National Computer System Engineering Research Institute of China

Beijing, China

National Computer System Engineering Research Institute of China

Beijing, China

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Xu X.,National Computer System Engineering Research Institute of China | Kang W.,National Computer System Engineering Research Institute of China | Fang Z.,National Computer System Engineering Research Institute of China | Sun B.,National Computer System Engineering Research Institute of China | Wang Y.,National Computer System Engineering Research Institute of China
International Journal of Computational Science and Engineering | Year: 2014

With the development of information technology and industrial automation, industrial control systems have become increasingly complicated which increases the difficulty to detect running errors. Errors usually lead to industry incidences, which in turn threaten civil property and even national economy. In this paper, we propose a novel safety mechanism based on data mining. Complex network analysis, data prediction, expert analysis and self-adaptive downgrade and upgrade techniques are also applied to solve safety problems in industrial control systems. To protect industrial control system, we define a safety protection status. The system can be switched into protection status by specific operations, which finally transfer the system back to safe status. Using data mining techniques, we propose a general safety mechanism which has four operations, including global safety, initiative safety, real-time safety, and self safety.Copyright © 2014 Inderscience Enterprises Ltd.


Liu J.,Key Laboratory of Data Engineering and Knowledge Engineering | Liu J.,Renmin University of China | Liu J.,National Computer System Engineering Research Institute of China | Chai Y.,Key Laboratory of Data Engineering and Knowledge Engineering | And 3 more authors.
IEEE Symposium on Mass Storage Systems and Technologies | Year: 2014

Data deduplication techniques improve cost efficiency by dramatically reducing space needs of storage systems. SSD-based data cache has been adopted to remedy the declining I/O performance induced by deduplication operations in the latency-sensitive primary storage. Unfortunately, frequent data updates caused by classical cache algorithms (e.g., FIFO, LRU, and LFU) inevitably slow down SSDs' I/O processing speed while significantly shortening SSDs' lifetime. To address this problem, we propose a new approach - PLC-Cache - to greatly improve the I/O performance as well as write durability of SSDs. PLC-Cache is conducive to amplifying the proportion of the Popular and Long-term Cached (PLC) data, which is infrequently written and kept in SSD cache in a long time period to catalyze cache hits, in an entire SSD written data set. PLC-Cache advocates a two-phase approach. First, non-popular data are ruled out from being written into SSDs. Second, PLC-Cache makes an effort to convert SSD written data into PLC-data as much as possible. Our experimental results based on a practical deduplication system indicate that compared with the existing caching schemes, PLC-Cache shortens data access latency by an average of 23.4%. Importantly, PLC-Cache improves the lifetime of SSD-based caches by reducing the amount of data written to SSDs by a factor of 15.7. © 2014 IEEE.


Li J.,CAS Beijing Institute of Acoustics | Xu Y.,CAS Beijing Institute of Acoustics | Xiong H.,CAS Beijing Institute of Acoustics | Wang Y.,National Computer System Engineering Research Institute of China
Proceedings - 2010 IEEE 2nd Symposium on Web Society, SWS 2010 | Year: 2010

Recently, much work have been done on text emotion classification. However, they mainly focused on the emotions expressed by authors instead of the readers. In addition, researches on simplified Chinese text emotion classification are extremely less. In this paper, we proposed a simplified Chinese text emotion classification based on readers' emotions. Mass of documents with readers' emotion tag are used as raw text sets, and Vector Space Model is used to represent each document. An emotion dictionary is created semi-automatically by using WordNet to build text vectors. We then train a Support Vector Machine classifier on preprocessed data with four emotion classes, and compared the predicate results with that from Naive Bayes classifier. Experiment results indicate that our approach performs much better on classify accuracy and efficiency. © 2010 IEEE.


Ning Y.,University of Chinese Academy of Sciences | Zhu T.,University of Chinese Academy of Sciences | Wang Y.,National Computer System Engineering Research Institute of China
ICPCA10 - 5th International Conference on Pervasive Computing and Applications | Year: 2010

When browsing news on the web, various emotions may be evoked in readers and furthermore cause different influence on their minds and life. We expect that emotional analysis and classification of text may provide good performance and significance to users surfing the Internet. Most previous research only focus on bi-emotion classification, that is, Positive and Negative, e.g., identifying whether a comment is for praising or criticizing. In this paper, we propose a χ2-based Chinese text emotion classification with five sentiment categories. We run two experiments, one uses sentiment words extracted from How Net and a Chinese thesaurus: TongYiCi CiLin, and the other is not. The results shows that adding affective words can make better prediction in the sentiment classification. ©2010 IEEE.


Fang Z.,National Computer System Engineering Research Institute of China | Ning Y.,University of Chinese Academy of Sciences | Zhu T.,University of Chinese Academy of Sciences
ICPCA10 - 5th International Conference on Pervasive Computing and Applications | Year: 2010

Internet is becoming an increasingly important platform for ordinary life and work. It is expected that keyword extraction can help people quickly find hot spots on the web, since keywords in a document provide important information about the content of the document. In this paper, we propose to use text clustering method based on semi-supervised learning to get focuses of social topics in a large amount of text. We develop a novel keyword extraction method named NATF-PDF, which is based on TFPDF algorithm, combined with supervised learning theory for keyword extraction. We compare its performance with TFIDF in comparison, and the results show that our method get better accuracy and recall ratio. ©2010 IEEE.


Zhang R.,National Computer System Engineering Research Institute of China | Sun B.,National Computer System Engineering Research Institute of China | Kang W.,National Computer System Engineering Research Institute of China | Zhu T.,University of Chinese Academy of Sciences
ICPCA10 - 5th International Conference on Pervasive Computing and Applications | Year: 2010

Nowadays, the development and application of the recommender system has grown greatly to cope with information overloading. Meanwhile, social networks come into being and become more and more popular. In this paper, a recommendation model based on social networks is proposed which can collect the users prǒle from the feedback and system log, then set up the social networks. According to the input keywords and types of recommender, more recommendation information can be generated. This model has been implemented as a recommendation module in an academic search system Gloss, deployed at the WSI Laboratory of Graduate University of Chinese Academy of Sciences. ©2010 IEEE.


Cui L.,CAS Institute of Psychology | Cui L.,National Computer System Engineering Research Institute of China | Li S.,CAS Institute of Psychology | Li S.,National Computer System Engineering Research Institute of China | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

Emotion identification, which aims to determine a person’s affective state automatically, has immense potential value in many areas, such as action tendency, health care, psychological detection and human-computer (robot) interaction. In this paper, we propose a novel method for identifying emotion from natural walking. After obtaining the three-axis acceleration data of wrist and ankle recorded by smartphone, we run a moving average filter window with different length w, then cut actual data into slices. 114 features are extracted from each slice, and Principal Component Analysis (PCA) is used for feature selection. We train SVM, Decision Tree, Multilayerperception, Random Tree and Random Forest classifiers, and compare the accuracy of emotion identification using different datasets (wrist vs. ankle) on different models. Results show that acceleration data from ankle has better performance in emotion identification than wrist. Among these models, SVM has the highest accuracy of 90.31 % for identifying anger vs. neutral, 89.76 % for happy vs. neutral, and 87.10 % for anger vs. happy. The model for identifying anger/neutral/happy yields the best accuracy of 85 %-78 %-78 %. The results show that we could identify people’s emotional states through the gait of walking with high accuracy. © Springer International Publishing Switzerland 2016.


Gao R.,University of Chinese Academy of Sciences | Hao B.,University of Chinese Academy of Sciences | Li H.,National Computer System Engineering Research Institute of China | Gao Y.,University of Chinese Academy of Sciences | Zhu T.,University of Chinese Academy of Sciences
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

The words that people use could reveal their emotional states, intentions, thinking styles, individual differences, etc. LIWC (Linguistic Inquiry and Word Count) has been widely used for psychological text analysis, and its dictionary is the core. The Traditional Chinese version of LIWC dictionary has been released, which is a translation of LIWC English dictionary. However, Simplified Chinese which is the world's most widely used language has subtle differences with Traditional Chinese. Furthermore, both English LIWC dictionary and Traditional Chinese version dictionary were both developed for relatively formal text. Microblog has become more and more popular in China nowadays. Original LIWC dictionaries take less consideration on microblog popular words, which makes it less applicable for text analysis on microblog. In this study, a Simplified Chinese LIWC dictionary is established according to LIWC categories. After translating Traditional Chinese dictionary into Simplified Chinese, five thousand words most frequently used in microblog are added into the dictionary. Four graduate students of psychology rated whether each word belonged in a category. The reliability and validity of Simplified Chinese LIWC dictionary were tested by these four judges. This new dictionary could contribute to all the text analysis on microblog in future. © Springer International Publishing 2013.


PubMed | National Computer System Engineering Research Institute of China, Hubei University of Economics and CAS Institute of Psychology
Type: Journal Article | Journal: PloS one | Year: 2016

The increasing need of automated analyzing web texts especially the short texts on Social Network Services (SNS) brings new demands of computerized text analysis instruments. The psychometric properties are the basis of the extensive use of these instruments such as the Linguistic Inquiry and Word Count (LIWC). For this study, Sina Weibo statuses were analyzed via rater coding and Simplified Chinese version of LIWC (SCLIWC), in order to evaluate the validity of SCLIWC in detecting psychological expressions in Weibo statuses (n = 60) and in identifying the psychological meaning of a single Weibo status (n = 11). Significant correlations between human ratings and SCLIWC scores and the high sensitivities of capturing single statuses with certain expressions identified by raters, proved the validity of SCLIWC in detecting psychological expressions. The results also suggested that, the efficiency of SCLIWC in detecting psychological expressions of SNS short texts could be higher if using status count scoring method, rather than the word count method as the common usage of LIWC. However, SCLIWC may not perform well in identifying the psychological meaning of a single piece of SNS short text because of its over-identification of target expressions. This study provided primary evidence of validity of SCLIWC, as well as the proper way of using it efficiently on SNS short texts.


Hu Y.,Anhui University of Science and Technology | Guo X.,National Computer System Engineering Research Institute of China | Zhu J.,National Computer System Engineering Research Institute of China
Proceedings of 2011 International Conference on Electronic and Mechanical Engineering and Information Technology, EMEIT 2011 | Year: 2011

A RFID tag based wireless sensor test platform is proposed in this paper, the system build a real-time monitoring of sensor network platform by using RFID tag and WSN technology. By the combination of the wireless sensor nodes, and RFID technology, and other basic technology of things, the building of an intelligent wireless sensor monitoring platform is proposed, which can not only expand the wireless sensor network are aware of the depth and breadth of information, but also the real-time monitoring and positioning quality of the items can be achieved. In this paper, the overall design of the system is proposed and the system routing and positioning protocol is analyzed, the core technologies in design are discussed, and the system is a overall solution for comprehensive RFID-based wireless sensor network to monitor. © 2011 IEEE.

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