A threshold secret sharing scheme protects content by dividing it into many pieces and distributing them among different servers. This scheme can also be utilized for the reliable delivery of important content. Thanks to this scheme, the receiver can still reconstruct the original content even if several pieces are lost during delivery due to a multiple-link failure. Nevertheless, the receiver cannot reconstruct the original content unless it receives pieces more than or equal to the threshold. This paper aims to obtain reliable delivery routes for the pieces, as this will minimize the probability that the receiver cannot reconstruct the original content. Although such a route optimization problem can be formulated using an integer linear programming (ILP) model, computation of globally optimum delivery routes based on the ILP model requires large amounts of computational resources. Thus, this paper proposes a lightweight method for computing suboptimum delivery routes. The proposed greedy method computes each of the delivery routes successively by using the conventional shortest route algorithm repeatedly. The link distances are adjusted iteratively on the basis of the given probability of failure on each link and they are utilized for the calculation of each shortest route. The results of a performance evaluation show that the proposed method can compute sub-optimum delivery routes efficiently thanks to the precise adjustment of the link distances, even in backbone networks on a real-world scale.

In this paper, we propose a new cross-layer scheme Cooperation between channel Access control and TCP Rate Adaptation (CATRA) aiming to manage TCP flow contention in multi-hop ad hoc networks. CATRA scheme collects useful information from MAC and physical layers to estimate channel utilization of the station. Based on this information, we adjust Contention Window (CW) size to control the contention between stations. It can also achieve fair channel access for fair channel access of each station and the efficient spatial channel usage. Moreover, the fair value of bandwidth allocation for each flow is calculated and sent to the Transport layer. Then, we adjust the sending rate of TCP flow to solve the contention between flows and the throughput of each flow becomes fairer. The performance of CATRA is examined on various multi-hop network topologies by using Network Simulator (NS-2).

This paper presents an optimal cooperative routing protocol (OCRP) aiming to improve the in-network cache utilization of the Content-Centric Networking (CCN). The objective of OCRP is to selectively aggregate the multiple flows of interest messages onto the same path in order to improve the cache utilization while mitigating the cache contention of the Content Stores (CSs) of CCN routers on the routing path. The proposed routing protocol consists of three processes: (1) Prefix Popularity Observation; (2) Prefix Group (Un)Subscription; and (3) Forwarding Information Base (FIB) Reconstruction. Prefix Popularity Observation observes the popularly cited prefixes to activate a prefix group (un)subscription function, which lets the Designated Router (DR) know which requester router wants to either join or leave a prefix group. Prefix Group (Un)Subscription lets the DR know which requester router is demanding to join or leave which prefix group. FIB Reconstruction reconstructs the FIB entries of the CCN routers involved in the newly computed optimal cooperative path of all prefix groups. The optimal routing path is obtained by binary linear optimization under a flow conservation constraint, cache contention mitigating constraint, and path length constraint. Two metrics of server load and round-trip hop distance are used to measure the performance of the proposed routing protocol. Simulation results from various network scenarios and various settings show advantages over the shortest path routing and our previously proposed cooperative routing schemes.

Because of the popularity of rich content, such as video files, the amount of traffic on the Internet continues to grow every year. Not only is the overall traffic increasing, but also the temporal fluctuations in traffic are increasing, and differences in the amounts of traffic between peak and off-peak periods are becoming very large. Consequently, efficient use of link bandwidth is becoming more challenging. In this paper, we propose a new system for content distribution: storage aware routing (SAR). With SAR, routers having large storage capacities can exploit those links that are underutilized. Our performance evaluations show that SAR can smooth the fluctuations in link utilization.

Presentation slide composition is an important job for knowledge workers. Instead of starting from scratch, users tend to make new presentation slides by reusing existing ones. A primary challenge in slide reuse is to select desired materials from a collection of existing slides. The state-of-the-art solution utilizes texts and images in slides as well as file names to help users to retrieve the materials they want. However, it only allows users to choose an entire slide as a query but does not support the search for a single element such as a few keywords, a sentence, an image, or a diagram. In this paper, we investigate content-based search for a variety of elements in presentation slides. Users may freely choose a slide element as a query. We propose different query processing methods to deal with various types of queries and improve the search efficiency. A system with a user-friendly interface is designed, based on which experiments are performed to evaluate the effectiveness and the efficiency of the proposed methods.

Information-centric networking (ICN) is a promising architecture and has attracted much attention in the area of future Internet architectures. As one of the key technologies in ICN, in-network caching can enhance content retrieval at a global scale without requiring any special infrastructure. In this paper, we propose a workload-aware caching policy, LRU-GT, which allows cache nodes to protect newly cached contents for a period of time (guard time) during which contents are protected from being replaced. LRU-GT can utilize the temporal locality and distinguish contents of different popularity, which are both the characteristics of the workload. Cache replacement is modeled as a semi-Markov process under the Independent Reference Model (IRM) assumption and a theoretical analysis proves that popular contents have longer sojourn time in the cache compared with unpopular ones in LRU-GT and the value of guard time can affect the cache hit ratio. We also propose a dynamic guard time adjustment algorithm to optimize the performance. Simulation results show that LRU-GT can reduce the average hops to get contents and improve cache hit ratio.

Screen content generally consists of text, images, and videos variously generated or captured by computers and other electronic devices. For the purpose of coding such screen content, we introduce alternative intra prediction (AIP) modes based on the emerging high efficiency video coding (HEVC) standard. With text and graphics, edges are much sharper and a large number of corners exist. These properties make it difficult to predict blocks using a one-directional intra prediction mode. The proposed method provides two-directional prediction by combining the existing vertical and horizontal prediction modes. Experiments show that our AIP modes provide an average BD-rate reduction of 2.8% relative to HEVC for general screen contents, and a 0.04% reduction for natural contents.

The relative arrangement, such as relative positions and orientations among objects, can play an important role in expressing the situation such as sports games and race scenes. In this paper, we propose a retargeting method that allows maintaining the relative arrangement. Our proposed retargeting method is based on a warping method which finds an optimal transformation by solving an energy minimization problem. To achieve protection of object arrangement, we introduce an energy that enforces all the objects and the relative positions among these objects to be transformed by the same transformation in the retargeting process. In addition, our method imposes the following three types of conditions in order to obtain more satisfactory results: protection of important regions, avoiding extreme deformation, and cropping with preservation of the balance of visual importance. Experimental results demonstrate that our proposed method maintains the relative arrangement while protecting important regions.

Seam carving, which preserves semantically important image content during resizing process, has been actively researched in recent years. This paper proposes a novel forensic technique to detect the trace of seam carving. We exploit the energy bias and noise level of images under analysis to reliably unveil the evidence of seam carving. Furthermore, we design a detector investigating the relationship among neighboring pixels to estimate the inserted seams. Experimental results from a large set of test images indicates the superior performance of the proposed methods for both seam carving and seam insertion.

In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.

This study proposes an integrated technology based on Proxy Mobile IPv6, which is a network-based protocol with mobility support, and a mobile content delivery network (CDN) that provides efficient content delivery management. The proposed architecture offers several benefits, such as the conservation of network resources because of reduced total traffic between hops and a reduced hop count.

Web page segmentation has a variety of benefits and potential web applications. Early techniques of web page segmentation are mainly based on machine learning algorithms and rule-based heuristics, which cannot be used for large-scale page segmentation. In this paper, we propose a formulated page segmentation method using visual semantics. Instead of analyzing the visual cues of web pages, this method utilizes three measures to formulate the visual semantics: layout tree is used to recognize the visual similar blocks; seam degree is used to describe how neatly the blocks are arranged; content similarity is used to describe the content coherent degree between blocks. A comparison experiment was done using the VIPS algorithm as a baseline. Experiment results show that the proposed method can divide a Web page into appropriate semantic segments.

In IEEE 802.11 standard, the contention window (CW) sizes are not efficient because it does not consider the system load. There has been several mechanisms to achieve the maximum throughput by the optimal CW. But some parameters such as the number of stations and system utilization are difficult to measure in WLAN systems. To solve this problem, we use the network allocation vector (NAV) which represents the transmission of other stations. This parameter can be used to measure the system load. Thus, the CW sizes can be estimated by the system load. In this paper, we derive the analytical model for the optimal CW sizes and the maximum throughput using the NAV and show the relationships between the CW sizes, the throughput and the NAV.

How do global warming and agriculture influence each other? It is possible to answer the question by searching knowledge about the relationship between global warming and agriculture. As exemplified by this question, strong demands exist for searching relationships between objects. Mining knowledge about relationships on Wikipedia has been studied. However, it is desired to search more diverse knowledge about relationships on the Web. By utilizing the objects constituting relationships mined from Wikipedia, we propose a new method to search images with surrounding text that include knowledge about relationships on the Web. Experimental results show that our method is effective and applicable in searching knowledge about relationships. We also construct a relationship search system named “Enishi” based on the proposed new method. Enishi supplies a wealth of diverse knowledge including images with surrounding text to help users to understand relationships deeply, by complementarily utilizing knowledge from Wikipedia and the Web.

Our prior papers proposed a traffic engineering scheme to further localize traffic in peer-assisted content delivery networks (CDNs). This scheme periodically combines the content files and allows them to obtain the combined content files while keeping the price unchanged from the single-content price in order to induce altruistic clients to download content files that are most likely to contribute to localizing network traffic. However, the selection algorithm in our prior work determined which and when content files should be combined according to the cache states of all clients, which is a kind of unrealistic assumption in terms of computational complexity. This paper proposes a new concept of virtual local server to reduce the computational complexity. We could say that the source server in our mechanism has a virtual caching network inside that reflects the cache states of all clients in the ‘actual’ caching network and combines content files based on the virtual caching network. In this paper, without determining virtual caching network according to the cache states of all clients, we approximately estimated the virtual caching network from the cache states of the virtual local server of the local domain, which is the aggregated cache state of only altruistic clients in a local domain. Furthermore, we proposed a content selection algorithm based on a virtual caching network. In this paper, we used news life-cycle model as a content model that had the severe changes in cache states, which was a striking instance of dynamic content models. Computer simulations confirmed that our proposed algorithm successfully localized network traffic.

Recently Peer-to-Peer Content Delivery Networks (P2P CDNs) have attracted considerable attention as a cost-effective way to disseminate digital contents to paid users in a scalable and dependable manner. However, due to its peer-to-peer nature, it faces threat from “colluders” who paid for the contents but illegally share them with unauthorized peers. This means that the detection of colluders is a crucial task for P2P CDNs to preserve the right of contents holders and paid users. In this paper, we propose two colluder detection schemes for P2P CDNs. The first scheme is based on the reputation collected from all peers participating in the network and the second scheme improves the quality of colluder identification by using a technique which is well known in the field of system level diagnosis. The performance of the schemes is evaluated by simulation. The simulation results indicate that even when 10% of authorized peers are colluders, our schemes identify all colluders without causing misidentifications.

The abundance of information published on the Internet makes filtering of hazardous Web pages a difficult yet important task. Supervised learning methods such as Support Vector Machines (SVMs) can be used to identify hazardous Web content. However, scalability is a big challenge, especially if we have to train multiple classifiers, since different policies exist on what kind of information is hazardous. We therefore propose two different strategies to train multiple SVMs for personalized Web content filters. The first strategy identifies common data clusters and then performs optimization on these clusters in order to obtain good initial solutions for individual problems. This initialization shortens the path to the optimal solutions and reduces the training time on individual training sets. The second approach is to train all SVMs simultaneously. We introduce an SMO-based kernel-biased heuristic that balances the reduction rate of individual objective functions and the computational cost of kernel matrix. The heuristic primarily relies on the optimality conditions of all optimization problems and secondly on the pre-calculated part of the whole kernel matrix. This strategy increases the amount of information sharing among learning tasks, thus reduces the number of kernel calculation and training time. In our experiments on inconsistently labeled training examples, both strategies were able to predict hazardous Web pages accurately (> 91%) with a training time of only 26% and 18% compared to that of the normal sequential training.

Recently, many kinds of content are being circulated within a great many service-specific overlay networks. When the content is not extremely delay-sensitive, content circulation between wireless terminals can be realized without additional resources by using off-peak periods in wireless access links. In such content circulation, peer-to-peer content multicast is a promising approach to reduce the load on the centralized server. However, to minimize battery drain, each wireless terminal can only forward content to a restricted number of neighboring terminals once it has received the content. This paper proposes an efficient forwarding scheme for peer-to-peer content multicast between the wireless terminals intermittently connected with the backhaul network. In the proposed scheme, a restricted number of terminals with an earlier start time of off-peak periods are selected to forward the content when the number of forwarding hops from the source terminal is less than or equal to a predetermined threshold. In contrast, a restricted number of terminals are selected randomly when the number of forwarding hops exceeds the threshold. This paper clarifies that the proposed hybrid forwarding scheme can multicast the content to many terminals within an arbitrarily restricted period. A guideline to determine the optimum threshold for switching the terminal selection method in the proposed hybrid scheme is derived from simulation results.

Machine-to-Machine (M2M) communication is expected to grow in networks of the future, where massive numbers of low cost, low function M2M terminals communicate in many-to-many manner in an extremely mobile and dynamic environment. We propose a network architecture called Data-centric Network (DCN) where communication is done using a data identifier (ID) and the dynamic data registered by mobile terminals can be retrieved by specifying the data ID. DCN mitigates the problems of prior arts, which are large size of routing table and transaction load of name resolution service. DCN introduces concept of route attraction and aggregation in which the related routes are attracted to an aggregation point and aggregated to reduce routing table size, and route optimization in which optimized routes are established routes to reduce access transaction load to the aggregation points. These allow the proposed architecture to deal with ever increasing number of data and terminals with frequent mobility and changes in data.

In this paper, we propose a novel content-aware image resizing method based on grid transformation. Our method focuses on not only keeping important regions unchanged but also keeping the aspect ratio of the main object in an image unchanged. The dual conditions can avoid distortion which often occurs when only using the former condition. Our method first calculates image importance. Next, we extract the main objects on an image by using image importance. Finally, we calculate the optimal grid transformation which suppresses changes in size of important regions and in the aspect ratios of the main objects. Our method uses lower and upper thresholds for transformation to suppress distortion due to extreme shrinking and enlargement. To achieve better resizing results, we introduce a boundary discarding process. This process can assign wider regions to important regions, reducing distortions on important regions. Experimental results demonstrate that our proposed method resizes images with less distortion than other resizing methods.