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With the rapid growth of cloud computing services, the high energy consumption of cloud data centers has become a critical concern of the cloud computing society. While virtual machine (VM) consolidation is often used to reduce energy consumption, excessive VM consolidation may lead to local hot spots and increase the risk of equipment failure. One possible solution to this problem is to utilize thermal-aware scheduling, but existing approaches have trouble realizing the balance between SLA and energy consumption. This paper proposes a novel method to manage cloud data center resources based on thermal management (TM-VMC), which optimizes total energy consumption and proactively prevents hot spots from a global perspective. Its VM consolidation process includes four phases where the VMs scheduler uses an improved ant colony algorithm (UACO) to find appropriate target hosts for VMs based on server temperature and utilization status obtained in real-time. Experimental results show that the TM-VMC approach can proactively avoid data center hot spots and significantly reduce energy consumption while maintaining low Service Level Agreement (SLA) violation rates compared to existing mainstream VM consolidation algorithms with workloads from real-world data centers. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things and cloud computing, HBase has become a good choice for massive data storage, and is efficient in reading and writing data. However, HBase is not supportive for multi-dimensional query of non-rowkey data, unconducive to data analysis and processing. To address this issue, we first analyze the constitution principle and deficiency of secondary index and clustering index, and select clustering index as the basis of optimization. Then, we choose the Hilbert curve in the space filling curve as the linearization technology, design the pre-partition algorithm and subspace partition algorithm, and realize the Hilbert-curve-based clustering index (HCIndex) which supports multi-dimensional point query and range query. Finally, the performance of HCIndex is verified by comparison experiments with HBase Scan, HiBase and CCIndex. The experimental results show that the query efficiency of HCIndex has been greatly improved at the expense of very limited storage space, which is necessary for storing index data and only 1.7 times the size of the original data table of HBase. Compared with HBase scan, the query efficiency of HCIndex's multi-dimensional point query and range query has been increased to more than 4 times and more than 2 times, respectively. Therefore, the proposed HCIndex is well suited for efficient multi-dimensional and complex queries of massive data in cloud storage systems. [ABSTRACT FROM AUTHOR]

Scholarly big data refer to the rapidly growing scholarly source of information, including a large number of authors, papers, and massive scale scholarly networks. Extracting the profile attributes for Web users is an important step in Web user analysis. For Web scholarly users, profile attributes extraction should integrate multi-source and heterogeneous information resources. However, the traditional extraction models have two main drawbacks: (1) The traditional models require manual feature selection based on specific domain knowledge; (2) The traditional models cannot adapt to the diversities of Scholarly Web pages and cannot discover the relationships between different target entities which are far apart in different domains. To address these issues, we propose a profile attributes extraction model, PAE-NN, based on a Bi-LSTM-CRF neural network. This model can automatically extract the characteristics and contextual representations of each extracting entity through a Recurrent Neural Network with end-to-end training. It takes advantage of the long-memory sequence characteristics of LSTM network to effectively discover the long-term dependencies on extracting entities. Our experimental results on published datasets from the SMPCUP2017 Open Academic Competition and Aminer demonstrate that the proposed PAE-NN model outperforms existing models in terms of extraction precision, recall, and F1-score with large-scale training data. [ABSTRACT FROM AUTHOR]

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Humans are arguably innately prepared to comprehend others' emotional expressions from subtle body movements. If robots or computers can be empowered with this capability, a number of robotic applications become possible. Automatically recognizing human bodily expression in unconstrained situations, however, is daunting given the incomplete understanding of the relationship between emotional expressions and body movements. The current research, as a multidisciplinary effort among computer and information sciences, psychology, and statistics, proposes a scalable and reliable crowdsourcing approach for collecting in-the-wild perceived emotion data for computers to learn to recognize body languages of humans. To accomplish this task, a large and growing annotated dataset with 9876 video clips of body movements and 13,239 human characters, named Body Language Dataset (BoLD), has been created. Comprehensive statistical analysis of the dataset revealed many interesting insights. A system to model the emotional expressions based on bodily movements, named Automated Recognition of Bodily Expression of Emotion (ARBEE), has also been developed and evaluated. Our analysis shows the effectiveness of Laban Movement Analysis (LMA) features in characterizing arousal, and our experiments using LMA features further demonstrate computability of bodily expression. We report and compare results of several other baseline methods which were developed for action recognition based on two different modalities, body skeleton and raw image. The dataset and findings presented in this work will likely serve as a launchpad for future discoveries in body language understanding that will enable future robots to interact and collaborate more effectively with humans. [ABSTRACT FROM AUTHOR]

Mobility and continuous connection entail the emergence of heterogeneous devices with multiple forms of interaction. However, it is challenging for developers and corporations to keep up with the devices and provide applications adapted to them. Besides, better user experiences attuned to users' needs and desires are increasingly in demand. User interfaces play a major role because they must be distributed through different devices and offer a customized experience for each user–device combination. We take advantage of the component-based applications easiness to build customized interfaces that can give optimal solutions to fulfill the requirements for adapting themselves to cross-device applications with multiple forms of interaction. User interaction on mashup interfaces can generate a great deal of data, which can be analyzed for improving the interaction and usefulness of the applications. In our paper, we have created a microservice-based architecture that generates datasets which contain the user behavior for further analysis. Therefore, the user experience and usability in distributed user interfaces may be improved through prediction models generated from the data. Each microservice autonomously fetches its own data and performs consistently so that it can transform datasets optimally by using feature engineering techniques. Thus, data analysis and algorithms can create accurate yet simple prediction models that provide useful knowledge to enhance the user experience. A REST API web service is added to each microservice to facilitate their communication with other microservices and/or third-party clients. The entire microservice architecture, including feature engineering and RESTful API web services for each microservice, offers an infrastructure to handle and process data interaction of cross-devices applications with multiple forms of interaction. This approach has been deployed in a real mashup application where new datasets have been created, processed and validated. [ABSTRACT FROM AUTHOR]

Annotating digital imagery of historical materials for the purpose of computer-based retrieval is a labor-intensive task for many historians and digital collection managers. We have explored the possibilities of automated annotation and retrieval of images from collections of art and cultural images. In this paper, we introduce the application of the ALIP (Automatic Linguistic Indexing of Pictures) system, developed at Penn State, to the problem of machine-assisted annotation of images of historical materials. The ALIP system learns the expertise of a human annotator on the basis of a small collection of annotated representative images. The learned knowledge about the domain-specific concepts is stored as a dictionary of statistical models in a computer-based knowledge base. When an un-annotated image is presented to ALIP, the system computes the statistical likelihood of the image resembling each of the learned statistical models and the best concept is selected to annotate the image. Experimental results, obtained using the Emperor image collection of the Chinese Memory Net project, are reported and discussed. The system has been trained using subsets of images and metadata from the Emperor collection. Finally, we introduce an integration of wavelet-based annotation and wavelet-based progressive displaying of very high resolution copyright-protected images. [ABSTRACT FROM AUTHOR]

Digital imagery for significant cultural and historical materials is an emerging research field that bridges people, culture, and technologies. In this paper, we first discuss the great importance of this field. Then we focus on its four interrelated subareas: (1) creation and preservation, (2) retrieval, (3) presentation and usability, and (4) applications and use. We propose several mechanisms to encourage collaboration and argue that the field has high potential impact on our digital society. Finally, we make specific recommendations on what to pursue in this field. [ABSTRACT FROM AUTHOR]

We present a novel technique, called 2-Phase Service Model, for streaming videos to home users in a limited-bandwidth environment. This scheme first delivers some number of non-adjacent data fragments to the client in Phase 1. The missing fragments are then transmitted in Phase 2 as the client is playing back the video. This approach offers many benefits. The isochronous bandwidth required for Phase 2 can be controlled within the capability of the transport medium. The data fragments received during Phase 1 can be used to provide an excellent preview of the video. They can also be used to facilitate VCR-style operations such as fast-forward and fast-reverse. Systems designed based on this method are less expensive because the fast-forward and fast-reverse versions of the video files are no longer needed. Eliminating these files also improves system performance because mapping between the regular files and their fast-forward and fast-reverse versions is no longer part of the VCR operations. Furthermore, since each client machine handles its own VCR-style interaction, this technique is very scalable. We provide simulation results to show that 2-Phase Service Model is able to handle VCR functions efficiently. We also implement a video player called {\em FRVplayer}. With this prototype, we are able to judge that the visual quality of the previews and VCR-style operations is excellent. These features are essential to many important applications. We discuss the application of FRVplayer in the design of a video management system, called VideoCenter. This system is intended for Internet applications such as digital video libraries. [ABSTRACT FROM AUTHOR]