A Novel Algorithm for Generating pseudo Random Number- - Gangyi Hu; Weili Kou; Sumeth Yuenyong; Jian Qu (Journal of Digital Information Management, 2020)
This paper proposes a pseudo random number generation algorithm based on cellular neural networks. It used the hyper-chaos characteristics of the cellular neural networks and sets the appropriate parameters to generate the pseudo random number. The experimental results show that, compared with other similar algorithms, this algorithm has the characteristics of simple operation, low complexity, large key space, and good randomness. It can meet the needs of secure communication and network information security, which has good application prospects.

A Noise-Robust Image Encryption Algorithm Based on Hyper Chaotic Cellular Neural Network - Gangyi Hu; Sumeth Yuenyong; Jian Qu; Jian Rong; Weili Kou (Journal of Digital Information Management, 2018)

A Noise-Robust Image Encryption Algorithm Based on Hyper Chaotic Cellular Neural Network- - Gangyi Hu; Sumeth Yuenyong; Jian Qu; Jian Rong; Weili Kou (Journal of Digital Information Management, 2018)
We propose an image encryption algorithm based on a 6-dimensional chaotic cellular neural network (CNN) that is robust to noise/missing pixels in the cipher image. We performed parameter search on the templates of the CNN in order to discover the parameters that leads to 6D chaotic evolution of the state, and then used the resulting chaotic sequence as the basis of encryption. The encryption process itself consists of shuffling the positions of image pixels based on the numerical value of the chaotic sequence; the second half of the encryption process consists of changing the shuffled image pixel values by performing XOR operation between the pixel values and the numerical value of the chaotic sequence. By using simple operations like sorting and XOR in the encryption process, the algorithm is robust to noise/ missing pixels in the cipher image. We illustrate this by comparing the robustness against 3 recently proposed chaos-based image encryption algorithms. The results show that our algorithm is competitive with the state-ofthe-art in term of encryption security, and superior in term of robustness.

Hot topic extraction and public opinion classification of tibetan texts - Xu, G.; Qiu, L.; Peng, X. (Journal of Digital Information Management, 2016)

Trajectory prediction of vessels based on data mining and machine learning - Qi, L.; Zheng, Z. (Journal of Digital Information Management, 2016)

Tibetan Word Sense Disambiguation Based on a Semantic knowledge Network Diagram - Qiu, L.; Jiang, X.; Ling, R. (Journal of Digital Information Management, 2015)

Analysis of information management and scheduling technology in hadoop - Weihua, M.; Hong, Z.; Qianmu, L.; Bin, X. (Journal of Digital Information Management, 2014)
Development of big data computing has brought many changes to society and social life is constantly digitized. 'How to handle vast amounts of data' has become a more and more fashionable topic. Hadoop is a distributed computing software framework, which includes HDFS and MapReduce distributed computing method, make distributed processing huge amounts of data possible. Then job scheduler determines the efficiency of Hadoop clusters and user experience. Under the premise of familiar with the mechanism of Hadoop's running tasks, make a full analysis of the existing Hadoop task scheduling algorithm, such as FIFO-Scheduler, Capacity-Scheduler, FairShare-Scheduler and LATE-Scheduler, found that the existing scheduling algorithms do not perceive the performance of the computing node, so that it cannot assign different tasks depending on the machine performance in heterogeneous Hadoop cluster.
Keywords: Cloud computing; Hadoop; Map Reduce; Task Scheduling

An indirect security trust method based on credibility evaluation - Xianghe, W.; Hong, Z.; Zhen, L.; Qianmu, L.; Bin, X. (Journal of Digital Information Management, 2014)
In existing trust models, the recommending services often provide a single trust value (computed by them during their interaction with the unknown entity in question) as a recommendation. However, a single trust value recommended by a recommender represents its subjective opinion about the unknown entity and cannot depict the real trust level very well under certain circumstances. To solve this problem, a fuzzy based credibility evaluation method for indirect trust computation is proposed. The calculation method cannot just incorporate a mechanism to determine the weight each valid recommendation should carry on aggregation process, but also distinguish between honest and dishonest recommendations in the meantime.
Keywords: Credibility Evaluation; Fuzzy based Evaluation; Indirect Recommendation; Reputation; Security Evaluation; Trust Mechanism

A SEIR model epidemic of virus on the online social network - Min, Y.; Qianmu, L.; Yaoliang, S. (Journal of Digital Information Management, 2014)
Vigorous development of computer technology has made computers play an integral role in people's lives, but at the same time propagation of the virus on a computer network also cost a great loss to the people. So the research on the mechanism of computer virus propagation and pointing out the key factors in the propagation of the virus has a significant influence on the prevention of computer virus and can also promote the development of relevant policies. The paper researched the influence of the user re-login frequency, the average number of friends on the user's friend list and the initial spread rate of the virus on the social networks during the epidemic of the propagation of the computer virus. By the means of mathematical analysis, an SEIR model to describe the epidemic of the virus on the online social network is established in this paper. The research shows that the re-login frequency of the user and the average friend number of the user would make a significant influence on the virus propagation. The two factors enhance the risk of a virus outbreak in the social network, at the same time.
Keywords: Complex network; Computer virus; Epidemic spreading; Social Network; Social networks; Transmission factor; Virus propagation

The analysis of network structure model and innovation network characteristics for industrial cluster: Using complicated network perspective - Qi, W. (Journal of Digital Information Management, 2013)
Industry cluster is a new feature under the background of globalization, informationization and integration. Along with the development of science and technology, the competition between industry clusters is gradually changed from the traditional cost competition into the innovation ability. The ability of innovation is not only concerned with single enterprise or institution's ablity but also concerned with the relationship built up by the enterprises in the industry cluster and the innovation environment around them. This article analyzed network structure and evolution of innovation networks on the basis of complicated network theory. According to the construction principle of complex network, we simulated the evolution process of innovation network structure of different types. And, we also analyzed the features and differences of two typical complex networks. The conclusion will provide suggestions for industry cluster to build up innovation system and upgrade the competition ability.
Keywords: Industry cluster; Innovation network; Network structure model

The statistical analysis of family names of donators for wenchuan - Yufan, L.; Qinghua, C. (Journal of Digital Information Management, 2013)
It is analyzed that the family names of personal donators who donated through China Construction Bank Corporation to Chinese Red Cross Foundation for the WenChuan earthquake. The distribution of family names, the first 100 family names and their shares, the probability of the same family names as well as the Gini coefficient are all given in this paper. A heavy disproportion is showed in the distribution, that is to say a small part of family names occupy the absolutely large proportion while most of them are rare. This character is similar to power-law that has been observed in many demonstrations about family names. However, the Kolmogorov-Smirnov test fails to support the hypothesis. Instead, part of the data shows an exponential characteristic. As an innovative sampling method, the discussion may help to know the family name's distribution of China.
Keywords: Distribution; Exponential; Powerlaw; The family name

A comparison of pattern classification approaches for structural damage identification - Qingqing, Z.; Qifeng, Z.; Yongpeng, N.; Fan, Y.; Jiayan, L.; Run, T.; Minda, L. (Journal of Digital Information Management, 2012)
A structural damage identification approach based on wavelet packet decomposition (WPD) and random forests (RF) was proposed and compared with other pattern classification approachs. The main procedure involves extracting energy features from vibration acceleration data through wavelet packet decomposition and then using these features as input for a RF classifier. The experiment was carried on an 8-storey steel shear building model in the case verification. The results show that the proposed method is effective for structure damage detection and can achieve a higher accuracy than other commonly used methods.
Keywords: Extraction algorithms; Pattern matching; Sensor identificaiton; Wavelet packet decomposition

Exploration in to reform in computer application technology major in higher vocational colleges based on tripartite linkage - Qiming, T. (Journal of Digital Information Management, 2012)
It has become an important subject to reform Computer Application Technology (CAT) major in higher vocational colleges against the backdrop of fierce competition. Firstly, this paper analyzes the prevailing problems in this major and their causes. Secondly, this paper proposes that reform in CAT major can only be realized based on the tripartite linkage of industry, enterprise and college. Thirdly, this paper further introduces the channel for major orientation, course system development and teaching staff building with the mechanism of tripartite linkage.
Keywords: Computer application technology major; Course system development; Higher vocational education; Tripartite linkage

Swarm-robot system based on wireless sensor network - Peng, J.-S.; Li, X.; Qin, Z.-Q.; Li, W. (Journal of Digital Information Management, 2012)
The study about the swarm-robot system is one of the most important directions of multitudinous robots system. It is an extraordinary challenged issue which how to communicate with each swarm-robot to cooperate together to accomplish a mission in numerous units. The swarm-robot system, which is based on CC2510 wireless sensor network, with the Radio Frequency of FHSS as the communication mode, has realized the swarm-robot unit sensor organ information coordination alternately fleet that improving the stability, interference immunity and transferability. The coordinating scheme possesses availability and practicability has been proved.
Cited By (since 1996):4Export Date: 10 July 2014
Keywords: CC2510; Coordination; FHSS; Swarm-robot

A taxonomic relationship learning approach for log ontology content event - Ming, S.; Qinghong, S.; Bo, C. (Journal of Digital Information Management, 2012)
To construct the log ontology is one of the main tasks of semantic Web usage mining. In order to discover the hierarchy of users' visit interesting for web sites, we propose a taxonomic relationship learning approach for content events on log ontology. In this method, event is used to express the users' visiting action, and content event is the semantic behavior of users' visiting to the page content of sites. This method extracts the taxonomic relationship of content event by web document cluster based on swarm intelligence, which combines web content mining and web usage mining. This method improves the results of semantic Web usage mining and provides more decision-making for optimizing the structure of Web sites. The simulation experimental results show that this method is effective and quite feasible to solve practical problems.
Keywords: Content event taxonomic relationship; Log ontology; Semantic web mining; Swarm intelligence

Word combination kernel for text categorization - Zhang, L.; Hu, X.; Qin, S. (Journal of Digital Information Management, 2012)
We proposed a novel kernel for text categorization. This kernel is an inner product in the feature space generated by all word combinations of specified length. A word combination is a collection of different words co-occurring in the same sentence. The word combination of length k is weighted by the k-th root of the product of the inverse document frequencies (IDF) of its words. A computationally simple and efficient algorithm was proposed to calculate this kernel. By restricting the words of a word combination to the same sentence and considering multi-word combinations, the word combination features can capture similarity at a more specific level than single words. By discarding word order, the word combination features are more compatible with the flexibility of natural language and the dimensionality this kernel can be reduced significantly compared to the word-sequence kernel. We conducted a series of experiments on the Reuters-21578 dataset and 20 Newsgroups dataset. This kernel consistently achieves better performance than the classical word kernel and word-sequence kernel on the two datasets. We also assessed the impact of word combination length on performance and compared the computing efficiency of this kernel to those of the word kernel and word-sequence kernel.
Keywords: Kernel methods; Machine learning; Support vector machine; Text classification; Word-combination kernel

Towards building ranking models with annual reports - Qiu, X.Y. (Journal of Digital Information Management, 2010)
The textual content of company annual reports has proven to contain predictive indicators for the company future performance. This paper addresses the general re search question of evaluating the effectiveness of applying machine learning and text mining techniques to building predictive models with annual reports. More specifically, we focus on these two questions: 1) the feasibility of build ing ranking models with annual reports to rank future firm performance and 2) the effect of integrating meta seman tic features to help improve and support our prediction. We compare models built with different ranking algorithms and document models. We evaluate our models with a simulated portfolio. Our results show significantly positive average returns over 5 years with a power law trend as we increase the ranking threshold. Adding meta features to document model has shown to improve ranking performance. The SVR & Meta-augemented model outperforms the others and pro vides potential for explaining the textual factors behind the prediction.
Cited By (since 1996):2Export Date: 10 July 2014
Keywords: Annual reports; Documentation models; Information retrieval; Ranking models

A query answering surveillance system for detecting and tracking moving objects using bounding rectangles - Sleit, A.; Almobaideen, W.; Qatawneh, M.; Barakat, A.; Saadeh, H. (Journal of Digital Information Management, 2009)
In many computer vision applications such as surveillance systems, detecting moving objects is the first step of information extraction. This paper introduces a query answering system that aims at responding to user queries without the need to scan the whole video tape. the proposed system is based on finding the minimum boundary rectangles (MBRs) of moving objects which appear in the video. It detects moving objects in the video frame set, keeps track of those objects, and saves information to answer future queries.
Keywords: Background subtraction; MBR; Object tracking; Rgb difference; Surveillance system

Anomaly detection through quasi-functional dependency analysis - Bruno, G.; Garza, P.; Quintarelli, E.; Rossato, R. (Journal of Digital Information Management, 2007)
Anomaly detection problems have been investigated in several research areas such as database, machine learning, knowledge discovery, and logic programming, with the main goal of identifying objects of a given population whose behavior is anomalous with respect to a set of commonly accepted rules that are part of the knowledge base. In this paper we focus our attention on the analysis of anomaly detection in databases. We propose a method, based on data mining algorithms, which allows one to infer the "normal behavior" of objects, by extracting frequent "rules" from a given dataset. These rules are described in the form of quasi-functional dependencies and mined from the dataset by using association rules. Our approach allows us to consequently analyze anomalies with respect to the previously inferred dependencies: given a quasi-functional dependency, it is possible to discover the related anomalies by querying either the original database or the association rules previously stored. By further investigating the nature of such anomalies, we can either derive the presence of erroneous data or highlight novel information which represents significant exceptions of frequent rules. Our method is independent of the considered database and directly infers rules from the data. The applicability of the proposed approach is validated through a set of experiments on XML databases, whose results are here reported.
Cited By (since 1996):5Export Date: 10 July 2014
Keywords: Anomaly detection; Association rules; Data mining; Qausi-functional dependencies

Robust self embedding watermarking technique in the DWT domain for digital colored images - Al-Tahan Al-Nu'aimi, A.; Qahwaji, R. (Journal of Digital Information Management, 2007)
In this work, a robust watermarking algorithm is proposed for colored digital images. The 24 bits/pixel RGB images are converted to YCbCr color format, and the Y part is converted to Discrete Wavelet Transform (DWT). The resulted LL part is resized to the original size of the host image and its blocks are scrambled using certain key. The watermark is another copy of the LL part and its pixels are, also, scrambled and each pixel is embedded in one of the scrambled blocks of the LL part. The algorithm takes into account the Human Visual System via relating the embedding process to the intensity of each block of the host image. We have used the average PSNR as a quality measure for the embedding stage. In all our experiments we got more than 68 dB which satisfies the PSNR bench mark. Also we got an average value of 0.15 for MAE, which is the robustness measure. This means that our algorithm is very robust and the watermark is still recognizable.
Cited By (since 1996):1Export Date: 10 July 2014
Keywords: Copyright; Digital images; Embedding; Robustness; Scrambling; Watermaking