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Articles 6661 - 6690 of 8722
Full-Text Articles in Entire DC Network
More Of A Receiver Than A Giver: Why Do People Unfollow In Twitter?, Haewoon Kwak, Sue Moon, Wonjae Lee
More Of A Receiver Than A Giver: Why Do People Unfollow In Twitter?, Haewoon Kwak, Sue Moon, Wonjae Lee
Research Collection School Of Computing and Information Systems
We propose a logistic regression model taking into ac- count two analytically different sets of factors–structure and action. The factors include individual, dyadic, and triadic properties between ego and alter whose tie breakup is under consideration. From the fitted model using a large-scale data, we discover 5 structural and 7 actional variables to have significant explanatory power for unfollow. One unique finding from our quantitative analysis is that people appreciate receiving acknowl- edgements from others even in virtually unilateral com- munication relationships and are less likely to unfollow them: people are more of a receiver than a giver.
Active Refinement Of Clone Anomaly Reports, Lucia, David Lo, Lingxiao Jiang, Aditya Budi
Active Refinement Of Clone Anomaly Reports, Lucia, David Lo, Lingxiao Jiang, Aditya Budi
Research Collection School Of Computing and Information Systems
Software clones have been widely studied in the recent literature and shown useful for finding bugs because inconsistent changes among clones in a clone group may indicate potential bugs. However, many inconsistent clone groups are not real bugs (true positives). The excessive number of false positives could easily impede broad adoption of clone-based bug detection approaches. In this work, we aim to improve the usability of clone-based bug detection tools by increasing the rate of true positives found when a developer analyzes anomaly reports. Our idea is to control the number of anomaly reports a user can see at a …
A New Framework For Privacy Of Rfid Path Authentication, Shaoying Cai, Robert H. Deng, Yingjiu Li, Yunlei Zhao
A New Framework For Privacy Of Rfid Path Authentication, Shaoying Cai, Robert H. Deng, Yingjiu Li, Yunlei Zhao
Research Collection School Of Computing and Information Systems
RFID-based path authentication enables supply chain managers to verify the exact path that a tag has taken. In this paper, we introduce a new oracle Move that models a tag's movement along a designed or an arbitrary path in a supply chain. With this oracle, we refine the existing security and privacy notions for RFID-based path authentication. In addition, we propose a new privacy notion, called path privacy, for RFID-based path authentication. Our privacy notion captures the privacy of both tag identity and path information in a single game. Compared to existing two-game based privacy notions, it is more rigorous, …
When A Friend In Twitter Is A Friend In Life, Wei Xie, Cheng Li, Feida Zhu, Ee-Peng Lim, Xueqing Gong
When A Friend In Twitter Is A Friend In Life, Wei Xie, Cheng Li, Feida Zhu, Ee-Peng Lim, Xueqing Gong
Research Collection School Of Computing and Information Systems
Twitter is a fast-growing online social network service (SNS) where users can "follow" any other user to receive his or her mini-blogs which are called "tweets". In this paper, we study the problem of identifying a user's off-line real-life social community, which we call the user'sTwitter off-line community, purely from examining Twitter network structure. Based on observations from our user-verified Twitter data and results from previous works, we propose three principles about Twitter off-line communities. Incorporating these principles, we develop a novel algorithm to iteratively discover the Twitter off-line community based on a new way of measuring user closeness. According …
Mobicon: Mobile Context Monitoring Platform: Incorporating Context-Awareness To Smartphone-Centric Personal Sensor Networks, Youngki Lee, Younghyun Ju, Chuihong Min, Jihun Yu, Junehwa Song
Mobicon: Mobile Context Monitoring Platform: Incorporating Context-Awareness To Smartphone-Centric Personal Sensor Networks, Youngki Lee, Younghyun Ju, Chuihong Min, Jihun Yu, Junehwa Song
Research Collection School Of Computing and Information Systems
In this demonstration, we will show MobiCon, a context monitoring platform; it runs over smartphones and sensor OSs, and facilitates development and deployment of everyday context-aware applications. For many years, lots of research efforts have been made in building low-cost, yet effective sensor networks for various application domains such as structural health monitoring of bridges, disaster recovery, automated ventilation of buildings. Integration of sensors into smartphones and the advent of wearable devices open a new opportunity for mobile applications to leverage in-situ user contexts such as his/her location, activity, social relationship, health status. In recent studies of mobile and pervasive …
Stochastic Dominance In Stochastic Dcops For Risk-Sensitive Applications, Nguyen Duc Thien, William Yeoh, Hoong Chuin Lau
Stochastic Dominance In Stochastic Dcops For Risk-Sensitive Applications, Nguyen Duc Thien, William Yeoh, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Distributed constraint optimization problems (DCOPs) are well-suited for modeling multi-agent coordination problems where the primary interactions are between local subsets of agents. However, one limitation of DCOPs is the assumption that the constraint rewards are without uncertainty. Researchers have thus extended DCOPs to Stochastic DCOPs (SDCOPs), where rewards are sampled from known probability distribution reward functions, and introduced algorithms to find solutions with the largest expected reward. Unfortunately, such a solution might be very risky, that is, very likely to result in a poor reward. Thus, in this paper, we make three contributions: (1) we propose a stricter objective for …
Ifalcon: A Neural Architecture For Hierarchical Planning, Budhitama Subagdja, Ah-Hwee Tan
Ifalcon: A Neural Architecture For Hierarchical Planning, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Hierarchical planning is an approach of planning by composing and executing hierarchically arranged predefined plans on the fly to solve some problems. This approach commonly relies on a domain expert providing all semantic and structural knowledge. One challenge is how the system deals with incomplete ill-defined knowledge while the solution can be achieved on the fly. Most symbolic-based hierarchical planners have been devised to allow the knowledge to be described expressively. However, in some cases, it is still difficult to produce the appropriate knowledge due to the complexity of the problem domain especially if the missing knowledge must be acquired …
Exerlink: Enabling Pervasive Social Exergames With Heterogeneous Exercise Devices, Taiwoo Park, Inseok Hwang, Uichin Lee, Sunghoon Ivan Lee, Chungkuk Yoo, Youngki Lee, Hyukjae Jang, Sungwon Peter Choe, Souneil Park, Junehwa Song
Exerlink: Enabling Pervasive Social Exergames With Heterogeneous Exercise Devices, Taiwoo Park, Inseok Hwang, Uichin Lee, Sunghoon Ivan Lee, Chungkuk Yoo, Youngki Lee, Hyukjae Jang, Sungwon Peter Choe, Souneil Park, Junehwa Song
Research Collection School Of Computing and Information Systems
We envision that diverse social exercising games, or exergames, will emerge, featuring much richer interactivity with immersive game play experiences. Further, the recent advances of mobile devices and wireless networking will make such social engagement more pervasive - people carry portable exergame devices (e.g., jump ropes) and interact with remote users anytime, anywhere. Towards this goal, we explore the potential of using heterogeneous exercise devices as game controllers for a multi-player social exergame; e.g., playing a boat paddling game with two remote exercisers (one with a jump rope, and the other with a treadmill). In this paper, we propose a …
Self-Organizing Neural Networks For Learning Air Combat Maneuvers, Teck-Hou Teng, Ah-Hwee Tan
Self-Organizing Neural Networks For Learning Air Combat Maneuvers, Teck-Hou Teng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper reports on an agent-oriented approach for the modeling of adaptive doctrine-equipped computer generated force (CGF) using a commercial-grade simulation platform known as CAE STRIVECGF. A self- organizing neural network is used for the adaptive CGF to learn and generalize knowledge in an online manner during the simulation. The challenge of defining the state space and action space and the lack of domain knowledge to initialize the adaptive CGF are addressed using the doctrine used to drive the non-adaptive CGF. The doctrine contains a set of specialized knowledge for conducting 1-v-1 dogfights. The hierarchical structure and symbol representation of …
Visualizing Media Bias Through Twitter, Jisun An, Meeyoung Cha, Gummadi, Krishna, Jon Crowcroft, Daniele Queria
Visualizing Media Bias Through Twitter, Jisun An, Meeyoung Cha, Gummadi, Krishna, Jon Crowcroft, Daniele Queria
Research Collection School Of Computing and Information Systems
Traditional media outlets are known to report political news in a biased way, potentially affecting the political beliefs of the audience and even altering their voting behaviors. Therefore, tracking bias in everyday news and building a platform where people can receive balanced news information is important. We propose a model that maps the news media sources along a dimensional dichotomous political spectrum using the co-subscriptions relationships inferred by Twitter links. By analyzing 7 million follow links, we show that the political dichotomy naturally arises on Twitter when we only consider direct media subscription. Furthermore, we demonstrate a real-time Twitter-based application …
Fast Semantic Diffusion For Large-Scale Context-Based Image And Video Annotation, Yu-Gang Jiang, Qi Dai, Jun Wang, Chong-Wah Ngo
Fast Semantic Diffusion For Large-Scale Context-Based Image And Video Annotation, Yu-Gang Jiang, Qi Dai, Jun Wang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Exploring context information for visual recognition has recently received significant research attention. This paper proposes a novel and highly efficient approach, which is named semantic diffusion, to utilize semantic context for large-scale image and video annotation. Starting from the initial annotation of a large number of semantic concepts (categories), obtained by either machine learning or manual tagging, the proposed approach refines the results using a graph diffusion technique, which recovers the consistency and smoothness of the annotations over a semantic graph. Different from the existing graph-based learning methods that model relations among data samples, the semantic graph captures context by …
Distributed Path Authentication For Dynamic Rfid-Enabled Supply Chains, Shaoying Cai, Yingjiu Li, Yunlei Zhao
Distributed Path Authentication For Dynamic Rfid-Enabled Supply Chains, Shaoying Cai, Yingjiu Li, Yunlei Zhao
Research Collection School Of Computing and Information Systems
In this paper, we propose a distributed path authentication solution for dynamic RFID-enabled supply chains to address the counterfeiting problem. Compared to existing general anti-counterfeiting solutions, our solution requires non sharing of item-level RFID information among supply chain parties, thus eliminating the requirement on high network bandwidth and fine-grained access control. Our solution is secure, privacy-preserving, and practical. It leverages on the standard EPCglobal network to share information about paths and parties in path authentication. Our solution can be implemented on standard EPC class 1 generation 2 tags with only 720 bits storage and no computational capability.
Mining Quantified Temporal Rules: Formalism, Algorithms, And Evaluation, David Lo, Ganesan Ramalingam, Venkatesh Prasad Ranganath, Kapil Vaswani
Mining Quantified Temporal Rules: Formalism, Algorithms, And Evaluation, David Lo, Ganesan Ramalingam, Venkatesh Prasad Ranganath, Kapil Vaswani
Research Collection School Of Computing and Information Systems
Libraries usually impose constraints on how clients should use them. Often these constraints are not well-documented. In this paper, we address the problem of recovering such constraints automatically, a problem referred to as specification mining. Given some client programs that use a given library, we identify constraints on the library usage that are (almost) satisfied by the given set of clients.The class of rules we target for mining combines simple binary temporal operators with state predicates (composed of equality constraints) and quantification. This is a simple yet expressive subclass of temporal properties (LTL formulae) that allows us to capture many …
Resource-Aware Video Multicasting Via Access Gateways In Wireless Mesh Networks, Wanqing Tu, Cormac Sreenan, Chun Tung Chou, Archan Misra, Sanjay Jha
Resource-Aware Video Multicasting Via Access Gateways In Wireless Mesh Networks, Wanqing Tu, Cormac Sreenan, Chun Tung Chou, Archan Misra, Sanjay Jha
Research Collection School Of Computing and Information Systems
This paper studies video multicasting in large scale areas using wireless mesh networks. The focus is on the use of Internet access gateways that allow a choice of alternative routes to avoid potentially lengthy and low capacity multi-hop wireless paths. A set of heuristic-based algorithms are described that together aim to maximize network capacity: the two-tier integrated architecture algorithm, the weighted gateway uploading algorithm, the link controlled routing tree algorithm, and the dynamic group management algorithm. These algorithms use different approaches to arrange nodes involved in video multicasting into a clustered and two-tier integrated architecture in which network protocols can …
Active Malware Analysis Using Stochastic Games, Simon Williamson, Pradeep Reddy Varakantham, Debin Gao, Chen Hui Ong
Active Malware Analysis Using Stochastic Games, Simon Williamson, Pradeep Reddy Varakantham, Debin Gao, Chen Hui Ong
Research Collection School Of Computing and Information Systems
Cyber security is increasingly important for defending computer systems from loss of privacy or unauthorised use. One important aspect is threat analysis - how does an attacker infiltrate a system and what do they want once they are inside. This paper considers the problem of Active Malware Analysis, where we learn about the human or software intruder by actively interacting with it with the goal of learning about its behaviours and intentions, whilst at the same time that intruder may be trying to avoid detection or showing those behaviours and intentions. This game-theoretic active learning is then used to obtain …
Adaptive Data Acquisition Strategies For Energy-Efficient, Smartphone-Based, Continuous Processing Of Sensor Streams, Lipyeow Lim, Archan Misra, Tianli Mo
Adaptive Data Acquisition Strategies For Energy-Efficient, Smartphone-Based, Continuous Processing Of Sensor Streams, Lipyeow Lim, Archan Misra, Tianli Mo
Research Collection School Of Computing and Information Systems
There is a growing interest in applications that utilize continuous sensing of individual activity or context, via sensors embedded or associated with personal mobile devices (e.g., smartphones). Reducing the energy overheads of sensor data acquisition and processing is essential to ensure the successful continuous operation of such applications, especially on battery-limited mobile devices. To achieve this goal, this paper presents a framework, called ACQUA, for ‘acquisition-cost’ aware continuous query processing. ACQUA replaces the current paradigm, where the data is typically streamed (pushed) from the sensors to the one or more smartphones, with a pull-based asynchronous model, where a smartphone retrieves …
Sammple: Detecting Semantic Indoor Activities In Practical Settings Using Locomotive Signatures, Zhixian Yan, Dipanjan Chakraborty, Archan Misra, Hoyoung Jeung, Karl Aberer
Sammple: Detecting Semantic Indoor Activities In Practical Settings Using Locomotive Signatures, Zhixian Yan, Dipanjan Chakraborty, Archan Misra, Hoyoung Jeung, Karl Aberer
Research Collection School Of Computing and Information Systems
We analyze the ability of mobile phone-generated accelerometer data to detect high-level (i.e., at the semantic level) indoor lifestyle activities, such as cooking at home and working at the workplace, in practical settings. We design a 2-T ier activity extraction framework (called SAMMPLE) for our purpose. Using this, we evaluate discriminatory power of activity structures along the dimension of statistical features and after a transformation to a sequence of individual locomotive micro-activities (e.g. sitting or standing). Our findings from 152 days of real-life behavioral traces reveal that locomotive signatures achieve an average accuracy of 77.14%, an improvement of 16.37% over …
Identifying Linux Bug Fixing Patches, Yuan Tian, Julia Lawall, David Lo
Identifying Linux Bug Fixing Patches, Yuan Tian, Julia Lawall, David Lo
Research Collection School Of Computing and Information Systems
In the evolution of an operating system there is a continuing tension between the need to develop and test new features, and the need to provide a stable and secure execution environment to users. A compromise, adopted by the developers of the Linux kernel, is to release new versions, including bug fixes and new features, frequently, while maintaining some older “longterm” versions. This strategy raises the problem of how to identify bug fixing patches that are submitted to the current version but should be applied to the longterm versions as well. The current approach is to rely on the individual …
Modeling Diffusion In Social Networks Using Network Properties, Duc Minh Luu, Ee Peng Lim, Tuan Anh Hoang, Chong Tat Freddy Chua
Modeling Diffusion In Social Networks Using Network Properties, Duc Minh Luu, Ee Peng Lim, Tuan Anh Hoang, Chong Tat Freddy Chua
Research Collection School Of Computing and Information Systems
"Diffusion of items occurs in social networks due to spreading of items through word of mouth and exogenous factors. These items may be news, products, videos, advertisements or contagious viruses. When a user purchases or consumes one of such items, we say that she adopts the item and she becomes an item adopter. Previous research has studied diffusion process at both the macro and micro levels. The former models the number of item adopters in the diffusion process while the latter determines which individuals adopt item. Both macro and micro level models have their merits and limitations. In this paper, …
Virality And Susceptibility In Information Diffusions, Tuan-Anh Hoang, Ee Peng Lim
Virality And Susceptibility In Information Diffusions, Tuan-Anh Hoang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Viral diffusion allows a piece of information to widely and quickly spread within the network of users through word-ofmouth. In this paper, we study the problem of modeling both item and user factors that contribute to viral diffusion in Twitter network. We identify three behaviorial factors, namely user virality, user susceptibility and item virality, that contribute to viral diffusion. Instead of modeling these factors independently as done in previous research, we propose a model that measures all the factors simultaneously considering their mutual dependencies. The model has been evaluated on both synthetic and real datasets. The experiments show that our …
Delayed Observation Planning In Partially Observable Domains, Pradeep Reddy Varakantham, Janusz Marecki
Delayed Observation Planning In Partially Observable Domains, Pradeep Reddy Varakantham, Janusz Marecki
Research Collection School Of Computing and Information Systems
Traditional models for planning under uncertainty such as Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs) assume that the observations about the results of agent actions are instantly available to the agent. In so doing, they are no longer applicable to domains where observations are received with delays caused by temporary unavailability of information (e.g. delayed response of the market to a new product). To that end, we make the following key contributions towards solving Delayed observation POMDPs (D-POMDPs): (i) We first provide an parameterized approximate algorithm for solving D-POMDPs efficiently, with desired accuracy; and (ii) We then propose …
Prioritized Shaping Of Models For Solving Dec-Pomdps, Pradeep Reddy Varakantham, William Yeoh, Prasanna Velagapudi, Paul Scerri
Prioritized Shaping Of Models For Solving Dec-Pomdps, Pradeep Reddy Varakantham, William Yeoh, Prasanna Velagapudi, Paul Scerri
Research Collection School Of Computing and Information Systems
An interesting class of multi-agent POMDP planning problems can be solved by having agents iteratively solve individual POMDPs, find interactions with other individual plans, shape their transition and reward functions to encourage good interactions and discourage bad ones and then recompute a new plan. D-TREMOR showed that this approach can allow distributed planning for hundreds of agents. However, the quality and speed of the planning process depends on the prioritization scheme used. Lower priority agents shape their models with respect to the models of higher priority agents. In this paper, we introduce a new prioritization scheme that is guaranteed to …
Trurepec: A Trust-Behavior-Based Reputation And Recommender System For Mobile Applications, Zheng Yan, Peng Zhang, Robert H. Deng
Trurepec: A Trust-Behavior-Based Reputation And Recommender System For Mobile Applications, Zheng Yan, Peng Zhang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Mobile applications are software packages that can be installed and executed in a mobile device. Which mobile application is trustworthy for a user to purchase, download, install, execute or recommend becomes a crucial issue that impacts its final success. This paper proposes TruBeRepec, a trust-behavior-based reputation and recommender system for mobile applications. We explore a model of trust behavior for mobile applications based on the result of a large-scale user survey. We further develop a number of algorithms that are used to evaluate individual user’s trust in a mobile application through trust behavior observation, generate the application’s reputation by aggregating …
Virtualization Based Password Protection Against Malware In Untrusted Operating Systems, Yueqiang Cheng, Xuhua Ding
Virtualization Based Password Protection Against Malware In Untrusted Operating Systems, Yueqiang Cheng, Xuhua Ding
Research Collection School Of Computing and Information Systems
Password based authentication remains as the mainstream user authentication method for most web servers, despite its known vulnerability to keylogger attacks. Most existing countermeasures are costly because they require a strong isolation of the browser and the operating system. In this paper, we propose KGuard, a password input protection system. Its security is based on the hardware-based virtualization without safeguarding the browser or OS. A security-conscious user can conveniently and securely activate or deactivate the password protection by using key combinations. We have implemented KGuard and experimented our prototype on Windows with Firefox. The results show that no significant performance …
Vertical Differentiation And A Comparison Of Online Advertising Models, Mei Lin, Xuqing Ke, Andrew B. Whinston
Vertical Differentiation And A Comparison Of Online Advertising Models, Mei Lin, Xuqing Ke, Andrew B. Whinston
Research Collection School Of Computing and Information Systems
Designing business models that take into consideration the role of advertising support is critical to the success of online services. In this paper, we address the challenges of these business model strategies and compare different ad revenue models. We use game theory to model vertical differentiation in both monopoly and duopoly settings, in which online service providers may offer an ad-free service, an ad-supported service, or a combination of these services. Offering both ad-free and ad-supported services is the optimal strategy for a monopolist because ad revenues compensate for the cannibalistic effect of vertical differentiation. In a duopoly equilibrium, exactly …
Overcoming The Challenges In Cost Estimation For Distributed Software Projects, Narayanasamy Ramasubbu, Rajesh Krishna Balan
Overcoming The Challenges In Cost Estimation For Distributed Software Projects, Narayanasamy Ramasubbu, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
We describe how we studied, in-situ, the operational processes of three large high process maturity distributed software development companies and discovered three common problems they faced with respect to early stage project cost estimation. We found that project managers faced significant challenges to accurately estimate project costs because the standard metrics-based estimation tools they used (a) did not effectively incorporate diverse distributed project configurations and characteristics, (b) required comprehensive data that was not fully available for all starting projects, and (c) required significant domain experience to derive accurate estimates. To address these challenges, we collaborated with practitioners at the three …
Memory Formation, Consolidation, And Forgetting In Learning Agents, Budhitama Susnagdja, Wenwen Wang, Ah-Hwee Tan, Yuan-Sin Tan, Loo-Nin Teow
Memory Formation, Consolidation, And Forgetting In Learning Agents, Budhitama Susnagdja, Wenwen Wang, Ah-Hwee Tan, Yuan-Sin Tan, Loo-Nin Teow
Research Collection School Of Computing and Information Systems
Memory enables past experiences to be remembered and acquired as useful knowledge to support decision making, especially when perception and computational resources are limited. This paper presents a neuropsychological- inspired dual memory model for agents, consisting of an episodic memory that records the agent's experience in real time and a semantic memory that captures factual knowledge through a parallel consolidation process. In addition, the model incorporates a natural forgetting mechanism that prevents memory overloading by removing transient memory traces. Our experimental study based on a real-time first-person-shooter video game has indicated that the memory consolidation and forgetting processes are not …
An Evolutionary Search Paradigm That Learns With Past Experiences, Liang Feng, Yew-Soon Ong, Ivor Tsang, Ah-Hwee Tan
An Evolutionary Search Paradigm That Learns With Past Experiences, Liang Feng, Yew-Soon Ong, Ivor Tsang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
A major drawback of evolutionary optimization approaches in the literature is the apparent lack of automated knowledge transfers and reuse across problems. Particularly, evolutionary optimization methods generally start a search from scratch or ground zero state, independent of how similar the given new problem of interest is to those optimized previously. In this paper, we present a study on the transfer of knowledge in the form of useful structured knowledge or latent patterns that are captured from previous experiences of problem-solving to enhance future evolutionary search. The essential contributions of our present study include the meme learning and meme selection …
Semi-Supervised Hierarchical Clustering For Personalized Web Image Organization, Lei Meng, Ah-Hwee Tan
Semi-Supervised Hierarchical Clustering For Personalized Web Image Organization, Lei Meng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Existing efforts on web image organization usually transform the task into surrounding text clustering. However, Current text clustering algorithms do not address the problem of insufficient statistical information for image representation and noisy tags which greatly decreases the clustering performance while increases the computational cost. In this paper, we propose a two-step semi-supervised hierarchical clustering algorithm, Personalized Hierarchical Theme-based Clustering (PHTC), for web image organization. In the first step, the Probabilistic Fusion ART (PF-ART) is proposed for grouping semantically similar images and simultaneously learning the probabilistic distribution of tag occurrence for mining the key tags/topics of clusters. In this way, …
Energy-Efficient Continuous Activity Recognition On Mobile Phones: An Activity-Adaptive Approach, Zhixian Yan, Vigneshwaran Subbaraju, Dipanjan Chakraborty, Archan Misra, Karl Aberer
Energy-Efficient Continuous Activity Recognition On Mobile Phones: An Activity-Adaptive Approach, Zhixian Yan, Vigneshwaran Subbaraju, Dipanjan Chakraborty, Archan Misra, Karl Aberer
Research Collection School Of Computing and Information Systems
Power consumption on mobile phones is a painful obstacle towards adoption of continuous sensing driven applications, e.g., continuously inferring individual’s locomotive activities (such as ‘sit’, ‘stand’ or ‘walk’) using the embedded accelerometer sensor. To reduce the energy overhead of such continuous activity sensing, we first investigate how the choice of accelerometer sampling frequency & classification features affects, separately for each activity, the “energy overhead” vs. “classification accuracy” tradeoff. We find that such tradeoff is activity specific. Based on this finding, we introduce an activity-sensitive strategy (dubbed “A3R” – Adaptive Accelerometer-based Activity Recognition) for continuous activity recognition, where the choice of …