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Preview: IEEE Intelligent Systems

IEEE Intelligent Systems



IEEE Intelligent Systems, a bimonthly publication of the IEEE Computer Society, covers new tools, techniques, concepts, and current research and development activities in intelligent systems. The magazine serves software engineers, systems designers, info



Published: Mon, 3 Nov 2014 15:35:04 GMT

 



PrePrint: Exploring alterations of brain networks of AD patients using WTC method
Objective: To explore the influences of different frequency bands on preprocessing of resting-state fMRI datasets used by the Wavelet Transform Coherence (WTC) method, and to study changes in the functional brain networks of AD patients. Method: Resting-state fMRI datasets of 10 AD patients and 11 healthy controls were collected in this study and time series of 90 brain regions defined by AAL (Automated Anatomical Labeling) were exacted after preprocessing. Wavelet transformation was performed for each time series, and a functional brain network were established in different frequencies (0.125Hz, 0.0625Hz) using the WTC (Wavelet Transform Coherence) method. The topology parameters of networks, containing global efficiency, clustering coefficient, average short paths length and small world property were calculated and averaged within each group. Result: The results imply that there are significant differences of topology parameters in networks of different frequencies. Likewise, statistical analysis of topology parameters of AD and HC (Healthy Controls) show that global efficiency, clustering coefficient and small world properties of AD all decreased by varying degrees, while the short path length of AD remained longer. Conclusion: Our research provides a theoretical basis for the choice of filter bands for data preprocessing in functional magnetic resonance imaging. The findings may serve as indicators for early diagnosis of AD patients.



PrePrint: A Framework for Generating Geospatial Social Computing Environment
Computational social science plays an important role in emergency management from a quantitative perspective. Reconstructing an individual-based social computing environment is crucial for both accurate computational experiments and determining optimal decisions. In this paper, we proposed a formalization method to define basic componential models in the artificial society and the inner logic in these models. A detailed generation process is presented, during the process, multi-source statistical data, social interactive behavior, and multilayer social networks are integrated together. As an evaluation of the propose framework, a virtual city of Beijing is reconstructed. For each household, all the family members are endowed with characteristics such as home address, household size, and family roles. Each person is endowed with demographic attributes, including age, gender, social role, correlated geographic locations, and multiple social relations. The generated synthetic population is statistically equivalent to the real population. Further, transmission experiments of influenza are carried out in the reconstructed computational environment, and individual daily interacting behavior is tracked and analyzed. The results indicate that the framework can provide an effective methodology to reconstruct the computing environment in high-resolution by using statistical data in low-resolution, leading to better prediction and management of emergencies.



PrePrint: Creating a Fine-Grained Corpus for Chinese Sentiment Analysis
Writing comments on products or news has become a popular activity in social media. The amount of opinionated text available online has been growing rapidly, thus increasing the need for techniques that can analyze opinions expressed in such text so that these texts can be easily absorbed by users. To date, most techniques are dependent on annotated corpora. However, existing corpora are almost sentence-level works that ignore important global sentiment information in other sentences. Besides, given the rise of advanced applications, more fine-grained corpora are needed, even at sentence-level, especially for Chinese. In this paper, we aim to create a fine-grained corpus for Chinese sentiment analysis, and more importantly, explore new sentiment analysis tasks through analyzing the annotated corpus. The proposed fine-grained annotation scheme in this paper not only introduces cross-sentence and global sentiment information (such as "target entity"), but also includes new sentence-level elements (such as "implicit aspect"). Based on this scheme, this corpus can provide a more fine-grained platform for researchers to study algorithms for advanced applications. In addition, an in-depth analysis on the annotated corpus is made and several important but ignored tasks, such as the target-aspect pair extraction task, are explored, which can give useful hints about future directions.



PrePrint: WaaS-Wisdom as a Service
An emerging hyper-world encompasses all human activities in a social-cyber-physical space. Its power derives from the Wisdom Web of Things (W2T) cycle, namely, "from things to data, information, knowledge, wisdom, services, humans, and then back to things." The W2T cycle leads to a harmonious symbiosis among humans, computers and things, which can be constructed by large-scale converging of intelligent information technology applications with an open and interoperable architecture. The recent advances in cloud computing, the Internet of Things, Web of Things, big data and other research fields have provided just such an open system architecture with resource sharing and services. The next step is therefore to develop an open and interoperable content architecture with intelligence sharing and services for the organization and transformation in the "data, information, knowledge and wisdom (DIKW)" hierarchy. This article introduces Wisdom as a Service (WaaS) as a content architecture based on the pay-as-you-go IT trend. The WaaS infrastructure and the main challenges in WaaS research and applications are discussed. A case study is also described. Relying on cloud computing and big data, WaaS provides a practical approach to realize the W2T cycle in the hyper-world for the coming age of ubiquitous intelligent IT applications.



Issue: May-June 2014 (Vol. 29 No.3)
IEEE Intelligent Systems



PrePrint: Interaction-Rich Transfer Learning for Collaborative Filtering with Heterogeneous User Feedbacks
A real recommender system can usually make use of more than one type of users' feedbacks, e.g., numerical ratings and binary ratings, in order to learn users' true preferences. In a recent work [10], a transfer learning algorithm called TCF is proposed to exploit such heterogeneous user feedbacks, which performs well via sharing data-independent knowledge and modeling data-dependent effect simultaneously. However, TCF is a batch algorithm and updates the model parameters only once after scanning the whole data, which may be not efficient enough for real systems. In this paper, we propose a novel and efficient transfer learning algorithm called iTCF (interaction-rich transfer by collective factorization), which extends the efficient CMF [13] algorithm with more interactions between the user-specific latent features. The assumption under iTCF is that the predictability w.r.t. the same user's rating behaviors in two related data is likely to be similar. Considering the shared predictability, we derive novel update rules for iTCF in a stochastic algorithmic framework. The advantages of iTCF include its efficiency comparing with TCF, and its higher prediction accuracy in comparison with CMF. Experimental results on three real-world data sets show the effectiveness of iTCF over the state-of-the-art methods.



PrePrint: A Network Evolution Model for Chinese traditional acquaintance networks
An evolution model of Chinese traditional acquaintance relationship networks on emphasizing individual heterogeneity and social culture is introduced in this paper. The model incorporates three distinct mechanisms that affect acquaintance network evolution and formation: heredity linking, variation linking and similarity-based disconnection. We have found that the degree distribution of Chinese traditional acquaintance networks is manifested in a piecewise approximation that combines a power-law form with an exponential cutoff and exponential distribution. Numerical results indicate that individuals maintaining a medium amount of connections far outweigh others, reflecting the characteristics of Guanxi centered society. The formation of acquaintance relationship networks is greatly affected by the Chinese special kinship culture. Our findings are supported by sociological statistical conclusions and offer a rational explanation for the nature of Chinese kinship networks. Our work provides an adequate framework for further research on dynamic human complex behaviors such as epidemic spreading and rumor propagation.



PrePrint: IEEE 1857 Standard Empowering Smart Video Surveillance Systems
IEEE 1857, Standard for Advanced Audio and Video Coding, was released as IEEE 1857-2013 in June 2013. Despite consisting of several different groups, the most significant feature of IEEE 1857-2013 is its surveillance groups, which can not only achieve at least twice coding efficiency on surveillance videos as H.264/AVC HP, but also should be the most recognition-friendly video coding standard till to now. This article presents an overview of IEEE 1857 surveillance groups, highlighting the background model based coding technology and recognition-friendly functionalities. We believe that IEEE 1857-2013 will bring new opportunities and drives to the research communities and industries on smart video surveillance systems.



PrePrint: An Adaptive Fusion Algorithm for Spam Detection
Spam detection has become a critical component in various online systems, like Email services, advertising engines, social media sites, etc. Diversity and dynamics are two main characteristics of spams, while one single online learner as deployed by many commercial systems is usually not sufficient to capture different aspects of spams, and thus may fail to learn the model parameters accurately. In this paper, we take Email services as an example, and present an adaptive fusion algorithm for spam detection (AFSD), which is a general content-based approach and can be applied to non-Email spam detection tasks with little additional effort. In our proposed algorithm, we (1) use n-grams of non-tokenized text strings to represent an Email, (2) introduce a link function in order to convert the prediction scores of online learners to be more comparable ones, (3) train the online learners in a mistake-driven manner via “thick thresholding” to obtain high competitive online learners, and (4) design update rules to adaptively integrate the online learners to capture different aspects of spams. We study the prediction performance of AFSD on five public competition datasets and one industry dataset, and observe that our algorithm achieves significantly better results than several state-of-the-art approaches, including the champion solutions of the corresponding competitions.



PrePrint: Supporting Trust Assessment and Decision-Making in Coalitions
Modern multi-organisational coalitions are capable of bringing diverse sets of capabilities, assets and information sources to bear on complex and dynamic operations. However, the successful completion of these operations places demands on the trust between coalition partners. When it is necessary to rely on other partners, decision-makers must be able to make rapid and effective trust assessments and decisions. In this paper, we introduce computational trust mechanisms, and illustrate how they may be employed in coalition information acquisition. We discuss how these mechanisms can draw on different sources of evidence to make assessments of trust, and arrive at decisions about how to act when trust can be supplemented by controls. Finally, we discuss future directions for these systems, and highlight some challenges that remain.