Recent years have witnessed a rapid increase in cyber-attacks through unauthorized accesses and DDoS attacks. Since packet classification is a fundamental technique to prevent such illegal communications, it has gained considerable attention. Packet classification is achieved with a linear search on a classification rule list that represents the packet classification policy. As such, a large number of rules can result in serious communication latency. To decrease this latency, the problem is formalized as optimal rule ordering (ORO). In most cases, this problem aims to find the order of rules that minimizes latency while satisfying the dependency relation of the rules, where rules ri and rj are dependent if there is a packet that matches both ri and rj and their actions applied to packets are different. However, there is a case in which although the ordering violates the dependency relation, the ordering satisfies the packet classification policy. Since such an ordering can decrease the latency compared to an ordering under the constraint of the dependency relation, we have introduced a new model, called relaxed optimal rule ordering (RORO). In general, it is difficult to determine whether an ordering satisfies the classification policy, even when it violates the dependency relation, because this problem contains unsatisfiability. However, using a zero-suppressed binary decision diagram (ZDD), we can determine it in a reasonable amount of time. In this paper, we present a simulated annealing method for RORO which interchanges rules by determining whether rules ri and rj can be interchanged in terms of policy violation using the ZDD. The experimental results show that our method decreases latency more than other heuristics.

Heterogeneous networks (HetNets) technology is expected to be applied in next generation cellular networks to boost system capacity. However, applying HetNets introduces a significant amount of interference among different tiers within the same cell. In this paper, we propose a weighted rank constrained rank minimization (WRCRM) based interference alignment (IA) approach for three-tier HetNets. The concept of RCRM is applied in a different way to deal with the basic characteristic of different tiers: their different interference tolerance. In the proposed WRCRM approach, interference components at different tiers are weighted with different weighting factors (WFs) to reflect their vulnerability to interference. First, we derive an inner and a loose outer bound on the achievable degrees of freedom (DoF) for the three-tier system that is modeled as a three-user mutually interfering broadcast channel (MIBC). Then, the derived bounds along with the well-known IA feasibility conditions are used to show the effectiveness of the proposed WRCRM approach. Results show that there exist WF values that maximize the achievable interference-free dimensions. Moreover, adjusting the required number of DoF according to the derived bounds improves the performance of the WRCRM approach.

Near real-time periodic tasks, which are popular in multimedia streaming applications, have deadline periods that are longer than the input intervals thanks to buffering. For such applications, the conventional frame-based schedulings cannot realize the optimal scheduling due to their shortsighted deadline assumptions. To realize globally energy-efficient executions of these applications, we propose a novel task scheduling algorithm, which takes advantage of the long deadline period. We confirm our approach can take advantage of the longer deadline period and reduce the average power consumption by up to 18%.

Based on the License Assisted Access (LAA) small cell architecture, the LAA coexisting with Wi-Fi heterogeneous networks provide LTE mobile users with high bandwidth efficiency as the unlicensed channels are shared among LAA and Wi-Fi. However, the LAA and Wi-Fi will affect each other when both systems are using the same unlicensed channel in the heterogeneous networks. In such a network, unlicensed band allocation for LAA and Wi-Fi is an important issue that may affect the quality of service (QoS) of both systems significantly. In this paper, we propose an analytical model and conduct simulation experiments to study two allocations for the unlicensed band: unlicensed full allocation (UFA), unlicensed time-division allocation (UTA), and the corresponding buffering mechanism for the LAA data packets. We evaluate the performance for these unlicensed band allocations schemes in terms of the acceptance rate of both LAA and Wi-Fi packet data in LAA buffer queue. Our study provides guidelines for designing channel occupation phase and the buffer size of LAA small cell.

It is of great importance to recommend collaborators for scholars in academic social networks, which can benefit more scientific research results. Facing the problem of data sparsity of co-author recommendation in academic social networks, a novel recommendation algorithm named HeteroRWR (Heterogeneous Random Walk with Restart) is proposed. Different from the basic Random Walk with Restart (RWR) model which only walks in homogeneous networks, HeteroRWR implements multiple random walks in a heterogeneous network which integrates a citation network and a co-authorship network to mine the k mostly valuable co-authors for target users. By introducing the citation network, HeteroRWR algorithm can find more suitable candidate authors when the co-authorship network is extremely sparse. Candidate recommenders will not only have high topic similarities with target users, but also have good community centralities. Analyses on the convergence and time efficiency of the proposed approach are presented. Extensive experiments have been conducted on DBLP and CiteSeerX datasets. Experimental results demonstrate that HeteroRWR outperforms state-of-the-art baseline methods in terms of precision and recall rate even in the case of incorporating an incomplete citation dataset.

Detecting phishing websites is imperative. Among several detection schemes, the promising ones are the visual similarity-based approaches. In those, targeted legitimate website's visual features referred to as signatures are stored in SDB (Signature Database) by the system administrator. They can only detect phishing websites whose signatures are highly similar to SDB's one. Thus, the system administrator has to register multiple signatures to detect various phishing websites and that cost is very high. This incurs the vulnerability of zero-day phishing attack. In order to address this issue, an auto signature update mechanism is needed. The naive way of auto updating SDB is expanding the scope of detection by adding detected phishing website's signature to SDB. However, the previous approaches are not suitable for auto updating since their similarity can be highly different among targeted legitimate website and subspecies of phishing website targeting that legitimate website. Furthermore, the previous signatures can be easily manipulated by attackers. In order to overcome the problems mentioned above, in this paper, we propose a hue signature auto update system for visual similarity-based phishing detection with tolerance to zero-day attack. The phishing websites targeting certain legitimate website tend to use the targeted website's theme color to deceive users. In other words, the users can easily distinguish phishing website if it has highly different hue information from targeted legitimate one (e.g. red colored Facebook is suspicious). Thus, the hue signature has a common feature among the targeted legitimate website and subspecies of phishing websites, and it is difficult for attackers to change it. Based on this notion, we argue that the hue signature fulfills the requirements about auto updating SDB and robustness for attackers' manipulating. This commonness can effectively expand the scope of detection when auto updating is applied to the hue signature. By the computer simulation with a real dataset, we demonstrate that our system achieves high detection performance compared with the previous scheme.

This paper presents high efficiency Class-E and compact Doherty power amplifiers (PAs) with novel harmonics termination for handset applications using a GaAs/InGaP heterojunction bipolar transistor (HBT) process. The novel harmonics termination circuit effectively reduces the insertion loss of the matching circuit, allowing a device with a compact size. The Doherty PA uses a lumped-element transformer which consists of metal-insulator-metal (MIM) capacitors on an IC substrate, a bonding-wire inductor and short micro-strip lines on a printed circuit board (PCB). The fabricated Class-E PA exhibits a power added efficiency (PAE) as high as 69.0% at 1.95GHz and as high as 67.6% at 2.535GHz. The fabricated Doherty PA exhibits an average output power of 25.5dBm and a PAE as high as 50.1% under a 10-MHz band width quadrature phase shift keying (QPSK) 6.16-dB peak-to-average-power-ratio (PAPR) LTE signal at 1.95GHz. The fabricated chip size is smaller than 1mm2. The input and output Doherty transformer areas are 0.5mm by 1.0mm and 0.7mm by 0.7mm, respectively.

Phase interferometer using baseline composed by uniform linear array (ULA) with stable phase center for estimating the angle of arrival (AOA) is always employed in the direction finding (DF) system. However, the phase center of antenna element could vary with the incident angle, frequency, multipath and so on. To deal with these problems, a novel method is proposed in this paper to calibrate the phase center over ultra-wideband (UWB). Meanwhile, the restrictions of this method are discussed. Numerical simulations reveal that higher accuracy and larger unambiguous angle range can be obtained by the proposed method.

As a generalization of perfect nonlinear (PN) functions, zero-difference balanced (ZDB) functions play an important role in coding theory, cryptography and communications engineering. Inspired by a foregoing work of Liu et al. [1], we present a class of ZDB functions with new parameters based on the cyclotomy in finite fields. Employing these ZDB functions, we obtain simultaneously optimal constant composition codes and perfect difference systems of sets.

An adaptive bit allocation scheme for zero-forcing (ZF) Tomlinson-Harashima precoding (THP) is proposed. The ZF-THP enables us to achieve feasible bit error rate (BER) performance when appropriate substream permutations are installed at the transmitter. In this study, the number of bits in each substream is adaptively allocated to minimize the average BER in fading environments. Numerical examples are provided to compare the proposed method with eigenbeam space division multiplexing (E-SDM) method.

This letter proposes a novel dynamic channel assignment (DCA) scheme to improve the downlink system capacity in heterogeneous networks (HetNets) with fractional frequency reuse (FFR). In the proposed DCA scheme, the macro base station (MBS) finds small-cell base stations (SBSs) that give strong interference to macro user equipments (MUEs) and then dynamically assigns subchannels to the SBSs to serve their small-cell user equipments (SUEs) according to the cross-tier interference information to MUEs. Through simulation results, it is shown that the proposed DCA scheme outperforms other schemes in terms of the total system capacity.

Previous works have studied the throughput allocation of the heterogeneous storage system consisting of SSD and HDD in the dynamic setting where users are not all present in the system simultaneously, but those researches make multiple servers as one large resource pool, and cannot cope with the multi-server environment. We design a dynamic throughput allocation mechanism named DAM, which can handle the throughput allocation of multiple heterogeneous servers in the dynamic setting, and can provide a number of desirable properties. The experimental results show that DAM can make one dynamic throughput allocation of multiple servers for making sure users' local allocations in each server, and can provide one efficient and fair throughput allocation in the whole system.

Kakuro is a popular logic puzzle, in which a player fills in all empty squares with digits from 1 to 9 so that the sum of digits in each (horizontal or vertical) line is equal to a given number, called a clue, and digits in each line are all different. In 2016, Bultel, Dreier, Dumas, and Lafourcade proposed a physical zero-knowledge proof protocol for Kakuro using a deck of cards; their proposed protocol enables a prover to convince a verifier that the prover knows the solution of a Kakuro puzzle without revealing any information about the solution. One possible drawback of their protocol would be that the protocol is not perfectly extractable, implying that a prover who does not know the solution can convince a verifier with a small probability; therefore, one has to repeat the protocol to make such an error become negligible. In this paper, to overcome this, we design zero-knowledge proof protocols for Kakuro having perfect extractability property. Our improvement relies on the ideas behind the copy protocols in the field of card-based cryptography. By executing our protocols with a real deck of physical playing cards, humans can practically perform an efficient zero-knowledge proof of knowledge for Kakuro.

As the demand of data reliability becomes more and more larger, most of today's storage systems adopt erasure codes to assure the data could be reconstructed when suffering from physical device failures. In order to fast recover the lost data from a single failure, recovery optimization methods have attracted a lot of attention in recent years. However, most of the existing optimization methods focus on homogeneous devices, ignoring the fact that the storage devices are usually heterogeneous. In this paper, we propose a new recovery optimization method named HSR (Heterogeneous Storage Recovery) method, which uses both loads and speed rate among physical devices as the optimization target, in order to further improve the recovery performance for heterogeneous devices. The experiment results show that, compared to existing popular recovery optimization methods, HSR method gains much higher recovery speed over heterogeneous storage devices.

Power line communication (PLC) networks play an important role in home networks and in next generation hybrid networks, which provide higher data rates (Gbps) and easier connectivity. The standard medium access control (MAC) protocol of PLC networks, IEEE 1901, uses a special carrier sense multiple access with collision avoidance (CSMA/CA) mechanism, in which the deferral counter technology is introduced to avoid unnecessary collisions. Although PLC networks have achieved great commercial success, MAC layer analysis for IEEE 1901 PLC networks received limited attention. Until now, a few studies used renewal theory and strong law of large number (SLLN) to analyze the MAC performance of IEEE 1901 protocol. These studies focus on saturated conditions and neglect the impacts of buffer size and traffic rate. Additionally, they are valid only for homogeneous traffic. Motivated by these limitations, we develop a unified and scalable analytical model for IEEE 1901 protocol in unsaturated conditions, which comprehensively considers the impacts of traffic rate, buffer size, and traffic types (homogeneous or heterogeneous traffic). In the modeling process, a multi-layer discrete Markov chain model is constructed to depict the basic working principle of IEEE 1901 protocol. The queueing process of the station buffer is captured by using Queueing theory. Furthermore, we present a detailed analysis for IEEE 1901 protocol under heterogeneous traffic conditions. Finally, we conduct extensive simulations to verify the analytical model and evaluate the MAC performance of IEEE 1901 protocol in PLC networks.

Smartphone users often want to customize the positions and functions of physical buttons to accommodate their own usage patterns; however, this is unfeasible for electronic mobile devices based on COTS (Commercial Off-The-Shelf) due to high production costs and hardware design constraints. In this letter, we present the design and implementation of customized virtual buttons that are localized using only common built-in sensors of electronic mobile devices. We develop sophisticated strategies firstly to detect when a user taps one of the virtual buttons, and secondly to locate the position of the tapped virtual button. The virtual-button scheme is implemented and demonstrated in a COTS-based smartphone. The feasibility study shows that, with up to nine virtual buttons on five different sides of the smartphone, the proposed virtual buttons can operate with greater than 90% accuracy.

Notes on two constructions of zero-difference balanced (ZDB) functions are made in this letter. Then ZDB functions over Ze×∏ki=0 Fqi are obtained. And it shows that all the known ZDB functions using cyclotomic cosets over Zn are special cases of a generic construction. Moreover, applications of these ZDB functions are presented.

Femtocell has been considered as a key promising technology to improve the capacity of a cellular system. However, the femtocells deployed inside a macrocell coverage are potentially suffered from excessive interference. This paper proposes a novel radio resource optimization in closed access femtocell networks based on bat algorithm. Bat algorithm is inspired by the behavior of bats in their echolocation process. While the original bat algorithm is designed to solve the complex optimization problem in continuous search space, the proposed modified bat algorithm extends the search optimization in a discrete search space which is suitable for radio resource allocation problem. The simulation results verify the convergence of the proposed optimization scheme to the global optimal solution and reveal that the proposed scheme based on modified bat algorithm facilitates the improvement of the femtocell network capacity.

This paper introduces a new noise generation algorithm for vocoder-based speech waveform generation. White noise is generally used for generating an aperiodic component. Since short-term white noise includes a zero-frequency component (ZFC) and inaudible components below 20 Hz, they are reduced in advance when synthesizing. We propose a new noise generation algorithm based on that for velvet noise to overcome the problem. The objective evaluation demonstrated that the proposed algorithm can reduce the unwanted components.

Heterogeneous defect prediction (HDP) is to detect the largest number of defective software modules in one project by using historical data collected from other projects with different metrics. However, these data can not be directly used because of different metrics set among projects. Meanwhile, software data have more non-defective instances than defective instances which may cause a significant bias towards defective instances. To completely solve these two restrictions, we propose unsupervised deep domain adaptation approach to build a HDP model. Specifically, we firstly map the data of source and target projects into a unified metric representation (UMR). Then, we design a simple neural network (SNN) model to deal with the heterogeneous and class-imbalanced problems in software defect prediction (SDP). In particular, our model introduces the Maximum Mean Discrepancy (MMD) as the distance between the source and target data to reduce the distribution mismatch, and use the cross-entropy loss function as the classification loss. Extensive experiments on 18 public projects from four datasets indicate that the proposed approach can build an effective prediction model for heterogeneous defect prediction (HDP) and outperforms the related competing approaches.