基于社会化标注的博客标签推荐方法

为了提高博客系统推荐标签的质量,分析了现有的标签推荐算法及相关技术,提出了一种基于社会化标注的博客标签推荐方法。该方法的优势在于:利用相似博客的社会化标签作为候选标签集,确保了推荐标签的全面性和可用性;基于TF-IDF相似度方法定义筛选步骤去除候选标签集中冗余和冷僻的标签,提高了推荐标签的准确性和高效性。实验结果表明了该方法的有效性。     

Monitoring air pollution by deep features and extreme learning machine

Smartphones have different kinds of applications that help to promote health and care of humans. This paper proposes a practical and low-cost method for predicting air pollution which is applicable to the smartphones based on an image taken by their camera. To find the best method, in the first approach, some convenionalconventional feature extraction methods including wavelet transform, scale-invariant feature transform and histogram of oriented gradients are implemented. Then, to reduce the dimension of the extracted feature vectors, principal component analysis is employed. For classification of the obtained reduced feature vectors, multilayer perceptron is employed. In the second approach, the performance of convolutional neural network (CNN) in classifying the sky images in terms of air quality is investigated. In CNN, the fully connected classifier can be replaced by other classifiers such as extreme learning machine (ELM). The results illustrate that if the deep features obtained by CNN are fed to the ELM, an accuracy of 66.92% in predicting the level of air quality is achieved, which is higher than the results of other previous and conventional methods.     

Representation learning on textual network with personalized PageRank

Representation learning on textual network or textual network embedding,which leverages rich textual information associated with the network structure to learn ...     

Improving music recommendation by incorporating social influence

In the past decades, a large number of music pieces are uploaded to the Internet every day through social networks, such as Last.fm, Spotify and YouTube, that concentrates on music and videos. We have been witnessing an ever-increasing amount of music data. At the same time, with the huge amount of online music data, users are facing an everyday struggle to obtain their interested music pieces. To solve this problem, music search and recommendation systems are helpful for users to find their favorite content from a huge repository of music. However, social influence, which contains rich information about similar interests between users and users’ frequent correlation actions, has been largely ignored in previous music recommender systems. In this work, we explore the effects of social influence on developing effective music recommender systems and focus on the problem of social influence aware music recommendation, which aims at recommending a list of music tracks for a target user. To exploit social influence in social influence aware music recommendation, we first construct a heterogeneous social network, propose a novel meta path-based similarity measure called WPC, and denote the framework of similarity measure in this network. As a step further, we use the topological potential approach to mine social influence in heterogeneous networks. Finally, in order to improve music recommendation by incorporating social influence, we present a factor graphic model based on social influence. Our experimental results on one real world dataset verify that our proposed approach outperforms current state-of-the-art music recommendation methods substantially.

     

Implicit relation-aware social recommendation with variational auto-encoder

World Wide Web - Integrating social networks as auxiliary information shows effectiveness in improving the performance for a recommendation task. Typical models usually characterize the user trust...     

Syntactic N-grams as machine learning features for natural language processing

In this paper we introduce and discuss a concept of syntactic n-grams (sn-grams). Sn-grams differ from traditional n-grams in the manner how we construct them, i.e., what elements are considered neighbors. In case of sn-grams, the neighbors are taken by following syntactic relations in syntactic trees, and not by taking words as they appear in a text, i.e., sn-grams are constructed by following paths in syntactic trees. In this manner, sn-grams allow bringing syntactic knowledge into machine learning methods; still, previous parsing is necessary for their construction. Sn-grams can be applied in any natural language processing (NLP) task where traditional n-grams are used. We describe how sn-grams were applied to authorship attribution. We used as baseline traditional n-grams of words, part of speech (POS) tags and characters; three classifiers were applied: support vector machines (SVM), naive Bayes (NB), and tree classifier J48. Sn-grams give better results with SVM classifier.     

Single machine scheduling with autonomous learning and induced learning

As an important management tool of winning competitive advantage, induced learning effect has been widely studied in empirical research area. But it is hardly considered in scheduling problems. In this paper, autonomous and induced learning are both taken into consideration. The investment of induced learning is interpreted as specialized time intervals to implement training, knowledge sharing and transferring etc. We present algorithms to determine jointly the optimal job sequence and the optimal position of induced learning intervals, with the objective of minimizing makespan.     

Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms

Multimedia Tools and Applications - While the RT-PCR is the silver bullet test for confirming the COVID-19 infection, it is limited by the lack of reagents, time-consuming, and the need for...     

Image deblurring with filters learned by extreme learning machine

Image deblurring is a basic and important task of image processing. Traditional filtering based image deblurring methods, e.g. enhancement filters, partial differential equation (PDE) and etc., are limited by the hypothesis that natural images and noise are with low and high frequency terms, respectively. Noise removal and edge protection are always the dilemma for traditional models.In this paper, we study image deblurring problem from a brand new perspective—classification. And we also generalize the traditional PDE model to a more general case, using the theories of calculus of variations. Furthermore, inspired by the theories of approximation of functions, we transform the operator-learning problem into a coefficient-learning problem by means of selecting a group of basis, and build a filter-learning model. Based on extreme learning machine (ELM) [1], [2], [3] and [4], an algorithm is designed and a group of filters are learned effectively. Then a generalized image deblurring model, learned filtering PDE (LF-PDE), is built.The experiments verify the effectiveness of our models and the corresponding learned filters. It is shown that our model can overcome many drawbacks of the traditional models and achieve much better results.     

Eye-tracking and social behavior preference-based recommendation system

The Journal of Supercomputing - With the popularization of wireless Internet technology and smartphones, the importance of recommendation systems, which analyze personality of a user using social...     

关联文本信息的网络表示学习推荐算法

通过基于随机游走的网络表示学习算法得到节点的低维嵌入向量,进而将其应用于推荐系统是推荐领域很流行的研究方向.针对当前基于随机游走的网络表示学习算法仅着重考虑了网络结构特性而忽略文本信息的问题,提出一种关联文本信息的网络表示学习推荐算法.首先在随机游走阶段,考虑到了节点文本间的相似度,联合结构和文本信息对下一游走节点进行...     

Image annotation in social networks using graph and multimodal deep learning features

Multimedia Tools and Applications - Thanks to the evolution of technology, we find a very large number of internet users who use social networks to react and share things with each other. These...     

Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey

This research synthesizes a taxonomy for classifying detection methods of new malicious code by Machine Learning (ML) methods based on static features extracted from executables. The taxonomy is then operationalized to classify research on this topic and pinpoint critical open research issues in light of emerging threats. The article addresses various facets of the detection challenge, including: file representation and feature selection methods, classification algorithms, weighting ensembles, as well as the imbalance problem, active learning, and chronological evaluation. From the survey we conclude that a framework for detecting new malicious code in executable files can be designed to achieve very high accuracy while maintaining low false positives (i.e. misclassifying benign files as malicious). The framework should include training of multiple classifiers on various types of features (mainly OpCode and byte n-grams and Portable Executable Features), applying weighting algorithm on the classification results of the individual classifiers, as well as an active learning mechanism to maintain high detection accuracy. The training of classifiers should also consider the imbalance problem by generating classifiers that will perform accurately in a real-life situation where the percentage of malicious files among all files is estimated to be approximately 10%.     

Rule-based document structure understanding with a fuzzy combination of layout and textual features

Document image processing is a crucial process in office automation and begins at the ‘OCR’ phase with difficulties in document ‘analysis’ and ‘understanding’. This paper presents a hybrid and comprehensive approach to document structure analysis. Hybrid in the sense that it makes use of layout (geometrical) as well as textual features of a given document. These features are the base for potential conditions which in turn are used to express fuzzy matched rules of an underlying rule base. Rules can be formulated based on features which might be observed within one specific layout object. However, rules can also express dependencies between different layout objects. In addition to its rule driven analysis, which allows an easy adaptation to specific domains with their specific logical objects, the system contains domain-independent markup algorithms for common objects (e.g., lists). Received June 19, 2000 / Revised November 8, 2000     

Integrating ontology technology with folksonomies for personalized social tag recommendation

Social tagging systems leverage social interoperability by facilitating the searching, sharing, and exchanging of tagging resources. A major drawback of existing social tagging systems is that social tags are used as keywords in keyword-based search. They focus on keywords and human interpretability rather than on computer interpretable semantic knowledge. Therefore, social tags are useful for information sharing and organizing, but they lack the computer-interpretability needed to facilitate a personalized social tag recommendation. An interesting issue is how to automatically generate a personalized social tag recommendation list to users when a resource is accessed by users. The novel solution proposed in this study is a hybrid approach based on semantic tag-based resource profile and user preference to provide personalized social tag recommendation. Experiments show that the Precision and Recall of the proposed hybrid approach effectively improves the accuracy of social tag recommendation.     

Preference dynamics with multimodal user-item interactions in social media recommendation

Recommender systems elicit the interests and preferences of individuals and make recommendations accordingly, a main challenge for expert and intelligent systems. An essential problem in recommender systems is to learn users’ preference dynamics, that is, the constant evolution of the explicit or the implicit information, which is diversified throughout time according to the user actions. Also, in real settings data sparsity degrades the recommendation accuracy. Hence, state-of-the-art methods exploit multimodal information of users-item interactions to reduce sparsity, but they ignore preference dynamics and do not capture users’ most recent preferences. In this article, we present a Temporal Collective Matrix Factorization (TCMF) model, making the following contributions: (i) we capture preference dynamics through a joint decomposition model that extracts the user temporal patterns, and (ii) co-factorize the temporal patterns with multimodal user-item interactions by minimizing a joint objective function to generate the recommendations. We evaluate the performance of TCMF in terms of accuracy and root mean square error, and show that the proposed model significantly outperforms state-of-the-art strategies.     

Leveraging pointwise prediction with learning to rank for top-N recommendation

World Wide Web - Pointwise prediction and Learning to Rank (L2R) are two hot strategies to model user preference in recommender systems. Currently, these two types of approaches are often...     

Credit rating by hybrid machine learning techniques

It is very important for financial institutions to develop credit rating systems to help them to decide whether to grant credit to consumers before issuing loans. In literature, statistical and machine learning techniques for credit rating have been extensively studied. Recent studies focusing on hybrid models by combining different machine learning techniques have shown promising results. However, there are various types of combination methods to develop hybrid models. It is unknown that which hybrid machine learning model can perform the best in credit rating. In this paper, four different types of hybrid models are compared by ‘Classification + Classification’, ‘Classification + Clustering’, ‘Clustering + Classification’, and ‘Clustering + Clustering’ techniques, respectively. A real world dataset from a bank in Taiwan is considered for the experiment. The experimental results show that the ‘Classification + Classification’ hybrid model based on the combination of logistic regression and neural networks can provide the highest prediction accuracy and maximize the profit.     

Malicious web content detection by machine learning

The recent development of the dynamic HTML gives attackers a new and powerful technique to compromise computer systems. A malicious dynamic HTML code is usually embedded in a normal webpage. The malicious webpage infects the victim when a user browses it. Furthermore, such DHTML code can disguise itself easily through obfuscation or transformation, which makes the detection even harder. Anti-virus software packages commonly use signature-based approaches which might not be able to efficiently identify camouflaged malicious HTML codes. Therefore, our paper proposes a malicious web page detection using the technique of machine learning. Our study analyzes the characteristic of a malicious webpage systematically and presents important features for machine learning. Experimental results demonstrate that our method is resilient to code obfuscations and can correctly determine whether a webpage is malicious or not.