In this paper, we propose a new method based on copula theory to evaluate the detection performance of a distributed-processing multistatic radar system (DPMRS). By applying the Gaussian copula to model the dependence of local decisions in a DPMRS as well as data fusion rules of AND, OR, and K/N, the performance of a DPMRS for detecting Swerling fluctuating targets can be easily evaluated even under non-Gaussian clutter with a nonuniform dependence matrix. The reliability and flexibility of this method are validated by applying the proposed method to a previous problem by other authors, and our other investigation results indicate its high potential for evaluating DPMRS performance in various cases involving different models of target and clutter.

It has become common practice for software projects to adopt third-party dependencies. Developers are encouraged to update any outdated dependency to remain safe from potential threats of vulnerabilities. In this study, we present an approach to aid developers show whether or not a vulnerable code is reachable for JavaScript projects. Our prototype, SōjiTantei, is evaluated in two ways (i) the accuracy when compared to a manual approach and (ii) a larger-scale analysis of 780 clients from 78 security vulnerability cases. The first evaluation shows that SōjiTantei has a high accuracy of 83.3%, with a speed of less than a second analysis per client. The second evaluation reveals that 68 out of the studied 78 vulnerabilities reported having at least one clean client. The study proves that automation is promising with the potential for further improvement.

The number of malware, including variants and new types, is dramatically increasing over the years, posing one of the greatest cybersecurity threats nowadays. To counteract such security threats, it is crucial to detect malware accurately and early enough. The recent advances in machine learning technology have brought increasing interest in malware detection. A number of research studies have been conducted in the field. It is well known that malware detection accuracy largely depends on the training dataset used. Creating a suitable training dataset for efficient malware detection is thus crucial. Different works usually use their own dataset; therefore, a dataset is only effective for one detection method, and strictly comparing several methods using a common training dataset is difficult. In this paper, we focus on how to create a training dataset for efficiently detecting malware. To achieve our goal, the first step is to clarify the information that can accurately characterize malware. This paper concentrates on threads, by treating them as important information for characterizing malware. Specifically, on the basis of the dynamic analysis log from the Alkanet, a system call tracer, we obtain the thread information and classify the thread information processing into four patterns. Then the malware detection is performed using the number of transitions of system calls appearing in the thread as a feature. Our comparative experimental results showed that the primary thread information is important and useful for detecting malware with high accuracy.

Horizon detection is useful in maritime image processing for various purposes, such as estimation of camera orientation, registration of consecutive frames, and restriction of the object search region. Existing horizon detection methods are based on edge extraction. For accuracy, they use multiple images, which are filtered with different filter sizes. However, this increases the processing time. In addition, these methods are not robust to blurting. Therefore, we developed a horizon detection method without extracting the candidates from the edge information by formulating the horizon detection problem as a global optimization problem. A horizon line in an image plane was represented by two parameters, which were optimized by an evolutionary algorithm (genetic algorithm). Thus, the local and global features of a horizon were concurrently utilized in the optimization process, which was accelerated by applying a coarse-to-fine strategy. As a result, we could detect the horizon line on high-resolution maritime images in about 50ms. The performance of the proposed method was tested on 49 videos of the Singapore marine dataset and the Buoy dataset, which contain over 16000 frames under different scenarios. Experimental results show that the proposed method can achieve higher accuracy than state-of-the-art methods.

In unlicensed spectrum, wireless communications employing carrier sense multiple access with collision avoidance (CSMA/CA) suffer from longer transmission delay time as the number of user terminals (UTs) increases, because packet collisions are more likely to occur. To cope with this problem, this paper proposes a new multiuser detection (MUD) scheme that uses both request-to-send (RTS) and enhanced clear-to-send (eCTS) for high-reliable and low-latency wireless communications. As in conventional MUD scheme, the metric-combining MUD (MC-MUD) calculates log likelihood functions called metrics and accumulates the metrics for the maximum likelihood detection (MLD). To avoid increasing the number of states for MLD, MC-MUD forces the relevant UTs to retransmit their packets until all the collided packets are correctly detected, which requires a kind of central control and reduces the system throughput. To overcome these drawbacks, the proposed scheme, which is referred to as cancelling MC-MUD (CMC-MUD), deletes replicas of some of the collided packets from the received signals, once the packets are correctly detected during the retransmission. This cancellation enables new UTs to transmit their packets and then performs MLD without increasing the number of states, which improves the system throughput without increasing the complexity. In addition, the proposed scheme adopts RTS and eCTS. One UT that suffers from packet collision transmits RTS before the retransmission. Then, the corresponding access point (AP) transmits eCTS including addresses of the other UTs, which have experienced the same packet collision. To reproduce the same packet collision, these other UTs transmit their packets once they receive the eCTS. Computer simulations under one AP conditions evaluate an average carrier-to-interference ratio (CIR) range in which the proposed scheme is effective, and clarify that the transmission delay time of the proposed scheme is shorter than that of the conventional schemes. In two APs environments that can cause the hidden terminal problem, it is demonstrated that the proposed scheme achieves shorter transmission delay times than the conventional scheme with RTS and conventional CTS.

DoS (Denial of Service) attacks are becoming one of the most serious security threats to global networks. We analyze the existing DoS detection methods and defense mechanisms in depth. In recent years, K-Means and improved variants have been widely examined for security intrusion detection, but the detection accuracy to data is not satisfactory. In this paper we propose a multi-dimensional space feature vector expansion K-Means model to detect threats in the network environment. The model uses a genetic algorithm to optimize the weight of K-Means multi-dimensional space feature vector, which greatly improves the detection rate against 6 typical Dos attacks. Furthermore, in order to verify the correctness of the model, this paper conducts a simulation on the NSL-KDD data set. The results show that the algorithm of multi-dimensional space feature vectors expansion K-Means improves the recognition accuracy to 96.88%. Furthermore, 41 kinds of feature vectors in NSL-KDD are analyzed in detail according to a large number of experimental training. The feature vector of the probability positive return of security attack detection is accurately extracted, and a comparison chart is formed to support subsequent research. A theoretical analysis and experimental results show that the multi-dimensional space feature vector expansion K-Means algorithm has a good application in the detection of DDos attacks.

The development of an efficient detection mechanism to determine malicious network traffic has been a critical research topic in the field of network security in recent years. This study implemented an intrusion-detection system (IDS) based on a machine learning algorithm to periodically convert and analyze real network traffic in the campus environment in almost real time. The focuses of this study are on determining how to improve the detection rate of an IDS and how to detect more non-well-known port attacks apart from the traditional rule-based system. Four new features are used to increase the discriminant accuracy. In addition, an algorithm for balancing the data set was used to construct the training data set, which can also enable the learning model to more accurately reflect situations in real environment.

Human gait analysis has been widely used in medical and health fields. It is essential to extract spatio-temporal gait features (e.g., single support duration, step length, and toe angle) by partitioning the gait phase and estimating the footprint position/orientation in such fields. Therefore, we propose a method to partition the gait phase given a foot position sequence using mutually constrained piecewise linear approximation with dynamic programming, which not only represents normal gait well but also pathological gait without training data. We also propose a method to detect footprints by accumulating toe edges on the floor plane during stance phases, which enables us to detect footprints more clearly than a conventional method. Finally, we extract four spatial/temporal gait parameters for accuracy evaluation: single support duration, double support duration, toe angle, and step length. We conducted experiments to validate the proposed method using two types of gait patterns, that is, healthy and mimicked hemiplegic gait, from 10 subjects. We confirmed that the proposed method could estimate the spatial/temporal gait parameters more accurately than a conventional skeleton-based method regardless of the gait pattern.

We propose a non-invasive and cost-effective method to automatically detect dementia by utilizing solely speech audio data. We extract paralinguistic features for a short speech segment and use Gated Convolutional Neural Networks (GCNN) to classify it into dementia or healthy. We evaluate our method on the Pitt Corpus and on our own dataset, the PROMPT Database. Our method yields the accuracy of 73.1% on the Pitt Corpus using an average of 114 seconds of speech data. In the PROMPT Database, our method yields the accuracy of 74.7% using 4 seconds of speech data and it improves to 80.8% when we use all the patient's speech data. Furthermore, we evaluate our method on a three-class classification problem in which we included the Mild Cognitive Impairment (MCI) class and achieved the accuracy of 60.6% with 40 seconds of speech data.

Depression endangers people's health conditions and affects the social order as a mental disorder. As an efficient diagnosis of depression, automatic depression detection has attracted lots of researcher's interest. This study presents an attention-based Long Short-Term Memory (LSTM) model for depression detection to make full use of the difference between depression and non-depression between timeframes. The proposed model uses frame-level features, which capture the temporal information of depressive speech, to replace traditional statistical features as an input of the LSTM layers. To achieve more multi-dimensional deep feature representations, the LSTM output is then passed on attention layers on both time and feature dimensions. Then, we concat the output of the attention layers and put the fused feature representation into the fully connected layer. At last, the fully connected layer's output is passed on to softmax layer. Experiments conducted on the DAIC-WOZ database demonstrate that the proposed attentive LSTM model achieves an average accuracy rate of 90.2% and outperforms the traditional LSTM network and LSTM with local attention by 0.7% and 2.3%, respectively, which indicates its feasibility.

The Dynamic Spectrum Sharing (DSS) system, which uses the frequency band allocated to incumbent systems (i.e., primary users) has attracted attention to expand the available bandwidth of the fifth-generation mobile communication (5G) systems in the sub-6GHz band. In Japan, a DSS system in the 2.3GHz band, in which the ARIB STD-B57-based Field Pickup Unit (FPU) is assigned as an incumbent system, has been studied for the secondary use of 5G systems. In this case, the incumbent FPU is a mobile system, and thus, the DSS system needs to use not only a spectrum sharing database but also radio sensors to detect primary signals with high accuracy, protect the primary system from interference, and achieve more secure spectrum sharing. This paper proposes highly efficient sensing methods for detecting the ARIB STD-B57-based FPU signals in the 2.3GHz band. The proposed methods can be applied to two types of the FPU signal; those that apply the Continuous Pilot (CP) mode pilot and the Scattered Pilot (SP) mode pilot. Moreover, we apply a sample addition method and a symbol addition method for improving the detection performance. Even in the 3GPP EVA channel environment, the proposed method can, with a probability of more than 99%, detect the FPU signal with an SNR of -10dB. In addition, we propose a quantized reference signal for reducing the implementation complexity of the complex cross-correlation circuit. The proposed reference signal can reduce the number of quantization bits of the reference signal to 2 bits for in-phase and 3 bits for orthogonal components.

Pedestrian detection is an essential but challenging task in computer vision, especially in crowded scenes due to heavy intra-class occlusion. In human visual system, head information can be used to locate pedestrian in a crowd because it is more stable and less likely to be occluded. Inspired by this clue, we propose a dual-task detector which detects head and human body simultaneously. Concretely, we estimate human body candidates from head regions with statistical head-body ratio. A head-body alignment map is proposed to perform relational learning between human bodies and heads based on their inherent correlation. We leverage the head information as a strict detection criterion to suppress common false positives of pedestrian detection via a novel pull-push loss. We validate the effectiveness of the proposed method on the CrowdHuman and CityPersons benchmarks. Experimental results demonstrate that the proposed method achieves impressive performance in detecting heavy-occluded pedestrians with little additional computation cost.

An autoencoder is trained to generate the background from the surveillance image by setting the training label as the shuffled input, instead of the input itself in a traditional autoencoder. Then the multi-scale features are extracted by a sparse autoencoder from the surveillance image and the corresponding background to detect foreground.

A method for detecting the timing of photodiode (PD) saturation without using an in-pixel time-to-digital converter (TDC) is proposed. Detecting PD saturation time is an approach to extend the dynamic range of a CMOS image sensor (CIS) without multiple exposures. In addition to accumulated charges in a PD, PD saturation time can be used as a signal related to light intensity. However, in previously reported CISs with detecting PD saturation time, an in-pixel TDC is used to detect and store PD saturation time. That makes the resolution of a CIS lower because an in-pixel TDC requires a large area. As for the proposed pixel circuit, PD saturation time is detected and stored as a voltage in a capacitor. The voltage is read and converted to a digital code by a column ADC after an exposure. As a result, an in-pixel TDC is not required. A signal-processing and calibration method for combining two signals, which are saturation time and accumulated charges, linearly are also proposed. Circuit simulations confirmed that the proposed method extends the dynamic range by 36 dB and its total dynamic range to 95 dB. Effectiveness of the calibration was also confirmed through circuit simulations.

Water detection is important for machine vision applications such as visual inspection and robot motion planning. In this paper, we propose an approach to per-pixel water detection on unknown surfaces with a hyperspectral image. Our proposed method is based on the water spectral characteristics: water is transparent for visible light but translucent/opaque for near-infrared light and therefore the apparent near-infrared spectral reflectance of a surface is smaller than the original one when water is present on it. Specifically, we use a linear combination of a small number of basis vector to approximate the spectral reflectance and estimate the original near-infrared reflectance from the visible reflectance (which does not depend on the presence or absence of water) to detect water. We conducted a number of experiments using real images and show that our method, which estimates near-infrared spectral reflectance based on the visible spectral reflectance, has better performance than existing techniques.

In this paper, we present an efficient and robust method for estimating Homography matrix for soccer field registration between a captured camera image and a soccer field model. The presented method first detects reliable field lines from the camera image through clustering. Constructing a novel directional feature of the intersection points of the lines in both the camera image and the model, the presented method then finds matching pairs of these points between the image and the model. Finally, Homography matrix estimations and validations are performed using the obtained matching pairs, which can reduce the required number of Homography matrix calculations. Our presented method uses possible intersection points outside image for the point matching. This effectively improves robustness and accuracy of Homography estimation as demonstrated in experimental results.

Feature detection and matching procedure require most of processing time in image matching where the time dramatically increases according to the number of feature points. The number of features is needed to be controlled for specific applications because of their processing time. This paper proposes a feature detection method based on significancy of local features. The feature significancy is computed for all pixels and higher significant features are chosen considering spatial distribution. The method contributes to reduce the number of features in order to match two images with maintaining high matching accuracy. It was shown that this approach was faster about two times in average processing time than FAST detector for natural scene images in the experiments.

Predicting the malfunction timing of wind turbines is essential for maintaining the high profitability of the wind power generation business. Machine learning methods have been studied using condition monitoring system data, such as vibration data, and supervisory control and data acquisition (SCADA) data, to detect and predict anomalies in wind turbines automatically. Autoencoder-based techniques have attracted significant interest in the detection or prediction of anomalies through unsupervised learning, in which the anomaly pattern is unknown. Although autoencoder-based techniques have been proven to detect anomalies effectively using relatively stable SCADA data, they perform poorly in the case of deteriorated SCADA data. In this letter, we propose a power-curve filtering method, which is a preprocessing technique used before the application of an autoencoder-based technique, to mitigate the dirtiness of SCADA data and improve the prediction performance of wind turbine degradation. We have evaluated its performance using SCADA data obtained from a real wind-farm.

This letter reports on the effectiveness of applying the K-singular value decomposition (SVD) dictionary learning to the electroencephalogram (EEG) compressed sensing framework with outlier detection and independent component analysis. Using the K-SVD dictionary matrix with our design parameter optimization, for example, at compression ratio of four, we improved the normalized mean square error value by 31.4% compared with that of the discrete cosine transform dictionary for CHB-MIT Scalp EEG Database.

Well annotated dataset is crucial to the training of object detectors. However, the production of finely annotated datasets for object detection tasks is extremely labor-intensive, therefore, cloud sourcing is often used to create datasets, which leads to these datasets tending to contain incorrect annotations such as inaccurate localization bounding boxes. In this study, we highlight a problem of object detection with noisy bounding box annotations and show that these noisy annotations are harmful to the performance of deep neural networks. To solve this problem, we further propose a framework to allow the network to modify the noisy datasets by alternating refinement. The experimental results demonstrate that our proposed framework can significantly alleviate the influences of noise on model performance.