コンピュータ・サイエンス : ACM computing surveys

Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

Multivariate Density Estimation

The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty in precisely learning users' sequential information and personalizing the recommendation model. To this end, we resort to the Metric Embedding method for the recommendation, which avoids drawbacks of the Matrix Factorization technique. We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. We further develop a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance. Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the state-of-the-art next POI recommendation methods.

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Personalized ranking metric embedding for next new POI recommendation

Point-of-interest (POI) recommendation is an important service to Location-Based Social Networks (LBSNs) that can benefit both users and businesses. In recent years, a number of POI recommender systems have been proposed, but there is still a lack of systematical comparison thereof. In this paper, we provide an all-around evaluation of 12 state-of-the-art POI recommendation models. From the evaluation, we obtain several important findings, based on which we can better understand and utilize POI recommendation models in various scenarios. We anticipate this work to provide readers with an overall picture of the cutting-edge research on POI recommendation.

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An experimental evaluation of point-of-interest recommendation in location-based social networks

Traditional optimization solvers tend to start the search from scratch by assuming zero prior knowledge about the task at hand. Generally speaking, the capabilities of solvers do not automatically grow with experience. In contrast, however, humans routinely make use of a pool of knowledge drawn from past experiences whenever faced with a new task. This is often an effective approach in practice as real-world problems seldom exist in isolation. Similarly, practically useful artificial systems are expected to face a large number of problems in their lifetime, many of which will either be repetitive or share domain-specific similarities. This view naturally motivates advanced optimizers that mimic human cognitive capabilities; leveraging on what has been seen before to accelerate the search toward optimal solutions of never before seen tasks. With this in mind, this paper sheds light on recent research advances in the field of global black-box optimization that champion the theme of automatic knowledge transfer across problems. We introduce a general formalization of transfer optimization , based on which the conceptual realizations of the paradigm are classified into three distinct categories, namely sequential transfer , multitasking , and multiform optimization . In addition, we carry out a survey of different methodological perspectives spanning Bayesian optimization and nature-inspired computational intelligence procedures for efficient encoding and transfer of knowledge building blocks. Finally, real-world applications of the techniques are identified, demonstrating the future impact of optimization engines that evolve as better problem-solvers over time by learning from the past and from one another.

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Insights on Transfer Optimization: Because Experience is the Best Teacher

The rise in computer networks and internet attacks has become alarming for most service providers. It has triggered the need for the development and implementation of intrusion detection systems (IDSs) to help prevent and or mitigate the challenges posed by network intruders. Over the years, intrusion detection systems have played and continue to play a very significant role in spotting network attacks and anomalies. Numerous researchers around the globe have proposed many IDSs to combat the threat of network invaders. However, most of the previously proposed IDSs have high rates of raising false alarms. Additionally, most existing models suffer the difficulty of detecting the different attack types, especially User-to-Root (U2R) and Remote-to-Local (R2L) attacks. These two types of attacks often appear to have lower detection accuracy for the existing models. Hence, in this paper, we propose a bidirectional Long-Short-Term-Memory (BiDLSTM) based intrusion detection system to handle the challenges mentioned above. To train and measure our model’s performance, we use the NSL-KDD dataset, a benchmark dataset for most IDSs. Experimental results show and validate the effectiveness of the BiDLSTM approach. It outperforms conventional LSTM and other state-of-the-art models in terms of accuracy, precision, recall, and F-score values. It also has a much more reduced false alarm rate than the existing models. Furthermore, the BiDLSTM model achieves a higher detection accuracy for U2R and R2L attacks than the conventional LSTM.

A bidirectional LSTM deep learning approach for intrusion detection

Feature selection is an import preprocessing approach for machine learning and text mining. It reduces the dimensions of high-dimensional data. A popular approach is based on information theoretic measures. Most of the existing methods used two- and three-dimensional mutual information terms that are ineffective in detecting higher-order feature interactions. To fill this gap, we employ two- through five-way interactions for feature selection. We first identify a relaxed assumption to decompose the mutual information-based feature selection problem into a sum of low-order interactions. A direct calculation of the decomposed interaction terms is computationally expensive. We employ five-dimensional joint mutual information, a computationally efficient measure, to estimate the interaction terms. We use the ‘maximum of the minimum’ nonlinear approach to avoid the overestimation of the feature significance. We also apply the proposed method to text categorization. To evaluate the performance of the proposed method, we compare it with eleven popular feature selection methods, eighteen benchmark data and seven text categorization data. Experimental results with four different types of classifiers provide concrete evidence that higher-order interactions are effective in improving feature selection methods.

Feature selection based on feature interactions with application to text categorization

Cloud computing requires a robust, scalable, and high-performance infrastructure. To provide a reliable and dependable cloud computing platform, it is necessary to build a self-diagnosis and self-healing system against various failures or downgrades. This paper is the first to study the self-healing function, a challenging topic in today's clouding computing systems, from the consequence-oriented point of view. To fulfill the self-diagnosis and self-healing requirements of efficiency, accuracy, and learning ability, a hybrid tool that takes advantages from Multivariate Decision Diagram and Naive Bayes Classifier is proposed. An example is used to demonstrate that this proposed approach is effective.

Self-healing and Hybrid Diagnosis in Cloud Computing

Multifactorial evolutionary algorithm (MFEA) exploits the parallelism of population-based evolutionaryalgorithm and provides an efficient way to evolve individuals for solving multiple tasks concurrently.Its efficiency is derived by implicitly transferring the genetic information among tasks.However, MFEA doesn?t distinguish the information quality in the transfer compromising the algorithmperformance. We propose a group-based MFEA that groups tasks of similar types and selectivelytransfers the genetic information only within the groups. We also develop a new selection criterionand an additional mating selection mechanism in order to strengthen the effectiveness andefficiency of the improved MFEA. We conduct the experiments in both the cross-domain and intra-domainproblems.

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A Group-based Approach to Improve Multifactorial Evolutionary Algorithm

Cloud computing is a new emerging technology aimed at large-scale resource sharing and service-oriented computing. To achieve the efficient use of cloud resources for supporting a cloud service, many important factors need to be considered, particularly, reliability, performance, and power consumption of the cloud service. Evaluation of these metrics is essential for further designing rational resource scheduling strategies. However, these metrics are closely related; they do affect one another. The cloud system should consider correlations among the metrics to make more precise evaluation. Most of the existing approaches and models handle these metrics separately, and thus they cannot be used to study the correlations. This paper presents a new hierarchical correlation model for analyzing and evaluating these correlated metrics, which encompasses Markov models, queuing theory, and a Bayesian approach. Various distinctive characteristics of the cloud system are investigated and captured in the model, such as multiple virtual machines (VMs) hosted on the same server, common cause failures of co-located VMs caused by server failures, and logical mapping mechanisms for multicore CPUs. Moreover, for evaluating and balancing the tradeoff between performance and power consumption, a tradeoff parameter and a pure profit optimization model are developed based on the presented correlation model. Numerical examples are provided.

Yanping Xiang

Papers

A bidirectional LSTM deep learning approach for intrusion detection

Feature selection based on feature interactions with application to text categorization

Self-healing and Hybrid Diagnosis in Cloud Computing

A Group-based Approach to Improve Multifactorial Evolutionary Algorithm

A Hierarchical Correlation Model for Evaluating Reliability, Performance, and Power Consumption of a Cloud Service