Nowadays, with the development of online social networks (OSN), a mass of online social information has been generated in OSN, which has triggered research on social recommendation. Collaborative filtering, as one of the most popular techniques in social recommendation, faces several challenges, such as data sparsity, cold-start users and prediction quality. The motivation of our work is to deal with the above challenges by effectively combining collaborative filtering technology with social information. The trust relationship has been identified as a useful means of using social information to improve the quality of recommendation. In this paper, we propose a trust-based recommendation approach which uses GlobalTrust (GT) to represent the trust value among users as neighboring nodes. A matrix factorization based on singular value decomposition is used to get a trust network built on the GT value. The recommendation results are obtained through a modified random walk algorithm called GlobalTrustWalker. Through experiments on a real-world sparser dataset, we demonstrate that the proposed approach can better utilize users' social trust information and improve the recommendation accuracy on cold-start users.

Analog and digital collaborative design techniques for wireless SoCs are reviewed in this paper. In wireless SoCs, delicate analog performance such as sensitivity of the receiver is easily degraded due to interferences from digital circuit blocks. On the other hand, an analog performance such as distortion is strongly compensated by digital assist techniques with low power consumption. In this paper, a sensitivity recovery technique using the analog and digital collaborative design, and digital assist techniques to achieve low-power and high-performance analog circuits are presented. Such analog and digital collaborative design is indispensable for wireless SoCs.

Unlimited requirements for system-on-chip (SoC) facilitate three-dimensional (3D) technology as a promising alternative for extending Moore's Law. In spite of many advantages 3D technology provides, 3D technology faces testing issues because of the complexity of 3D design. Therefore, resolving the problem of test optimization and reducing test cost are crucial challenges. In this paper, we propose a novel optimization mechanism of 3D SoCs to minimize test time for mid-bond testing. To make our proposed mechanism more practical, we discuss test cost in mid-bond testing with consideration of manufacturing influence factors. Experimental results on ITC'02 SoC benchmark circuits show that our proposed mechanism reduces mid-bond test time by around 73% on average compared with one baseline solution, furthermore, the mechanism also proves its capacity in test cost reduction.

In a previous study, we proposed a technique to recommend candidate verbs for a method name so that developers can consistently use various verbs. In this study, we improve the rule extraction technique proposed in this previous study. Moreover, we confirm that the rank of each correct verb recommended by the new technique is higher than that by the previous technique.

Routing is a challenging issue in mobile social networks (MSNs) because of time-varying links and intermittent connectivity. In order to enable nodes to make right decisions while forwarding messages, exploiting social relationship has become an important method for designing efficient routing protocols in MSNs. In this paper, we first use the temporal evolution graph model to accurately capture the dynamic topology of the MSN. Based on the model, we introduce the social relationship metric for detecting the quality of human social relationship from contact history records. Utilizing this metric, we propose social relationship based betweenness centrality metric to identify influential nodes to ensure messages forwarded by the nodes with stronger social relationship and higher likelihood of contacting other nodes. Then, we present SRBet, a novel social-based forwarding algorithm, which utilizes the aforementioned metric to enhance routing performance. Simulations have been conducted on two real world data sets and results demonstrate that the proposed forwarding algorithm achieves better performances than the existing algorithms.

In social websites, users acquire information from adjacent neighbors as well as distant users by seeking along hyperlinks, and therefore, information diffusions, also seen as processes of “user infection”, show both cascading and jumping routes in social networks. Currently, existing analysis suffers from the difficulty in distinguishing between the impacts of information seeking behaviors, i.e. random walks, and other factors leading to user infections. To this end, we present a mechanism to recognize and measure influences of random walks on information diffusions. Firstly, we propose the concept of information propagation structure (IPS), which is also a directed acyclic graph, to represent frequent information diffusion routes in social networks. In IPS, we represent “jumping routes” as virtual arcs and regard them as the traces of random walks. Secondly, we design a frequent IPS mining algorithm (FIPS). By considering descendant node infections as a consequence of ancestor node infections in IPS, we can use a Bayesian network to model each IPS, and learn parameters based on the records of information diffusions passing through the IPS. Finally, we present a quantitative description method of random walks influence, the method is based on Bayesian probabilistic inferring in IPS, which is used to determine the ancestors, whose infection causes the infection of target users. We also employ betweenness centralities of arcs to evaluate contributions of random walks to certain infections. Experiments are carried out with real datasets and simulations. The results show random walks are influential in early and steady phases of information diffusions. They help diffusions pass through some topology limitations in social networks.

Association rule mining is a powerful data mining tool, and it can be used to discover unknown patterns from large volumes of data. However, people often have to face the risk of disclosing sensitive information when data is shared with different organizations. The association rule mining techniques may be improperly used to find sensitive patterns which the owner is unwilling to disclose. One of the great challenges in association rule mining is how to protect the confidentiality of sensitive patterns when data is released. Association rule hiding refers to sanitize a database so that certain sensitive association rules cannot be mined out in the released database. In this study, we proposed a new method which hides sensitive rules by removing some items in a database to reduce the support or confidence levels of sensitive rules below specified thresholds. Based on the information of positive border rules and negative border rules contained in transactions, the proposed method chooses suitable candidates for modification aimed at reducing the side effects and the data distortion degree. Comparative experiments on real datasets and synthetic datasets demonstrate that the proposed method can hide sensitive rules with much fewer side effects and database modifications.

With recent developments in machine learning technology, the predictions by systems incorporating machine learning can now have a significant impact on the lives and activities of individuals. In some cases, predictions made by machine learning can result unexpectedly in unfair treatments to individuals. For example, if the results are highly dependent on personal attributes, such as gender or ethnicity, hiring decisions might be discriminatory. This paper investigates the neutralization of a probabilistic model with respect to another probabilistic model, referred to as a viewpoint. We present a novel definition of neutrality for probabilistic models, η-neutrality, and introduce a systematic method that uses the maximum likelihood estimation to enforce the neutrality of a prediction model. Our method can be applied to various machine learning algorithms, as demonstrated by η-neutral logistic regression and η-neutral linear regression.

This paper presents an automatic method to track soccer players in soccer video recorded from a single camera where the occurrence of pan-tilt-zoom can take place. The automatic object tracking is intended to support texture extraction in a free viewpoint video authoring application for soccer video. To ensure that the identity of the tracked object can be correctly obtained, background segmentation is performed and automatically removes commercial billboards whenever it overlaps with the soccer player. Next, object tracking is performed by an attribute matching algorithm for all objects in the temporal domain to find and maintain the correlation of the detected objects. The attribute matching process finds the best match between two objects in different frames according to their pre-determined attributes: position, size, dominant color and motion information. Utilizing these attributes, the experimental results show that the tracking process can handle occlusion problems such as occlusion involving more than three objects and occluded objects with similar color and moving direction, as well as correctly identify objects in the presence of camera movements.

Much research which has shown the usage of social ties could improve the location predictive performance, but as the strength of social ties is varying constantly with time, using the movement data of user's close friends at different times could obtain a better predictive performance. A hybrid Markov location prediction algorithm based on dynamic social ties is presented. The time is divided by the absolute time (week) to mine the long-term changing trend of users' social ties, and then the movements of each week are projected to the workdays and weekends to find the changes of the social circle in different time slices. The segmented friends' movements are compared to the history of the user with our modified cross-sample entropy to discover the individuals who have the relatively high similarity with the user in different time intervals. Finally, the user's historical movement data and his friends' movements at different times which are assigned with the similarity weights are combined to build the hybrid Markov model. The experiments based on a real location-based social network dataset show the hybrid Markov location prediction algorithm could improve 15% predictive accuracy compared with the location prediction algorithms that consider the global strength of social ties.

At present, wireless local area networks (WLANs) based on IEEE802.11 are widely deployed in both private premises and public areas. In a public environment offering several access points (APs), a station (STA) needs to choose which AP to associate with. In this paper, we propose a distributed association control scheme with user guidance to increase users' utility based on uplink and downlink throughputs of individual stations. As part of the scheme, we also present a simple throughput estimation method that considers physical data rate, traffic demand, and frame length in both uplink and downlink. Basically, in the proposed scheme, an AP selects a user and suggests that the user moves to another AP if certain conditions are met. The user then decides whether to accept the suggestion or not in a self-interested manner or in a voluntary manner for the benefit of all users including the user's own self. Through simulations under this condition, we confirm that our distributed association control scheme can improve user utility and fairness even though the channel quality of the new AP is unknown in advance.

In wireless LANs, wireless clients are associated with one of access points (APs) to obtain network connectivity, and the AP performs network traffic relay between the wired infrastructure and wireless clients. If a client with a low transmission rate is associated with an AP, the throughput performance of all the clients that are associated with the AP is significantly degraded because of the long channel usage time of the low-rate client. Therefore, it is important to select an appropriate AP when a new client joins the wireless LAN to prevent the performance degradation. In this paper, we propose a traffic control that determines the feasible data traffic from an AP to the clients on the basis of the trade-off relationship between the equal-throughput and equal-airtime traffic allocation policies. We then propose a network-wide association algorithm that allows a client to be associated with the AP that can provide the highest throughput improvement. Simulation results indicate that the proposed algorithm achieves the better aggregate throughput and throughput fairness performances in IEEE 802.11 WLANs.

In recent years, applications of complex-valued neural networks have become wide spread. Quaternions are an extension of complex numbers, and neural networks with quaternions have been proposed. Because quaternion algebra is non-commutative algebra, we can consider two orders of multiplication to calculate weighted input. However, both orders provide almost the same performance. We propose hybrid quaternionic Hopfield neural networks, which have both orders of multiplication. Using computer simulations, we show that these networks outperformed conventional quaternionic Hopfield neural networks in noise tolerance. We discuss why hybrid quaternionic Hopfield neural networks improve noise tolerance from the standpoint of rotational invariance.

This paper focuses on system level simulation of heterogeneous networks (HetNet). Aiming at the imbalance offloading of macro cell and pico cell under the macro-pico coexistence case, we propose an adaptive cell-specific association strategy for HetNet to ensure that users can be served equitably by both macro cell and pico cell. The traditional cell range expansion (CRE) scheme with bias-based cell association has fixed bias values for all pico cells. Our proposal, on the other hand, sets different thresholds of attached users for all MeNB (macro enhanced node B) and PeNBs (pico enhanced node B), and all cell-specific biases are obtained by the proposed adaptive association strategy according to different cell-specific predefined thresholds. With this strategy, the load imbalance between MeNB and different PeNBs is well alleviated, and hence the entire network performance is elevated. Moreover, due to the newly deployed low-power nodes in HetNets, the achieved spectral efficiency of users, especially for cell edge users, is also affected by the downlink inter-cell interference. The idea we put forward is to combine the frequency and power coordination, and so suppress the inter-cell interference. Finally in this paper, we present some numerical results to verify the effectiveness of our proposed approach.

Recommender systems, which provide users with recommendations of content suited to their needs, have received great attention in today's online business world. However, most recommendation approaches exploit only a single source of input data and suffer from the data sparsity problem and the cold start problem. To improve recommendation accuracy in this situation, additional sources of information, such as friend relationship and user-generated tags, should be incorporated in recommendation systems. In this paper, we revise the user-based collaborative filtering (CF) technique, and propose two recommendation approaches fusing user-generated tags and social relations in a novel way. In order to evaluate the performance of our approaches, we compare experimental results with two baseline methods: user-based CF and user-based CF with weighted friendship similarity using the real datasets (Last.fm and Movielens). Our experimental results show that our methods get higher accuracy. We also verify our methods in cold-start settings, and our methods achieve more precise recommendations than the compared approaches.

In this paper, we present new methods for learning the individual patterns of a card user's transaction amount and the region in which he or she uses the card, for a given period, and for determining whether the specified transaction is allowable in accordance with these learned user transaction patterns. Then, we classify legitimate transactions and fraudulent transactions by setting thresholds based on the learned individual patterns.

It is well-known that program readability is important for maintenance tasks. Method names are important identifiers for program readability because they are used for understanding the behavior of methods without reading a part of the program. Although developers can create a method name by arbitrarily choosing a verb and objects, the names are expected to represent the behavior consistently. However, it is not easy for developers to choose verbs and objects consistently since each developer may have a different notion of a suitable lexicon for method names. In this paper, we propose a technique to recommend candidate verbs for a method name so that developers can use various verbs consistently. We recommend candidate verbs likely to be used as a part of a method name, using association rules extracted from existing methods. To evaluate our technique, we have extracted rules from 445 open source projects written in Java and confirmed the accuracy of our approach by applying the extracted rules to several open source applications. As a result, we found that 84.9% of the considered methods in four projects are recommended the existing verb. Moreover, we found that 73.2% of the actual renamed methods in six projects are recommended the correct verb.

Social recommendation systems that make use of the user's social information have recently attracted considerable attention. These recommendation approaches partly solve cold-start and data sparsity problems and significantly improve the performance of recommendation systems. The essence of social recommendation methods is to utilize the user's explicit social connections to improve recommendation results. However, this information is not always available in real-world recommender systems. In this paper, a solution to this problem of explicit social information unavailability is proposed. The existing user-item rating matrix is used to compute implicit social information, and then an ISRec (implicit social recommendation algorithm) which integrates this implicit social information and the user-item rating matrix for social recommendation is introduced. Experimental results show that our method performs much better than state-of-the-art approaches; moreover, complexity analysis indicates that our approach can be applied to very large datasets because it scales linearly with respect to the number of observations in the matrices.

While online social networking is a popular way for people to share information, it carries the risk of unintentionally disclosing personal information. One way to reduce this risk is to anonymize personal information in messages before they are posted. Furthermore, if personal information is somehow disclosed, the person who disclosed it should be identifiable. Several methods developed for anonymizing personal information in natural language text simply remove sensitive phrases, making the anonymized text message unnatural. Other methods change the message by using synonymization or structural alteration to create fingerprints for detecting disclosure, but they do not support the creation of a sufficient number of fingerprints for friends of an online social network user. We have developed a system for anonymizing personal information in text messages that generalizes sensitive phrases. It also creates a sufficient number of fingerprints of a message by using synonyms so that, if personal information is revealed online, the person who revealed it can be identified. A distribution metric is used to ensure that the degree of anonymization is appropriate for each group of friends. A threshold is used to improve the naturalness of the fingerprinted messages so that they do not catch the attention of attackers. Evaluation using about 55,000 personal tweets in English demonstrated that our system creates sufficiently natural fingerprinted messages for friends and groups of friends. The practicality of the system was demonstrated by creating a web application for controlling messages posted on Facebook.

A 10-gigabit Ethernet passive optical network (10G-EPON) is promising for the next generation of access networks. A protocol processor for 10G-EPON needs to not only achieve 10-Gbps throughput but also to have protocol extendibility for various potential services. However, the conventional protocol processor does not have the ability to install additional protocols after chip fabrication, due to its hardware-based architecture. This paper presents a software-hardware cooperative protocol processor for 10G-EPON that provides the protocol extendibility. To achieve the software-hardware cooperation, the protocol processor newly employs a software-hardware partitioning technique driven by the timing requirements of 10G-EPON and a software-hardware interface circuit with event FIFO to absorb performance difference between software and hardware. The fabricated chip with this protocol processor properly works cooperatively and is able to accept newly standardized protocols. This protocol processor enables network operators to install additional service protocols adaptively for their own services.