Products and services nowadays need personal information from consumers in order to personalize their goods to best fit consumers. At the present, the online environment is the biggest source of consumers' personal information. However, online privacy has become the major concern of consumers. A personal information trading platform has been proposed as a medium for collecting consumers' personal information in exchange for monetary incentive. This study proposes a new approach to requesting personal attributes which can adapt with consumers' personal information disclosure behavior and aims to increase the disclosure of personal information without increasing of monetary incentive. To develop this new adaption method, we developed the valuation of a personal information method without using currency. The probability and graph mining techniques were used to valuating personal attributes. Then, we displayed the relationships of personal attributes disclosure in the hierarchy and proposed a method for valuating personal information disclosure. The valuation method was used in the evaluations, which were compared with the disclosure of personal information results from the consumers. After the evaluation was completed, the result showed that the new approach can significantly increase the disclosure of consumers' personal information.
A use of an electric outlet by a consumer forces the outlet manager to pay for the consumer's power usage in current electrical power systems. Even if a consumer uses an outlet managed by another person, one bill for both indoor and outdoor charging information should be required to the consumer in their contract with the utility company. For this purpose, we define a model for the Smart Grid security and propose a Secure Payment Protocol for Charging Information over Smart grid, SPaCIS for short, as a protocol satisfying the model. Our model provides for the unlinkability of consumers as well as for the undeniability and unforgeability of billing information using digital signatures and identity federations. SPaCIS is also efficient in the sense that time complexity is constant relatively to a trivial use such as an individual verification for each signatures, unless a verification error happens. We furthermore evaluate performance of SPaCIS via cryptographic implementation, and simulate SPaCIS in a case that one thousand users generate thirty signatures. Then, we show that SPaCIS with ECDSA can be executed within 6.30msec for signing and 21.04msec for verification of signatures, and conclude that SPaCIS is fairly practical.
Technological development in communications and electronics has made the growing expansion of the Internet of Things (IoT). IoT is expected to make a great impact to our society because smart devices in IoT are easily integrated into existing service. As a result, standardization of technologies to support the IoT is becoming more important to realize a smart society through different service domains. This paper presents a survey on the current state of the art of standards for IoT technologies and gives a brief introduction to related standards and recent research areas in IoT. Finally, it also proposes an idea of the future platform of scalable IoT systems. The proposed idea employs IP mobility technologies to realize inter-operability among IoT devices in different networks.
As an innovation of driver assistance technology, this research aims to develop an “Autonomous Intelligent Driving System” to prevent risk of accidents and enhance driving safety for elderly drivers in order to vitalize current aged society. The proposed system focuses on two key technologies: Risk-predictive driving intelligence model and Shared control between the driver and the assistance system. The first key technology is to embed an experienced driver model for recovering degraded performances of recognition, decision-making and operation of drivers. In the driver assistance system design, the experienced driver model contains knowledge-based “risk-prediction mechanism” to avoid accidents in risky driving situations. For instance, when passing unsignalized intersections with poor visibility, it is known that experienced drivers predict the appearance of sudden-crossing pedestrians or bicycles and then slow down the vehicle when approaching such poor visibility area and also prepare to brake in order to avoid potential collisions that might occur. The second key point is “Shared control.” This research does not aim to develop a fully-autonomous driving vehicle for them, but aims to develop an advanced driver assistance system for preventing accidents in the case that the intervention by braking or steering is needed, as well as reducing driving workload. Therefore, to realize good cooperative characteristics between the driver and the system, the shared control concept is applied to optimize the assistance level for braking and steering maneuver, minimizing the interference human driver driving maneuver. The Driving Simulator and the test vehicle are used to verify the effectiveness of the proposed intelligent driving system.
This article discusses a novel method to strengthen the collaboration between Internet service providers (ISPs) and content delivery networks (CDNs). CDNs are becoming the primary data delivery method in information communication technology environments because information sharing via networks is becoming the driving force of the future Internet. Moreover, it is anticipated that network routers will be equipped with additional processing power and storage modules for providing efficient end-user services. Consequently, this article studies the effectiveness of introducing a Service-oriented Router (SoR) to strengthen the ISP-CDN collaboration to leverage DNS-based request redirection in CDNs. In contrast, the proposed method yields better performance in user redirection and network resource utilization, suggesting that using SoR may a future business model which addresses adequate ISP-CDN collaboration.
Recently, cloud systems composed of heterogeneous hardware have been increased to utilize progressed hardware power. However, to program applications for heterogeneous hardware to achieve high performance needs much technical skill and is difficult for users. Therefore, to achieve high performance easily, this paper proposes a PaaS which analyzes application logics and offloads computations to GPU and FPGA automatically when users deploy applications to clouds.
There have been several studies on object detection and activity recognition on a table conducted thus far. Most of these studies use image processing with cameras or a specially configured table with electrodes and an RFID reader. In private homes, methods using cameras are not preferable since cameras might invade the privacy of inhabitants and give them the impression of being monitored. In addition, it is difficult to apply the specially configured system to off-the-shelf tables. In this work, we propose a system that recognizes activities conducted on a table and identifies which user conducted the activities with load cells only. The proposed system uses four load cells installed on the four corners of the table or under the four legs of the table. User privacy is protected because only the data on actions through the load cells is obtained. Load cells are easily installed on off-the-shelf tables with four legs and installing our system does not change the appearance of the table. The results of experiments using a table we manufactured revealed that the weight error was 38g, the position error was 6.8cm, the average recall of recognition for four activities was 0.96, and the average recalls of user identification were 0.65 for ten users and 0.89 for four users.
In this paper we consider the (legal) representative in governmental ICT services and propose a secure private mail box system in which a message sent to the pupil is re-encrypted by the proxy server. This process enables the representative to decrypt the message. We also show its formal description of the protocols and evaluate the security by ProVerif model checking tool.
We describe a method for decentralized task/area partitioning for coordination in cleaning/sweeping domains with learning to identify the easy-to-dirty areas. Ongoing advances in computer science and robotics have led to applications for covering large areas that require coordinated tasks by multiple control programs including robots. Our study aims at coordination and cooperation by multiple agents, and we discuss it using an example of the cleaning tasks to be performed by multiple agents with potentially different performances and capabilities. We then developed a method for partitioning the target area on the basis of their performances in order to improve the overall efficiency through their balanced collective efforts. Agents, i.e., software for controlling devices and robots, autonomously decide in a cooperative manner how the task/area is partitioned by taking into account the characteristics of the environment and the differences in agents' software capability and hardware performance. During this partitioning process, agents also learn the locations of obstacles and the probabilities of dirt accumulation that express what areas are easy to be dirty. Experimental evaluation showed that even if the agents use different algorithms or have the batteries with different capacities resulting in different performances, and even if the environment is not uniform such as different locations of easy-to-dirty areas and obstacles, the proposed method can adaptively partition the task/area among the agents with the learning of the probabilities of dirt accumulations. Thus, agents with the proposed method can keep the area clean effectively and evenly.
Increasing the size of parallel corpora for less-resourced language pairs is essential for machine translation (MT). To address the shortage of parallel corpora between Chinese and Japanese, we propose a method to construct a quasi-parallel corpus by inflating a small amount of Chinese-Japanese corpus, so as to improve statistical machine translation (SMT) quality. We generate new sentences using analogical associations based on large amounts of monolingual data and a small amount of parallel data. We filter over-generated sentences using two filtering methods: one based on BLEU and the second one based on N-sequences. We add the obtained aligned quasi-parallel corpus to a small parallel Chinese-Japanese corpus and perform SMT experiments. We obtain significant improvements over a baseline system.
In eye-tracking-based reading behavior research, gaze sampling errors often negatively affect gaze-to-word mapping. In this paper, we propose a method for more accurate mapping by first taking adjacent horizontally progressive fixations as segments, and then classifying the segments into six classes using a random forest classifier. The segments are then reconstructed based on the classification, and are associated with a document line using a dynamic programming algorithm. The combination of segment-to-line mapping and transition classification achieved 87% mapping accuracy. We also witnessed a reduction of manual annotation time when the mapping was used as an annotation guiding tool.
Data stream management systems (DSMSs) are suitable for managing and processing continuous data at high input rates with low latency. For advanced driver assistance including autonomous driving, embedded systems use a variety of onboard sensor data with communications from outside the vehicle. Thus, the software developed for such systems must be able to handle large volumes of data and complex processing. We develop a platform that integrates and manages data in an automotive embedded system using a DSMS. However, because automotive data processing, which is distributed in in-vehicle networks of the embedded system, is time-critical and must be reliable to reduce sensor noise, it is difficult to identify conventional DSMSs that meet these requirements. To address these new challenges, we develop an automotive embedded DSMS (AEDSMS). This AEDSMS precompiles high-level queries into executable query plans when designing automotive systems that demand time-criticality. Data stream processing is distributed in in-vehicle networks appropriately, where real-time scheduling and senor data fusion are also applied to meet deadlines and enhance the reliability of sensor data. The main contributions of this paper are as follows: (1) we establish a clear understanding of the challenges faced when introducing DSMSs into the automotive field; (2) we propose an AEDSMS to tackle these challenges; and (3) we evaluate the AEDSMS during run-time for advanced driver assistance.
This paper addresses the issues in the task of annotating geographical entities on microblogs and reports the preliminary results of our efforts to annotate Japanese microblog texts. Unlike prior work, we aim at annotating not only geographical location entities but also facility entities, such as stations, restaurants and schools. We discuss (i) how to build a gazetteer of geographical entities with a sufficiently broad coverage, (ii) what types ambiguities that need to be considered, (iii) why the annotator tends to disagree, and (iv) what technical problems should be addressed to automate the task of annotating the geographical entities. All the annotation data and the annotation guidelines are publicly available for research purposes from our web site.