With the rapid proliferation of various cloud storage services in recent years, the development of technology to efficiently search data while ensuring its confidentiality during cloud usage is an important issue. The technology that enables keyword searches on encrypted files using previously set keywords is called searchable symmetric encryption (SSE). In this paper, we propose a method formally representing encrypted document, and verify the security of SSE using the formal verification tool ProVerif. Our proposed method considers the channel-type terms of ProVerif as a Document that includes different keywords to verify the indistinguishability of encrypted documents.

With the popularization of smartphones, SNS, which allows easy message exchange, has become popular. On the other hand, a metaverse has attracted attention recently. In the metaverse, participants interact with each other through avatars in a virtual space. Therefore, if we can show participants the existing SNS space as the metaverse, we can provide participants with more intuitive use cases. This manner is beneficial for elderly people who are unfamiliar with using smartphones. In this paper, we propose LINE metaverse. LINE is the most popular messaging application in Japan. The LINE metaverse is characterized by replacing the bot mechanism used for marketing using SNS with a metaverse agent. In the LINE metaverse, elderly people can exchange messages via avatars on the metaverse.

Hexagonal pattern nets made of polyester monofilament are widely used for fish cages and rockfall prevention due to their durability. Although these nets are manufactured by automatic knitting machines, factory workers constantly monitor the process to prevent abnormalities as it is difficult to distinguish abnormalities. Therefore, it is necessary to automatically, quickly and accurately detect net abnormalities during production. On the other hand, subsequent tension adjusting operations for anomaly clearance must be still performed by factory workers. To prevent mesh abnormalities in knitting machines, therefore, it is not enough just to be able to detect abnormalities with high accuracy; it is also important to obtain feedbacks on the subsequent factory worker’s anomaly clearance operations. This article proposes image features that achieve the requirement and a method for obtaining these features from net images. It further proposes a learning method to identify abnormalities with high accuracy using such images. We shows that the identification accuracy of our method is as high as that of the Convolutional Neural Network (CNN) based method, which is well known by its high performance in detecting anomalies using image data, even when not enough training data is available. In addition, we show that it is easy to estimate which strings need to be adjusted to clear the anomaly by obtaining these proposed image features, without training the classifier. Furthermore, this proposed method was implemented on an actual knitting machine as an experiment, which confirmed that it can detect mesh abnormalities in real-time.

In recent years, image inspections have become a useful tool in the early diagnosis of rheumatoid arthritis (RA). However, evaluations in RA ultrasound inspections are visual inspections by doctors, evaluation tends to become somewhat subjective, meaning that it is difficult for less-experienced doctors to make an assessment. In this study, therefore, we propose a system that alleviates the burden on doctors, by automating RA image diagnosis, that is to say an RA image diagnosis support system using ultrasound images. With this system, as it is necessary to automate RA image diagnosis, it is necessary to propose an image classification method with higher classification accuracy. Therefore, in this paper, we propose an image classification method with higher precision, by comparing methods of image classification. We also show our evaluation results. As a result, it is considered that image classification via CNN using automatic extraction images of synovial thickening surrounding areas is effective for image classification of ultrasound images for the purpose of automating RA image diagnosis.

With the growing global demand for marine products, offshore aquaculture, which enables large-scale aquaculture compared to conventional coastal aquaculture, is drawing attention. In offshore aquaculture, the occurrence of red tide due to residual food can be suppressed by the circulation of ocean currents, and the environmental load can be reduced. On the other hand, since the farmed cage is located in a remote area, there are problems such as the cost of transporting food, the storage of a large amount of food, and the management of facilities in the event of a typhoon. In addition, it is especially important to manage the environmental information in the cage in order to understand the health condition of farmed fish in remote areas. If a device that measures water quality, which is one of the environmental information, is left in the sea for a long period of time, dirt such as algae will adhere and the measurement performance will deteriorate. This time, we developed an air pressure control system that allows the water quality sensor in the sea to enter the water only when measuring, and implemented it in the observation device. This observation device enables long-term observation of various water quality data.

Many subjective experiments have been performed to develop objective speech intelligibility measures, but the novel coronavirus outbreak has made it very difficult to conduct experiments in a laboratory. One solution is to perform remote testing using crowdsourcing; however, because we cannot control the listening conditions, it is unclear whether the results are entirely reliable. In this study, we compared speech intelligibility scores obtained in remote and laboratory experiments. The results showed that the mean and standard deviation (SD) of the remote experiments' speech reception threshold (SRT) were higher than those of the laboratory experiments. However, the variance in the SRTs across the speech-enhancement conditions revealed similarities, implying that remote testing results may be as useful as laboratory experiments to develop an objective measure. We also show that the practice session scores correlate with the SRT values. This is a priori information before performing the main tests and would be useful for data screening to reduce the variability of the SRT distribution.

Currently, a communication robot like an AI speaker has become popular as one of consumer services. As realized high functions seen in cooperation of network and home appliances by them, hurdles for the elderly in operating such advanced IT devices are still high. On the other hand, RPA (Robotic Process Automation) attracts attention for productivity improvement of business processing. However, there are few examples that applied RPA to consumer services. It is caused that there is not common sense about an application method of RPA for consumer services. Therefore, if we could define the application method of RPA for consumer services, we could develop consumer services familiar with the elderly. This paper gives some examples of our developed consumer services for the elderly by communication robots after summarizing requirements for applying RPA to consumer services. Then, we inspect the effectiveness of the consumer services based on RPA. Finally, we make clear a RPA basic model for consumer services.

Symbolic verification tools such as ProVerif can analyze the security of cryptographic protocols automatically. However, if the formal definitions of cryptologic functionalities are incomplete, then the results will be incorrect. Unfortunately, there has so far been no way to analyze the symbolically behavior of such functionalities in the formalization. Furthermore, we have not been able to describe attacker models using such tools. In this paper, we propose a method of defining cryptologic functionalities that uses ProVerif to verify their cryptographic requirements. In addition, by using symbolic verifiers, the proposed method makes it possible to verify cryptographically meaningful security requirements. We therefore expect that this method will contribute to formally defining advanced cryptologic functionalities and accurately verifying the security of cryptographic protocols involving such functionalities.

We developed SNS Door Phone by making an interphone system an IoT device. We integrated SNS and QR-code recognition function with an interphone system. Thanks to connection with SNS, we can know the visit of the parcel delivery service anytime through SNS even if during going out. Thanks to introduction of QR-code recognition function, if a parcel deliveryman only showed the QR-code of the parcel in front of SNS Door Phone, the re-delivery operation information would be sent to a user automatically through SNS. Then, the user can call or ask re-delivery arrangement using smart phone without inputting any additional data. We can consider this kind of seamless re-delivery operation to be a good example of Robotic Process Automation.

There are many obstacles for wheelchair users outside. These items are not an issue for people without physical impairment, but are major obstacles for wheelchair users. Due to these obstacles, wheelchair users may feel anxiety besides familiar paths, leading to less desire to go outside. In this study, we propose a system for collecting social barrier-free information from the various sensors attached to general-purpose wheelchairs and constantly feeding back the latest barrier-free street view information to wheelchair users, that is to say an “on-demand barrier-free street view system.” With this system, wheelchair users can confirm safe routes using the street view; thus, their range of movement can be increased. Furthermore, shared road information is information that was gathered by wheelchair users; therefore, it is a verification that the path is actually passable with a wheelchair. It provides a feeling of security for wheelchair users to go out. In this paper, we show the results of an evaluation experiment using this system.

We propose a SNS Agency Robot that can be used for the interactive communication between elderly people and younger generation via existing Social Networking Service (SNS). This robot system has been implemented on a cloud service and a single board computer embedded in a human-type robot, which is equipped with a microphone, camera, speaker, sensors, and network access function, so that elderly people can transmit and receive information by voice via SNS without using smartphones. We employed LINE that is a proprietary application for instant communications on electronic devices such as smartphones. In LINE, we need to select a message destination address before sending messages. On the other hand, our proposed robot is basically operated by voice due to realizing the simple user interface. Therefore, we proposed a message exchange learning-type destination estimation method that enables elderly people not to express a message destination address explicitly. We developed the prototype system including the message exchange learning-type destination estimation method. Then, we confirmed the effectiveness of the message destination estimation through the demonstration experiment at the house for the elderly with the care service.

The capacity for secret key agreement for permutation-invariant and symmetric sources under a sampling attack is investigated. The supremum of the normalized secret key capacity is introduced, where the supremum is taken over all permutation-invariant sources or all symmetric sources and the normalized secret key capacity is the secret key capacity divided by the description length of the symbol. It is proved that the supremum of the normalized secret key capacity bound under a sampling attack is close to 1/m for permutation-invariant sources and O(1/m) for symmetric sources, where and m is the number of Eve's sources

This paper considers the capacity for secret key agreement under a sampling attack. Given the number of the eavesdropper's sources, we evaluate the secret key capacity bound which is defined as the supremum of the secret key capacity divided by the description length of the alphabet, where the supremum is taken over a set of probability distributions corresponding to the correlated sources. In particular, we consider symmetric sources and permutation-invariant sources. We derive inequalities which show the scaling of the secret key capacity bound with the number of the eavesdropper's sources