Obje...

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IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Donglan Cai Li Yu Yun Gao School of Information Science and Engineering Yunnan University Kunming China

ISBN: (纸本)9781538665664;9781538665657

Staple combines the correlation filter with the color probability model have recently achieved excellent performance. However, the model drift caused by the frame-by-frame update strategy also greatly affects the tracking robustness. In addition, we founded that the sparse model update strategy can effectively avoid the model drift with improving the speed and robustness. In this paper, we proposed a high-confidence discrimination via maximum response for object tracking. First, the confidence of the tracking results was discriminated based on the maximum response values in a period. Second, object model was updated based on the tracked result which the highest-confidence in a period. the experimental results showed that the proposed algorithm outperformed the far more sophisticated trackers when dealing with occlusion and severe deformation. the AVe on the OTBIOO benchmark is 0.633 and the speed reaches 85 fps, which is higher than Staple AVe 9%.

关键词： Target tracking predictive models Correlation Strain Computational modeling Benchmark testing

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