High dimensional data arising from diverse scientific research fields and industrial development have led to increased interest in sparse learning due to model parsimony and computational advantage. With the assumption of sparsity, many computational problems can be handled efficiently in practice. Structured sparse learning encodes the structural information of the variables and has been quite successful in numerous research fields. With various types of structures discovered, sorts of structured regularizations have been proposed. These regularizations have greatly improved the efficacy of sparse learning algorithms through the use of specific structural information. In this article, we present a systematic review of structured sparse learning including ideas, formulations, algorithms, and applications. We present these algorithms in the unified framework of minimizing the sum of loss and penalty functions, summarize publicly accessible software implementations, and compare the computational complexity of typical optimization methods to solve structured sparse learning problems. In experiments, we present applications in unsupervised learning, for structured signal recovery and hierarchical image reconstruction, and in supervised learning in the context of a novel graph-guided logistic regression.

We present a neuro-heuristic computing platform for finding the solution for initial value problems (IVPs) of nonlinear pantograph systems based on functional differential equations (P-FDEs) of different orders. In this scheme, the strengths of feed-forward artificial neural networks (ANNs), the evolutionary computing technique mainly based on genetic algorithms (GAs), and the interior-point technique (IPT) are exploited. Two types of mathematical models of the systems are constructed with the help of ANNs by defining an unsupervised error with and without exactly satisfying the initial conditions. The design parameters of ANN models are optimized with a hybrid approach GA–IPT, where GA is used as a tool for effective global search, and IPT is incorporated for rapid local convergence. The proposed scheme is tested on three different types of IVPs of P-FDE with orders 1–3. The correctness of the scheme is established by comparison with the existing exact solutions. The accuracy and convergence of the proposed scheme are further validated through a large number of numerical experiments by taking different numbers of neurons in ANN models.

An increasing number of underwater gliders have been applied to lake monitoring. Lakes have a limited vertical space. Therefore, good space-saving capacity is required for underwater gliders to enlarge the spacing between monitoring waypoints. This paper presents a space-saving steering method under a small pitch angle (SPA) for appearance-fixed underwater gliders. Steering under an SPA increases the steering angle in per unit vertical space. An amended hydrodynamic model for both small and large attack angles is presented to help analyze the steering process. Analysis is conducted to find the optimal parameters of net buoyancy and roll angle for steering under an SPA. A lake trial with a prototype tiny underwater glider (TUG) is conducted to inspect the applicability of the presented model. The trial results show that steering under an SPA saves vertical space, unlike that under a large pitch angle. Simulation results of steering are consistent with the trial results. In addition, multiple-waypoint trial shows that monitoring with steering under an SPA covers a larger horizontal displacement than that without steering.

Optimized task scheduling is one of the most important challenges to achieve high performance in multiprocessor environments such as parallel and distributed systems. Most introduced task-scheduling algorithms are based on the so-called list scheduling technique. The basic idea behind list scheduling is to prepare a sequence of nodes in the form of a list for scheduling by assigning them some priority measurements, and then repeatedly removing the node with the highest priority from the list and allocating it to the processor providing the earliest start time (EST). Therefore, it can be inferred that the makespans obtained are dominated by two major factors: (1) which order of tasks should be selected (sequence subproblem); (2) how the selected order should be assigned to the processors (assignment subproblem). A number of good approaches for overcoming the task sequence dilemma have been proposed in the literature, while the task assignment problem has not been studied much. The results of this study prove that assigning tasks to the processors using the traditional EST method is not optimum; in addition, a novel approach based on the ant colony optimization algorithm is introduced, which can find far better solutions.

The security of cryptographic systems is a major concern for cryptosystem designers, even though cryptography algorithms have been improved. Side-channel attacks, by taking advantage of physical vulnerabilities of cryptosystems, aim to gain secret information. Several approaches have been proposed to analyze side-channel information, among which machine learning is known as a promising method. Machine learning in terms of neural networks learns the signature (power consumption and electromagnetic emission) of an instruction, and then recognizes it automatically. In this paper, a novel experimental investigation was conducted on field-programmable gate array (FPGA) implementation of elliptic curve cryptography (ECC), to explore the efficiency of side-channel information characterization based on a learning vector quantization (LVQ) neural network. The main characteristics of LVQ as a multi-class classifier are that it has the ability to learn complex non-linear input-output relationships, use sequential training procedures, and adapt to the data. Experimental results show the performance of multi-class classification based on LVQ as a powerful and promising approach of side-channel data characterization.

Industrial control systems (ICSs) are widely used in critical infrastructures, making them popular targets for attacks to cause catastrophic physical damage. As one of the most critical components in ICSs, the programmable logic controller (PLC) controls the actuators directly. A PLC executing a malicious program can cause significant property loss or even casualties. The number of attacks targeted at PLCs has increased noticeably over the last few years, exposing the vulnerability of the PLC and the importance of PLC protection. Unfortunately, PLCs cannot be protected by traditional intrusion detection systems or antivirus software. Thus, an effective method for PLC protection is yet to be designed. Motivated by these concerns, we propose a non-invasive power-based anomaly detection scheme for PLCs. The basic idea is to detect malicious software execution in a PLC through analyzing its power consumption, which is measured by inserting a shunt resistor in series with the CPU in a PLC while it is executing instructions. To analyze the power measurements, we extract a discriminative feature set from the power trace, and then train a long short-term memory (LSTM) neural network with the features of normal samples to predict the next time step of a normal sample. Finally, an abnormal sample is identified through comparing the predicted sample and the actual sample. The advantages of our method are that it requires no software modification on the original system and is able to detect unknown attacks effectively. The method is evaluated on a lab testbed, and for a trojan attack whose difference from the normal program is around 0.63%, the detection accuracy reaches 99.83%.

One of the key challenges for question answering is to bridge the lexical gap between questions and answers because there may not be any matching word between them. Machine translation models have been shown to boost the performance of solving the lexical gap problem between question-answer pairs. In this paper, we introduce an attention-based deep learning model to address the answer selection task for question answering. The proposed model employs a bidirectional long short-term memory (LSTM) encoder-decoder, which has been demonstrated to be effective on machine translation tasks to bridge the lexical gap between questions and answers. Our model also uses a step attention mechanism which allows the question to focus on a certain part of the candidate answer. Finally, we evaluate our model using a benchmark dataset and the results show that our approach outperforms the existing approaches. Integrating our model significantly improves the performance of our question answering system in the TREC 2015 LiveQA task.

Visual tracking, which has been widely used in many vision fields, has been one of the most active research topics in computer vision in recent years. However, there are still challenges in visual tracking, such as illumination change, object occlusion, and appearance deformation. To overcome these difficulties, a reliable point assignment (RPA) algorithm based on wavelet transform is proposed. The reliable points are obtained by searching the location that holds local maximal wavelet coefficients. Since the local maximal wavelet coefficients indicate high variation in the image, the reliable points are robust against image noise, illumination change, and appearance deformation. Moreover, a Kalman filter is applied to the detection step to speed up the detection processing and reduce false detection. Finally, the proposed RPA is integrated into the tracking-learning-detection (TLD) framework with the Kalman filter, which not only improves the tracking precision, but also reduces the false detections. Experimental results showed that the new framework outperforms TLD and kernelized correlation filters with respect to precision, f-measure, and average overlap in percent.

The special characteristics of the mobile environment, such as limited bandwidth, dynamic topology, heterogeneity of peers, and limited power, pose additional challenges on mobile peer-to-peer (MP2P) networks. Trust management becomes an essential component of MP2P networks to promote peer transactions. However, in an MP2P network, peers frequently join and leave the network, which dynamically changes the network topology. Thus, it is difficult to establish long-term and effective trust relationships among peers. In this paper, we propose a dynamic grouping based trust model (DGTM) to classify peers. A group is formed according to the peers’ interests. Within a group, mobile peers share resources and tend to keep stable trust relationships. We propose three peer roles (super peers, relay peers, and ordinary peers) and two novel trust metrics (intragroup trust and intergroup trust). The two metrics are used to accurately measure the trust between two peers from the same group or from different groups. Simulations illustrate that our proposed DGTM always achieves the highest successful transaction rate and the best communication overhead under different circumstances.

In this paper, an optimal design of linear antenna arrays having microstrip patch antenna elements has been carried out. Cat swarm optimization (CSO) has been applied for the optimization of the control parameters of radiation pattern of an antenna array. The optimal radiation patterns of isotropic antenna elements are obtained by optimizing the current excitation weight of each element and the inter-element spacing. The antenna arrays of 12, 16, and 20 elements are taken as examples. The arrays are designed by using MATLAB computation and are validated through Computer Simulation Technology-Microwave Studio (CST-MWS). From the simulation results it is evident that CSO is able to yield the optimal design of linear antenna arrays of patch antenna elements.

This paper investigates the secrecy performance of maximal ratio combining (MRC) and selection combining (SC) with imperfect channel state information (CSI) in the physical layer. In a single-input multiple-output (SIMO) wiretap channel, a source transmits confidential messages to the destination equipped with M antennas using the MRC/SC scheme to process the received multiple signals. An eavesdropper equipped with N antennas also adopts the MRC/SC scheme to promote successful eavesdropping. We derive the exact and asymptotic closed-form expressions for the ergodic secrecy capacity (ESC) in two cases: (1) MRC with weighting errors, and (2) SC with outdated CSI. Moreover, two important indicators, namely high signal-to-noise ratio (SNR) slope and high SNR power offset, which govern ESC at the high SNR region, are derived. Finally, simulations are conducted to validate the accuracy of our proposed analytical models. Results indicate that ESC rises with the increase of the number of antennas and the received SNR at the destination, and fades with the increase of those at the eavesdropper. Another finding is that the high SNR slope is constant, while the high SNR power offset is correlated with the number of antennas at both the destination and the eavesdropper.