Recently, the controllability of complex networks has become a hot topic in the field of network science, where the driver nodes play a key and central role. Therefore, studying their structural characteristics is of great significance to understand the underlying mechanism of network controllability. In this paper, we systematically investigate the nodal centrality of driver nodes in controlling complex networks, we find that the driver nodes tend to be low in-degree but high out-degree nodes, and most of driver nodes tend to have low betweenness centrality but relatively high closeness centrality. We also find that the tendencies of driver nodes towards eigenvector centrality and Katz centrality show very similar behaviors, both high eigenvector centrality and high Katz centrality are avoided by driver nodes. Finally, we find that the driver nodes towards PageRank centrality demonstrate a polarized distribution, i.e., the vast majority of driver nodes tend to be low PageRank nodes whereas only few driver nodes tend to be high PageRank nodes.

This paper proposes two high-fidelity and computationally efficient neural voice conversion (VC) methods based on a direct waveform modification using spectral differentials. The conventional spectral-differential VC method with a minimum-phase filter achieves high-quality conversion for narrow-band (16 kHz-sampled) VC but requires heavy computational cost in filtering. This is because the minimum phase obtained using a fixed lifter of the Hilbert transform often results in a long-tap filter. Furthermore, when we extend the method to full-band (48 kHz-sampled) VC, the computational cost is heavy due to increased sampling points, and the converted-speech quality degrades due to large fluctuations in the high-frequency band. To construct a short-tap filter, we propose a lifter-training method for data-driven phase reconstruction that trains a lifter of the Hilbert transform by taking into account filter truncation. We also propose a frequency-band-wise modeling method based on sub-band multi-rate signal processing (sub-band modeling method) for full-band VC. It enhances the computational efficiency by reducing sampling points of signals converted with filtering and improves converted-speech quality by modeling only the low-frequency band. We conducted several objective and subjective evaluations to investigate the effectiveness of the proposed methods through implementation of the real-time, online, full-band VC system we developed, which is based on the proposed methods. The results indicate that 1) the proposed lifter-training method for narrow-band VC can shorten the tap length to 1/16 without degrading the converted-speech quality, and 2) the proposed sub-band modeling method for full-band VC can improve the converted-speech quality while reducing the computational cost, and 3) our real-time, online, full-band VC system can convert 48 kHz-sampled speech in real time attaining the converted speech with a 3.6 out of 5.0 mean opinion score of naturalness.

3D ultrasound imagers require low-noise amplifier (LNA) with much lower power consumption and smaller chip area than conventional 2D imagers because of the huge amount of transducer channels. This paper presents a low-power small-size LNA with a novel current-reuse circuitry for 3D ultrasound imaging systems. The proposed LNA is composed of a differential common source amplifier and a source-follower driver which share the current without using inductors. The LNA was fabricated in a 0.18-μm CMOS process with only 0.0056mm2. The measured results show a gain of 21dB and a bandwidth of 9MHz. The proposed LNA achieves an average noise density of 11.3nV/√Hz, and the 2nd harmonic distortion below -40dBc with 0.1-Vpp input. The supply current is 85μA with a 1.8-V power supply, which is competitive with conventional LNAs by finer CMOS process.

An active gate driver IC generates arbitrary switching waveform is proposed to reduce the switching loss, the voltage overshoot, and the electromagnetic interference (EMI) by optimizing the switching pattern. However, it is hard to find optimal switching pattern because the switching pattern has huge possible combinations. In this paper, the method to estimate the switching loss and the voltage overshoot from the switching pattern with neural network (NN) is proposed. The implemented NN model obtains reasonable learning results for data-sets.

Driver drowsiness estimation is one of the important tasks for preventing car accidents. Most of the approaches are binary classification that classify a driver is significantly drowsy or not. Multi-level drowsiness estimation, that detects not only significant drowsiness but also moderate drowsiness, is helpful to a safer and more comfortable car system. Existing approaches are mostly based on conventional temporal measures which extract temporal information related to eye states, and these measures mainly focus on detecting significant drowsiness for binary classification. For multi-level drowsiness estimation, we propose two temporal measures, average eye closed time (AECT) and soft percentage of eyelid closure (Soft PERCLOS). Existing approaches are also based on a time domain convolutional neural network (CNN) as deep neural network models, of which layers are linked sequentially. The network model extracts features mainly focusing on mono-temporal resolution. We found that features focusing on multi-temporal resolution are effective to multi-level drowsiness estimation, and we propose a parallel linked time-domain CNN to extract the multi-temporal features. We collected an own dataset in a real environment and evaluated the proposed methods with the dataset. Compared with existing temporal measures and network models, Our system outperforms the existing approaches on the dataset.

This paper analyzes the system-level performance of Storage Class Memory (SCM)/NAND flash hybrid solid-state drives (SSDs) and SCM/NAND flash/NAND flash tri-hybrid SSDs in difference types of NAND flash memory. There are several types of NAND flash memory, i.e. 2-dimensional (2D) or 3-dimensional (3D), charge-trap type (CT) and floating-gate type (FG) and multi-level cell (MLC) or triple-level cell (TLC). In this paper, the following four types of NAND flash memory are analyzed: 1) 3D CT TLC, 2) 3D FG TLC, 3) 2D FG TLC, and 4) 2D FG MLC NAND flash. Regardless of read- and write-intensive workloads, SCM/NAND flash hybrid SSD with low cost 3D CT TLC NAND flash achieves the best performance that is 20% higher than that with higher cost 2D FG MLC NAND flash. The performance improvement of 3D CT TLC NAND flash can be obtained by the short write latency. On the other hand, in case of tri-hybrid SSD, SCM/3D CT TLC/3D CT TLC NAND flash tri-hybrid SSD improves the performance 102% compared to SCM/2D FG MLC/3D CT TLC NAND flash tri-hybrid SSD. In addition, SCM/2D FG MLC/2D FG MLC NAND flash tri-hybrid SSD shows 49% lower performance than SCM/2D FG MLC/3D CT TLC NAND flash tri-hybrid SSD. Tri-hybrid SSD flash with 3D CT TLC NAND flash is the best performance in tri-hybrid SSD thanks to larger block size and word-line (WL) write. Therefore, in 3D CT TLC NAND flash based SSDs, higher cost MLC NAND flash is not necessary for hybrid SSD and tri-hybrid SSD for data center applications.

We develop a deep neural network (DNN) for detecting driver drowsiness in videos. The proposed DNN model that receives driver's faces extracted from video frames as inputs consists of three components - a convolutional neural network (CNN), a convolutional control gate-based recurrent neural network (ConvCGRNN), and a voting layer. The CNN is to learn facial representations from global faces which are then fed to the ConvCGRNN to learn their temporal dependencies. The voting layer works like an ensemble of many sub-classifiers to predict drowsiness state. Experimental results on the NTHU-DDD dataset show that our model not only achieve a competitive accuracy of 84.81% without any post-processing but it can work in real-time with a high speed of about 100 fps.

The Cyber-Physical Integrated Society (CPIS) is being formed with the fusion of cyber-space and the real-world. In this paper, we will discuss Data-Driven Decision-Making (DDDM) support systems to solve social problems in the CPIS. First, we introduce a Web of Resources (WoR) that uses Web booking log data for destination data management. Next, we introduce an Internet of Persons (IoP) system to visualize individual and group flows of people by analyzing collected Wi-Fi usage log data. Specifically, we present examples of how WoR and IoP visualize flows of groups of people that can be shared across different industries, including telecommunications carriers and railway operators, and policy decision support for local, short-term events. Finally, the importance of data-driven training of human resources to support DDDM in the future CPIS is discussed.

Data-driven untying of a recursive autoencoder (RAE) is proposed for utterance intent classification for spoken dialogue systems. Although an RAE expresses a nonlinear operation on two neighboring child nodes in a parse tree in the application of spoken language understanding (SLU) of spoken dialogue systems, the nonlinear operation is considered to be intrinsically different depending on the types of child nodes. To reduce the gap between the single nonlinear operation of an RAE and intrinsically different operations depending on the node types, a data-driven untying of autoencoders using part-of-speech (PoS) tags at leaf nodes is proposed. When using the proposed method, the experimental results on two corpora: ATIS English data set and Japanese data set of a smartphone-based spoken dialogue system showed improved accuracies compared to when using the tied RAE, as well as a reasonable difference in untying between two languages.

We propose an object detector using a sliding window method for an event-driven camera which outputs a subtracted frame (usually a binary value) when changes are detected in captured images. Since sliding window skips unchanged portions of the output, the number of target object area candidates decreases dramatically, which means that our system operates faster and with lower power consumption than a system using a straightforward sliding window approach. Since the event-driven camera output consists of binary precision frames, an all binarized convolutional neural network (ABCNN) can be available, which means that it allows all convolutional layers to share the same binarized convolutional circuit, thereby reducing the area requirement. We implemented our proposed method on the Xilinx Inc. Zedboard and then evaluated it using the PETS 2009 dataset. The results showed that our system outperformed BCNN system from the viewpoint of detection performance, hardware requirement, and computation time. Also, we showed that FPGA is an ideal method for our system than mobile GPU. From these results, our proposed system is more suitable for the embedded systems based on stationary cameras (such as security cameras).

This paper presents an ultra-low-power class-AB bulk-driven operational transconductance amplifier operating in the subthreshold region. Employing the partial positive feedback in current mirrors, the effective transconductance and output voltage swing are enhanced considerably without additional power consumption and layout area. Both traditional and proposed OTAs are designed and simulated for a 180 nm CMOS process. They dissipate an ultra low power of 192 nW. The proposed OTA features not only a DC gain enhancement of 14 dB but also a slew rate improvement of 200%. In addition, the improved gain leads to a 5.3 times wider unity-gain bandwidth than that of the traditional OTA.

Many users are exposed to threats of drive-by download attacks through the Web. Attackers compromise vulnerable websites discovered by search engines and redirect clients to malicious websites created with exploit kits. Security researchers and vendors have tried to prevent the attacks by detecting malicious data, i.e., malicious URLs, web content, and redirections. However, attackers conceal parts of malicious data with evasion techniques to circumvent detection systems. In this paper, we propose a system for detecting malicious websites without collecting all malicious data. Even if we cannot observe parts of malicious data, we can always observe compromised websites. Since vulnerable websites are discovered by search engines, compromised websites have similar traits. Therefore, we built a classifier by leveraging not only malicious but also compromised websites. More precisely, we convert all websites observed at the time of access into a redirection graph and classify it by integrating similarities between its subgraphs and redirection subgraphs shared across malicious, benign, and compromised websites. As a result of evaluating our system with crawling data of 455,860 websites, we found that the system achieved a 91.7% true positive rate for malicious websites containing exploit URLs at a low false positive rate of 0.1%. Moreover, it detected 143 more evasive malicious websites than the conventional content-based system.

The number of infected hosts on enterprise networks has been increased by drive-by download attacks. In these attacks, users of compromised popular websites are redirected toward websites that exploit vulnerabilities of a browser and its plugins. To prevent damage, detection of infected hosts on the basis of proxy logs rather than blacklist-based filtering has started to be researched. This is because blacklists have become difficult to create due to the short lifetime of malicious domains and concealment of exploit code. To detect accesses to malicious websites from proxy logs, we propose a system for detecting malicious URL sequences on the basis of three key ideas: focusing on sequences of URLs that include artifacts of malicious redirections, designing new features related to software other than browsers, and generating new training data with data augmentation. To find an effective approach for classifying URL sequences, we compared three approaches: an individual-based approach, a convolutional neural network (CNN), and our new event de-noising CNN (EDCNN). Our EDCNN reduces the negative effects of benign URLs redirected from compromised websites included in malicious URL sequences. Evaluation results show that only our EDCNN with proposed features and data augmentation achieved a practical classification performance: a true positive rate of 99.1%, and a false positive rate of 3.4%.

In this paper, we developed saccade-induced line displays including flashing period controllers. The displays speeded up the flashing period of one line using LED drivers and Arduino Uno equipped with AVR microcomputers. It was shown that saccades were easily induced when the observer alternately looks at the two fast flashing line displays apart. Also, we were able to find the optimum flashing period using a controller that can speed up the flashing period and change its speed. We found that the relationship between the viewing angle of the observer and the optimum flashing period is almost proportional.

We have developed the high speed bandpass liquid crystal filter with narrow full width at half maximum (FWHM) of 5nm for real-time multi spectral imaging systems. We have successfully achieved short wavelength-switching time of 30ms by the optimization of phase retardation of thin liquid crystal cells.

The traditional Markov prediction methods of the taxi destination rely only on the previous 2 to 3 GPS points. They negelect long-term dependencies within a taxi trajectory. We adopt a Recurrent Neural Network (RNN) to explore the long-term dependencies to predict the taxi destination as the multiple hidden layers of RNN can store these dependencies. However, the hidden layers of RNN are very sensitive to small perturbations to reduce the prediction accuracy when the amount of taxi trajectories is increasing. In order to improve the prediction accuracy of taxi destination and reduce the training time, we embed suprisal-driven zoneout (SDZ) to RNN, hence a taxi destination prediction method by regularized RNN with SDZ (TDPRS). SDZ can not only improve the robustness of TDPRS, but also reduce the training time by adopting partial update of parameters instead of a full update. Experiments with a Porto taxi trajectory data show that TDPRS improves the prediction accuracy by 12% compared to RNN prediction method in literature[4]. At the same time, the prediction time is reduced by 7%.

Simulation-based verification of hardware designs, in particular, register-transfer-level (RTL) designs, has been widely used, and has been one of the major bottlenecks in design processes. One of the approaches is coverage-driven verification, of its target is improvement of some metric called coverage. In a prior work of ours, we have proposed a coverage-driven verification using both randomly generated simulation patterns and patterns generated by a SAT (satisfiability) solver, and have shown its effectiveness. In this paper, we extend this approach with an SMT (satisfiability modulo theory) solver, which can handle arithmetic relations among integer, floating-point or bit-vector variables. Experimental results show that the more arithmetic modules are included, the more an SMT-based method gets superior to the method using only a SAT solver.

In recent years, the number of traffic accidents caused by elderly drivers has increased in Japan. However, cars are an important mode of transportation for the elderly. Therefore, to ensure safe driving, a system that can assist elderly drivers is required. We propose a driver-agent system that provides support to elderly drivers during and after driving and encourages them to improve their driving. This paper describes the prototype system and the analysis conducted of the teaching records of a human instructor, the impression caused by the instructions on a subject during driving, and subjective evaluation of the driver-agent system.

NAND flash memories used in solid-state drives (SSDs) will be replaced with storage-class memories (SCMs), which are comparable with NAND flash in their cost, and with DRAM in their speed. This paper describes the performance difference of the SCM/NAND flash hybrid SSD and the SCM-based SSD with between sector-unit read (512 Byte) and page-unit read (16 KByte, NAND flash page-size) using synthetic and real workload. Also, effect of the SCM read-unit size on SSD performance are analyzed. When SCM write/read latency is 0.1 us, performance difference of the SCM/NAND flash hybrid SSD with between page- and sector-unit read is about 1% and 6% at most for the write-intensive and read-intensive workloads, respectively. However, performance of the SCM-based SSD is significantly improved when sector-unit read is used because extra read latency does not occur. Especially, the SCM-based SSD IOPS is improved by 131% for proj_3 (read-hot-random), because its read request size is small but its read request ratio is large. This paper also shows IOPS of SCM-based SSD write/read with sector-unit read can be predicted by the average write/read request size of workloads.

In order to reduce the memory cell errors in real-usage of NAND flash-based SSD, real usage-based precise reliability test for NAND flash of SSDs has been proposed. Reliability of the NAND flash memories of the SSDs is seriously degraded as the scaling of memory cells. However, conventional simple reliability tests of read-disturb and data-retention cannot give the same result as the real-life VTH shift and memory cell errors. To solve this problem, the proposed reliability test precisely reproduces the real memory cell failures by emulating the complicated read, write, and data-retention with SSD emulator. In this paper, the real-life VTH shift and memory cell errors between two generations of NAND flash memory with different characterized real workloads are provided. Using the proposed test method, 1.6-times BER difference is observed when write-cold and read-hot workload (hm_1) and write-hot and read-hot workload (prxy_1) are compared in 1Ynm MLC NAND flash. In addition, by NAND flash memory scaling from 1Xnm to 1Ynm generations, the discrepancy of error numbers between the conventional reliability test result and actual reliability measured by proposed reliability test is increased by 6.3-times. Finally, guidelines for read reference voltage shifts and strength of ECCs are given to achieve high memory cell reliability for various workloads.