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Timely and accurate traffic classification and application characterization are becoming increasingly important with many applications in wired and wireless networks, e.g., traffic engineering, security monitoring, and quality of service (QoS). In particular, Software Defined Networking (SDN) is a new networking paradigm that has great impact on future IP networks and 5G wireless networks. In SDN...
An improved KNN text classification algorithm based on Simhash has been proposed by introducing Simhash and the average Hamming distance of adjacent texts as a unit, which solves the problems caused by data imbalance and the large computational overhead in the traditional KNN text classification algorithms. Experimental results demonstrate that the proposed algorithm performs a higher precision, a...
Big data technology is used to integrate, clean up and analyze the data of student management system, educational administration system, and campus card consumption system and so on in this paper. The characteristics of high risk students are extracted and selected, and the prediction model is constructed, which can be used to predict the high risk students scientifically, reasonably and effectively,...
With the rapid spread of Internet and the mobile web, the number of news pages is increasing quickly as well as the content of news becomes highly dynamic. It's difficult for normal users to obtain specific information contained in a mass of news streams. So it's of great research significance to study how to analyze massive news, detect and track news hotspots automatically. This research proposes...
Call detail records (CDRs) collected at telecommunication networks have been well studied to reveal human behaviors such as voice service usage, contact regularities and mobility patterns. With the advances in big data technology, carriers can now collect, store and analyze more data, such as records of user mobile web activities, at scales much larger than CDRs. In this paper, we study 14 features...
Early detection of depression is important to improve human well-being. This paper proposes a new method to detect depression through time-frequency analysis of Internet behaviors. We recruited 728 postgraduate students and obtained their scores on a depression questionnaire (Zung Self-rating Depression Scale, SDS) and digital records of Internet behaviors. By time-frequency analysis, we built classification...
The lasting popularity of many social Q&A websites, such as Yahoo! Answers and ResearchGate, has become valuable knowledge repositories for people to search for answers to questions in various aspects in life. Finding the most relevant questions is often a non-trivial task, and a fine-grained classification system of questions will be an important aid. Existing work mainly focused on classifying...
With the rapid development of the Web, Internet gambling has become a global problem, which causes nontrivial social impacts. Despite of its prosperity, in the major countries such as United States, Russia, and mainland China, Internet gambling is explicitly prohibited, and in the most remaining countries, Internet gambling is under strict regulations. However, there are so many websites that it is...
In this paper, we study multi-class classification of tweets, where we introduce highly efficient dimensionality reduction techniques suitable for online processing of high dimensional feature vectors generated from freely-worded text. As for the real life case study, we work on tweets in the Turkish language, however, our methods are generic and can be used for other languages as clearly explained...
Short message strings are widely prevalent in the age of social networking. Taking Facebook as an example, a user may have many other users in his contact list. However, at any given time frame, the user interacts with only a small subset of these users. In this paper, we propose a recommender system that determines which users have common interests based on the content of the short message strings...
Computers and Smartphone's becomes vital part of everyday life and hence use of internet becomes more and more. Due to internet, computers are becomes vulnerable of different kinds of security threats. Therefore it is required that we need to have efficient security method in order to avoid leakage of important data or misuse of data. This security method is called as Intrusion Detection System (IDS)...
In recent decades, webpages are becoming an increasingly important visual information source. Compared with natural images, webpages are different in many ways. For example, webpages are usually rich in semantically meaningful visual media (text, pictures, logos, and animations), which make the direct application of some traditional low-level saliency models ineffective. Besides, distinct web-viewing...
Identification of malware's family is an intricate process whose success and accuracy depends on different factors. These factors are mainly related to the process of extracting of meaningful and distinctive features from a set of malware samples, modeling malware via its static or dynamic features and particularly techniques used to classify malware samples. In this paper, we propose a new malware...
We present an approach for automatic annotation of commercial videos from an arts-and-crafts domain with the aid of textual descriptions. The main focus is on recognizing both manipulation actions (e.g. cut, draw, glue) and the tools that are used to perform these actions (e.g. markers, brushes, glue bottle). We demonstrate how multiple visual cues such as motion descriptors, object presence, and...
Now a day's computer networks are very popular, so network attacks are inevitable. As a consequence, any complete security package includes a network Intrusion Detection System (nIDS). This work focuses on nIDSs which work by scanning the network traffic. We have combined classifiers based on packet header information with classifiers based on payload distribution to increase detection rates in non-flood...
Detecting anomalous traffic on the Internet has remained an issue of concern for the community of security researchers over the years. Advances in computing performance, in terms of processing power and storage, have allowed the use of resource-intensive intelligent algorithms, to detect intrusive activities, in a timely manner. Naïve Bayes is a statistical inference learning algorithm with promise...
In order to improve detection efficiency of on-line web news stream, we propose a new method to accomplish detection task with window-adding, named entity recognition and suffix tree clustering. In our method, we make full use of informative elements of news stream(such as date, place, person and so on) to help detection process, and this method decreases text similarity computation greatly. Experimental...
Discovering the correct dataset efficiently is critical for computations and effective simulations in scientific experiments. In contrast to searching web documents over the Internet, massive binary datasets are difficult to browse or search. Users must select a reliable data publisher from the large collection of data services available over the Internet. Once a publisher is selected, the user must...
In this paper, we have proposed a highlight based adaptive transmission (HBAT) system to address the soccer video transmission over unpredictable wireless network. The proposed system employs video analysis to rank the highlight level of each frame within its semantic structure, performs optimized temporal operation such as frame dropping on real time stream to maximize remaining ratio of the highlights...
Collaborative filtering is the most widely used and successful technology for building recommender systems. However it faces challenges of scalability and recommendation accuracy. Collaborative filtering can be divided into memory based and model based. The former is more accurate while the latter performs better in scalability. This paper proposes a hybrid user model. The recommender system based...
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