Ising machines have attracted attention as they are expected to solve combinatorial optimization problems at high speed with Ising models corresponding to those problems. An induced subgraph isomorphism problem is one of the decision problems, which determines whether a specific graph structure is included in a whole graph or not. The problem can be represented by equality constraints in the words of combinatorial optimization problem. By using the penalty functions corresponding to the equality constraints, we can utilize an Ising machine to the induced subgraph isomorphism problem. The induced subgraph isomorphism problem can be seen in many practical problems, for example, finding out a particular malicious circuit in a device or particular network structure of chemical bonds in a compound. However, due to the limitation of the number of spin variables in the current Ising machines, reducing the number of spin variables is a major concern. Here, we propose an efficient Ising model mapping method to solve the induced subgraph isomorphism problem by Ising machines. Our proposed method theoretically solves the induced subgraph isomorphism problem. Furthermore, the number of spin variables in the Ising model generated by our proposed method is theoretically smaller than that of the conventional method. Experimental results demonstrate that our proposed method can successfully solve the induced subgraph isomorphism problem by using the Ising-model based simulated annealing and a real Ising machine.

Surveillance through aerial systems is in place for years. Such systems are expensive, and a large fleet is in operation around the world without upgrades. These systems have low resolution and multiple analog cameras on-board, with Digital Video Recorders (DVRs) at the control station. Generated digital videos have multi-scenes from multi-feeds embedded in a single video stream and lack video stabilization. Replacing on-board analog cameras with the latest digital counterparts requires huge investment. These videos require stabilization and other automated video analysis prepossessing steps before passing it to the mosaicing algorithm. Available mosaicing software are not tailored to segregate feeds from different cameras and scenes, automate image enhancements, and stabilize before mosaicing (image stitching). We present "AirMatch", a new automated system that first separates camera feeds and scenes, then stabilize and enhance the video feed of each camera; generates a mosaic of each scene of every feed and produce a super quality mosaic by stitching mosaics of all feeds. In our proposed solution, state-of-the-art video analytics techniques are tailored to work on videos from vintage cameras in aerial applications. Our new framework is independent of specialized hardware requirements and generates effective mosaics. Affine motion transform with smoothing Gaussian filter is selected for the stabilization of videos. A histogram-based method is performed for scene change detection and image contrast enhancement. Oriented FAST and rotated BRIEF (ORB) is selected for feature detection and descriptors in video stitching. Several experiments on a number of video streams are performed and the analysis shows that our system can efficiently generate mosaics of videos with high distortion and artifacts, compared with other commercially available mosaicing software.

Connected services have been under development in the automotive industry. Meanwhile, the volume of predictive notifications that utilize travel-related data is increasing, and there are concerns that drivers cannot process such an amount of information or do not accept and follow such predictive instructions straightforwardly because the information provided is predicted. In this work, an interactive voice system using two agents is proposed to realize notifications that can easily be accepted by drivers and enhance the reliability of the system by adding contextual information. An experiment was performed using a driving simulator to compare the following three forms of notifications: (1) notification with no contextual information, (2) notification with contextual information using one agent, and (3) notification with contextual information using two agents. The notification content was limited to probable near-miss incidents. The results of the experiment indicate that the driver may decelerate more with the one- and two-agent notification methods than with the conventional notification method. The degree of deceleration depended the number of times the notification was provided and whether there were cars parked on the streets.

With the development and spread of Internet of Things (IoT) technology, various kinds of data are now being generated from IoT devices. Some data generated from IoT devices depend on geographical location and time, and we refer to them as spatio-temporal data (STD). Since the “locally produced and consumed” paradigm of STD use is effective for location-dependent applications, the authors have previously proposed a vehicle-based STD retention system. However, in low vehicle density environments, the data retention becomes difficult due to the decrease in the number of data transmissions in this method. In this paper, we propose a new data transmission control method for data retention in the low vehicle density environments.

In this paper, the issue of malicious URL detection is investigated. Firstly a P system is proposed. Then the new P system is introduced to design the optimization algorithm of BP neural network to achieve the malicious URL detection with better performance. In the end some examples are included and corresponding experimental results display the advantage and effectiveness of the optimization algorithm proposed.

Encrypted traffic identification is to predict traffic types of encrypted traffic. A deep residual convolution network is proposed for this task. The Softmax classifier is fused with its angular variant, which sets an angular margin to achieve better discrimination. The proposed method improves representation learning and reaches excellent results on the public dataset.

This work introduces a measurement model to estimate the naturalness of vibrato. We carried out a subjective evaluation using a mean opinion score (MOS). We then built a measurement model by using two-dimensional Gaussian functions. We found that three Gaussian functions can measure naturalness with an error of 4.0%.