Security-related issues have become more significant due to the proliferation of IT. Collating security-related information in a database improves security. For example, Common Vulnerabilities and Exposures (CVE) is a security knowledge repository containing descriptions of vulnerabilities about software or source code. Although the descriptions include various entities, there is not a uniform entity structure, making security analysis difficult using individual entities. Developing a consistent entity structure will enhance the security field. Herein we propose a method to automatically label select entities from CVE descriptions by applying the Named Entity Recognition (NER) technique. We manually labeled 3287 CVE descriptions and conducted experiments using a machine learning model called BERT to compare the proposed method to labeling with regular expressions. Machine learning using the proposed method significantly improves the labeling accuracy. It has an f1 score of about 0.93, precision of about 0.91, and recall of about 0.95, demonstrating that our method has potential to automatically label select entities from CVE descriptions.

Ising machines can find optimum or quasi-optimum solutions of combinatorial optimization problems efficiently and effectively. It is known that, when a good initial solution is given to an Ising machine, we can finally obtain a solution closer to the optimal solution. However, several Ising machines cannot directly accept an initial solution due to its computational nature. In this paper, we propose a method to give quasi-initial solutions into Ising machines that cannot directly accept them. The proposed method gives the positive or negative external magnetic field coefficients (magnetic field controlling term) based on the initial solutions and obtains a solution by using an Ising machine. Then, the magnetic field controlling term is re-calculated every time an Ising machine repeats the annealing process, and hence the solution is repeatedly improved on the basis of the previously obtained solution. The proposed method is applied to the capacitated vehicle routing problem with an additional constraint (constrained CVRP) and the max-cut problem. Experimental results show that the total path distance is reduced by 5.78% on average compared to the initial solution in the constrained CVRP and the sum of cut-edge weight is increased by 1.25% on average in the max-cut problem.

Distributed compressive video sensing (DCVS) has received considerable attention due to its potential in source-limited communication, e.g., wireless video sensor networks (WVSNs). Multi-hypothesis (MH) prediction, which treats the target block as a linear combination of hypotheses, is a state-of-the-art technique in DCVS. The common approach is under the supposition that blocks that are dissimilar from the target block are given lower weights than blocks that are more similar. This assumption can yield acceptable reconstruction quality, but it is not suitable for scenarios with more details. In this paper, based on the joint sparsity model (JSM), the authors present a Tikhonov-regularized MH prediction scheme in which the most similar block provides the similar common portion and the others blocks provide respective unique portions, differing from the common supposition. Specifically, a new scheme for generating hypotheses and a Euclidean distance-based metric for the regularized term are proposed. Compared with several state-of-the-art algorithms, the authors show the effectiveness of the proposed scheme when there are a limited number of hypotheses.

System administrators and security officials of an organization need to deal with vulnerable IT assets, especially those with severe vulnerabilities, to minimize the risk of these vulnerabilities being exploited. The Common Vulnerability Scoring System (CVSS) can be used as a means to calculate the severity score of vulnerabilities, but it currently requires human operators to choose input values. A word-level Convolutional Neural Network (CNN) has been proposed to estimate the input parameters of CVSS and derive the severity score of vulnerability notes, but its accuracy needs to be improved further. In this paper, we propose a character-level CNN for estimating the severity scores. Experiments show that the proposed scheme outperforms conventional one in terms of accuracy and how errors occur.

Multi-hypothesis prediction technique, which exploits inter-frame correlation efficiently, is widely used in block-based distributed compressive video sensing. To solve the problem of inaccurate prediction in multi-hypothesis prediction technique at a low sampling rate and enhance the reconstruction quality of non-key frames, we present a resample-based hybrid multi-hypothesis scheme for block-based distributed compressive video sensing. The innovations in this paper include: (1) multi-hypothesis reconstruction based on measurements reorganization (MR-MH) which integrates side information into the original measurements; (2) hybrid multi-hypothesis (H-MH) reconstruction which mixes multiple multi-hypothesis reconstructions adaptively by resampling each reconstruction. Experimental results show that the proposed scheme outperforms the state-of-the-art technique at the same low sampling rate.

In this paper, we first analyze the discriminative power in the Best Match (BM) 25 formula and provide its calculation method from the Bayesian point of view. The resulting, derived discriminative power is quite similar to the exponential inverse document frequency (EIDF) that we have previously proposed [1] but retains more preferable theoretical advantages. In our previous paper [1], we proposed the EIDF in the framework of the probabilistic information retrieval (IR) method BM25 to address the instance search task, which is a specific object search for videos using an image query. Although the effectiveness of our EIDF was experimentally demonstrated, we did not consider its theoretical justification and interpretation. We also did not describe the use of region-of-interest (ROI) information, which is supposed to be input to the instance search system together with the original image query showing the instance. Therefore, here, we justify the EIDF by calculating the discriminative power in the BM25 from the Bayesian viewpoint. We also investigate the effect of the ROI information for improving the instance search accuracy and propose two search methods incorporating the ROI effect into the BM25 video ranking function. We validated the proposed methods through a series of experiments using the TREC Video Retrieval Evaluation instance search task dataset.

We observed Ga focused-ion-beam (FIB) irradiation effect onto diamond-like carbon (DLC) free-space nanowiring (FSW) fabricated by focused-ion-beam chemical vapor deposition (FIB-CVD). A bended FIB-CVD FSW completely strained after Ga-FIB irradiation with raster scanning. This is probably caused by generation of compression stresses onto the surface of FSW, because the surface state of the nanowire changed with Ga-FIB irradiation. Transmission electron microscope (TEM) study indicates that Ga of FSW core part disappeared after Ga-FIB irradiation and a near-edge X-ray absorption fine structure (NEXAFS) analysis revealed C-Ga bond formation onto the surface. This is attributed to a movement of Ga from the core region to the surface, and/or an adsorption of Ga onto the surface by Ga-FIB scanned irradiation. The transformation of FSW is not only fascinating as physical phenomenon, but also effective for fabricating various 3-dimensional nanodevices equipped with nanowires utilized as electric wiring.

With the proliferation of smart grids and the construction of various electric IT systems and networks, a next-generation substation automation system (SAS) based on IEC 61850 has been agreed upon as a core element of smart grids. However, research on security vulnerability analysis and quantification for automated substations is still in the preliminary phase. In particular, it is not suitable to apply existing security vulnerability quantification approaches to IEC 61850-based SAS because of its heterogeneous characteristics. In this paper, we propose an IEC 61850-based SAS network modeling and evaluation approach for security vulnerability quantification. The proposed approach uses network-level and device groupings to categorize the characteristic of the SAS. In addition, novel attack scenarios are proposed through a zoning scheme to evaluate the network model. Finally, an MTTC (Mean Time-to-Compromise) scheme is used to verify the proposed network model using a sample attack scenario.

We propose a novel technique to grow the single-walled carbon nanotubes (SWNTs) with specific chirality at the desired position using free electron laser (FEL) irradiation during growth and surface treatment. As a result, only the semiconducting SWNTs grew at the area between triangle electrodes, where the ozone treatment was done to be hydrophilic when an alcohol chemical vapor deposition (ACCVD) process was carried out with the 800 nm FEL irradiation. Although the number of possible chiral index is 22 in the SWNTs grown without the FEL irradiation, the number is much reduced to be 8 by the FEL.

In this paper, we report the fabrication and device characteristics of InP/InGaAs double heterojunction bipolar transistors (DHBTs) with buried SiO2 wires. The SiO2 wires were buried in the collector and subcollector layers by metalorganic chemical vapor deposition toward reduction of the base-collector capacitance under the base electrode. A current gain of 22 was obtained at an emitter current density of 1.25 MA/cm2 for a DHBT with an emitter width of 400 nm. The DC characteristics of DHBTs with buried SiO2 wires were the same as those of DHBTs without buried SiO2 wires on the same substrate. A current gain cutoff frequency (fT) of 213 GHz and a maximum oscillation frequency (fmax) of 100 GHz were obtained at an emitter current density of 725 kA/cm2.

In this paper, a new heuristic algorithm is proposed to optimize the power domain clustering in controlling-value-based (CV-based) power gating technology. In this algorithm, both the switching activity of sleep signals (p) and the overall numbers of sleep gates (gate count, N) are considered, and the sum of the product of p and N is optimized. The algorithm effectively exerts the total power reduction obtained from the CV-based power gating. Even when the maximum depth is kept to be the same, the proposed algorithm can still achieve power reduction approximately 10% more than that of the prior algorithms. Furthermore, detailed comparison between the proposed heuristic algorithm and other possible heuristic algorithms are also presented. HSPICE simulation results show that over 26% of total power reduction can be obtained by using the new heuristic algorithm. In addition, the effect of dynamic power reduction through the CV-based power gating method and the delay overhead caused by the switching of sleep transistors are also shown in this paper.

This letter proposes a nonlinear DCT discriminant feature extraction approach for face recognition. The proposed approach first selects appropriate DCT frequency bands according to their levels of nonlinear discrimination. Then, this approach extracts nonlinear discriminant features from the selected DCT bands by presenting a new kernel discriminant method, i.e. the improved kernel discriminative common vector (KDCV) method. Experiments on the public FERET database show that this new approach is more effective than several related methods.

Current collapse phenomenon is a well known obstacle in the AlGaN/GaN HEMTs. In order to improve the surface stability of HEMTs, we have investigated the SiN passivation film deposited by T-CVD, and we found that it improves both gate leakage current and current collapse phenomenon [1]. Moreover, we compared the T-CVD and PE-CVD passivation films, on high electric field DC and RF characteristics. We found that T-CVD SiN passivation film improves BVds-off by 30% because of the reduction of gate leakage current. It also improved ηd in the output power characteristics by load-pull measurement, which indicates the decrease of the current collapse phenomenon. Also we fabricated a multi-fingered 50 W-class AlGaN/GaN HEMT with T-CVD SiN passivation film and achieved 61.2% of high drain efficiency at frequency of 2.14 GHz, which was 3.6 points higher than that with PE-CVD SiN passivation film.

In AlGaN/GaN high electron mobility transistors (HEMTs), drain current reduction by current collapse phenomenon is a big obstacle for a high efficient operation of power amplifier application. In this study, we investigated the effects of SiN passivation film quality on the electrical characteristics of AlGaN/GaN HEMTs. First, we conducted some experiments to investigate the relationship between electrical characteristics of AlGaN/GaN HEMTs and various conditions of SiN passivation film by plasma enhanced chemical vapor deposition (PE-CVD). We found that both gate current leakage and current collapse were improved simultaneously by SiN passivation film deposited by optimized condition of NH3 and SiH4 gas flow. It is found that the critical parameter in the optimization is a IN-H/ ISi-H ratio measured by Fourier transforms infrared spectroscopy (FT-IR) spectra. Next, a thermal CVD SiN was applied to the passivation film to be investigated from the same point of view, because a thermal CVD SiN is well known to have good quality with low hydrogen content and high IN-H/ISi-H ratio. We confirmed that the thermal CVD SiN passivation could improve much further both of the gate leakage current and the current collapse in AlGaN/GaN-HEMTs. Furthermore, we tried to apply the thermal CVD SiN to the gate insulator in MIS (Metal Insulator Semiconductor) structure of AlGaN/GaN HEMTs. The thermal CVD SiN passivation was more suitable for the gate insulator than PE-CVD SiN passivation in a view of reducing current collapse phenomena. It could be believed that the thermal CVD SiN film is superior to the PE-CVD SiN film to achieve good passivation and gate insulator film for AlGaN/GaN HEMTs due to the low hydrogen content and the high IN-H/ISi-H ratio.

This paper presents the selective epitaxial growth (SEG) properties of reduced pressure chemical vapor deposition (RPCVD) at low temperatures (LT) of 675-725 with high aspect ratio mask of dielectric films. The SEG process could be explained in conjunction with the loading effect, the mask pattern shape/size, and the process parameters of RPCVD. The growth rates showed a large non-uniformity up to 40% depending upon the pattern size of the dielectric mask films, but as the SEG film becomes thicker, the growth rate difference converged on 15% between the narrow 2-µm and the wide 100-µm patterns. The evolution of SEG was controlled dominantly by the surface migration control at the initial stage, and converted to the surface topology control. The design of pattern size and distribution with dummy patterns must be useful to accomplish the reliable and uniform LT-SEG.

The historical review of Taiwan's researching activities on the features of PECVD grown SiOx are also included to realize the performance of Si nanocrystal based MOSLED made by such a Si-rich SiOx film with embedded Si nanocrystals on conventional Si substrate. A surface nano-roughened Si substrate with interfacial Si nano-pyramids at SiOx/Si interface are also reviewed, which provide the capabilities of enhancing the surface roughness induced total-internal-reflection relaxation and the Fowler-Nordheim tunneling based carrier injection. These structures enable the light emission and extraction from a metal-SiOx-Si MOSLED.

Effect of the post-growth annealing on the morphology of a Ge mesa selectively grown on Si was studied from the viewpoint of near-infrared photodiode applications. By ultrahigh-vacuum chemical vapor deposition, Ge mesas were selectively grown at 600 on Si (001) substrates partially covered with SiO2 masks. The as-grown Ge mesas showed trapezoidal cross-sections having a top (001) surface and {311} sidewall facets, as similar to previous reports. However, after the subsequent post-growth annealing at ~800 in the ultrahigh-vacuum chamber, the mesas were deformed into rounded shapes having a depression at the center and mounds near the edges. Such a deformation cannot be observed for the samples annealed once after cooled and exposed to the air. The residual hydrogen atoms on the Ge surface from the germane (GeH4) decomposition is regarded as a trigger to the observed morphological instability, while the final mesa shape is determined in order to minimize a sum of the surface and/or strain energies.

Three-dimensional nanostructure fabrication has been demonstrated by 30 keV Ga+ focused-ion-beam chemical-vapor-deposition (FIB-CVD) using a phenanthrene (C14H10) source as a precursor. Microstructure plastic arts is advocated as a new field using micro-beam technology, presenting one example of micro-wine-glass with 2.75 µm external diameter and 12 µm height. The deposition film is a diamondlike amorphous carbon. A large Young's modulus that exceeds 600 GPa seems to present great possibilities for various applications. Producing of three-dimensional nanostructure is discussed. Micro-coil, nanoelectrostatic actuator, and nano-space-wiring with 0.1 µm dimension are demonstrated as parts of nanomechanical system. Furthermore, nanoinjector and nanomanipulator are also fabricated as a novel nano-tool for manipulation and analysis of subcellular organelles.

Carbon nanotubes (CNTs) offer unique properties such as highest current density exceeding 109 A/cm2, ultra-high thermal conductivity as high as that of diamond, ballistic transport along the tube and extremely high mechanical strength with high aspect ratio of more than 1000. Because of these remarkable properties, they have been expected for use as future wiring materials to solve several problems, for examples, stress and electro-migration, heat removal and fabrication of a small-sized via in future LSIs. In this paper, we demonstrate present status of CNT material technologies and the potential of metallic CNT vias. In particular, we report our original catalytic nano-particle technique for controlling the diameter and density of CNTs. We have succeeded in forming a 40-nm via with the CNT density of 91011/cm2, which is the highest density ever reported. The low temperature CVD growth and the electrical properties of CNT vias are also discussed.

A systematic approach to the analysis and design of a bi-directional Cuk converter for the cell voltage balancing control of a series-connected lithium-ion battery string is presented in this paper. The proposed individual cell equalizers (ICE) are designed to operate at discontinuous-capacitor-voltage mode (DCVM) to achieve the zero-voltage switching (ZVS) for reducing the switching loss of the bi-directional DC/DC converters. Simulation and experimental results show that the proposed battery equalization scheme can not only enhance the bi-directional battery equalization performance, but also can reduce the switching loss during the equalization period. Two designed examples are demonstrated, the switch power losses are significantly reduced by 52.8% from the MOSFETs and the equalization efficiency can be improved by 68-86.9% using the proposed DCVM ZVS battery equalizer under the specified cell equalization process. The charged/discharged capacity of the lithium-ion battery string is increased by using the proposed ICEs equipped in the battery string.