The paper employed an Alexnet, which is a deep learning framework, to automatically diagnose the damages of wind power generator blade surfaces. The original images of wind power generator blade surfaces were captured by machine visions of a 4-rotor UAV (unmanned aerial vehicle). Firstly, an 8-layer Alexnet, totally including 21 functional sub-layers, is constructed and parameterized. Secondly, the Alexnet was trained with 10000 images and then was tested by 6-turn 350 images. Finally, the statistic of network tests shows that the average accuracy of damage diagnosis by Alexnet is about 99.001%. We also trained and tested a traditional BP (Back Propagation) neural network, which have 20-neuron input layer, 5-neuron hidden layer, and 1-neuron output layer, with the same image data. The average accuracy of damage diagnosis of BP neural network is 19.424% lower than that of Alexnet. The point shows that it is feasible to apply the UAV image acquisition and the deep learning classifier to diagnose the damages of wind turbine blades in service automatically.

The monocone antenna is a type of monopole antenna that has wideband characteristics. This paper proposes a low-profile monocone antenna with a planar inverted-F structure. The characteristics of the proposed antenna are analyzed through a simulation. The results demonstrate that the low-profile antenna offers wideband performance, and the relative bandwidth of VSWR ≤ 2 is found to be more than 190%. In addition, miniaturization of the monocone antenna is elucidated. The proposed antenna is prototyped, and the validity of the simulation is verified through measurements.

In this paper, we explore how to enhance the physical layer security performance in downlink cellular networks through cooperative jamming technology. Idle user equipments (UE) are used to cooperatively transmit jamming signal to confuse eavesdroppers (Eve). We propose a threshold-based jammer selection scheme to decide which idle UE should participate in the transmission of jamming signal. Threshold conditions are carefully designed to decrease interference to legitimate channel, while maintain the interference to the Eves. Moreover, fewer UE are activated, which is helpful for saving energy consumptions of cooperative UEs. Analytical expressions of the connection and secrecy performances are derived, which are validated through Monte Carlo simulations. Theoretical and simulation results reveal that our proposed scheme can improve connection performance, while approaches the secrecy performance of [12]. Furthermore, only 43% idle UEs of [12] are used for cooperative jamming, which helps to decrease energy consumption of network.

In this paper, we investigate a novel joint multi-relay and jammer selection (JMRJS) scheme in order to improve the physical layer security of wireless networks. In the JMRJS scheme, all the relays succeeding in source decoding are selected to assist in the source signal transmission and meanwhile, all the remaining relay nodes are employed to act as friendly jammers to disturb the eavesdroppers by broadcasting artificial noise. Based on the more general Nakagami-m fading channel, we analyze the security performance of the JMRJS scheme for protecting the source signal against eavesdropping. The exact closed-form expressions of outage probability (OP) and intercept probability (IP) for the JMRJS scheme over Nakagami-m fading channel are derived. Moreover, we analyze the security-reliability tradeoff (SRT) of this scheme. Simulation results show that as the number of decode-and-forward (DF)relay nodes increases, the SRT of the JMRJS scheme improves notably. And when the transmit power is below a certain value, the SRT of the JMRJS scheme consistently outperforms the joint single-relay and jammer selection (JSRJS) scheme and joint equal-relay and jammer selection (JERJS) scheme respectively. In addition, the SRT of this scheme is always better than that of the multi-relay selection (MRS) scheme.

The bulk execution of a sequential algorithm is to execute it for many different inputs in turn or at the same time. It is known that the bulk execution of an oblivious sequential algorithm can be implemented to run efficiently on a GPU. The bulk execution supports fine grained bitwise parallelism, allowing it to achieve high acceleration over a straightforward sequential computation. The main contribution of this work is to present a Bitwise Parallel Bulk Computation (BPBC) to accelerate the Smith-Waterman Algorithm (SWA) using the affine gap penalty. Thus, our idea is to convert this computation into a circuit simulation using the BPBC technique to compute multiple instances simultaneously. The proposed BPBC technique for the SWA has been implemented on the GPU and CPU. Experimental results show that the proposed BPBC for the SWA accelerates the computation by over 646 times as compared to a single CPU implementation and by 6.9 times as compared to a multi-core CPU implementation with 160 threads.

Adaptive Volterra filters (AVFs) are usually used to identify nonlinear systems, such as loudspeaker systems, and ordinary adaptive algorithms can be used to update the filter coefficients of AVFs. However, AVFs require huge computational complexity even if the order of the AVF is constrained to the second order. Improving calculation efficiency is therefore an important issue for the real-time implementation of AVFs. In this paper, we propose a novel sub-band AVF with high calculation efficiency for second-order AVFs. The proposed sub-band AVF consists of four parts: input signal transformation for a single sub-band AVF, tap length determination to improve calculation efficiency, switching the number of sub-bands while maintaining the estimation accuracy, and an automatic search for an appropriate number of sub-bands. The proposed sub-band AVF can improve calculation efficiency for which the dominant nonlinear components are concentrated in any frequency band, such as loudspeakers. A simulation result demonstrates that the proposed sub-band AVF can realize higher estimation accuracy than conventional efficient AVFs.

Hand-dorsa vein recognition is solved based on the convolutional activations of the pre-trained deep convolutional neural network (DCNN). In specific, a novel task-specific cross-convolutional-layer pooling is proposed to obtain the more representative and discriminative feature representation. Rigorous experiments on the self-established database achieves the state-of-the-art recognition result, which demonstrates the effectiveness of the proposed model.

A travel route recommendation service that recommends a sequence of points of interest for tourists traveling in an unfamiliar city is a very useful tool in the field of location-based social networks. Although there are many web services and mobile applications that can help tourists to plan their trips by providing information about sightseeing attractions, travel route recommendation services are still not widely applied. One reason could be that most of the previous studies that addressed this task were based on the orienteering problem model, which mainly focuses on the estimation of a user-location relation (for example, a user preference). This assumes that a user receives a reward by visiting a point of interest and the travel route is recommended by maximizing the total rewards from visiting those locations. However, a location-location relation, which we introduce as a transition pattern in this paper, implies useful information such as visiting order and can help to improve the quality of travel route recommendations. To this end, we propose a travel route recommendation method by combining location and transition knowledge, which assigns rewards for both locations and transitions.

We propose a novel hue-preserving tone mapping scheme. Various tone mapping operations have been studied so far, but there are very few works on color distortion caused in image tone mapping. First, LDR images produced from HDR ones by using conventional tone mapping operators (TMOs) are pointed out to have some distortion in hue values due to clipping and rounding quantization processing. Next,we propose a novel method which allows LDR images to have the same maximally saturated color values as those of HDR ones. Generated LDR images by the proposed method have smaller hue degradation than LDR ones generated by conventional TMOs. Moreover, the proposed method is applicable to any TMOs. In an experiment, the proposed method is demonstrated not only to produce images with small hue degradation but also to maintain well-mapped luminance, in terms of three objective metrics: TMQI, hue value in CIEDE2000, and the maximally saturated color on the constant-hue plane in the RGB color space.

Over the past recent decades, numerous programming languages have expanded to embrace multi-paradigms such as the fusion of object-oriented and functional programming. For example, Java, one of the most famous object-oriented programming languages, introduced a number of functional idioms in 2014. This evolution enables developers to achieve various benefits from both paradigms. However, we do not know how Java developers use functional idioms actually. Additionally, the extent to which, while there are several criticisms against the idioms, the developers actually accept and/or use the idioms currently remains unclear. In this paper, we investigate the actual use status of three functional idioms (Lambda Expression, Stream, and Optional) in Java projects by mining 100 projects containing approximately 130,000 revisions. From the mining results, we determined that Lambda Expression is utilized in 16% of all the examined projects, whereas Stream and Optional are only utilized in 2% to 3% of those projects. It appears that most Java developers avoid using functional idioms just because of keeping compatibility Java versions, while a number of developers accept these idioms for reasons of readability and runtime performance improvements. Besides, when they adopt the idioms, Lambda Expression frequently consists of a single statement, and Stream is used to operate the elements of a collection. On the other hand, some developers implement Optional using deprecated methods. We can say that good usage of the idioms should be widely known among developers.

In this paper, we study domain adaptation of semantic role classification. Most systems utilize the supervised method for semantic role classification. But, these methods often suffer severe performance drops on out-of-domain test data. The reason for the performance drops is that there are giant feature differences between source and target domain. This paper proposes a framework called Adversarial Domain Adaption Network (ADAN) to relieve domain adaption of semantic role classification. The idea behind our method is that the proposed framework can derive domain-invariant features via adversarial learning and narrow down the gap between source and target feature space. To evaluate our method, we conduct experiments on English portion in the CoNLL 2009 shared task. Experimental results show that our method can largely reduce the performance drop on out-of-domain test data.

Convolutional Neural Networks (CNNs) usually have millions or even billions of parameters, which make them hard to be deployed into mobile devices. In this work, we present a novel filter-level pruning method to alleviate this issue. More concretely, we first construct an undirected fully connected graph to represent a pre-trained CNN model. Then, we employ the spectral clustering algorithm to divide the graph into some subgraphs, which is equivalent to clustering the similar filters of the CNN into the same groups. After gaining the grouping relationships among the filters, we finally keep one filter for one group and retrain the pruned model. Compared with previous pruning methods that identify the redundant filters by heuristic ways, the proposed method can select the pruning candidates more reasonably and precisely. Experimental results also show that our proposed pruning method has significant improvements over the state-of-the-arts.

For cross-corpus speech emotion recognition (SER), how to obtain effective feature representation for the discrepancy elimination of feature distributions between source and target domains is a crucial issue. In this paper, we propose a Target-adapted Subspace Learning (TaSL) method for cross-corpus SER. The TaSL method trys to find a projection subspace, where the feature regress the label more accurately and the gap of feature distributions in target and source domains is bridged effectively. Then, in order to obtain more optimal projection matrix, ℓ1 norm and ℓ2,1 norm penalty terms are added to different regularization terms, respectively. Finally, we conduct extensive experiments on three public corpuses, EmoDB, eNTERFACE and AFEW 4.0. The experimental results show that our proposed method can achieve better performance compared with the state-of-the-art methods in the cross-corpus SER tasks.

In ID-based user authentications, a privacy problem can occur, since the service provider (SP) can accumulate the user's access history from the user ID. As a solution to that problem, group signatures have been researched. One of important issues in the group signatures is the user revocation. Previously, an efficient revocable scheme with signing/verification of constant complexity was proposed by Libert et al. In this scheme, users are managed by a binary tree, and a list of data for revoked users, called a revocation list (RL), is used for revocation. However, the scheme suffers from the large RL. Recently, an extended scheme has been proposed by Sadiah and Nakanishi, where the RL size is reduced by compressing RL. On the other hand, there is a problem that some overhead occurs in the authentication as a price for reducing the size of RL. In this paper, we propose an extended scheme where the authentication is speeded up by reducing the number of Groth-Sahai (GS) proofs. Furthermore, we implemented it on a PC to show the effectiveness. The verification time is about 30% shorter than that of the previous scheme by Sadiah and Nakanishi.

In the large-scale multi-UAV systems, the direct link may be invalid for two remote nodes on account of the constrained power or complex communication environment. Idle UAVs may work as relays between the sources and destinations to enhance communication quality. In this letter, we investigate the opportunistic relay selection for the UAVs dynamic network. On account of the time-varying channel states and the variable numbers of sources and relays, relay selection becomes much more difficult. In addition, information exchange among all nodes may bring much cost and it is difficult to implement in practice. Thus, we propose a decentralized relay selection approach based on mood-driven mechanism to combat the dynamic characteristics, aiming to maximize the total capacity of the network without information exchange. With the proposed approach, the sources can make decisions only according to their own current states and update states according to immediate rewards. Numerical results show that the proposed approach has attractive properties.

Spoofing attacks are one of the biggest concerns for most biometric recognition systems. This will be also the case with silhouette-based gait recognition in the near future. So far, gait recognition has been fortunately out of the scope of spoofing attacks. However, it is becoming a real threat with the rapid growth and spread of deep neural network-based multimedia generation techniques, which will allow attackers to generate a fake video of gait silhouettes resembling a target person's walking motion. We refer to such computer-generated fake silhouettes as gait silhouette clones (GSCs). To deal with the future threat caused by GSCs, in this paper, we propose a supervised method for discriminating GSCs from genuine gait silhouettes (GGSs) that are observed from actual walking people. For training a good discriminator, it is important to collect training datasets of both GGSs and GSCs which do not differ from each other in any aspect other than genuineness. To this end, we propose to generate a training set of GSCs from GGSs by transforming them using multiple autoencoders. The generated GSCs are used together with their original GGSs for training the discriminator. In our experiments, the proposed method achieved the recognition accuracy of up to 94% for several test datasets, which demonstrates the effectiveness and the generality of the proposed method.

Free/libre open source software (FLOSS) are being rapidly employed in several companies and organizations, because it can be modified and used for free. Hence, the use of FLOSS could contribute to its originally intended benefits and to the competence of its users. In this study, we analyzed the effect of using FLOSS on related competences. We investigated the change in the competences through an empirical study before and after the use of FLOSS among project participants. Consequently, it was confirmed that the competences of the participants improved after utilizing FLOSS.

Indexing is one of the fields where the non-volatile memory (NVM) has the advantages of byte-addressable characteristics and fast read/write speed. The existing index structures for NVM have been developed based on the fact that the size of cache line and the atomicity guarantee unit of NVM are different and they tried to overcome the weakness of consistency from the difference. To overcome the weakness, an expensive flush operation is required which results in a lower performance than a basic B+tree index. Recent studies have shown that the I/O units of the NVM can be matched with the atomicity guarantee units under limited circumstances. In this paper, we propose a Cache line sized Atomic Write B+tree (CAWBT), which is a minimal B+tree structure that shows higher performance than a basic b+ tree and designed for NVM. CAWBT has almost same performance compared to basic B+tree without consistency guarantee and shows remarkable performance improvement compared to other B+tree indexes for NVM.

File systems based on persistent memory deploy Copy-on-Write (COW) or logging to guarantee data consistency. However, COW has a write amplification problem and logging has a double write problem. Both COW and logging increase write traffic on persistent memory. In this work, we present adaptive differential logging and zero-copy logging for persistent memory. Adaptive differential logging applies COW or logging selectively to each block. If the updated size of a block is smaller than or equal to half of the block size, we apply logging to the block. If the updated size of a block is larger than half of the block size, we apply COW to the block. Zero-copy logging treats an user buffer on persistent memory as a redo log. Zero-copy logging does not incur any additional data copy. We implement adaptive differential logging and zero-copy logging on both NOVA and PMFS file systems. Our measurement on real workloads shows that adaptive differential logging and zero-copy logging get 150.6% and 149.2% performance improvement over COW, respectively.

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.