In order to evaluate the vehicle detection method, it is necessary to know the correct vehicle position considered as “ground truth”. We propose indices considering subjective evaluation in vehicle detection utilizing IoU. Subjective evaluation experiments were carried out with respect to misregistration from ground truth in vehicle detection.

Security researchers/vendors detect malicious websites based on several website features extracted by honeyclient analysis. However, web-based attacks continue to be more sophisticated along with the development of countermeasure techniques. Attackers detect the honeyclient and evade analysis using sophisticated JavaScript code. The evasive code indirectly identifies vulnerable clients by abusing the differences among JavaScript implementations. Attackers deliver malware only to targeted clients on the basis of the evasion results while avoiding honeyclient analysis. Therefore, we are faced with a problem in that honeyclients cannot analyze malicious websites. Nevertheless, we can observe the evasion nature, i.e., the results in accessing malicious websites by using targeted clients are different from those by using honeyclients. In this paper, we propose a method of extracting evasive code by leveraging the above differences to investigate current evasion techniques. Our method analyzes HTTP transactions of the same website obtained using two types of clients, a real browser as a targeted client and a browser emulator as a honeyclient. As a result of evaluating our method with 8,467 JavaScript samples executed in 20,272 malicious websites, we discovered previously unknown evasion techniques that abuse the differences among JavaScript implementations. These findings will contribute to improving the analysis capabilities of conventional honeyclients.

Hard-type oscillators for ultrahigh frequency applications were proposed based on resonant tunneling diodes (RTDs) and a HEMT trigger circuit. The hard-type oscillators initiate oscillation only after external excitation. This is advantageous for suppressing the spurious oscillation in the bias line, which is one of the most significant problems in the RTD oscillators. We first investigated a series-connected circuit of a resistor and an RTD for constructing a hard-type oscillator. We carried out circuit simulation using the practical device parameters. It was demonstrated that the stable oscillation can be obtained for such oscillators. Next, we proposed to use series-connected RTDs for the gain block of the hard-type oscillators. The series circuits of RTDs show the negative differential resistance in very narrow regions, or no regions at all, which makes impossible to use such circuits for the conventional soft-type oscillators. However, with the trigger circuit, they can be used for hard-type oscillators. We confirmed the oscillation and the bias stability of these oscillators, and also demonstrated that the voltage swing can be easily increased by increasing the number of RTDs connected in series. This is promising method to overcome the power restriction of the RTD oscillators.

Damage caused by malware has become a serious problem. The recent rise in the spread of evasive malware has made it difficult to detect it at the pre-infection timing. Malware detection at post-infection timing is a promising approach that fulfills this gap. Given this background, this work aims to identify likely malware-infected devices from the measurement of Internet traffic. The advantage of the traffic-measurement-based approach is that it enables us to monitor a large number of endhosts. If we find an endhost as a source of malicious traffic, the endhost is likely a malware-infected device. Since the majority of malware today makes use of the web as a means to communicate with the C&C servers that reside on the external network, we leverage information recorded in the HTTP headers to discriminate between malicious and benign traffic. To make our approach scalable and robust, we develop the automatic template generation scheme that drastically reduces the amount of information to be kept while achieving the high accuracy of classification; since it does not make use of any domain knowledge, the approach should be robust against changes of malware. We apply several classifiers, which include machine learning algorithms, to the extracted templates and classify traffic into two categories: malicious and benign. Our extensive experiments demonstrate that our approach discriminates between malicious and benign traffic with up to 97.1% precision while maintaining the false positive rate below 1.0%.

Advances in fingerprint authentication technology have led to it being used in a growing range of personal devices such as PCs and smartphones. However, they have also made it possible to capture fingerprints remotely with a digital camera, putting the target person at risk of illegal log-ins and identity theft. This article shows how fingerprint captured in this manner can be authenticated and how people can protect their fingerprints against surreptitious photography. First we show that photographed fingerprints have enough information to spoof fingerprint authentication systems by demonstrating with “fake fingers” made from such photographs. Then we present a method that defeats the use of surreptitious photography without preventing the use of legitimate fingerprint authentication devices. Finally, we demonstrate that an implementation of the proposed method called “BiometricJammer,” a wearable device put on a fingertip, can effectively prevent the illegal acquisition of fingerprints by surreptitious photography while still enabling contact-based fingerprint sensors to respond normally.

Establishing drivers' trust in the automated driving system is critical to the success of automated vehicles. The focus of this paper is learning what drivers of automated vehicles need to feel confident during braking events. In this study, 10 participants drove a test vehicle and each experienced 24 different deceleration settings. Prior to each drive, it was indicated to each participant what the expected brake starting and stopping positions would be. During each drive, participants maintained a set speed, and then stopped the vehicle when they saw a signal to apply the brakes. After each drive, the participants were asked what their perceived safety level was during the deceleration setting they just experienced. The results revealed that ‘jerk’ movements have significant influence on drivers' perceived safety. For this study, we have named this jerk movement impression jerk (IJ). Using IJ, clearly divides the secure and anxious feelings of the drivers along with individual differences.

There are two extensions of oblivious polynomial evaluation (OPE), OPEE (oblivious polynomial evaluation in the exponent) and OPEE2. At TCC 2015, Hazay showed two OPEE2 protocols. In this paper, we first show that her first OPEE2 protocol does not run in polynomial time if the computational DH assumption holds. We next present a constant round OPEE protocol under the DDH assumption.

In this paper, a multi-data and multi-ACK verified selective forwarding attacks (SFAs) detection scheme is proposed for containing SFAs. In our scheme, each node (in addition to the nodes in the hotspots area) generates multiple acknowledgement (ACK) message for each received packet to confirm the normal packet transmission. In multiple ACK message, one ACK is returned along the data forwarding path, other ACKs are returned along different routing paths, and thus malicious nodes can be located accurately. At the same time, source node send multiple data routing, one is primary data routing, the others are backup data routing. Primary data is routed to sink directly, but backup data is routed to nodes far from sink, and then waits for the returned ACK of sink when primary data is routed to sink. If a node doesn't receive the ACK, the backup data is routed to sink, thus the success rate of data transmission and lifetime can be improved. For this case, the MDMA scheme has better potential to detect abnormal packet loss and identify suspect nodes as well as resilience against attack. Theoretical analysis and experiments show that MDMA scheme has better ability for ensuring success rate of data transmission, detecting SFA and identifying malicious nodes.

We propose an exact algorithm to determine the satisfiability of oblivious read-twice branching programs. Our algorithm runs in $2^{left(1 - Omega(rac{1}{log c}) ight)n}$ time for instances with n variables and cn nodes.

Traffic is a key aspect of everyday life. Its study, as it happens with other complex phenomena, has found in simulation a basic tool. However, the use of simulations faces important limitations. Building them requires considering different aspects of traffic (e.g. urbanism, car features, and individual drivers) with their specific theories, that must be integrated to provide a coherent model. There is also a variety of simulation platforms with different requirements. Many of these problems demand multi-disciplinary teams, where the different backgrounds can hinder the communication and validation of simulations. The Model-Driven Engineering (MDE) of simulations has been proposed in other fields to address these issues. Such approaches develop graphical Modelling Languages (MLs) that researchers use to model their problems, and then semi-automatically generate simulations from those models. Working in this way promotes communication, platform independence, incremental development, and reutilisation. This paper presents the first steps for a MDE framework for traffic simulations. It introduces a tailored extensible ML for domain experts. The ML is focused on human actions, so it adopts an Agent-Based Modelling perspective. Regarding traffic aspects, it includes concepts commonly found in related literature following the Driver-Vehicle-Environment model. The language is also suitable to accommodate additional theories using its extension mechanisms. The approach is supported by an infrastructure developed using Eclipse MDE projects: the ML is specified with Ecore, and a model editor and a code generator tools are provided. A case study illustrates how to develop a simulation based on a driver's behaviour theory for a specific target platform using these elements.

Analog and digital collaborative design techniques for wireless SoCs are reviewed in this paper. In wireless SoCs, delicate analog performance such as sensitivity of the receiver is easily degraded due to interferences from digital circuit blocks. On the other hand, an analog performance such as distortion is strongly compensated by digital assist techniques with low power consumption. In this paper, a sensitivity recovery technique using the analog and digital collaborative design, and digital assist techniques to achieve low-power and high-performance analog circuits are presented. Such analog and digital collaborative design is indispensable for wireless SoCs.

A novel dynamic element matching (DEM) method, called binary-tree random DEM (BTR-DEM), is presented for a Nyquist-rate current-steering digital-to-analog converter (DAC). By increasing or decreasing the number of unit current sources randomly at the same time, the BTR-DEM encoding reduces switch transition glitches. A 5-bit current-steering DAC with the BTR-DEM technique is implemented in a 65-nm CMOS technology. The measured spurious free dynamic range (SFDR) attains 42 dB for a sample rate of 100 MHz and shows little dependence on signal frequency.

The harmonious coloring of an undirected simple graph is a vertex coloring such that adjacent vertices are assigned different colors and each pair of colors appears together on at most one edge. The harmonious chromatic number of a graph is the least number of colors used in such a coloring. The harmonious chromatic number of a path is known, whereas the problem to find the harmonious chromatic number is NP-hard even for trees with pathwidth at most 2. Hence, we consider the harmonious coloring of trees with pathwidth 1, which are also known as caterpillars. This paper shows the harmonious chromatic number of a caterpillar with at most one vertex of degree more than 2. We also show the upper bound of the harmonious chromatic number of a 3-regular caterpillar.

Prediction of seasonal infectious disease spread is traditionally dealt with as a function of time. Typical methods are time series analysis such as ARIMA (autoregressive, integrated, and moving average) or ANN (artificial neural networks). However, if we regard the time series data as the matrix form, e.g., consisting of yearly magnitude in row and weekly trend in column, we may expect to use a different method (matrix approach) to predict the disease spread when seasonality is dominant. The MD (matrix decomposition) method is the one method which is used in recommendation systems. The other is the IRT (item response theory) used in ability evaluation systems. In this paper, we apply these two methods to predict the disease spread in the case of infectious gastroenteritis caused by norovirus in Japan, and compare the results obtained by using two conventional methods in forecasting, ARIMA and ANN. We have found that the matrix approach is simple and useful in prediction for the seasonal infectious disease spread.

In wireless sensor networks, or WSNs, a malicious node is able to cover itself by switching between good and bad behaviors. Even when running under a reputation mechanism, such a node can still behave maliciously now and then so long as its reputation is within the acceptable level. To address this inconsistent behavior issue, a combined approach of statistic reputation and time series is proposed in this study, in which the negative binomial reputation is applied to rate the nodes' reputation and concept of time series is borrowed to analyze the reputation results. Simulations show that the proposed method can effectively counter inconsistent behavior nodes and thus improves the overall system performance.

Fault localization is essential for conducting effective program repair. However, preliminary studies have shown that existing fault localization approaches do not take the requirements of automatic repair into account, and therefore restrict the repair performance. To address this issue, this paper presents the first study on designing fault localization approaches for automatic program repair, that is, we propose a fault localization approach using failure-related contexts in order to improve automatic program repair. The proposed approach first utilizes program slicing technique to construct a failure-related context, then evaluates the suspiciousness of each element in this context, and finally transfers the result of evaluation to automatic program repair techniques for performing repair on faulty programs. The experimental results demonstrate that the proposed approach is effective to improve automatic repair performance.

A new trigger circuit based on up/down counter is proposed. This trigger circuit consists of a up/down counter and a pulse conversion circuit. Compared with a trigger circuit based on 32-bit counter, the proposed trigger circuit occupies less circuit area and consumes less power consumption, while the trigger process can be inversed, increasing the controllability of the Trojan.

Oblivious RAM (ORAM) schemes, the concept introduced by Goldreich and Ostrovsky, are very useful technique for protecting users' privacy when storing data in remote untrusted servers and running software on untrusted systems. However they are usually considered impractical due to their huge overhead. In order to reduce overhead, many improvements have been presented. Thanks to these improvements, ORAM schemes can be considered practical on cloud environment where users can expect huge storage and high computational power. Especially for private information retrieval (PIR), some literatures demonstrated they are usable. Also dedicated PIRs have been proposed and shown that they are usable in practice. Yet, they are still impractical for protecting software running on untrusted systems. We first survey recent researches on ORAM and PIR. Then, we present a practical software-based memory protection scheme applicable to several environments. The main feature of our scheme is that it records the history of accesses and uses the history to hide the access pattern. We also address implementing issues of ORAM and propose practical solutions for these issues.

Recent progress in research on the finite element method (FEM) for optical waveguide design and analysis is reviewed, focusing on the author's works. After briefly reviewing fundamentals of FEM such as a theoretical framework, a conventional nodal element, a newly developed edge element to eliminate nonphysical, spurious solutions, and a perfectly matched layer to avoid undesirable reflections from computational window edges, various FEM techniques for a guided-mode analysis, a beam propagation analysis, and a waveguide discontinuity analysis are described. Some design examples are introduced, including current research activities on multi-core fibers.

Software watermarking is a digital technique used to protect software by embedding some secret information as identification in order to discourage software piracy and unauthorized modification. Watermarking is still a relatively new field and has good potential in protecting software from privacy threats. However, there appears to be a security vulnerability in the watermark trigger behaviour, and has been frequently attacked. By tracing the watermark trigger behaviour, attackers can easily intrude into the software and locate and expose the watermark for modification. In order to address this problem, we propose an algorithm that obscures the watermark trigger behaviour by utilizing buffer overflow. The code of the watermark trigger behaviour is removed from the software product itself, making it more difficult for attackers to trace the software. Experiments show that the new algorithm has promising performance in terms of the imperceptibility of software watermark.