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Showing papers by "Daqing Zhang published in 2013"


Journal ArticleDOI
TL;DR: The bi-directional effects between human and opportunistic IoT are characterized, the technical challenges faced by this new research field are discussed, and a reference architecture for developing opportunistic Internet of Things systems is proposed.

318 citations


Journal ArticleDOI
Gang Pan1, Guande Qi1, Zhaohui Wu1, Daqing Zhang, Shijian Li1 
TL;DR: It is found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear patterns corresponding to the land-use classes of these regions, particularly for recognizing the social function of urban land by using one year's trace data from 4000 taxis.
Abstract: Detailed land use, which is difficult to obtain, is an integral part of urban planning. Currently, GPS traces of vehicles are becoming readily available. It conveys human mobility and activity information, which can be closely related to the land use of a region. This paper discusses the potential use of taxi traces for urban land-use classification, particularly for recognizing the social function of urban land by using one year's trace data from 4000 taxis. First, we found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear patterns corresponding to the land-use classes of these regions. Second, with six features designed to characterize the pick-up/set-down pattern, land-use classes of regions could be recognized. Classification results using the best combination of features achieved a recognition accuracy of 95%. Third, the classification results also highlighted regions that changed land-use class from one to another, and such land-use class transition dynamics of regions revealed unusual real-world social events. Moreover, the pick-up/set-down dynamics could further reflect to what extent each region is used as a certain class.

271 citations


Journal ArticleDOI
TL;DR: In this article, the authors provide an exhaustive survey of the work on mining taxi traces and provide a formalization of the data sets, along with an overview of different mechanisms for preprocessing the data.
Abstract: Vehicles equipped with GPS localizers are an important sensory device for examining people’s movements and activities. Taxis equipped with GPS localizers serve the transportation needs of a large number of people driven by diverse needs; their traces can tell us where passengers were picked up and dropped off, which route was taken, and what steps the driver took to find a new passenger. In this article, we provide an exhaustive survey of the work on mining these traces. We first provide a formalization of the data sets, along with an overview of different mechanisms for preprocessing the data. We then classify the existing work into three main categories: social dynamics, traffic dynamics and operational dynamics. Social dynamics refers to the study of the collective behaviour of a city’s population, based on their observed movements; Traffic dynamics studies the resulting flow of the movement through the road network; Operational dynamics refers to the study and analysis of taxi driver’s modus operandi. We discuss the different problems currently being researched, the various approaches proposed, and suggest new avenues of research. Finally, we present a historical overview of the research work in this field and discuss which areas hold most promise for future research.

248 citations


01 Jan 2013
TL;DR: An exhaustive survey of the work on mining traces of taxis equipped with GPS localizers, which discusses the different problems currently being researched, the various approaches proposed, and suggest new avenues of research.
Abstract: Vehicles equipped with GPS localizers are an important sensory device for examining people’s movements and activities. Taxis equipped with GPS localizers serve the transportation needs of a large number of people driven by diverse needs; their traces can tell us where passengers were picked up and dropped off, which route was taken, and what steps the driver took to find a new passenger. In this article, we provide an exhaustive survey of the work on mining these traces. We first provide a formalization of the data sets, along with an overview of different mechanisms for preprocessing the data. We then classify the existing work into three main categories: social dynamics, traffic dynamics and operational dynamics. Social dynamics refers to the study of the collective behaviour of a city’s population, based on their observed movements; Traffic dynamics studies the resulting flow of the movement through the road network; Operational dynamics refers to the study and analysis of taxi driver’s modus operandi. We discuss the different problems currently being researched, the various approaches proposed, and suggest new avenues of research. Finally, we present a historical overview of the research work in this field and discuss which areas hold most promise for future research.

235 citations


Proceedings ArticleDOI
01 May 2013
TL;DR: This research proposes a hybrid user location preference model by combining the preference extracted from check-ins and text-based tips which is processed using sentiment analysis techniques and develops a location based social matrix factorization algorithm that takes both user social influence and venue similarity influence into account in location recommendation.
Abstract: Although online recommendation systems such as recommendation of movies or music have been systematically studied in the past decade, location recommendation in Location Based Social Networks (LBSNs) is not well investigated yet. In LBSNs, users can check in and leave tips commenting on a venue. These two heterogeneous data sources both describe users' preference of venues. However, in current research work, only users' check-in behavior is considered in users' location preference model, users' tips on venues are seldom investigated yet. Moreover, while existing work mainly considers social influence in recommendation, we argue that considering venue similarity can further improve the recommendation performance. In this research, we ameliorate location recommendation by enhancing not only the user location preference model but also recommendation algorithm. First, we propose a hybrid user location preference model by combining the preference extracted from check-ins and text-based tips which are processed using sentiment analysis techniques. Second, we develop a location based social matrix factorization algorithm that takes both user social influence and venue similarity influence into account in location recommendation. Using two datasets extracted from the location based social networks Foursquare, experiment results demonstrate that the proposed hybrid preference model can better characterize user preference by maintaining the preference consistency, and the proposed algorithm outperforms the state-of-the-art methods.

227 citations


Journal ArticleDOI
TL;DR: The proposed isolation-based online anomalous trajectory (iBOAT) is evaluated through extensive experiments on large-scale taxi data, and it shows that iBOAT achieves state-of-the-art performance, with a remarkable performance of the area under a curve (AUC) ≥ 0.99.
Abstract: Trajectories obtained from Global Position System (GPS)-enabled taxis grant us an opportunity not only to extract meaningful statistics, dynamics, and behaviors about certain urban road users but also to monitor adverse and/or malicious events. In this paper, we focus on the problem of detecting anomalous routes by comparing the latter against time-dependent historically “normal” routes. We propose an online method that is able to detect anomalous trajectories “on-the-fly” and to identify which parts of the trajectory are responsible for its anomalousness. Furthermore, we perform an in-depth analysis on around 43 800 anomalous trajectories that are detected out from the trajectories of 7600 taxis for a month, revealing that most of the anomalous trips are the result of conscious decisions of greedy taxi drivers to commit fraud. We evaluate our proposed isolation-based online anomalous trajectory (iBOAT) through extensive experiments on large-scale taxi data, and it shows that iBOAT achieves state-of-the-art performance, with a remarkable performance of the area under a curve (AUC) ≥ 0.99.

169 citations


Journal ArticleDOI
TL;DR: This paper extracts the “embedded” intelligence about individual, environment, and society, which can augment existing IoT systems with user, ambient, and social awareness, and attempts to enhance the IoT with intelligence and awareness under the W2T vision.
Abstract: The Internet of Things (IoT) represents the future technology trend of sensing, computing, and communication. Under the Wisdom Web of Things (W2T) vision, the next-generation Internet will promote harmonious interaction among humans, computers, and things. Current research on IoT is primarily conducted from the perspective of identifying, connecting, and managing objects. In this paper, however, we attempt to enhance the IoT with intelligence and awareness under the W2T vision. By exploring the various interactions between humans and the IoT, we extract the "embedded" intelligence about individual, environment, and society, which can augment existing IoT systems with user, ambient, and social awareness. The characteristics, major applications, research issues, the reference architecture, as well as our ongoing efforts to embedded intelligence are also presented and discussed.

120 citations


Proceedings ArticleDOI
08 Sep 2013
TL;DR: This paper first collects user check-ins and tips from Foursquare and uses them as direct user feedback on locations, and extracts users' sentiment about locations and associated entities from tips to characterize their fine-grained location preference, which is incorporated into personalized location ranking using tensor factorization techniques.
Abstract: The crowdsourced digital footprints from Location Based Social Networks (LBSNs) contain not only rich information about locations, but also individual's feeling about locations and associated entities. This new data source provides us with an unprecedented opportunity to massively and cheaply collect location related information, and to subtly characterize individual's fine-grained preference about those places and associated entities. In this paper, we propose SEALs - a fine-grained preference-aware location search framework leveraging the crowdsourced traces in LBSNs. We first collect user check-ins and tips from Foursquare and use them as direct user feedback on locations. Second, we extract users' sentiment about locations and associated entities from tips to characterize their fine-grained location preference. Third, we incorporate such fine-grained user preference into personalized location ranking using tensor factorization techniques. Experimental results show that SEALs can achieve better location ranking comparing to the state-of-the-art solutions.

97 citations


Proceedings ArticleDOI
18 Mar 2013
TL;DR: A two-phase approach based on the crowd-sourced GPS data for night-bus route planning by leveraging taxi GPS traces is proposed, which develops two heuristic algorithms to automatically generate candidate bus routes and selects the best route which expects the maximum number of passengers under the given conditions.
Abstract: Taxi GPS traces provide us with rich information about the human mobility pattern in modern cities. Instead of designing the bus route based on inaccurate human survey regarding people's mobility pattern, we intend to address the night-bus route planning issue by leveraging taxi GPS traces. In this paper, we propose a two-phase approach based on the crowd-sourced GPS data for night-bus route planning. In the first phase, we develop a process to cluster “hot” areas with dense passenger pick-up/drop-off, and then propose effective methods to split big “hot” areas into clusters and identify a location in each cluster as a candidate bus stop. In the second phase, given the bus route origin, destination, candidate bus stops as well as bus operation time constraints, we derive several effective rules to build bus routing graph and prune the invalid stops and edges iteratively. We further develop two heuristic algorithms to automatically generate candidate bus routes, and finally we select the best route which expects the maximum number of passengers under the given conditions. To validate the effectiveness of the proposed approach, extensive empirical studies are performed on a real-world taxi GPS data set which contains more than 1.57 million passenger delivery trips, generated by 7,600 taxis for a month in Hangzhou, China.

90 citations


Proceedings ArticleDOI
21 Oct 2013
TL;DR: This paper proposes an extended F-formation system for robust detection of interactions and interactants, which employs a heat-map based feature representation for each individual, namely Interaction Space (IS), to model their location, orientation, and temporal information.
Abstract: In the context of a social gathering, such as a cocktail party, the memorable moments are generally captured by professional photographers or by the participants. The latter case is often undesirable because many participants would rather enjoy the event instead of being occupied by the photo-taking task. Motivated by this scenario, we propose the use of a set of cameras to automatically take photos. Instead of performing dense analysis on all cameras for photo capturing, we first detect the occurrence and location of social interactions via F-formation detection. In the sociology literature, F-formation is a concept used to define social interactions, where each detection only requires the spatial location and orientation of each participant. This information can be robustly obtained with additional Kinect depth sensors. In this paper, we propose an extended F-formation system for robust detection of interactions and interactants. The extended F-formation system employs a heat-map based feature representation for each individual, namely Interaction Space (IS), to model their location, orientation, and temporal information. Using the temporally encoded IS for each detected interactant, we propose a best-view camera selection framework to detect the corresponding best view camera for each detected social interaction. The extended F-formation system is evaluated with synthetic data on multiple scenarios. To demonstrate the effectiveness of the proposed system, we conducted a user study to compare our best view camera ranking with human's ranking using real-world data.

63 citations


Proceedings ArticleDOI
08 Sep 2013
TL;DR: In this article, the authors propose effSense, which reduces the data transmission cost of non-data-plan users by maximally offloading the data to Bluetooth/WiFi gateways or data-plan encountered to relay the sensed data to the server; it reduces energy consumption of data plan users by uploading data in parallel with a call or using less energy demand networks.
Abstract: Energy consumption and mobile data cost are two key factors affecting users' willingness to participate in crowdsensing tasks. While data-plan users are mostly concerned about the energy consumption, non-data-plan users are more sensitive to data transmission cost incurred. Traditional ways of data collection in mobile crowdsensing often go to two extremes: either uploading the sensed data online in real-time or fully offline after the whole sensing task is finished. In this paper, we propose effSense - a novel energy-efficient and cost-effective data uploading framework leveraging the delay-tolerant mechanisms. Specifically, effSense reduces the data cost of non-data-plan users by maximally offloading the data to Bluetooth/WiFi gateways or data-plan users encountered to relay the data to the server; it reduces energy consumption of data-plan users by uploading data in parallel with a call or using less-energy demand networks (e.g. Bluetooth). By leveraging the prediction of critical events such as user's future calls or encounters, effSense selects the optimal uploading scheme for both types of users. Our evaluation with MIT Reality Mining and Nodobo datasets show that effSense can save 55%~65% energy and 45%~50% data cost for the two types of users, respectively, compared with the traditional uploading schemes.

Proceedings ArticleDOI
20 Aug 2013
TL;DR: A method to predict the waiting time for a passenger at a given time and spot from historical taxi trajectories is presented and a large-scale real taxi GPS trace dataset is carried out to verify the proposed method.
Abstract: To achieve smart cities, real-world trace data sensed from the GPS-enabled taxi system, which conveys underlying dynamics of people movements, could be used to make urban transportation services smarter. As an example, it will be very helpful for passengers to know how long it will take to find a taxi at a spot, since they can plan their schedule and choose the best spot to wait. In this paper, we present a method to predict the waiting time for a passenger at a given time and spot from historical taxi trajectories. The arrival model of passengers and that of vacant taxis are built from the events that taxis arrive at and leave a spot. With the models, we could simulate the passenger waiting queue for a spot and infer the waiting time. The experiment with a large-scale real taxi GPS trace dataset is carried out to verify the proposed method.

Journal ArticleDOI
TL;DR: A novel GPS-based taxi system which can detect ongoing anomalous passenger delivery behaviors leveraging the proposed iBOAT method is presented and the effectiveness of the system with large scale real life taxi GPS records while serving 200,000 taxis is evaluated.
Abstract: GPS-equipped taxis can be considered as pervasive sensors and the large-scale digital traces produced allow us to reveal many hidden facts about the city dynamics and human behaviors. In this paper we present a novel GPS-based taxi system which can detect ongoing anomalous passenger delivery behaviors leveraging our proposed iBOAT method. To achieve real time monitoring, we reduce the response time of iBOAT by more than five times with an inverted index mechanism adopted. We evaluate the effectiveness of the system with large scale real life taxi GPS records while serving 200,000 taxis. With this system, we obtain about 0.44 million anomalous trajectories out of 7.35 million taxi delivery trips, which correspond to 7600 taxis' GPS records in one month time in the city of Hangzhou, China. Through further analysis of these anomalous trajectories, we observe that: (1) Over 60 % of the anomalous trajectories are "detours" that travel longer distances and time than normal trajectories; (2) The average trip length of drivers with high-detour tendency is 20 % longer than that of normal drivers; (3) The length of anomalous sub-trajectories is usually less than a third of the entire trip, and they tend to begin in the first two thirds of the journey; (4) Although longer distance results in a greater taxi fare, a higher tendency to take anomalous detours does not result in higher monthly revenue; and (5) Taxis with a higher income usually spend less time finding new passengers and deliver them in faster speed.

Journal ArticleDOI
TL;DR: A study of understanding social relationship evolution by using real-life anonymized mobile phone data shows that social relationships (not only reciprocal friends and non-friends, but non-reciprocal friends) can be likely predicted by usingreal-world sensing data.
Abstract: Mobile and pervasive computing technologies enable us to obtain real-world sensing data for sociological studies, such as exploring human behaviors and relationships. In this paper, we present a study of understanding social relationship evolution by using real-life anonymized mobile phone data. First, we define a friendship as a directed relation, i.e., person A regards another person B as his or her friend but not necessarily vice versa. Second, we recognize human friendship from a supervised learning perspective. The Support Vector Machine (SVM) approach is adopted as the inference model to predict friendship based on a variety of features extracted from the mobile phone data, including proximity, outgoing calls, outgoing text messages, incoming calls, and incoming text messages. Third, we demonstrate the social relation evolution process by using the social balance theory. For the friendship prediction, we achieved an overall recognition rate of 97.0 % by number and a class average accuracy of 89.8 %. This shows that social relationships (not only reciprocal friends and non-friends, but non-reciprocal friends) can be likely predicted by using real-world sensing data. With respect to the friendship evolution, we verified that the principles of reciprocality and transitivity play an important role in social relation evolution.

Book
18 Nov 2013
TL;DR: This book demonstrates how mobile social networks can be inferred from users' physical interactions both with the environment and with others, as well as how users behave around them and how their behavior differs on mobile vs. traditional online social networks.
Abstract: The use of contextually aware, pervasive, distributed computing, and sensor networks to bridge the gap between the physical and online worlds is the basis of mobile social networking. This book shows how applications can be built to provide mobile social networking, the research issues that need to be solved to enable this vision, and how mobile social networking can be used to provide computational intelligence that will improve daily life. With contributions from the fields of sociology, computer science, human-computer interaction and design, this book demonstrates how mobile social networks can be inferred from users' physical interactions both with the environment and with others, as well as how users behave around them and how their behavior differs on mobile vs. traditional online social networks.

Proceedings ArticleDOI
18 Dec 2013
TL;DR: This paper proposes a novel approach to address the problem of ranking areas by popularity of a business category by exploiting user-generated contents from location based social networks, which are cheap, fine-grained, and abundant.
Abstract: Ranking areas by popularity of a business category is an essential problem for business planning. Traditional approaches rely on economic and demographic factors nearby. However, the acquisition of relevant data is usually expensive. In this paper we propose a novel approach to address this problem by exploiting user-generated contents from location based social networks, which are cheap, fine-grained, and abundant. Particularly, by analyzing a dataset collected from Foursquare in Paris, we attain the customer distribution across all categories in each area. With the help of data mining methods, the popularity (i.e., the number of customers) of a particular business category can be estimated from popularities of other nearby categories, and then can be ranked accordingly. The evaluation shows that these methods significantly outperform the passenger volume based method.

Proceedings ArticleDOI
18 Mar 2013
TL;DR: MemPhone, a new system that addresses various human memory needs by using the mobile tagging technique, can augment memory externalization and recall, and build object-based social networks (OBSNs) to enhance memory sharing.
Abstract: Human memory is important yet often not easy to be handled in daily life. Many challenges are raised, such as how to enhance memory recall and reminiscence, how to facilitate memory sharing in terms of people's social nature. This paper proposes MemPhone, a new system that addresses various human memory needs by using the mobile tagging (e.g., RFID, barcodes) technique. By linking human memory or experience with associated physical objects, MemPhone can i) augment memory externalization and recall, and ii) build object-based social networks (OBSNs) to enhance memory sharing. By embedding physical contexts into SNs, the OBSN can strengthen friendships by enabling serendipity discovering and nurture new connections among people with shared memories. Early studies indicate that our system can facilitate memory recall and shared memory discovery.

Journal ArticleDOI
TL;DR: This special section aims to explore intelligent systems and related applications for socially aware computing, which aims to leverage the large-scale and diverse sensing devices that can be deployed in human daily lives to recognize individual behaviors, discover group interaction patterns, and support communication and collaboration.
Abstract: The recent advance of pervasive computing technologies promises to significantly enhance capabilities for data capture and data analysis. In this socially aware era, such technologies hold great promise and challenge for using the sensory data to understand human behavior, human mobility, human activities, and ultimately to help solve human social problems. The integration of pervasive computing and social computing has resulted in an emerging new research field in computer science—Socially Aware Computing. While the concept of social awareness has been developed in the field of Computer Supported Cooperative Work for decades, the notion of socially aware computation and communication has only recently been introduced by Alex Pentland [2005]. Socially Aware Computing brings light to the design of new software methodology, infrastructure, data analysis, and applications. This paradigm aims to leverage the large-scale and diverse sensing devices that can be deployed in human daily lives to recognize individual behaviors, discover group interaction patterns, and support communication and collaboration. Intelligent systems powered by artificial intelligence play an important role in realizing socially aware computing in various aspects, such as sensing, processing, and supporting human interaction. This special section aims to explore intelligent systems and related applications for socially aware computing. Submissions to this special issue came from an open call for papers. We received a total of 16 submissions of which 5 articles were accepted after three rounds of rigorous reviews. A large number of reviewers assisted us in the review process. In order to ensure high reviewing standards, three to four reviewers evaluated each article.

Journal ArticleDOI
01 Mar 2013
TL;DR: To deal with the mobile entity problem raised in cross-domain context sharing, a transparent query mechanism that enables applications to obtain context information about mobile entities from remote domains is proposed.
Abstract: With the development of pervasive computing techniques, the world will be filled with interconnected context-aware domains (e.g., homes, offices, hospitals, etc.). While the previous studies focused solely on the management of contexts produced in a single domain, in this paper we discuss the challenges to be addressed for cross-domain context management. By analyzing the requirements from several scenarios, we identify two context producer---consumer patterns in multi-domain environments. Furthermore, to deal with the mobile entity problem raised in cross-domain context sharing, a transparent query mechanism that enables applications to obtain context information about mobile entities from remote domains is proposed. Two prototype applications--smart home and community services in a smart campus--have been developed to demonstrate the key features and usefulness of cross-domain context management. Initial experiments have also been conducted to evaluate the performance of our system.

Book ChapterDOI
19 Jun 2013
TL;DR: A data mining method is proposed to extract the most frequent sequential sequences of steps inside each individual activity of a set of daily activities so that these patterns can be used to model human daily activities for activity recognition purpose, or to directly instruct/prompt elders with impaired memory when they perform daily routines.
Abstract: One of the most challenging issues faced by many elders is the over-decreasing independence mainly caused by impaired physical, cognitive, and/or sensory abilities. Activity recognition can be used to help elders live longer in their own homes independently, by providing assurance of safety, instructing performance of activity and assessing cognitive status. In this work, we propose to discover both intra- and inter-activity association patterns from daily routines of elderly people. Specifically, a data mining method is proposed to extract the most frequent sequential sequences of steps inside each individual activity (i.e., intra-activity pattern) and activities (i.e., inter-activity pattern) of a set of daily activities. These patterns can then be used to model human daily activities for activity recognition purpose, or to directly instruct/prompt elders with impaired memory when they perform daily routines. The experimental results conducted on two individuals’ datasets of daily activities show that our proposed approach is workable to discover these association patterns.

Proceedings ArticleDOI
18 Mar 2013
TL;DR: This talk will present a new research direction called “social and community intelligence (SCI)” as a natural extension of context-aware computing in the era of crowd sensing, with emphasis on extracting community and society level context, and introduce the work in mining large scale taxi GPS data, mobile phone data and social media data for enabling innovative applications in smart cities.
Abstract: Since the seminal work of Schilit and Theimer on context-awareness in 1994, great research progress has been made in context-aware computing field. Due to limited deployment scale of sensors and devices, in early years context-aware computing focused mainly on understanding and exploiting personal context in single smart spaces. As a result of the recent explosion of sensor-equipped mobile phones, the phenomenal growth of Internet and social network services, the broader use of the Global Positioning System (GPS) in all types of public transportation, and the extensive deployment of sensor network and WiFi in both indoor and outdoor environments, the digital footprints left by people while interacting with cyber-physical spaces are accumulating with an unprecedented speed and scale. The technology trend towards crowd sensing is creating new challenges and opportunities for context-aware computing - with huge amount, large scale, multi-modal, different granularity, diverse quality of data from various data sources. In this talk, I will present a new research direction called “social and community intelligence (SCI)” as a natural extension of context-aware computing in the era of crowd sensing, with emphasis on extracting community and society level context; in particular I will introduce our work in mining large scale taxi GPS data, mobile phone data and social media data for enabling innovative applications in smart cities. Finally I will briefly summarize the difference between traditional context-aware computing and SCI in terms of data acquisition, modeling, inference, storage and context inferred.

Proceedings ArticleDOI
08 Sep 2013
TL;DR: This paper proposes an autonomic system for activity scheduling in MoSoN communities that allows flexible activity proposition while efficiently handling the user conflicts, and can schedule multiple simultaneous activities in real-time while incurring low message and time cost.
Abstract: Mobile social network (MoSoN) signifies an emerging area in the social computing research built on top of the mobile communications and wireless networking. It allows virtual community formation among like minded users to share data and to organize collaborative social activities at commonly agreed upon places and times. Such an activity scheduling in real-time is non-trivial as it requires tracing multiple users' profiles, preferences, and other spatio-temporal contexts, like location, availability, etc. Inherent conflicts among users regarding choices of places and time slots further complicates unanimous decision making. In this paper, we propose an autonomic system for activity scheduling in MoSoN communities. Our system allows flexible activity proposition while efficiently handling the user conflicts. As evident from our simulation results and analysis, our system can schedule multiple simultaneous activities in real-time while incurring low message and time cost.

Book ChapterDOI
Chao Chen1, Daqing Zhang1, Lin Sun1, Mossaab Hariz1, Bruno Jean-Bart 
19 Jun 2013
TL;DR: A comprehensive system that enables coordinators to manage care-givers and elders in an efficient way to improve service quality and a statistical study about the collected data and the reported alerts is offered.
Abstract: Elderly care is facing the challenge of the disequilibrium between the increased number of old people and the low number of personnel in the elderly care. The emerging pervasive technology has revolutionised the way of assistance in elderly care. Current solutions usually focus too much on technology, and fail to address the usability issues. In this paper, we offer a comprehensive system for both elderly care providers and elders. The system enables coordinators to manage care-givers and elders in an efficient way to improve service quality. For instance, care-givers can be scheduled in a real-time manner with mobile phones. We also deploy several sensors in their homes to monitor daily routines to ensure their safety. Alerts will be sent and accessible by coordinators immediately once detected, then elderly care services can be provided accordingly. We test our system in a real home for over 2 months. Finally, we offer a statistical study about the collected data and the reported alerts.

Journal ArticleDOI
30 Sep 2013
TL;DR: The work to understand the review activities by analyzing the anonymized review data of two conferences (ACM SIGCOMM and UIC) gets some interesting knowledge, which is significant for interpreting how the reviewers give their reviews in academic conferences.
Abstract: Human activities have been investigated and applied in various fields, such as contextaware computing, search engine, social network services, location-based services, automated visual surveillance, and multimodal human–computer interaction. However, the human activities in the review process of academic conferences have seldom been explored. There is no doubt that review process plays an important role in deciding whether a paper can be accepted or not. In this paper, we present our work to understand the review activities by analyzing the anonymized review data of two conferences (ACM SIGCOMM and UIC). The descriptive statistics and the data mining technology are adopted in the analysis. We got some interesting knowledge, which is significant for interpreting how the reviewers give their reviews in academic conferences, such as the relationships between the score, confidence and review length, and reviewer activity patterns.