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A Performance-Sensitive Malware Detection System Using Deep Learning On Mobile Devices, Ruitao Feng, Sen Chen, Xiaofei Xie, Guozhu Meng, Shang-Wei Lin, Yang Liu
A Performance-Sensitive Malware Detection System Using Deep Learning On Mobile Devices, Ruitao Feng, Sen Chen, Xiaofei Xie, Guozhu Meng, Shang-Wei Lin, Yang Liu
Research Collection School Of Computing and Information Systems
Currently, Android malware detection is mostly performed on server side against the increasing number of malware. Powerful computing resource provides more exhaustive protection for app markets than maintaining detection by a single user. However, apart from the applications (apps) provided by the official market (i.e., Google Play Store), apps from unofficial markets and third-party resources are always causing serious security threats to end-users. Meanwhile, it is a time-consuming task if the app is downloaded first and then uploaded to the server side for detection, because the network transmission has a lot of overhead. In addition, the uploading process also suffers …
On Very Large Scale Test Collection For Landmark Image Search Benchmarking, Zhiyong Cheng, Jialie Shen
On Very Large Scale Test Collection For Landmark Image Search Benchmarking, Zhiyong Cheng, Jialie Shen
Research Collection School Of Computing and Information Systems
High quality test collections have been becoming more and more important for the technological advancement in geo-referenced image retrieval and analytics. In this paper, we present a large scale test collection to support robust performance evaluation of landmark image search and corresponding construction methodology. Using the approach, we develop a very large scale test collection consisting of three key components: (1) 355,141 images of 128 landmarks in five cities across three continents crawled from Flickr; (2) different kinds of textual features for each image, including surrounding text (e.g. tags), contextual data (e.g. geo-location and upload time), and metadata (e.g. uploader …
Not You Too? Distilling Local Contexts Of Poor Cellular Network Performance Through Participatory Sensing, Huiguang Liang, Ido Nevat, Hyong S. Kim, Hwee-Pink Tan, Wai-Leong Yeow
Not You Too? Distilling Local Contexts Of Poor Cellular Network Performance Through Participatory Sensing, Huiguang Liang, Ido Nevat, Hyong S. Kim, Hwee-Pink Tan, Wai-Leong Yeow
Research Collection School Of Computing and Information Systems
Cellular service subscribers are increasingly reliant on cellular data services for all kinds of mobile applications. Oftentimes, when subscribers experience frustratingly high network delays and timeouts, they like to know whether their experiences are shared by other users nearby. The question that is often asked is essentially this: “is it just me, or do others around me face the same problem?” In this paper, we describe how we use Tattle, a distributed real-time participatory sensing and monitoring framework, to glean network performance information from users nearby. Tattle relies on recent advances in peer-to-peer device networking, such as Wi-Fi Direct, Bluetooth …
Where Am I? Characterizing And Improving The Localization Performance Of Off-The-Shelf Mobile Devices Through Cooperation, Huiguang Liang, Hyong S. Kim, Hwee-Pink Tan, Wai-Leong Yeow
Where Am I? Characterizing And Improving The Localization Performance Of Off-The-Shelf Mobile Devices Through Cooperation, Huiguang Liang, Hyong S. Kim, Hwee-Pink Tan, Wai-Leong Yeow
Research Collection School Of Computing and Information Systems
We are increasingly reliant on cellular data services for many types of day-to-day activities, from hailing a cab, to searching for nearby restaurants. Geo-location has become a ubiquitous feature that underpins the functionality of such applications. Network operators can also benefit from accurate mobile terminal localization in order to quickly detect and identify location-related network performance issues, such as coverage holes and congestion, based on mobile measurements. Current implementations of mobile localization on the wildly-popular Android platform depend on either the Global Positioning System (GPS), Android's Network Location Provider (NLP), or a combination of both. In this paper, we extensively …
Iot+Small Data: Transforming In-Store Shopping Analytics And Services, Meera Radhakrishnan, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Balan
Iot+Small Data: Transforming In-Store Shopping Analytics And Services, Meera Radhakrishnan, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Balan
Research Collection School Of Computing and Information Systems
We espouse a vision of small data-based immersive retail analytics, where a combination of sensor data, from personal wearable-devices and store-deployed sensors & IoT devices, is used to create real-time, individualized services for in-store shoppers. Key challenges include (a) appropriate joint mining of sensor & wearable data to capture a shopper’s product level interactions, and (b) judicious triggering of power-hungry wearable sensors (e.g., camera) to capture only relevant portions of a shopper’s in-store activities. To explore the feasibility of our vision, we conducted experiments with 5 smartwatch-wearing users who interacted with objects placed on cupboard racks in our lab (to …
On The Unreliability Of Bug Severity Data, Yuan Tian, Nasir Ali, David Lo, Ahmed E. Hassan
On The Unreliability Of Bug Severity Data, Yuan Tian, Nasir Ali, David Lo, Ahmed E. Hassan
Research Collection School Of Computing and Information Systems
Severity levels, e.g., critical and minor, of bugs are often used to prioritize development efforts. Prior research efforts have proposed approaches to automatically assign the severity label to a bug report. All prior efforts verify the accuracy of their approaches using human-assigned bug reports data that is stored in software repositories. However, all prior efforts assume that such human-assigned data is reliable. Hence a perfect automated approach should be able to assign the same severity label as in the repository – achieving a 100% accuracy. Looking at duplicate bug reports (i.e., reports referring to the same problem) from three open-source …
Experiences With Performance Tradeoffs In Practical, Continuous Indoor Localization, Azeem J. Khan, Vikash Ranjan, Trung-Tuan Luong, Rajesh Krishna Balan, Archan Misra
Experiences With Performance Tradeoffs In Practical, Continuous Indoor Localization, Azeem J. Khan, Vikash Ranjan, Trung-Tuan Luong, Rajesh Krishna Balan, Archan Misra
Research Collection School Of Computing and Information Systems
This paper describes our experiences and observations with the first version of a localization system that continuous tracks the indoor location of a large number of consumer mobile devices. Unlike past work that focuses principally on the accuracy of the location tracking algorithm, we study the performance of the localization system in terms of key additional metrics: scalability and energy-efficiency, which can sometimes conflict with the desire for high accuracy. To ensure that our solution can handle both Android and iOS-based mobile devices (& other closed mobile platforms), we adapt the conventional client-side fingerprinting-based localization approaches to develop a novel …
Multi-Echelon Repairable Item Inventory System With Limited Repair Capacity Under Nonstationary Demands, Hoong Chuin Lau, Huawei Song
Multi-Echelon Repairable Item Inventory System With Limited Repair Capacity Under Nonstationary Demands, Hoong Chuin Lau, Huawei Song
Research Collection School Of Computing and Information Systems
Classical multi-echelon repairable item inventory models are based either on steady-state analysis or infinite repair capacity, which may not work well in situations when the demand is nonstationary, or repair capacity is limited. In this paper, we propose an analytical model for evaluating system performance that works well under limited repair capacity and nonstationary demands. Following the METRIC methodology, we then develop an optimisation algorithm to solve the corrective maintenance problem in military logistics. Experimental results show that our approach yields good solutions efficiently. This work has also resulted in a software that has been field-tested by a military organisation.
A Multimodal And Multilevel Ranking Framework For Content-Based Video Retrieval, Steven C. H. Hoi, Michael R. Lyu
A Multimodal And Multilevel Ranking Framework For Content-Based Video Retrieval, Steven C. H. Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
One critical task in content-based video retrieval is to rank search results with combinations of multimodal resources effectively. This paper proposes a novel multimodal and multilevel ranking framework for content-based video retrieval. The main idea of our approach is to represent videos by graphs and learn harmonic ranking functions through fusing multimodal resources over these graphs smoothly. We further tackle the efficiency issue by a multilevel learning scheme, which makes the semi-supervised ranking method practical for large-scale applications. Our empirical evaluations on TRECVID 2005 dataset show that the proposed multimodal and multilevel ranking framework is effective and promising for content-based …
A Multimodal And Multilevel Ranking Framework For Content-Based Video Retrieval, Steven C. H. Hoi, Michael R. Lyu
A Multimodal And Multilevel Ranking Framework For Content-Based Video Retrieval, Steven C. H. Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
One critical task in content-based video retrieval is to rank search results with combinations of multimodal resources effectively. This paper proposes a novel multimodal and multilevel ranking framework for content-based video retrieval. The main idea of our approach is to represent videos by graphs and learn harmonic ranking functions through fusing multimodal resources over these graphs smoothly. We further tackle the efficiency issue by a multilevel learning scheme, which makes the semi-supervised ranking method practical for large-scale applications. Our empirical evaluations on TRECVID 2005 dataset show that the proposed multimodal and multilevel ranking framework is effective and promising for content-based …
Cache Invalidation And Replacement Strategies For Location-Dependent Data In Mobile Environments, Baihua Zheng, Jianliang Xu, Dik Lun Lee
Cache Invalidation And Replacement Strategies For Location-Dependent Data In Mobile Environments, Baihua Zheng, Jianliang Xu, Dik Lun Lee
Research Collection School Of Computing and Information Systems
Mobile location-dependent information services (LDISs) have become increasingly popular in recent years. However, data caching strategies for LDISs have thus far received little attention. In this paper, we study the issues of cache invalidation and cache replacement for location-dependent data under a geometric location model. We introduce a new performance criterion, called caching efficiency, and propose a generic method for location-dependent cache invalidation strategies. In addition, two cache replacement policies, PA and PAID, are proposed. Unlike the conventional replacement policies, PA and PAID take into consideration the valid scope area of a data value. We conduct a series of simulation …
Performance Analysis Of Interconnected Lans With Server/Client Configuration, Jiangling Du, Robert H. Deng, Chi Chung Ko
Performance Analysis Of Interconnected Lans With Server/Client Configuration, Jiangling Du, Robert H. Deng, Chi Chung Ko
Research Collection School Of Computing and Information Systems
In this paper, we study the end-to-end performance of interconnected local area networks (LAN) with server/client configuration. The system uses bridges to connect two token-ring LANs through a high-speed communication link. A server station located on one LAN receives requests from client-stations on the same LAN as well as on the remote LAN, processes the requests, and returns responses to the client-stations. The end-to-end connections of the interconnected network are modelled as single-chain and multiple-chain closed queueing systems, which are solved by an iterative algorithm based on the MVA (mean value analysis) method. The performance examples are shown in terms …
Performance Of A Token-Passing System With Batch Arrivals And Its Application To File Transfers, Robert H. Deng, Xuanyu Zhang, Kuan Tase Huang
Performance Of A Token-Passing System With Batch Arrivals And Its Application To File Transfers, Robert H. Deng, Xuanyu Zhang, Kuan Tase Huang
Research Collection School Of Computing and Information Systems
This paper investigates the performance of token-passing systems with limited service and Poisson arrivals. For pure Poisson arrivals, the Laplace-Stieltjes Transform (LST) of an approximate customer/packet waiting time distribution is derived and expressed as a functional equation, from which the approximate mean and variance of waiting time are obtained; for batch Poisson arrivals, an approximate mean of waiting time is derived. Mean waiting time approximations are compared against both simulation results and other results presented in the literature; the variance approximation is evaluated by comparing it to simulation results only, since no other results on variance have been reported so …