This paper proposes a null-space expansion scheme for multiuser massive MIMO transmission in order to suppress inter-user interference (IUI) triggered by the temporal variation of the channel. The downlink multiuser MIMO channel capacity of time varying channels is severely degraded since IUI must be suppressed at the transmitter side by using past estimated channel state information at the transmitter side (CSIT). Massive MIMO has emerged as one of the most promising technologies for further capacity enhancement by increasing the number of base station (BS) antenna elements. Exploiting the excess degrees of freedom (DoFs) inherent in massive MIMO, a BS with the proposed IUI suppression scheme performs multiple null-steering for each UE (User Equipment) antenna element, which expands the null-space dimension. Computer simulations show that the proposed scheme has superior IUI suppression performance to the existing channel prediction scheme in time varying channels.

The increase in network access devices and demand for high quality of service (QoS) by the users have led to insufficient capacity for the network operators. Moreover, the existing control equipment and mechanisms are not flexible and agile enough for the dynamically changing environment of heterogeneous cellular networks (HetNets). This non-agile control plane is hard to scale with ever increasing traffic demand and has become the performance bottleneck. Furthermore, the new HetNet architecture requires tight coordination and cooperation for the densely deployed small cell base stations, particularly for interference mitigation and dynamic frequency reuse and sharing. These issues further complicate the existing control plane and can cause serious inefficiencies in terms of users' quality of experience and network performance. This article presents an SDN control framework for energy efficient downlink/uplink scheduling in HetNets. The framework decouples the control plane from data plane by means of a logically centralized controller with distributed agents implemented in separate entities of the network (users and base stations). The scheduling problem consists of three sub-problems: (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. Moreover, these sub-problems are coupled and must be solved simultaneously. We formulate the DL/UL scheduling in HetNet as an optimization problem and use the Markov approximation framework to propose a distributed economical algorithm. Then, we divide the algorithm into three sub-routines for (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. These sub-routines are then implemented on different agents of the SDN framework. We run extensive simulation to validate our proposal and finally, present the performance analysis.

The translation based language model (TRLM) is state-of-the-art method to solve the lexical gap problem of the question retrieval in the community-based question answering (cQA). Some researchers tried to find methods for solving the lexical gap and improving the TRLM. In this paper, we propose a new dependency based model (DM) for the question retrieval. We explore how to utilize the results of a dependency parser for cQA. Dependency bigrams are extracted from the dependency parser and the language model is transformed using the dependency bigrams as bigram features. As a result, we obtain the significant improved performances when TRLM and DM approaches are effectively combined.

The development of smart devices has led to the growth of Location-Based Social Networking Services (LBSNSs). In this paper, we introduce an l-Close Range Friends query that finds all l-hop friends of a user within a specified range. We also propose a query processing method on Social Grid Index (SGI). Using real datasets, the performance of our method is evaluated.

This paper illustrates various content sharing systems that take advantage of cloud's storage and computational resources as well as their supporting conventional technologies. First, basic technology concepts supporting cloud-based systems from a client-server to cloud computing as well as their relationships and functional linkages are shown. Second, the taxonomy of cloud-based system models from the aspect of multiple clouds' interoperability is explained. Interoperability can be categorized into provider-centric and client-centric scenarios. Each can be further divided into federated clouds, hybrid clouds, multi-clouds and aggregated service by broker. Third, practical cloud-based systems related to contents sharing are reported and their characteristics are discussed. Finally, future direction of cloud-based content sharing is suggested.

A modular serial pipelined sorting architecture for continuous input sequences is presented. It supports continuous sequences, whose lengths can be dynamically changed, and does so using a very simple control strategy. It consists of identical serial cascaded sorting cells, and lends itself to high frequency implementation with any number of sorting cells, because both data and control signals are pipelined. With L cascaded sorting cells, it produces a fully sorted result for sequences whose length N is equal to or less than L+1; for longer sequences, the largest L elements are sorted out. Being modularly designed, several independent smaller sorters can be dynamically configured to form a larger sorter.

In this paper, we investigate a user and antenna joint selection problem in multi-user large-scale MIMO downlink networks, where a BS with N transmit antennas serves K users, and N is much larger than K. The BS activates only S(S≤N) antennas for data transmission to reduce hardware cost and computation complexity, and selects the set of users to which data is to be transmitted by maximizing the sum-rate. The optimal user and antenna joint selection scheme based on exhaustive search causes considerable computation complexity. Thus, we propose a new joint selection algorithm with low complexity and analyze the performance of the proposed scheme in terms of sum-rate and complexity. When S=7, N=10, K=5, and SNR=10dB, the sum-rate of the proposed scheme is 5.1% lower than that of the optimal scheme, while the computation complexity of the proposed scheme is reduced by 99.0% compared to that of the optimal scheme.

PPDP (Privacy-Preserving Data Publishing) is technology that discloses personal information while protecting individual privacy. k-anonymity is a privacy model that should be achieved in PPDP. However, k-anonymity does not guarantee privacy against adversaries who have knowledge of even a few uncommon individuals in a population. In this paper, we propose a new model, called k-presence-secrecy, that prevents such adversaries from inferring whether an arbitrary individual is included in a personal data table. We also propose an algorithm that satisfies the model. k-presence-secrecy is a practical model because an algorithm that satisfies it requires only a PPDP target table as personal information, whereas previous models require a PPDP target table and almost all the background knowledge of adversaries. Our experiments show that, whereas an algorithm satisfying only k-anonymity cannot protect privacy, even against adversaries who have knowledge for one uncommon individual in a population, our algorithm can do so with less information loss and shorter execution time.

This paper proposes a new antenna array design of Massive MIMO for capacity enhancement in line of sight (LOS) environments. Massive MIMO has two key problems: the heavy overhead of feeding back the channel state information (CSI) for very large number of transmission and reception antenna element pairs and the huge computation complexity imposed by the very large scale matrixes. We have already proposed a practical application of Massive MIMO, that is, Massive Antenna Systems for Wireless Entrance links (MAS-WE), which can clearly solve the two key problems of Massive MIMO. However, the conventional antenna array arrangements; e.g. uniform planar array (UPA) or uniform circular array (UCA) degrade the system capacity of MAS-WE due to the channel spatial correlation created by the inter-element spacing. When the LOS component dominates the propagation channel, the antenna array can be designed to minimize the inter-user channel correlation. We propose an antenna array arrangement to control the grating-lobe positions and achieve very low channel spatial correlation. Simulation results show that the proposed arrangement can reduce the spatial correlation at CDF=50% value by 80% compared to UCA and 75% compared to UPA.

Multi-view video streaming plays an important role in new interactive and augmented video applications such as telepresence, remote surgery, and entertainment. For those applications, interactive multi-view video transmission schemes have been proposed that aim to reduce the amount of video traffic. Specifically, these schemes only encode and transmit video frames, which are potentially displayed by users, based on periodical feedback from the users. However, existing schemes are vulnerable to frame loss, which often occurs during transmissions, because they encode most video frames using inter prediction and inter-view prediction to reduce traffic. Frame losses induce significant quality degradation due to the collapse of the decoding operations. To improve the loss resilience, we propose an encoding/decoding system, Frame Popularity-based Multi-view Video Streaming (FP-MVS), for interactive multi-view video streaming services. The main idea of FP-MVS is to assign intra (I) frames in the prediction structure for less/more popular (i.e., few/many observed users) potential frames in order to mitigate the impact of a frame loss. In addition, FP-MVS utilizes overlapping and non-overlapping areas between all user's potential frames to prevent redundant video transmission. Although each intra-frame has a large data size, the video traffic can be reduced within a network constraint by combining multicast and unicast for overlapping and non-overlapping area transmissions. Evaluations using Joint Multi-view Video Coding (JMVC) demonstrated that FP-MVS achieves higher video quality even in loss-prone environments. For example, our scheme improves video quality by 11.81dB compared to the standard multi-view video encoding schemes at the loss rate of 5%.

In this work, we study efficient scheduling with network coding in a scalable video coding (SVC) multicast system. Transmission consists of two stages. The original SVC packets are multicasted by the server in the first stage and the lost packets are retransmitted in the second stage. With deadline constraint, the consumer can be only satisfied when the requested packets are received before expiration. Further, the hierarchical encoding architecture of SVC introduces extra decoding delay which poses a challenge for providing acceptable reconstructed video quality. To solve these problems, instantly decodable network coding is applied for reducing the decoding delay, and a novel packet weighted policy is designed to better describe the contribution a packet can make in upgrading the recovered video quality. Finally, an online packet scheduling algorithm based on the maximal weighted clique is proposed to improve the delay, deadline miss ratio and users' experience. Multiple characteristics of SVC packets, such as the packet utility, the slack time and the number of undelivered/wanted packets, are jointly considered. Simulation results prove that the proposed algorithm requires fewer retransmissions and achieves lower deadline miss ratio. Moreover, the algorithm enjoys fine recovery video quality and provides high user satisfaction.

The Android pattern unlock is a widely adopted graphical password system that requires a user to draw a secret pattern connecting points arranged in a grid. The theoretical security of pattern unlock can be defined by the number of possible patterns. However, only upper bounds of the number of patterns have been known except for 3×3 and 4×4 grids for which the exact number of patterns was found by brute-force enumeration. In this letter, we present the first lower bound by computing the minimum number of visible points from each point in various subgrids.

In this paper, we introduce a self-constructive Normalized Gaussian Network (NGnet) for online learning tasks. In online tasks, data samples are received sequentially, and domain knowledge is often limited. Then, we need to employ learning methods to the NGnet that possess robust performance and dynamically select an accurate model size. We revise a previously proposed localized forgetting approach for the NGnet and adapt some unit manipulation mechanisms to it for dynamic model selection. The mechanisms are improved for more robustness in negative interference prone environments, and a new merge manipulation is considered to deal with model redundancies. The effectiveness of the proposed method is compared with the previous localized forgetting approach and an established learning method for the NGnet. Several experiments are conducted for a function approximation and chaotic time series forecasting task. The proposed approach possesses robust and favorable performance in different learning situations over all testbeds.

With the popularization of Internet of things (IoT), the interaction between human and IoT has become a daily life. In this interaction, the objects of IoT usually require access to personal data, which are often sensitive. We propose a lightweight privacy-preserving model based on the trust evaluation that it can effectively protect privacy based on simple threshold detection. The key issue we address in this work is how to construct trust model so that non trusted objects were prevented from accessing private data. This work can be considered as a lightweight approach to access control for privacy-preservation. The main algorithm in the proposed model is a kind of dynamic self-adjusting trust evaluation mechanism that uses a combination of interaction information occurs between the human and the Internet of things, between the human and the human. According to the given threshold, the trust model can determine the data level of object access in the IoT. We have implemented a prototype of the proposed scheme, thereby demonstrating the feasibility of the proposed scheme on resource-constrained devices.

With the rapid development of information and Web technologies, people are facing ‘information overload’ in their daily lives. The personalized recommendation system (PRS) is an effective tool to assist users extract meaningful information from the big data. Collaborative filtering (CF) is one of the most widely used personalized recommendation techniques to recommend the personalized products for users. However, the conventional CF technique has some limitations, such as the low accuracy of of similarity calculation, cold start problem, etc. In this paper, a PRS model based on the Support Vector Machine (SVM) is proposed. The proposed model not only considers the items' content information, but also the users' demographic and behavior information to fully capture the users' interests and preferences. An improved Particle Swarm Optimization (PSO) algorithm is also proposed to improve the performance of the model. The efficiency of the proposed method is verified by multiple benchmark datasets.

We have developed membrane distributed Bragg reflector (DBR) lasers on thermally oxidized Si substrate (SiO2/Si substrate) to evaluate the parameters of the on-Si lasers we have been developing. The lasers have InGaAsP-based multi-quantum wells (MQWs) grown on InP substrate. We used direct bonding to transfer this active epitaxial layer to SiO2/Si substrate, followed by epitaxial growth of InP to fabricate a buried-heterostructure (BH) on Si. The lateral p-i-n structure was formed by thermal diffusion of Zn and ion implantation of Si. For the purpose of evaluating laser parameters such as internal quantum efficiency and internal loss, we fabricated long-cavity lasers that have 200- to 600-µm-long active regions. The fabricated DBR lasers exhibit threshold currents of 1.7, 2.1, 2.8, and 3.7mA for active-region lengths of 200, 300, 400, and 600µm, respectively. The differential quantum efficiency also depends on active-region length. In addition, the laser characteristics depend on the distance between active region and p-doped region. We evaluated the internal loss to be 10.2cm-1 and internal quantum efficiency to be 32.4% with appropriate doping profile.

In this paper, we propose two secure multiuser multiple-input multiple-output (MIMO) transmission approaches based on interference alignment (IA) in the presence of an eavesdropper. To deal with the information leakage to the eavesdropper as well as the interference signals from undesired transmitters (Txs) at desired receivers (Rxs), our approaches aim to design the transmit precoding and receive subspace matrices to minimize both the total inter-main-link interference and the wiretapped signals (WSs). The first proposed IA scheme focuses on aligning the WSs into proper subspaces while the second one imposes a new structure on the precoding matrices to force the WSs to zero. In each proposed IA scheme, the precoding matrices and the receive subspaces at the legitimate users are alternatively selected to minimize the cost function of a convex optimization problem for every iteration. We provide the feasible conditions and the proofs of convergence for both IA approaches. The simulation results indicate that our two IA approaches outperform the conventional IA algorithm in terms of the average secrecy sum rate.

With the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.

We present a new notion of public-key encryption, called multi-divisible on-line/off-line encryptions, in which partial ciphertexts can be computed and made publicly available for the recipients before the recipients' public key and/or the plaintexts are determined. We formalize its syntax and define several security notions with regard to the level of divisibility, the number of users, and the number of encryption (challenge) queries per user. Furthermore, we show implications and separations between these security notions and classify them into three categories. We also present concrete multi-divisible on-line/off-line encryption schemes. The schemes allow the computationally-restricted and/or bandwidth-restricted devices to transmit ciphertexts with low computational overhead and/or low-bandwidth network.

Service chaining (SC) is a method for realizing a service by transferring flows among several service functions (SFs) that process packets. A route among SFs is called a service path (SP). Service chaining is being developed to reduce costs, increase flexibility, and shorten time-to-market. SC technologies are expected to be applied to carrier networks so that large communication carriers benefit from them. We assume that SPs process the traffic of services that treat all users in the same way such as an Internet access service for home users. An SP processes flows from several users. We do not assume that each SP is assigned to a user. Because a carrier network accommodates many users, each service will be heavily utilized. Therefore, it is assumed that the amount of traffic of a service is larger than the resource of an SF apparatus. Several SPs are required to process the traffic. SPs are supposed to meet two requirements. One is guaranteeing minimum bandwidth. The other is reducing the number of SF apparatuses, i.e., high resource utilization. Resource utilization depends on the combination of the resource quantities of SF apparatuses. Network operators have to determine the bandwidth of each SP within the range from the minimum bandwidth to the resource quantities of SF apparatuses to maximize resource utilization. Methods for determining the bandwidth of each SP have not been proposed for meeting the two requirements. Therefore, we propose a resource allocation method for this purpose. The proposed method determines the bandwidth of each SP on the basis of the combination of the resource quantities of SF apparatuses for guaranteeing the minimum bandwidth and maximizing resource utilization and allocates necessary resources to each SP. We also evaluate the proposed method and confirm that it can guarantee the minimum bandwidth of SPs and achieve high resource utilization regardless of the combination of the resource quantities of SF apparatuses. Although SF apparatuses are generally produced without considering the combinations of resource quantities of SF apparatuses in SPs, the proposed method can provide more options for selecting SF apparatuses.