Efficient community maintenance for dynamic social networks

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Detecting community pacemakers of burst topic in Twitter

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Energy cost minimization with job security guarantee in Internet data center

With the proliferation of various big data applications and resource demand from Internet data centers (IDCs), the energy cost has been skyrocketing, and it attracts a great deal of attention and brings many energy optimization management issues. However, the security problem for a wide range of applications, which has been overlooked, is another critical concern and even ranked as the greatest challenge in IDC. In this paper, we propose an energy cost minimization (ECM) algorithm with job security guarantee for IDC in deregulated electricity markets. Randomly arriving jobs are routed to a FIFO queue, and a heuristic algorithm is devised to select security levels for guaranteeing job risk probability constraint. Then, the energy optimization problem is formulated by taking the temporal diversity of electricity price into account. Finally, an online energy cost minimization algorithm is designed to solve the problem by Lyapunov optimization framework which offers provable energy cost optimization and delay guarantee. This algorithm can aggressively and adaptively seize the timing of low electricity price to process workloads and defer delay-tolerant workloads execution when the price is high. Based on the real-life electricity price, simulation results prove the feasibility and effectiveness of proposed algorithm

Clasificación de sentimientos semi-supervisada y dependiente de objetivo para micro- blogs

The wealth of opinions expressed in micro-blogs, such as tweets, motivated researchers to develop techniques for automatic opinion detection. However, accuracies of such techniques are still limited. Moreover, current techniques focus on detecting sentiment polarity regardless of the topic (target) discussed. Detecting sentiment towards a specific target, referred to as target-dependent sentiment classification, has not received adequate researchers’ attention. Literature review has shown that all target-dependent approaches use supervised learning techniques. Such techniques need a large number of labeled data. However, labeling data in social media is cumbersome and error prone. The research presented in this paper addresses this issue by employing semi-supervised learning techniques for target-dependent sentiment classification. Semisupervised learning techniques make use of labeled as well as unlabeled data. In this paper, we present a new semi-supervised learning technique that uses less number of labeled micro-blogs than that used by supervised learning techniques. Experiment results have shown that the proposed technique provides comparable accuracy.Facultad de Informátic

Clasificación de sentimientos semi-supervisada y dependiente de objetivo para micro- blogs

The wealth of opinions expressed in micro-blogs, such as tweets, motivated researchers to develop techniques for automatic opinion detection. However, accuracies of such techniques are still limited. Moreover, current techniques focus on detecting sentiment polarity regardless of the topic (target) discussed. Detecting sentiment towards a specific target, referred to as target-dependent sentiment classification, has not received adequate researchers’ attention. Literature review has shown that all target-dependent approaches use supervised learning techniques. Such techniques need a large number of labeled data. However, labeling data in social media is cumbersome and error prone. The research presented in this paper addresses this issue by employing semi-supervised learning techniques for target-dependent sentiment classification. Semisupervised learning techniques make use of labeled as well as unlabeled data. In this paper, we present a new semi-supervised learning technique that uses less number of labeled micro-blogs than that used by supervised learning techniques. Experiment results have shown that the proposed technique provides comparable accuracy.Facultad de Informátic

neurosent pdi at semeval 2018 task 3 understanding irony in social networks through a multi domain sentiment model

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Clasificación de sentimientos semi-supervisada y dependiente de objetivo para micro- blogs

The wealth of opinions expressed in micro-blogs, such as tweets, motivated researchers to develop techniques for automatic opinion detection. However, accuracies of such techniques are still limited. Moreover, current techniques focus on detecting sentiment polarity regardless of the topic (target) discussed. Detecting sentiment towards a specific target, referred to as target-dependent sentiment classification, has not received adequate researchers’ attention. Literature review has shown that all target-dependent approaches use supervised learning techniques. Such techniques need a large number of labeled data. However, labeling data in social media is cumbersome and error prone. The research presented in this paper addresses this issue by employing semi-supervised learning techniques for target-dependent sentiment classification. Semisupervised learning techniques make use of labeled as well as unlabeled data. In this paper, we present a new semi-supervised learning technique that uses less number of labeled micro-blogs than that used by supervised learning techniques. Experiment results have shown that the proposed technique provides comparable accuracy.Facultad de Informátic