ALBERT

Description: The degree distribution is an important characteristic of complex networks. In many data analysis applications, the networks should be represented as fixed-length feature vectors and therefore the feature extraction from the degree distribution is a necessary step. Moreover, many applications need a similarity function for comparison of complex networks based on their degree distributions. Such a similarity measure has many applications, including classification and clustering of network instances, evaluation of network sampling methods, anomaly detection and study of epidemic dynamics. The existing methods are unable to effectively capture the similarity of degree distributions, particularly when the corresponding networks have different sizes. In this paper, we propose a feature extraction method and a similarity function for the degree distributions in complex networks. We propose to calculate the feature values based on the mean and standard deviation of the node degrees in order to decrease the effect of the network size on the extracted features. Experiments on a wide range of real and artificial networks confirms the accuracy, stability and effectiveness of the proposed method.

Description: Given a query photo characterizing a location-aware landmark shot by a user, landmark retrieval is about returning a set of photos ranked in their similarities to the query. Existing studies on landmark retrieval focus on conducting a matching process between candidate photos and a query photo by exploiting location-aware visual features. Notwithstanding the good results achieved, these approaches are based on an assumption that a landmark of interest is well-captured and distinctive enough to be distinguished from others. In fact, distinctive landmarks may be badly selected, e.g. changes on viewpoints or angles. This will discourage the recognition results if a biased query photo is issued. In this paper, we present a novel technique that exploits user communities in social media networks. Given a biased query photo containing some landmarks taken by a user, we select multiple users to complement this user for retrieval. Multiple photos are then used to enrich the query photo, constituting a more representative yet robust multi-query set. A pattern mining method is developed to obtain a compact feature representation of photos from the multi-query set. Such a representation is utilized to efficiently query the database so as to improve retrieval results. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of our approach.

Description: Social media have been used by some companies to support customer co-creation in recent years. However, few academic studies have been done to investigate the use of social media in customer co-creation. To understand the current state-of-the-art and future trends about the use of social media in customer co-creation, we conducted two studies to analyze relevant posts on blogs and social media-based online forums. This study reveals some interesting patterns, themes and future trends in this specific area. Recommendations are given to help managers engage in co-creation activities with customers.

Description: Mobile message push has become a ubiquitous technology in various applications such as online resource sharing, traffic surveillance, mobile health care and environmental monitoring. In mobile terminals, energy efficiency optimization is one of the most important issues due to battery power limitations, resource constraints and quality-of-service (QoS) requirements. Considering the timely delivery, network load and terminal diversity, this paper proposes an adaptive message push strategy (AMPS) for energy efficiency optimization in mobile terminals. In AMPS, running parameters including energy parameter, operating system (OS) version and connection/polling cost in mobile terminal are first acquired and sent to the server together with the requisition data, and then the dispatching module will automatically choose a message pushing mode between polling-based and connection-based ones. The AMPS was tested in real environments using mobile phones with different OSs. Experiment results show that AMPS can efficiently optimize energy exploitation with dynamic tradeoff between terminal using time and QoS performance in comparison with polling-based and connection-based message push strategies.

Description: Wireless sensor networks (WSNs) are vulnerable to the unfavorable funneling effect. The optimization of WSN clustering is a natural way to suppress the funneling effect. WSN clusters involve the edge effect that was undervalued in existing techniques. We propose an optimal clustering routing protocol GreenOCR to reduce the detrimental influence of the funnel effect and minimize the energy consumption in WSNs. Our work focuses on the approximate unequal optimal clustering and dropping energy consumption arising from the edge effect. First, according to the data repeat rate among overlapped clusters, we estimate the actual data compression ratio to offset the negative influence of the edge effect and save WSN energy. Secondly, we reduce the issue of minimizing the total energy consumption in a WSN to a nonlinear programming (NLP). We have proved that this NLP problem is NP complete. Third, we turn over to exploring an approximate optimal clustering and propose an approximate optimal clustering algorithm. A GreenOCR enabled WSN clustering minimizes the energy consumption in the whole network and extends the lifetime of the WSN. The simulation experiment shows that GreenOCR outperforms its rivals in alleviating the funnel effect.

Description: To improve the video semantic analysis for video surveillance, a new video semantic analysis method based on the kernel discriminative sparse representation (KSVD) and weighted K nearest neighbors (KNN) is proposed in this paper. A discriminative model is built by introducing a kernel discriminative function to the KSVD dictionary optimization algorithm, mapping the sparse representation features into a high-dimensional space. The optimal dictionary is then generated and applied to compute the sparse representations of video features. For video semantic analysis, a weighted KNN algorithm based on the optimal sparse representation is proposed. In the algorithm, a kernel function is introduced to establish discrimination about sparse representation features and the classification vote result is weighted, the purpose of which is to improve the accuracy and rationality for video semantic analysis. The experimental results show that the proposed method significantly improves the discrimination of sparse representation features when compared with the traditional KSVD-based support vector machine method. The method can effectively detect the concept and event, which can be potentially useful for improving the video surveillance.

Description: In cloud computing, companies usually use high-end storage systems to guarantee the I/O performance of virtual machines (VM). These storage systems cost a lot of energy for their high performance. In this paper, we propose an EEVS, a deduplication-based energy efficiency storage system for VM storage. We firstly investigate some VM image files with general operating systems. With the analysis result, we find there are many redundant data blocks that bring extra energy cost VM storage. Therefore, in the EEVS, we design an online-deduplication mechanism to reduce these redundant data without service interruption, while traditional deduplication technology is used for offline backup. Based on the system design, we implement an EEVS with the existing cloud platform. Since this mechanism needs considerable computing resources, we design a deduplication selection algorithm such that the storage energy consumption is minimized for a given set of VMs with limited resources for deduplication. Experiment results in a para-virtualization environments of the EEVS show that energy consumption is reduced by even up to 66% with negligible performance degradation.

Description: Smarter use of the renewable energy produced by solar panels reduces the return time of the investment necessary for their installation. This improvement consequently motivates more households to use solar panels so as to not only help protect the environment, but also better use the expensive energy. The difference in tariff prices at different hours of the day is one such opportunity for smarter use of solar electricity: we propose and implement a real-time strategy to more economically use the produced solar electrical energy by forecasting future demand of a few days ahead and by using that energy at the most economical time. Evaluation of the proposed technique in an educational building showed that this scheme improves financial advantage of solar panels by 41% compared with the direct connection of production of solar panels to the grid, or using the stored solar energy completely unawares, hence it can reduce the return time of investment by the same amount. Moreover, since our technique reduces power usage from the utility grid at peak tariff hours, it is one way to move toward a uniform consumption at the suppliers’ level that leads to better use and higher quality and stability.

Description: In vehicular networks, since reliable end-to-end paths between data source and destination seldom exist, replication-based routing protocols are widely used to increase the delivery ratio and reduce the transfer delay. However, after a data message is delivered, its replicas still exist and greatly waste network resources, such as transmission bandwidth and storage spaces. In mobile multimedia communications, the large size of multimedia data greatly aggravates this situation. In this paper, we propose a GPS-based replica deletion scheme with anti-packet distribution for vehicular networks, namely RAD. It utilizes vehicle-to-vehicle and vehicle-to-roadside-unit communications to remove redundant data replicas by a certain number of anti-packets. A roadside unit at each crossing distributes its collected anti-packets to nearby vehicles moving for different directions according to the geographical locations of the to-be-deleted targets. This distribution increases the delivery probability of these anti-packets. Experiment results in a real taxi network show that, compared with others, RAD accelerates replica deletion with less extra overhead

Description: The uncertainty and extreme large scale of cloud resources make task scheduling very difficult which affects the user quality of experience and probably result in a waste of cloud resources and energy consumption. Moreover, some resources stay in an unusable state for extended time. To take into account these problems a cloud resource evaluation model is proposed, termed Entropy Optimization Evaluation and ant colony clustering Model (EOEACCM). The model releases long-term unavailable resources to save energy. First, by mean of the entropy increasing minimum principle, the proposed model can maximize the system utilization and balance profits of both cloud resource providers and users. As a consequence, it can shorten task completion time. Secondly, the model narrows the task scheduling size and achieves optimal scheduling by clustering. To make the model more suitable for the dynamics of cloud resources, the model design improves pheromone update policies by fixing total path length in each function cycle when clustering by the ant colony algorithm. Evaluation of results using EOEACCM demonstrate that it may be applicable for resource management strategies for migration and release, an application which can effectively save energy. The proposed model was evaluated by simulation. Experiment results showed the positive effect of user satisfaction from entropy optimization, as well as scheduling time from clustering. Moreover, when the scale of tasks was large, this clustering algorithm performed much better than others. The clustering model also demonstrated better adaptability when some cloud resources were joined or terminated.