Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.

Software diversity can be utilized in cyberspace security to defend against the zero-day attacks. Existing researches have proved the effectiveness of diversity in bringing security benefits, but few of them touch the problem that whether there is a positive correlation between the security and the diversity. In addition, there is little guidance on how to construct an effective diversified system. For that, this paper develops two diversity metrics based on system attribute matrix, proposes a diversity measurement and verifies the effectiveness of the measurement. Through several simulations on the diversified systems which use voting strategy, the relationship between diversity and security is analyzed. The results show that there is an overall positive correlation between security and diversity. Though some cases are against the correlation, further analysis is made to explain the phenomenon. In addition, the effect of voting strategy is also discussed through simulations. The results show that the voting strategy have a dominant impact on the security, which implies that security benefits can be obtained only with proper strategies. According to the conclusions, some guidance is provided in constructing a more diversified as well as securer system.

In this paper, we consider a wide family of λ-quasi-twisted (λ-QT) codes of index 2 and provide a bound on the minimum Hamming distance. Moreover, we give a sufficient condition for dual containing with respect to Hermitian inner product of these involved codes. As an application, some good stabilizer quantum codes over small finite fields F2 or F3 are obtained from the class of λ-QT codes.

A Multipath TCP (MPTCP) connection uses multiple subflows (i.e., TCP flows), each of which traverses over a wireless link, enabling throughput and resilience enhancements in mobile wireless networks. However, to achieve the benefits, the subflows are necessarily initialized (i.e., must complete TCP handshakes) and sequentially attached to the MPTCP connection. In the standard (MPTCPST), MPTCP initialization raises several problems. First, the TCP handshake of opening subflow is generally associated with a predetermined network. That leads to degraded MPTCP performance when the network does not have the lowest latency among available ones. Second, the first subflow's initialization needs to be successful before the next subflow can commence its attempt to achieve initialization. Therefore, the resilience of multiple paths fails when the first initialization fails. This paper proposes a novel method for MPTCP initialization, namely MPTCPSD (i.e., MPTCP with SYN duplication), which can solve the problems. MPTCPSD duplicates the first SYN and attempts to establish TCP handshakes for all subflows simultaneously, hence inherently improves the loss-resiliency. The subflow that achieves initialization first, is selected as the first subflow, consequently solving the first problem. We have implemented and extensively evaluated MPTCPSD in comparison to MPTCPST. In an emulated network, the evaluation results show that MPTCPSD has better performance that MPTCPST with the scenarios of medium and short flows. Moreover, MPTCPSD outperforms MPTCPST in the case that the opening subflow fails. Moreover, a real network evaluation proves that MPTCPSD efficiently selects the lowest delay network among three ones for the first subflow regardless of the preconfigured default network. Additionally, we propose and implement a security feature for MPTCPSD, that prevents the malicious subflow from being established by a third party.

In recent years, multi-party computation (MPC) frameworks based on replicated secret sharing schemes (RSSS) have attracted the attention as a method to achieve high efficiency among known MPCs. However, the RSSS-based MPCs are still inefficient for several heavy computations like algebraic operations, as they require a large amount and number of communication proportional to the number of multiplications in the operations (which is not the case with other secret sharing-based MPCs). In this paper, we propose RSSS-based three-party computation protocols for modular exponentiation, which is one of the most popular algebraic operations, on the case where the base is public and the exponent is private. Our proposed schemes are simple and efficient in both of the asymptotic and practical sense. On the asymptotic efficiency, the proposed schemes require O(n)-bit communication and O(1) rounds,where n is the secret-value size, in the best setting, whereas the previous scheme requires O(n2)-bit communication and O(n) rounds. On the practical efficiency, we show the performance of our protocol by experiments on the scenario for distributed signatures, which is useful for secure key management on the distributed environment (e.g., distributed ledgers). As one of the cases, our implementation performs a modular exponentiation on a 3,072-bit discrete-log group and 256-bit exponent with roughly 300ms, which is an acceptable parameter for 128-bit security, even in the WAN setting.

Accurate estimation of the state-of-charge is a crucial need for the battery, which is the most important power source in electric vehicles. To achieve better estimation result, an accurate battery model with optimum parameters is required. In this paper, a gradient-free optimization technique, namely tree seed algorithm (TSA), is utilized to identify specific parameters of the battery model. In order to strengthen the search ability of TSA and obtain more quality results, the original algorithm is improved. On one hand, the DE/rand/2/bin mechanism is employed to maintain the colony diversity, by generating mutant individuals in each time step. On the other hand, the control parameter in the algorithm is adaptively updated during the searching process, to achieve a better balance between the exploitation and exploration capabilities. The battery state-of-charge can be estimated simultaneously by regarding it as one of the parameters. Experiments under different dynamic profiles show that the proposed method can provide reliable and accurate estimation results. The performance of conventional algorithms, such as genetic algorithm and extended Kalman filter, are also compared to demonstrate the superiority of the proposed method in terms of accuracy and robustness.

Joint transmit and receive antenna selection (JTRAS) for transceive spatial modulation (TRSM) is investigated in this paper. A couple of low-complexity and efficient JTRAS algorithms are proposed to improve the reliability of TRSM systems by maximizing the minimum Euclidean distance (ED) among all received signals. Specifically, the QR decomposition based ED-JTRAS achieves near-optimal error performance with a moderate complexity reduction as compared to the optimal ED-JTRAS method. The singular value decomposition based ED-JTRAS achieves sub-optimal error performance with a significant complexity reduction. Simulation results show that the proposed methods remarkably improve the system reliability in both uncorrelated and spatially correlated Rayleigh fading channels, as compared to the conventional norm based JTRAS method.

We numerically demonstrate a possibility on-off keying (OOK) type of modulation over tens gigabits per second for sub-terahertz radiation in our proposed wireless transmitter device structure towards radio over fiber (RoF) technology. The integrated device consists of an InP-based compound semiconductor resonant tunneling diode (RTD) adjacent to an InP-based photo diode (PD), a self-complementary type of bow-tie antenna (BTA), external microstrip lines. These integration structures are carefully designed to obtain robust relaxation oscillation (RO) due to the negative differential conductance (NDC) characteristic of the RTD and the nonlinearity of the NDC. Moreover, the device is designed to exhibit OOK modulation of RO due to photo current from the PD inject into the RTD. Electromagnetic simulations and nonlinear equivalent circuit model of the whole device structure are established to perform large signal analysis numerically with considerations of previously measured characteristics of the triple-barrier RTD.

This paper proposes an improved closed-form method for moving source localization using time difference of arrival (TDOA), frequency difference of arrival (FDOA) and differential Doppler rate measurements. After linearizing the measurement equations by introducing three additional parameters, a rough estimate is obtained by using the weighted least-square (WLS) estimator. To further refine the estimate, the relationship between additional parameters and source location is utilized. The proposed method gives a final closed-form solution without iteration or the extra mathematics operations used in existing methods by employing the basic idea of WLS processing. Numerical examples show that the proposed method exhibits better robustness and performance compared with several existing methods.

In this letter, the differential uniformity of power function f(x)=xe over GF(3m) is studied, where m≥3 is an odd integer and $e=rac{3^m-3}{4}$. It is shown that Δf≤3 and the power function is not CCZ-equivalent to the known ones. Moreover, we consider a family of ternary cyclic code C(1,e), which is generated by mω(x)mωe(x). Herein, ω is a primitive element of GF(3m), mω(x) and mωe(x) are minimal polynomials of ω and ωe, respectively. The parameters of this family of cyclic codes are determined. It turns out that C(1,e) is optimal with respect to the Sphere Packing bound.

Intrusion detection system (IDS) is a device or software to monitor a network system for malicious activity. In terms of detection results, there could be two types of false, namely, the false positive (FP) which incorrectly detects normal traffic as abnormal, and the false negative (FN) which incorrectly judges malicious traffic as normal. To protect the network system, we expect that FN should be minimized as low as possible. However, since there is a trade-off between FP and FN when IDS detects malicious traffic, it is difficult to reduce the both metrics simultaneously. In this paper, we propose a sequential classifiers combination method to reduce the effect of the trade-off. The single classifier suffers a high FN rate in general, therefore additional classifiers are sequentially combined in order to detect more positives (reduce more FN). Since each classifier can reduce FN and does not generate much FP in our approach, we can achieve a reduction of FN at the final output. In evaluations, we use NSL-KDD dataset, which is an updated version of KDD Cup'99 dataset. WEKA is utilized as a classification tool in experiment, and the results show that the proposed approach can reduce FN while improving the sensitivity and accuracy.

In order to solve the problem in Automatic Identification System (AIS) that the signal in the target slot cannot be correctly received due to partial overlap of signals in adjacent time slots, the paper introduces a new criterion: maximum expected signal power (MESP) and proposes a novel beamforming algorithm based on generalized singular value decomposition (GSVD) and orthogonal projection. The algorithm employs GSVD to estimate the signal subspace, and adopts orthogonal projection to project the received signal onto the orthogonal subspace of the non-target signal. Then, beamforming technique is used to maximize the output power of the target signal on the basis of MESP. Theoretical analysis and simulation results show the effectiveness of the proposed algorithm.

Very few existing works about inertial sensor based air-writing focused on writing constraints' effects on recognition performance. We proposed a LSTM-based system and made several quantitative analyses under different constraints settings against CHMM, DTW-AP and CNN. The proposed system shows its advantages in accuracy, real-time performance and flexibility.

Malware-infected hosts have typically been detected using network-based Intrusion Detection Systems on the basis of characteristic patterns of HTTP requests collected with dynamic malware analysis. Since attackers continuously modify malicious HTTP requests to evade detection, novel HTTP requests sent from new malware samples need to be exhaustively collected in order to maintain a high detection rate. However, analyzing all new malware samples for a long period is infeasible in a limited amount of time. Therefore, we propose a system for efficiently collecting HTTP requests with dynamic malware analysis. Specifically, our system analyzes a malware sample for a short period and then determines whether the analysis should be continued or suspended. Our system identifies malware samples whose analyses should be continued on the basis of the network behavior in their short-period analyses. To make an accurate determination, we focus on the fact that malware communications resemble natural language from the viewpoint of data structure. We apply the recursive neural network, which has recently exhibited high classification performance in the field of natural language processing, to our proposed system. In the evaluation with 42,856 malware samples, our proposed system collected 94% of novel HTTP requests and reduced analysis time by 82% in comparison with the system that continues all analyses.

To solve the low accuracy problem of the recommender system for long term users, in this paper, we propose a top-N-balanced sequential recommendation based on recurrent neural network. We postulated and verified that the interactions between users and items is time-dependent in the long term, but in the short term, it is time-independent. We balance the top-N recommendation and sequential recommendation to generate a better recommender list by improving the loss function and generation method. The experimental results demonstrate the effectiveness of our method. Compared with a state-of-the-art recommender algorithm, our method clearly improves the performance of the recommendation on hit rate. Besides the improvement of the basic performance, our method can also handle the cold start problem and supply new users with the same quality of service as the old users.

To address the bandwidth bottleneck that exists between LSI chips, we have proposed a novel, high-sensitivity receiver circuit for differential optical transmission on a silicon optical interposer. Both anodes and cathodes of the differential photodiodes (PDs) were designed to be connected to a transimpedance amplifier (TIA) through coupling capacitors. Reverse bias voltage was applied to each of the differential PDs through load resistance. The proposed receiver circuit achieved double the current signal amplitude of conventional differential receiver circuits. The frequency response of the receiver circuit was analyzed using its equivalent circuit, wherein the temperature dependence of the PD was implemented. The optimal load resistances of the PDs were determined to be 5kΩ by considering the tradeoff between the frequency response and bias voltage drop. A small dark current of the PD was important to reduce the voltage drop, but the bandwidth degradation was negligible if the dark current at room temperature was below 1µA. The proposed circuit achieved 3-dB bandwidths of 18.9 GHz at 25°C and 13.7 GHz at 85°C. Clear eye openings in the TIA output waveforms for 25-Gbps 27-1 pseudorandom binary sequence signals were obtained at both temperatures.

As big data attracts attention in a variety of fields, research on data exploration for analyzing large-scale scientific data has gained popularity. To support exploratory analysis of scientific data, effective summarization and visualization of the target data as well as seamless cooperation with modern data management systems are in demand. In this paper, we focus on the exploration-based analysis of scientific array data, and define a spatial V-Optimal histogram to summarize it based on the notion of histograms in the database research area. We propose histogram construction approaches based on a general hierarchical partitioning as well as a more specific one, the l-grid partitioning, for effective and efficient data visualization in scientific data analysis. In addition, we implement the proposed algorithms on the state-of-the-art array DBMS, which is appropriate to process and manage scientific data. Experiments are conducted using massive evacuation simulation data in tsunami disasters, real taxi data as well as synthetic data, to verify the effectiveness and efficiency of our methods.

Network embedding has attracted an increasing amount of attention in recent years due to its wide-ranging applications in graph mining tasks such as vertex classification, community detection, and network visualization. Network embedding is an important method to learn low-dimensional representations of vertices in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt the so-called Skip-gram model in Word2vec. However, as a bag-of-words model, the skip-gram model mainly utilized the local structure information. The lack of information metrics for vertices in global network leads to the mix of vertices with different labels in the new embedding space. To solve this problem, in this paper we propose a Network Representation Learning method with Deep Metric Learning, namely DML-NRL. By setting the initialized anchor vertices and adding the similarity measure in the training progress, the distance information between different labels of vertices in the network is integrated into the vertex representation, which improves the accuracy of network embedding algorithm effectively. We compare our method with baselines by applying them to the tasks of multi-label classification and data visualization of vertices. The experimental results show that our method outperforms the baselines in all three datasets, and the method has proved to be effective and robust.

Let G=(V,E) be an unweighted simple graph. A distance-d independent set is a subset I ⊆ V such that dist(u, v) ≥ d for any two vertices u, v in I, where dist(u, v) is the distance between u and v. Then, Maximum Distance-d Independent Set problem requires to compute the size of a distance-d independent set with the maximum number of vertices. Even for a fixed integer d ≥ 3, this problem is NP-hard. In this paper, we design an exact exponential algorithm that calculates the size of a maximum distance-3 independent set in O(1.4143n) time.

In this paper, we consider the partial gathering problem of mobile agents in arbitrary networks. The partial gathering problem is a generalization of the (well-investigated) total gathering problem, which requires that all the agents meet at the same node. The partial gathering problem requires, for a given positive integer g, that each agent should move to a node and terminate so that at least g agents should meet at each of the nodes they terminate at. The requirement for the partial gathering problem is no stronger than that for the total gathering problem, and thus, we clarify the difference on the move complexity between them. First, we show that agents require Ω(gn+m) total moves to solve the partial gathering problem, where n is the number of nodes and m is the number of communication links. Next, we propose a deterministic algorithm to solve the partial gathering problem in O(gn+m) total moves, which is asymptotically optimal in terms of total moves. Note that, it is known that agents require Ω(kn+m) total moves to solve the total gathering problem in arbitrary networks, where k is the number of agents. Thus, our result shows that the partial gathering problem is solvable with strictly fewer total moves compared to the total gathering problem in arbitrary networks.