Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (5591)
- Computer Sciences (5576)
- Databases and Information Systems (2329)
- Software Engineering (1250)
- Artificial Intelligence and Robotics (1149)
-
- Numerical Analysis and Scientific Computing (724)
- Engineering (708)
- Information Security (686)
- Graphics and Human Computer Interfaces (581)
- Business (502)
- Social and Behavioral Sciences (501)
- Computer Engineering (349)
- Theory and Algorithms (341)
- Operations Research, Systems Engineering and Industrial Engineering (335)
- Programming Languages and Compilers (241)
- OS and Networks (219)
- Communication (186)
- Public Affairs, Public Policy and Public Administration (184)
- Transportation (167)
- Medicine and Health Sciences (160)
- Education (150)
- Social Media (148)
- Management Information Systems (140)
- E-Commerce (116)
- Data Storage Systems (109)
- Health Information Technology (98)
- International and Area Studies (87)
- Asian Studies (86)
- Digital Communications and Networking (82)
- Higher Education (78)
- Keyword
-
- Deep learning (90)
- Machine learning (88)
- Artificial intelligence (55)
- Reinforcement learning (53)
- Cloud computing (51)
-
- Privacy (49)
- Data mining (47)
- Online learning (44)
- Security (44)
- Social media (43)
- Empirical study (42)
- Optimization (42)
- Training (42)
- Visualization (41)
- Task analysis (39)
- Access control (38)
- Feature extraction (37)
- Neural networks (37)
- Deep Learning (34)
- Software engineering (34)
- Blockchain (33)
- Machine Learning (31)
- Twitter (31)
- Anomaly detection (30)
- Large Language Models (30)
- Privacy-preserving (30)
- Natural language processing (29)
- Singapore (29)
- Clustering (28)
- Collaboration (28)
- Publication Year
Articles 4741 - 4770 of 5707
Full-Text Articles in Entire DC Network
A Review Of The Professionalization Of The Software Industry: Has It Made Software Engineering A Real Profession?, Heng Ngee Mok
A Review Of The Professionalization Of The Software Industry: Has It Made Software Engineering A Real Profession?, Heng Ngee Mok
Research Collection School Of Computing and Information Systems
Every industry strives to be called a "profession", and software engineering is no exception. This paper attempts to define "profession" from three different perspectives and provides a chronological narration of the professionalization efforts of major IT bodies such as the IEEE Computer Society, Association of Computing Machinery and British Computer Society to promote software engineering from "occupation" to "profession". The outcome of this professionalization process is then examined against the three vastly different definitions of "profession" to qualitatively gauge the success of the professionalization process.
Do Online Reviews Reflect A Product's True Perceived Quality? An Investigation Of Online Movie Reviews Across Cultures, Noi Sian Koh, Nan Hu, Eric K. Clemons
Do Online Reviews Reflect A Product's True Perceived Quality? An Investigation Of Online Movie Reviews Across Cultures, Noi Sian Koh, Nan Hu, Eric K. Clemons
Research Collection School Of Computing and Information Systems
When does the reported average of online ratings match perceived average assessment? We apply behavioral theory to capture intentions in rating online movie reviews in two dissimilar countries - China and the United States. We argue that consumers' rating behaviors are affected by cultural influences. Based on data collected from imdb.com and douban.com, we found significant differences across raters from these two different cultures. Additionally, we examined how cultural elements influence rating behavior for a hybrid culture - Singapore. Under-reporting bias occurs when consumers with extreme opinions are more likely to report their opinions than consumers with moderate reviews, resulting …
Dual Phase Learning For Large Scale Video Gait Recognition, Jialie Shen, Hwee Hwa Pang, Dacheng Tao, Xuelong Li
Dual Phase Learning For Large Scale Video Gait Recognition, Jialie Shen, Hwee Hwa Pang, Dacheng Tao, Xuelong Li
Research Collection School Of Computing and Information Systems
Accurate gait recognition from video is a complex process involving heterogenous features, and is still being developed actively. This article introduces a novel framework, called GC2F, for effective and efficient gait recognition and classification. Adopting a ”refinement-and-classification” principle, the framework comprises two components: 1) a classifier to generate advanced probabilistic features from low level gait parameters; and 2) a hidden classifier layer (based on multilayer perceptron neural network) to model the statistical properties of different subject classes. To validate our framework, we have conducted comprehensive experiments with a large test collection, and observed significant improvements in identification accuracy relative to …
Introducing Communication In Dis-Pomdps With Locality Of Interaction, Makoto Tasaki, Yuichi Yabu, Yuki Iwanari, Makoto Yokoo, Janusz Marecki, Pradeep Reddy Varakantham, Milind Tambe
Introducing Communication In Dis-Pomdps With Locality Of Interaction, Makoto Tasaki, Yuichi Yabu, Yuki Iwanari, Makoto Yokoo, Janusz Marecki, Pradeep Reddy Varakantham, Milind Tambe
Research Collection School Of Computing and Information Systems
The Networked Distributed POMDPs (ND-POMDPs) can model multiagent systems in uncertain domains and has begun to scale-up the number of agents. However, prior work in ND-POMDPs has failed to address communication. Without communication, the size of a local policy at each agent within the ND-POMDPs grows exponentially in the time horizon. To overcome this problem, we extend existing algorithms so that agents periodically communicate their observation and action histories with each other. After communication, agents can start from new synchronized belief state. Thus, we can avoid the exponential growth in the size of local policies at agents. Furthermore, we introduce …
Teleoph: A Secure Real-Time Teleophthalmology System, Yongdong Wu, Zhou Wei, Haixia Yao, Zhigang Zhao, Lek Heng Ngoh, Robert H. Deng, Shengsheng Yu
Teleoph: A Secure Real-Time Teleophthalmology System, Yongdong Wu, Zhou Wei, Haixia Yao, Zhigang Zhao, Lek Heng Ngoh, Robert H. Deng, Shengsheng Yu
Research Collection School Of Computing and Information Systems
Teleophthalmology (TeleOph) is an electronic counterpart of today's face-to-face, patient-to-specialist ophthalmology system. It enables one or more ophthalmologists to remotely examine a patient's condition via a confidential and authentic communication channel. Specifically, TeleOph allows a trained nonspecialist in a primary clinic to screen the patients with digital instruments (e.g., camera, ophthalmoscope). The acquired medical data are delivered to the hospital where an ophthalmologist will review the data collected and, if required, provide further consultation for the patient through a real-time secure channel established over a public Internet network. If necessary, the ophthalmologist is able to further sample the images/video of …
On The Potential Of Limitation-Oriented Malware Detection And Prevention Techniques On Mobile Phones, Qiang Yan, Robert H. Deng, Yingjiu Li, Tieyan Li
On The Potential Of Limitation-Oriented Malware Detection And Prevention Techniques On Mobile Phones, Qiang Yan, Robert H. Deng, Yingjiu Li, Tieyan Li
Research Collection School Of Computing and Information Systems
The malware threat for mobile phones is expected to increase with the functionality enhancement of mobile phones. This threat is exacerbated with the surge in population of smart phones instilled with stable Internet access which provides attractive targets for malware developers. Prior research on malware protection has focused on avoiding the negative impact of the functionality limitations of mobile phones to keep the performance cost within the limitations of mobile phones. Being different, this paper investigates the positive impact of these limitations on suppressing the development of mobile malware. We study the state-of-the-art mobile malware, as well as the progress …
Does Co-Opetition Change The Game? A Bayesian Analysis Of Participation Strategy In An Industry Standard-Setting Organization, Robert J. Kauffman, Benjamin B. M. Shao, Juliana Y. Tsai
Does Co-Opetition Change The Game? A Bayesian Analysis Of Participation Strategy In An Industry Standard-Setting Organization, Robert J. Kauffman, Benjamin B. M. Shao, Juliana Y. Tsai
Research Collection School Of Computing and Information Systems
Across different industries, we observe the emergence of industry standard-setting organizations (SSO) that focus on the creation of industry-specific process standards. Firms adopt a strategy of co-opetition, the combination of cooperation and competition, during standards development. We propose that a firm's co-opetition strategy is revealed to some extent by when it elects to participate in an industry SSO. Bayesian information updating offers an effective empirical modeling perspective for studying firms' participation strategies when managers' beliefs and uncertainties are factored into their decision-making. We analyze a panel data set of 73 U.S. public firms over a 20-year period. These firms are …
Scalable Multi-Core Model Checking Fairness Enhanced Systems, Yang Liu, Jun Sun, Jin Song Dong
Scalable Multi-Core Model Checking Fairness Enhanced Systems, Yang Liu, Jun Sun, Jin Song Dong
Research Collection School Of Computing and Information Systems
Rapid development in hardware industry has brought the prevalence of multi-core systems with shared-memory, which enabled the speedup of various tasks by using parallel algorithms. The Linear Temporal Logic (LTL) model checking problem is one of the difficult problems to be parallelized or scaled up to multi-core. In this work, we propose an on-the-fly parallel model checking algorithm based on the Tarjan’s strongly connected components (SCC) detection algorithm. The approach can be applied to general LTL model checking or with different fairness assumptions. Further, it is orthogonal to state space reduction techniques like partial order reduction. We enhance our PAT …
Wake Up Or Fall Asleep: Value Implication Of Trusted Computing, Nan Hu, Jianhui Huang, Ling Liu, Yingjiu Li, Dan Ma
Wake Up Or Fall Asleep: Value Implication Of Trusted Computing, Nan Hu, Jianhui Huang, Ling Liu, Yingjiu Li, Dan Ma
Research Collection School Of Computing and Information Systems
More than 10 years have passed since trusted computing (TC) technology was introduced to the market; however, there is still no consensus about its value. The increasing importance of user and enterprise security and the security promised by TC, coupled with the increasing tension between the proponents and the opponents of TC, make it timely to investigate the value relevance of TC in terms of both capital market and accounting performance. Based on both price and volume studies, we found that news releases related to the adoption of the TC technology had no information content. All investors, regardless of whether …
Cyber Attacks: Does Physical Boundary Matter?, Qiu-Hong Wang, Seung-Hyun Kim
Cyber Attacks: Does Physical Boundary Matter?, Qiu-Hong Wang, Seung-Hyun Kim
Research Collection School Of Computing and Information Systems
Information security issues are characterized with interdependence. Particularly, cyber criminals can easily cross national boundaries and exploit jurisdictional limitations between countries. Thus, whether cyber attacks are spatially autocorrelated is a strategic issue for government authorities and a tactic issue for insurance companies. Through an empirical study of cyber attacks across 62 countries during the period 2003-2007, we find little evidence on the spatial autocorrelation of cyber attacks at any week. However, after considering economic opportunity, IT infrastructure, international collaboration in enforcement and conventional crimes, we find strong evidence that cyber attacks were indeed spatially autocorrelated as they moved over time. …
Coherent Phrase Model For Efficient Image Near-Duplicate Retrieval, Yiqun Hu, Xiangang Cheng, Liang-Tien Chia, Xing Xie, Deepu Rajan, Ah-Hwee Tan
Coherent Phrase Model For Efficient Image Near-Duplicate Retrieval, Yiqun Hu, Xiangang Cheng, Liang-Tien Chia, Xing Xie, Deepu Rajan, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper presents an efficient and effective solution for retrieving image near-duplicate (IND) from image database. We introduce the coherent phrase model which incorporates the coherency of local regions to reduce the quantization error of the bag-of-words (BoW) model. In this model, local regions are characterized by visual phrase of multiple descriptors instead of visual word of single descriptor. We propose two types of visual phrase to encode the coherency in feature and spatial domain, respectively. The proposed model reduces the number of false matches by using this coherency and generates sparse representations of images. Compared to other method, the …
Enabling Secure Secret Updating For Unidirectional Key Distribution In Rfid-Enabled Supply Chains, Shaoying Cai, Tieyan Li, Changshe Ma, Yingjiu Li, Robert H. Deng
Enabling Secure Secret Updating For Unidirectional Key Distribution In Rfid-Enabled Supply Chains, Shaoying Cai, Tieyan Li, Changshe Ma, Yingjiu Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
In USENIX Security 08, Juels, Pappu and Parno proposed a secret sharing based mechanism to alleviate the key distribution problem in RFID-enabled supply chains. Compared to existing pseudonym based RFID protocols, the secret sharing based solution is more suitable for RFID-enabled supply chains since it does not require a database of keys be distributed among supply chain parties for secure ownership transfer of RFID tags. However, this mechanism cannot resist tag tracking and tag counterfeiting attacks in supply chain systems. It is also not convenient for downstream supply chain parties to adjust the size of RFID tag collections in recovering …
On Strategies For Imbalanced Text Classification Using Svm: A Comparative Study, Aixin Sun, Ee Peng Lim, Ying Liu
On Strategies For Imbalanced Text Classification Using Svm: A Comparative Study, Aixin Sun, Ee Peng Lim, Ying Liu
Research Collection School Of Computing and Information Systems
Many real-world text classification tasks involve imbalanced training examples. The strategies proposed to address the imbalanced classification (e.g., resampling, instance weighting), however, have not been systematically evaluated in the text domain. In this paper, we conduct a comparative study on the effectiveness of these strategies in the context of imbalanced text classification using Support Vector Machines (SVM) classifier. SVM is the interest in this study for its good classification accuracy reported in many text classification tasks. We propose a taxonomy to organize all proposed strategies following the training and the test phases in text classification tasks. Based on the taxonomy, …
A New Approach For Anonymous Password Authentication, Yanjiang Yang, Jianying Zhou, Jian Weng, Feng Bao
A New Approach For Anonymous Password Authentication, Yanjiang Yang, Jianying Zhou, Jian Weng, Feng Bao
Research Collection School Of Computing and Information Systems
Anonymous password authentication reinforces password authentication with the protection of user privacy. Considering the increasing concern of individual privacy nowadays, anonymous password authentication represents a promising privacy-preserving authentication primitive. However, anonymous password authentication in the standard setting has several inherent weaknesses, making its practicality questionable. In this paper, we propose a new and efficient approach for anonymous password authentication. Our approach assumes a different setting where users do not register their passwords to the server; rather, they use passwords to protect their authentication credentials. We present a concrete scheme, and get over a number of challenges in securing password-protected credentials …
To Trust Or Not To Trust? Predicting Online Trusts Using Trust Antecedent Framework, Viet-An Nguyen, Ee Peng Lim, Jing Jiang, Aixin Sun
To Trust Or Not To Trust? Predicting Online Trusts Using Trust Antecedent Framework, Viet-An Nguyen, Ee Peng Lim, Jing Jiang, Aixin Sun
Research Collection School Of Computing and Information Systems
This paper analyzes the trustor and trustee factors that lead to inter-personal trust using a well studied Trust Antecedent framework in management science. To apply these factors to trust ranking problem in online rating systems, we derive features that correspond to each factor and develop different trust ranking models. The advantage of this approach is that features relevant to trust can be systematically derived so as to achieve good prediction accuracy. Through a series of experiments on real data from Epinions, we show that even a simple model using the derived features yields good accuracy and outperforms MoleTrust, a trust …
On The Untraceability Of Anonymous Rfid Authentication Protocol With Constant Key-Lookup, Bing Liang, Yingjiu Li, Tieyan Li, Robert H. Deng
On The Untraceability Of Anonymous Rfid Authentication Protocol With Constant Key-Lookup, Bing Liang, Yingjiu Li, Tieyan Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
In ASIACCS'08, Burmester, Medeiros and Motta proposed an anonymous RFID authentication protocol (BMM protocol [2]) that preserves the security and privacy properties, and achieves better scalability compared with other contemporary approaches. We analyze BMM protocol and find that some of security properties (especial untraceability) are not fulfilled as originally claimed. We consider a subtle attack, in which an adversary can manipulate the messages transmitted between a tag and a reader for several continuous protocol runs, and can successfully trace the tag after these interactions. Our attack works under a weak adversary model, in which an adversary can eavesdrop, intercept and …
Computationally Secure Hierarchical Self-Healing Key Distribution For Heterogeneous Wireless Sensor Networks, Yanjiang Yang, Jianying Zhou, Robert H. Deng, Feng Bao
Computationally Secure Hierarchical Self-Healing Key Distribution For Heterogeneous Wireless Sensor Networks, Yanjiang Yang, Jianying Zhou, Robert H. Deng, Feng Bao
Research Collection School Of Computing and Information Systems
Self-healing group key distribution is a primitive aimed to achieve robust key distribution in wireless sensor networks (WSNs) over lossy communication channels. However, all the existing self-healing group key distribution schemes in the literature are designed for homogenous WSNs that do not scale. In contract, heterogeneous WSNs have better scalability and performance. We are thus motivated to study self-healing group key distribution for heterogeneous WSNs. In particular, we propose the concept of hierarchical self-healing group key distribution, tailored to the heterogeneous WSN architecture; we further revisit and adapt Dutta et al.’s model to the setting of hierarchical self-healing group …
Denial-Of-Service Attacks On Host-Based Generic Unpackers, Limin Liu, Jiang Ming, Zhi Wang, Debin Gao, Chunfu Jia
Denial-Of-Service Attacks On Host-Based Generic Unpackers, Limin Liu, Jiang Ming, Zhi Wang, Debin Gao, Chunfu Jia
Research Collection School Of Computing and Information Systems
With the advance of packing techniques, a few generic and automatic unpackers have been proposed. These unpackers are designed to automatically unpack packed binaries without specific knowledge of the packing techniques used. In this paper, we present an automatic packer with which packed malware forges spurious unpacking behaviors that lead to a denial-of-service attack on host-based generic unpackers. We present the design, implementation, and evaluation of the proposed packer and malware produced using the proposed packer, and show the success of denial-of-service attacks on host-based generic unpackers.
Adaptive Type-2 Fuzzy Maintenance Advisor For Offshore Power Systems, Zhaoxia Wang, C. S. Chang, Fan Yang, W. W. Tan
Adaptive Type-2 Fuzzy Maintenance Advisor For Offshore Power Systems, Zhaoxia Wang, C. S. Chang, Fan Yang, W. W. Tan
Research Collection School Of Computing and Information Systems
Proper maintenance strategies are very desirable for minimizing the operational and maintenance costs of power systems without sacrificing reliability. Condition-based maintenance has largely replaced time-based maintenance because of the former's potential economic benefits. As offshore substations are often remotely located, they experience more adverse environments, higher failures, and therefore need more powerful analytical tools than their onshore counterpart. As reliability information collected during operation of an offshore substation can rarely avoid uncertainties, it is essential to obtain consistent estimates of reliability measures under changing environmental and operating conditions. Some attempts with type-1 fuzzy logic were made with limited success in …
Managing Supply Uncertainty With An Information Market, Zhiling Guo, Fang Fang, Andrew B. Whinston
Managing Supply Uncertainty With An Information Market, Zhiling Guo, Fang Fang, Andrew B. Whinston
Research Collection School Of Computing and Information Systems
We propose a market-based information aggregation mechanism to manage the supply side uncertainty in the supply chain. In our analytical model, a simple supply chain consists of a group of retailers who order a homogeneous product from two suppliers. The two suppliers differ in their ability to fulfill orders – one always delivers orders and the other fulfills orders probabilistically. We model the supply chain decisions as a Stackelberg game where the supplier who has uncertain reliability decides a wholesale price before the retailers who independently receive signals about the supplier’s reliability determine their sourcing strategies. We then propose an …
Learning Bregman Distance Functions And Its Application For Semi-Supervised Clustering, Lei Wu, Rong Jin, Steven C. H. Hoi, Jianke Zhu, Nenghai Yu
Learning Bregman Distance Functions And Its Application For Semi-Supervised Clustering, Lei Wu, Rong Jin, Steven C. H. Hoi, Jianke Zhu, Nenghai Yu
Research Collection School Of Computing and Information Systems
Learning distance functions with side information plays a key role in many machine learning and data mining applications. Conventional approaches often assume a Mahalanobis distance function. These approaches are limited in two aspects: (i) they are computationally expensive (even infeasible) for high dimensional data because the size of the metric is in the square of dimensionality; (ii) they assume a fixed metric for the entire input space and therefore are unable to handle heterogeneous data. In this paper, we propose a novel scheme that learns nonlinear Bregman distance functions from side information using a nonparametric approach that is similar to …
Ensemble And Individual Noise Reduction Method For Induction-Motor Signature Analysis, Zhaoxia Wang, C.S. Chang, Tw Chua, W.W Tan
Ensemble And Individual Noise Reduction Method For Induction-Motor Signature Analysis, Zhaoxia Wang, C.S. Chang, Tw Chua, W.W Tan
Research Collection School Of Computing and Information Systems
Unlike a fixed-frequency power supply, the voltagesupplying an inverter-fed motor is heavily corrupted by noises,which are produced from high-frequency switching leading tonoisy stator currents. To extract useful information from statorcurrentmeasurements, a theoretically sound and robust denoisingmethod is required. The effective filtering of these noisesis difficult with certain frequency-domain techniques, such asFourier transform or Wavelet analysis, because some noises havefrequencies overlapping with those of the actual signals, andsome have high noise-to-frequency ratios. In order to analyze thestatistical signatures of different types of signals, a certainnumber is required of the individual signals to be de-noisedwithout sacrificing the individual characteristic and quantity ofthe …
Online Fault Detection Of Induction Motors Using Independent Component Analysis And Fuzzy Neural Network, Zhaoxia Wang, C. S. Chang, X. German, W.W. Tan
Online Fault Detection Of Induction Motors Using Independent Component Analysis And Fuzzy Neural Network, Zhaoxia Wang, C. S. Chang, X. German, W.W. Tan
Research Collection School Of Computing and Information Systems
This paper proposes the use of independent component analysis and fuzzy neural network for online fault detection of induction motors. The most dominating components of the stator currents measured from laboratory motors are directly identified by an improved method of independent component analysis, which are then used to obtain signatures of the stator current with different faults. The signatures are used to train a fuzzy neural network for detecting induction-motor problems such as broken rotor bars and bearing fault. Using signals collected from laboratory motors, the robustness of the proposed method for online fault detection is demonstrated for various motor …
Mining Communities In Networks: A Solution For Consistency And Its Evaluation, Haewoon Kwak, Yoonchan Choi, Young-Ho Eom, Hawoong Jeong, Sue Moon
Mining Communities In Networks: A Solution For Consistency And Its Evaluation, Haewoon Kwak, Yoonchan Choi, Young-Ho Eom, Hawoong Jeong, Sue Moon
Research Collection School Of Computing and Information Systems
Online social networks pose significant challenges to computer scientists, physicists, and sociologists alike, for their massive size, fast evolution, and uncharted potential for social computing. One particular problem that has interested us is community identification. Many algorithms based on various metrics have been proposed for communities in networks [18, 24], but a few algorithms scale to very large networks. Three recent community identification algorithms, namely CNM [16], Wakita [59], and Louvain [10], stand out for their scalability to a few millions of nodes. All of them use modularity as the metric of optimization. However, all three algorithms produce inconsistent communities …
Trust Relationship Prediction Using Online Product Review Data, Nan Ma, Ee Peng Lim, Viet-An Nguyen, Aixin Sun
Trust Relationship Prediction Using Online Product Review Data, Nan Ma, Ee Peng Lim, Viet-An Nguyen, Aixin Sun
Research Collection School Of Computing and Information Systems
Trust between users is an important piece of knowledge that can be exploited in search and recommendation.Given that user-supplied trust relationships are usually very sparse, we study the prediction of trust relationships using user interaction features in an online user generated review application context. We show that trust relationship prediction can achieve better accuracy when one adopts personalized and cluster-based classification methods. The former trains one classifier for each user using user-specific training data. The cluster-based method first constructs user clusters before training one classifier for each user cluster. Our proposed methods have been evaluated in a series of experiments …
Trust-Oriented Composite Services Selection And Discovery, Lei Li, Yan Wang, Ee Peng Lim
Trust-Oriented Composite Services Selection And Discovery, Lei Li, Yan Wang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In Service-Oriented Computing (SOC) environments, service clients interact with service providers for consuming services. From the viewpoint of service clients, the trust level of a service or a service provider is a critical issue to consider in service selection and discovery, particularly when a client is looking for a service from a large set of services or service providers. However, a service may invoke other services offered by different providers forming composite services. The complex invocations in composite services greatly increase the complexity of trust-oriented service selection and discovery. In this paper, we propose novel approaches for composite service representation, …
Minimum Latency Broadcasting In Multiradio, Multichannel, Multirate Wireless Meshes, Junaid Qadir, Chuntung Chou, Archan Misra, Joo Ghee Lim
Minimum Latency Broadcasting In Multiradio, Multichannel, Multirate Wireless Meshes, Junaid Qadir, Chuntung Chou, Archan Misra, Joo Ghee Lim
Research Collection School Of Computing and Information Systems
We address the problem of minimizing the worst-case broadcast delay in multi-radio multi-channel multi-rate (MR2-MC) wireless mesh networks (WMN). The problem of 'efficient' broadcast in such networks is especially challenging due to the numerous interrelated decisions that have to be made. The multi-rate transmission capability of WMN nodes, interference between wireless transmissions, and the hardness of optimal channel assignment adds complexity to our considered problem. We present four heuristic algorithms to solve the minimum latency broadcast problem for such settings and show that the 'best' performing algorithms usually adapt themselves to the available radio interfaces and channels. We also study …
Analyzing The Video Popularity Characteristics Of Large-Scale User Generated Content Systems, Meeyoung Cha, Haewoon Kwak, Pablo Rodriguez, Yong-Yeol Ahn, Sue Moon
Analyzing The Video Popularity Characteristics Of Large-Scale User Generated Content Systems, Meeyoung Cha, Haewoon Kwak, Pablo Rodriguez, Yong-Yeol Ahn, Sue Moon
Research Collection School Of Computing and Information Systems
User generated content (UGC), now with millions of video producers and consumers, is re-shaping the way people watch video and TV. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and generating new business opportunities. Compared to traditional video-on-demand (VoD) systems, UGC services allow users to request videos from a potentially unlimited selection in an asynchronous fashion. To better understand the impact of UGC services, we have analyzed the world's largest UGC VoD system, YouTube, and a popular similar system in Korea, Daum Videos. In this paper, we first empirically show …
Distance Metric Learning From Uncertain Side Information With Application To Automated Photo Tagging, Lei Wu, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Nenghai Yu
Distance Metric Learning From Uncertain Side Information With Application To Automated Photo Tagging, Lei Wu, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Nenghai Yu
Research Collection School Of Computing and Information Systems
Automated photo tagging is essential to make massive unlabeled photos searchable by text search engines. Conventional image annotation approaches, though working reasonably well on small testbeds, are either computationally expensive or inaccurate when dealing with large-scale photo tagging. Recently, with the popularity of social networking websites, we observe a massive number of user-tagged images, referred to as "social images", that are available on the web. Unlike traditional web images, social images often contain tags and other user-generated content, which offer a new opportunity to resolve some long-standing challenges in multimedia. In this work, we aim to address the challenge of …
Mining Globally Distributed Frequent Subgraphs In A Single Labeled Graph, Xing Jiang, Hui Xiong, Chen Wang, Ah-Hwee Tan
Mining Globally Distributed Frequent Subgraphs In A Single Labeled Graph, Xing Jiang, Hui Xiong, Chen Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Recent years have observed increasing efforts on graph mining and many algorithms have been developed for this purpose. However, most of the existing algorithms are designed for discovering frequent subgraphs in a set of labeled graphs only. Also, the few algorithms that find frequent subgraphs in a single labeled graph typically identify subgraphs appearing regionally in the input graph. In contrast, for real-world applications, it is commonly required that the identified frequent subgraphs in a single labeled graph should also be globally distributed. This paper thus fills this crucial void by proposing a new measure, termed G-Measure, to find globally …