Details of a Researcher - Nakazawa, Jin

Papers - Nakazawa, Jin

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A mobility-aware pub/sub architecture for short-lived data in smart cities

T Kawasaki, T Okoshi, J Nakazawa

Proceedings of the 18th Conference on Embedded Networked Sensor Systems, 645-646 2020

iOS crowd–sensing won’t hurt a bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform

Y Nishiyama, D Ferreira, Y Eigen, W Sasaki, T Okoshi, J Nakazawa, ...

International Conference on Human-Computer Interaction, 223-243 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)) 12203 LNCS 223 - 243 2020

Research paper (international conference proceedings), Joint Work, ISSN 9783030503437

The latest smartphones have advanced sensors that allow us to recognize human and environmental contexts. They operate primarily on Android and iOS, and can be used as sensing platforms for research in various fields owing to their ubiquity in society. Mobile sensing frameworks help to manage these sensors easily. However, Android and iOS are constructed following different policies, requiring developers and researchers to consider framework differences during research planning, application development, and data collection phases to ensure sustainable data collection. In particular, iOS imposes strict regulations on background data collection and application distribution. In this study, we design, implement, and evaluate a mobile sensing framework for iOS, namely AWARE-iOS, which is an iOS version of the AWARE Framework. Our performance evaluations and case studies measured over a duration of 288 h on four types of devices, show the risks of continuous data collection in the background and explore optimal practical sensor settings for improved data collection. Based on these results, we develop guidelines for sustainable data collection on iOS.

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GANonymizer: Image Anonymization Method Integrating Object Detection and Generative Adversarial Network

T Tanimura, M Kawano, T Yonezawa, J Nakazawa

EAI International Conference on IoT in Urban Space, 109-121 (EAI/Springer Innovations in Communication and Computing) 109 - 121 2020

Joint Work, ISSN 25228595

Sharing and analyzing image data from ubiquitous urban cameras must enable us to understand and predict various contexts of the city. Meanwhile, since such image data always contains privacy data such as people and cars, we cannot easily share and analyze the data through the Internet for the viewpoint of privacy protection. As a result, most of urban image data are only kept/shared within the camera owners, or even discarded to reduce risks of privacy data leakage. To solve the privacy problem and accelerate sharing of urban image data, we propose GANonymizer that automatically detects and removes objects related to privacy from the urban images. GANonymizer combines two neural networks: (1) a network which detects objects related to privacy such as persons and cars in an input image using object detection network and (2) a network that removes the detected objects naturally as though they are not exist originally. Through our experiment of applying GANonymizer to urban video images, we confirmed that GANonymizer partially achieved natural removal of objects related to privacy.

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Civic Crowdsensing Through Location-Aware Virtual Monsters

J Nakazawa

Distributed, Ambient and Pervasive Interactions: 8th International … (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)) 12203 LNCS 464 - 476 2020

Joint Work, ISSN 9783030503437

We present a new model for encouraging people to get involved with monitoring and taking part in the life of cities. Cities could be smarter if IoT and people could serve as engaged and pro-active data resources (i.e., crowd sensing). This study tackles two challenges: methods by which the privacy of people who act as sensors/actuators can be guaranteed and methods to create a unified programming model for crowd sensors alongside other IoT functions. To achieve these goals, we introduce a new concept called Lokemon (Location Monster). Each sensing space is characterized as a personified target. Lokemon asks users to imagine themselves to be monsters associated with target spots when achieving sensing tasks. Lokemon is also expressed as a PubSub node so that the data from Lokemon can be easily accessed in the same way as data from IoT is assessed. The article explains the concept of Lokemon and its programming model. We report our evaluation of the effectiveness of Lokemon in a campus experiment that was performed for four weeks.

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CityFlow: Supporting Spatial-Temporal Edge Computing for Urban Machine Learning Applications

M Kawano, T Yonezawa, T Tanimura, NK Giang, M Broadbent, R Lea, ...

EAI International Conference on IoT in Urban Space, 3-15 (EAI/Springer Innovations in Communication and Computing) 3 - 15 2020

Joint Work, ISSN 25228595

A growing trend in smart cities is the use of machine learning techniques to gather city data, formulate learning tasks and models, and use these to develop solutions to city problems. However, although these processes are sufficient for theoretical experiments, they often fail when they meet the reality of city data and processes, which by their very nature are highly distributed, heterogeneous, and exhibit high degrees of spatial and temporal variance. In order to address those problems, we have designed and implemented an integrated development environment called CityFlow that supports developing machine learning applications. With CityFlow, we can develop, deploy, and maintain machine learning applications easily by using an intuitive data flow model. To verify our approach, we conducted two case studies: deploying a road damage detection application to help monitor transport infrastructure and an automatic labeling application in support of a participatory sensing application. These applications show both the generic applicability of our approach, and its ease of use; both critical if we wish to deploy sophisticated ML based applications to smart cities.

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AffectiveNemo: Promoting Owner-Fish Interaction by Displaying Observed Fish Emotion with Speech Bubble

N Isokawa, J Nakazawa, T Okoshi

Proceedings of the Sixth International Conference on Animal-Computer … (ACM International Conference Proceeding Series) 18 - 5 2019.11

Research paper (international conference proceedings), Joint Work, ISSN 9781450376938

Aquarium fish is one of the most common household companion animals for humans. Keeping and appreciating aquarium fish enriches us and provides comfort to our lives. However, difficulty in communicating and understanding their fish may negatively impact the person's motivation to continuously take care of their fish. In order to enhance human-fish interaction, we propose “AffectiveNemo”, an ACI system for aquarium fish, that estimates and displays “observed emotion” (defined as fish's emotional states that the system estimates based on observations by humans), via speech bubbles shown on an LCD display placed behind the aquarium. The system estimates the observed emotion by analyzing the movement of the fish in the aquarium, and combining this data along with the emotions that the humans perceive the fish to have. We evaluated the accuracy of each module as a preliminary experiment, conducted a user study on the influence of displaying observed emotion, and evaluated the utility of the system. Our findings concluded that the system can correctly detect the observed emotion, further enhance viewer sensitivity by displaying the observed emotion of aquarium fish that humans are perceptive, and promote the interest in aquarium fish.

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SleepThermo: The affect of in-cloth monitored body temperature change during sleep on human well-being

K Katsumata, Y Noda, N Isokawa, S Katayama, T Okoshi, J Nakazawa

Adjunct Proceedings of the 2019 ACM International Joint Conference on … (UbiComp/ISWC 2019- - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers) 1174 - 1177 2019.09

Research paper (international conference proceedings), Joint Work, ISSN 9781450368698

With the advent of 24/7 technology-driven society, there are rising discomforts and increasing concerns over sleep. Quality of sleep critically affects human well-being and everyday performance. A good night’s sleep, therefore, is essential to prepare one’s mind and body for the next day. In this paper, we propose SleepThermo, a system that identifies the affect of in-cloth monitored body temperature change during sleep on human well-being. The primary purpose of this research is to determine the relationship between in-clothing body temperature and mental/physical conditions, and then elicit positive insights to improve sleep quality. Our evaluation proved that there is a potential for body temperature to be used in identifying mental and physical conditions. In addition, personalized models achieved better balanced accuracy than overall models in all subjects.

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WellComp 2019: second international workshop on computing for well-being

T Okoshi, J Nakazawa, JG Ko, F Kawsar, S Pirttikangas

Research paper (international conference proceedings), Joint Work, ISSN 9781450368698

With the advancements in ubiquitous computing, ubicomp technology has deeply spread into our daily lives, including office work, home and house-keeping, health management, transportation, or even urban living environments. Furthermore, beyond the initial metric of computing, such as “efficiency” and “productivity”, the benefits that people (users) benefit on a well-being perspective based on such ubiquitous technology has been greatly paid attention in the recent years. In our second “WellComp” (Computing for Well-being) workshop, we intensively discuss about the contribution of ubiquitous computing towards users’ well-being that covers physical, mental, and social wellness (and their combinations), from the viewpoints of various different layers of computing. Having strong international organization members in various ubicomp research domains, WellComp 2019 will bring together researchers and practitioners from the academia and industry to explore versatile topics related to wellbeing and ubiquitous computing.

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Situation-Aware Emotion Regulation of Conversational Agents with Kinetic Earables

S Katayama, A Mathur, M Van den Broeck, T Okoshi, J Nakazawa, ...

2019 8th International Conference on Affective Computing and Intelligent … (2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019) 725 - 731 2019.09

Research paper (international conference proceedings), Joint Work, ISSN 9781728138886

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Demo: Motivating long-term dietary habit modification through mobile MR gamification

Katsumata K, Eigen Y, Noda Y, Tsuruoka M, Hashiba S, Numoto S, Katayama S, Okoshi T, Nakazawa J

MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services) 671 - 672 2019.06

Research paper (international conference proceedings), Joint Work, ISSN 9781450366618

In correlation with the socio-economic development, changes in people’s lifestyle brought about significant impact on dietary patterns. Though public concerns over healthy eating are increasing, many are still uncertain when choosing a well balanced meal amid welter of information. In this paper, we propose “KomaFLens”, a mobile system and application built for Microsoft HoloLens, which aims to motivate long term dietary habit modification through gamification. The primary purpose of this research is to enhance the users’ nutritional knowledge and to guide them to make healthier choices in their diet. Our preliminary evaluation revealed interesting points for discussion regarding the procedure for capturing food labels. Streamlining the operational method to boost tractability will improve the accuracy when recording food intakes.

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Demo: Situation-aware conversational agent with kinetic earables

Katayama S, Mathur A, Okoshi T, Nakazawa J, Kawsar F

Research paper (international conference proceedings), Joint Work, ISSN 9781450366618

Conversational agents are increasingly becoming digital partners of our everyday computing experiences offering a variety of purposeful information and utility services. Although rich on competency, these agents are entirely oblivious to their users’ situational and emotional context today and incapable of adjusting their interaction style and tone contextually. To this end, we present a first-of-its-kind situation-aware conversational agent on kinetic earable that dynamically adjusts its conversation style, tone, volume in response to users emotional, environmental, social and activity context gathered through speech prosody, ambient sound and motion signatures. In particular the system is composed of the following components: • Perception Builder: This component is responsible for building an approximate view of user’s momentary experience by sensing his/her 1) physical activity, 2) emotional state, 3) social context and 4) environmental context using different purpose-built acoustic and motion sensory models [4, 5]. • Conversation Builder: This component enables a user to interact with the agent using a predefined dialogue base, and for this demo, we have used Dialogflow [1] populated with a set of situation-specific dialogues. • Affect Adapter: This component is responsible for guiding the adaptation strategy for the agent’s response corresponding to the user’s context, taking into account the output of the perception builder and a data-driven rule engine. We have devised a set of adaptation rules using multiple quantitative and qualitative studies that describe the prosody, volume and speed to shape agents response. • Text-to-Speech Builder: This component is responsible for synthesising the agent’s response in a voice that accurately reflects a user’s situation using IBM Bluemix Voice service [2]. This synthesis process interplays various voice attributes, e.g., pitch, rate, breathiness, glottal tension etc. to transform agents voice according to the rule of the Affect Adapter.

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CityFlow: exploiting edge computing for large scale smart city applications

NK Giang, VCM Leung, M Kawano, T Yonezawa, J Nakazawa, R Lea, ...

2019 IEEE International Conference on Big Data and Smart Computing (BigComp … (2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings) 1 - 4 2019.04

Joint Work, ISSN 9781538677896

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Cruisers: An automotive sensing platform for smart cities using door-to-door garbage collecting trucks

Y Chen, J Nakazawa, T Yonezawa, H Tokuda

Ad Hoc Networks 85, 32-45 (Ad Hoc Networks) 85 32 - 45 2019.03

Research paper (scientific journal), Joint Work, ISSN 15708705

Due to the advance in information and communication technologies, smart cities have become an increasingly appealing vision. One indispensable basis in the research, development and implementation of smart cities is urban sensing technology. Automotive sensing is a novel urban sensing technology that equips sensors to vehicles, like buses, taxi cabs or garbage trucks and utilizes their mobility to help to conduct urban sensing tasks. In this paper, we present Cruisers, an automotive sensing platform for smart cities, based on the following ideas: a) Garbage collecting trucks are used to host sensors, b) Cellular communication network is used to deliver sensed data to servers, and c) Proxy server(s) are adopted to convert the format of sensed data to required ones, such as sampling rate, spatial interval or pub/sub protocol. To validate the feasibility of Cruisers, we implement a real system that consists of 66 customized sensing modulars integrating typical environmental and mobility sensors and deploy them into the garbage collecting trucks at Fujisawa, a medium-sized city in Japan. Performance evaluation is conducted to investigate the performance of Cruisers in terms of scalability, data publishing delay and spatial coverage.

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Resilient Edge: A scalable, robust network function backend

Y Hayakawa, K Yasukata, J Nakazawa, M Honda

IEICE Transactions on Information and Systems 102 (3), 550-558 (IEICE Transactions on Information and Systems) E102D ( 3 ) 550 - 558 2019.03

Joint Work, ISSN 09168532

Increasing hardware resources, such as multi-core and multi-socket CPUs, memory capacity and high-speed NICs, impose significant challenges on Network Function Virtualization (NFV) backends. They increase the potential numbers of per-server NFs or tenants, which requires a packet switching architecture that is not only scalable to large number of virtual ports, but also robust to attacks on the data plane. This is a real problem; a recent study has reported that Open vSwitch, a widely used software switch, had a buffer-overflow bug in its data plane that results the entire SDN domain to be hijacked by worms propagated in the network. In order to address this problem, we propose REdge. It scales to thousands of virtual ports or NFs (as opposed to hundreds in the current state-of-the art), and protect modular, flexible packet switching logic against various bugs, such as buffer overflow and other unexpected operations using static program checking. When 2048 NFs are active and packets are distributed to them based on the MAC or IP addresses, REdge achieves 3.16 Mpps or higher packet forwarding rates for 60 byte packets and achieves the wire rate for 1500 byte packets in the 25 Gbps link.

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OmimamoriNet: an outdoor positioning system based on Wi-SUN FAN network

Y Chen, M Sakamura, J Nakazawa, T Yonezawa, A Tsuge, Y Hamada

2018 Eleventh International Conference on Mobile Computing and Ubiquitous … (2018 11th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2018) 1 - 6 2019.02

Joint Work, ISSN 9784907626341

We propose in this paper an outdoor positioning system based on Wi-SUN FAN network, the goal of which is to protect the elderly, young children and even pets via estimating their locations in a city. In order to achieve long-term portability and network-side positioning, the system does not directly rely on GPS receiver mounted on terminals but use machine learning for location estimation via the received signal strength indication (RSSI) measurements. In particular, the system consists of Wi-SUN beacons, Wi-SUN base-stations and vehicular devices. A beacon, attached to the one to be positioned, broadcasts wireless signal periodically so that its location can be estimated using machine learning algorithms from the RSSIs measured at multiple base-stations that are densely deployed over a city to construct an ad hoc network. Using the mobility of vehicles that roam over a city routinely, such like garbage collection trucks, buses and taxies. Vehicular devices containing both a Wi-SUN beacon and a GPS are used to collect RSSIs and the corresponding GPS coordinates to train the estimation models. We develop a prototype system consisting of 9 base-stations and deploy it to our university campus to conduct a field experiment to validate the proposed approach. Offline analysis on the data collected from the experiment showed that a RandomForest learner performs best among four selected learning algorithms using the default parameters of Weka 3.8, which achieves a mean absolute error of 35.43m and a root mean squared error of 44.21m, respectively. Evaluation on network performance is also conducted.

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Damaged lane markings detection method with label propagation

T Nukita, Y Kishimoto, Y Iida, M Kawano, T Yonezawa, J Nakazawa

2018 IEEE 24th International Conference on Embedded and Real-Time Computing … (Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018) 203 - 208 2019.01

Joint Work, ISSN 9781538677599

We propose a damaged traffic lane detection method ensuring high accuracy in spite of only a few number of supervised data which are labeled traffic lane images. In general, supervised machine learning approach is very powerful for image classification. However, preparing a large amount of supervised data is time-consuming task because labeling damaged or not damaged is usually done manually through visual inspection of images. Thus, lowering the cost of labeling data is a great concern. To this end, we adopt a semi-supervised machine learning approach which learns from both labeled and unlabeled data by constructing graph based on the image similarity. We captured a large amount of the road lane images. Then, we constructed graph structure whose nodes are the road lane images and whose edges are the similarity between the images. In several nodes, we assigned labels which denote "damaged" or "not damaged." Finally we utilized the label propagation, which made it possible to infer the labels of the unlabeled data from the labeled data. These estimation resulted in the accuracy rate over 85% from the supervised data, which accounted for only 1.8% of the total data.

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Deepcounter: Using deep learning to count garbage bags

K Mikami, Y Chen, J Nakazawa, Y Iida, Y Kishimoto, Y Oya

Joint Work, ISSN 9781538677599

This paper proposes DeepCounter, an automotive sensing system where deep learning based image processing technology is used to automatically count the number of collected garbage bags from the video taken by a camera mounted on the rear of a garbage truck in order to sense a fine-grain spatio-temporal distribution on the amount of disposed garbage in cities that is envisioned to be helpful to develop novel applications related to garbage collection there. A prototype system is implemented on a GPU-integrated signal-board computer. A detection-tracking-counting (DTC) algorithm is developed and implemented based on the single shot multibox detector (SSD), a well-known real-time object detection algorithm. Experimental evaluation validates the feasibility of the proposed approach using video of realistic garbage collection in Fujisawa city, Japan.

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PerCom PhD Forum 2019: PhD Forum on Pervasive Computing and Communications 2019-Program

V Kalogeraki, J Nakazawa

2019 IEEE International Conference on Pervasive Computing and Communications … (2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019) 2019

ISSN 9781538691519

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Continuous shape changing locomotion of 32-legged spherical robot

H Nozaki, Y Kujirai, R Niiyama, Y Kawahara, T Yonezawa, J Nakazawa

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems … (IEEE International Conference on Intelligent Robots and Systems) 2721 - 2726 2018.12

Research paper (international conference proceedings), Joint Work, ISSN 9781538680940

Shape changing robot is an approach towards locomotion on uncertain terrain due to its omni-directional features. However, the current locomotion method for such robots rely on discontinuous rolling. We propose a free form locomotion: an omni directional continuous crawling for deformable robots. This method introduce continuous shifting of contact surface similar to amoeba movement. A Mochibot that has thirty two telescopic legs is developed to verify the proposed locomotion method. Through the experiments, we have confirmed that the robot can track smooth paths: straight, smooth, and hand written curves. We also evaluate errors between desired and measured trajectories of the robot.

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Poster abstract: Using deep learning to count garbage bags

Mikami K, Chen Y, Nakazawa J

SenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems (SenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems) 329 - 330 2018.11

Joint Work, ISSN 9781450359528

The information of daily garbage diposal can be used to develop many appealing applications in smart cities. This poster introduces DeepCounter, an automotive sensing system to providing a fine-grained spatio-temporal distribution on the amount of disposed garbage bags. In the system, deep learning based image processing is used to automatically count the number of collected garbage bags from the video taken by a camera mounted on the rear of a garbage truck. A prototype system is implemented and experimental evaluation validates the feasibility of our proposal using realistic garbage collection videos in Fujisawa city, Japan.

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