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Articles 6451 - 6480 of 8479
Full-Text Articles in Computer Sciences
Mydeal: The Context-Aware Urban Shopping Assistant, Kartik Muralidharan, Swapna Gottipati, Jing Jiang, Narayan Ramasubbu, Rajesh Krishna Balan
Mydeal: The Context-Aware Urban Shopping Assistant, Kartik Muralidharan, Swapna Gottipati, Jing Jiang, Narayan Ramasubbu, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
A common problem in large Urban cities, of the sort seen in Asia, is the huge number of retail options available in the city. In particular, it is not uncommon to find multiple malls, each with hundreds of stores inside, just a short distance from each other in almost every part of these cities. These factors make it incredibly hard for consumers to identify stores of interest to them in any particular mall.In response, a number of shopping assistance applications have been created for mobile phones.However, these applications mostly just allow users to know which stores are where or to …
A Model Checker For Hierarchical Probabilistic Real-Time Systems, Songzheng Song, Jun Sun, Yang Liu, Jin Song Dong
A Model Checker For Hierarchical Probabilistic Real-Time Systems, Songzheng Song, Jun Sun, Yang Liu, Jin Song Dong
Research Collection School Of Computing and Information Systems
Real-life systems are usually hard to control, due to their complicated structures, quantitative time factors and even stochastic behaviors. In this work, we present a model checker to analyze hierarchical probabilistic real-time systems. A modeling language called PRTS is used to specify such systems, and automatic zone-abstraction approach, which is probability preserving, is used to generate finite state MDP. We have implemented PRTS in model checking framework PAT so that friendly user interface can be used to edit, simulate and verify PRTS models. Some experiments are conducted to show our tool’s efficiency.
Translating Pddl Into Csp# - The Pat Approach, Yi Li, Jing Sun, Jin Song Dong, Yang Liu, Jun Sun
Translating Pddl Into Csp# - The Pat Approach, Yi Li, Jing Sun, Jin Song Dong, Yang Liu, Jun Sun
Research Collection School Of Computing and Information Systems
Model checking provides a way to automatically verify hardware and software systems, whereas the goal of planning is to produce a sequence of actions that leads from the initial state to the desired goal state. Recently research indicates that there is a strong connection between model checking and planning problem solving. In this paper, we investigate the feasibility of using a newly developed model checking framework, Process Analysis Toolkit (PAT), to serve as a planning solution provider for upper layer applications. We first carried out a number of experiments on different planning tools in order to compare their performance and …
Parameter Synthesis For Hierarchical Concurrent Real-Time Systems, Étienne André, Yang Liu, Jun Sun, Jin Song Dong
Parameter Synthesis For Hierarchical Concurrent Real-Time Systems, Étienne André, Yang Liu, Jun Sun, Jin Song Dong
Research Collection School Of Computing and Information Systems
Modeling and verifying complex real-time systems, involving timing delays, are notoriously difficult problems. Checking the correctness of a system for one particular value for each delay does not give any information for other values. It is hence interesting to reason parametrically, by considering that the delays are parameters (unknown constants) and synthesize a constraint guaranteeing a correct behavior. We present here Parametric Stateful Timed CSP, a language capable of specifying hierarchical real-time systems with complex data structures. Although we prove that the synthesis is undecidable in general, we present an algorithm for efficient parameter synthesis that behaves well in practice.
K-Partite Graph Reinforcement And Its Application In Multimedia Information Retrieval, Yue Gao, Meng Wang, Rongrong Ji, Zheng-Jun Zha, Jialie Shen
K-Partite Graph Reinforcement And Its Application In Multimedia Information Retrieval, Yue Gao, Meng Wang, Rongrong Ji, Zheng-Jun Zha, Jialie Shen
Research Collection School Of Computing and Information Systems
In many example-based information retrieval tasks, example query actually contains multiple sub-queries. For example, in 3D object retrieval, the query is an object described by multiple views. In content-based video retrieval, the query is a video clip that contains multiple frames. Without prior knowledge, the most intuitive approach is to treat the sub-queries equally without difference. In this paper, we propose a k-partite graph reinforcement approach to fuse these sub-queries based on the to-be-retrieved database. The approach first collects the top retrieved results. These results are regarded as pseudo-relevant samples and then a k-partite graph reinforcement is performed on these …
Enhancing Access Privacy Of Range Retrievals Over B+Trees, Hwee Hwa Pang, Jilian Zhang, Kyriakos Mouratidis
Enhancing Access Privacy Of Range Retrievals Over B+Trees, Hwee Hwa Pang, Jilian Zhang, Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
Users of databases that are hosted on shared servers cannot take for granted that their queries will not be disclosed to unauthorized parties. Even if the database is encrypted, an adversary who is monitoring the I/O activity on the server may still be able to infer some information about a user query. For the particular case of a B+-tree that has its nodes encrypted, we identify properties that enable the ordering among the leaf nodes to be deduced. These properties allow us to construct adversarial algorithms to recover the B+-tree structure from the I/O traces generated by range queries. Combining …
Finding Bursty Topics From Microblogs, Qiming Diao, Jing Jiang, Feida Zhu, Ee Peng Lim
Finding Bursty Topics From Microblogs, Qiming Diao, Jing Jiang, Feida Zhu, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Microblogs such as Twitter reflect the general public’s reactions to major events. Bursty topics from microblogs reveal what events have attracted the most online attention. Although bursty event detection from text streams has been studied before, previous work may not be suitable for microblogs because compared with other text streams such as news articles and scientific publications, microblog posts are particularly diverse and noisy. To find topics that have bursty patterns on microblogs, we propose a topic model that simultaneousy captures two observations: (1) posts published around the same time are more likely to have the same topic, and (2) …
Decision Support For Agent Populations In Uncertain And Congested Environments, Pradeep Reddy Varakantham, Shih-Fen Cheng, Geoff Gordon, Asrar Ahmed
Decision Support For Agent Populations In Uncertain And Congested Environments, Pradeep Reddy Varakantham, Shih-Fen Cheng, Geoff Gordon, Asrar Ahmed
Research Collection School Of Computing and Information Systems
This research is motivated by large scale problems in urban transportation and labor mobility where there is congestion for resources and uncertainty in movement. In such domains, even though the individual agents do not have an identity of their own and do not explicitly interact with other agents, they effect other agents. While there has been much research in handling such implicit effects, it has primarily assumed deterministic movements of agents. We address the issue of decision support for individual agents that are identical and have involuntary movements in dynamic environments. For instance, in a taxi fleet serving a city, …
Joint Learning For Coreference Resolution With Markov Logic, Yang Song, Jing Jiang, Xin Zhao, Sujian Li, Houfeng Wang
Joint Learning For Coreference Resolution With Markov Logic, Yang Song, Jing Jiang, Xin Zhao, Sujian Li, Houfeng Wang
Research Collection School Of Computing and Information Systems
Pairwise coreference resolution models must merge pairwise coreference decisions to generate final outputs. Traditional merging methods adopt different strategies such as the best first method and enforcing the transitivity constraint, but most of these methods are used independently of the pairwise learning methods as an isolated inference procedure at the end. We propose a joint learning model which combines pairwise classification and mention clustering with Markov logic. Experimental results show that our joint learning system outperforms independent learning systems. Our system gives a better performance than all the learning-based systems from the CoNLL-2011 shared task on the same dataset. Compared …
Identifying Event-Related Bursts Via Social Media Activities, Xin Zhao, Baihan Shu, Jing Jiang, Yang Song, Hongfei Yan, Xiaoming Li
Identifying Event-Related Bursts Via Social Media Activities, Xin Zhao, Baihan Shu, Jing Jiang, Yang Song, Hongfei Yan, Xiaoming Li
Research Collection School Of Computing and Information Systems
Activities on social media increase at a dramatic rate. When an external event happens, there is a surge in the degree of activities related to the event. These activities may be temporally correlated with one another, but they may also capture different aspects of an event and therefore exhibit different bursty patterns. In this paper, we propose to identify event-related bursts via social media activities. We study how to correlate multiple types of activities to derive a global bursty pattern. To model smoothness of one state sequence, we propose a novel function which can capture the state context. The experiments …
A Pollution Attack To Public-Key Watermarking Schemes, Yongdong Wu, Robert H. Deng
A Pollution Attack To Public-Key Watermarking Schemes, Yongdong Wu, Robert H. Deng
Research Collection School Of Computing and Information Systems
Public-key watermarking schemes are required to possess two desirable properties: allowing everyone to determine whether a watermark exists in an image or not and ensuring high detection probability in case of malicious modification. In this paper we propose an attack which pollutes the watermark embedded in an image with an optimal colored noise so as to fool the detector of the underlying public-key watermarking scheme. We further show how to apply the proposed pollution attack to public-key subspace watermarking schemes to generate pirated images of high quality but of low detection probability. Our experiment results demonstrate that the proposed pollution …
An Economic Analysis Of The Online Counterfeit Market And The Impact Of Anti-Counterfeit Technology, Xiong Zhang, Zhiling Guo, Wei Thoo Yue
An Economic Analysis Of The Online Counterfeit Market And The Impact Of Anti-Counterfeit Technology, Xiong Zhang, Zhiling Guo, Wei Thoo Yue
Research Collection School Of Computing and Information Systems
Counterfeiting causes hundreds of billions dollars of losses around the world every year. Due to the growing prominence of online commerce, the seriousness of the situation could soon become much worse. Hence, reaching a clear understanding of the fundamental economic incentives behind this practice is of vital importance. In this paper, we investigate a problem within which a firm selling a counterfeit product engages in price competition with a firm that sells an authentic product to a population of heterogeneous consumers. An online intermediary acts as the facilitator of both firms’ transactions and may consequently be liable for any counterfeit …
Lagrangian Relaxation Techniques For Scalable Spatial Conservation Planning, Akshat Kumar, Xiaojian Wu, Shlomo Zilberstein
Lagrangian Relaxation Techniques For Scalable Spatial Conservation Planning, Akshat Kumar, Xiaojian Wu, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
We address the problem of spatial conservation planning in which the goal is to maximize the expected spread of cascades of an endangered species by strategically purchasing land parcels within a given budget. This problem can be solved by standard integer programming methods using the sample average approximation (SAA) scheme. Our main contribution lies in exploiting the separable structure present in this problem and using Lagrangian relaxation techniques to gain scalability over the flat representation. We also generalize the approach to allow the application of the SAA scheme to a range of stochastic optimization problems. Our iterative approach is highly …
On-Line Portfolio Selection With Moving Average Reversion, Bin Li, Steven C. H. Hoi
On-Line Portfolio Selection With Moving Average Reversion, Bin Li, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied in some real datasets, leading to poor performance when the assumption does not hold. To overcome the limitation, this article proposes a multiple-period mean reversion, or so-called Moving Average Reversion (MAR), and a …
Exact Soft Confidence-Weighted Learning, Jialei Wang, Steven C. H. Hoi
Exact Soft Confidence-Weighted Learning, Jialei Wang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
In this paper, we propose a new Soft Confidence-Weighted (SCW) online learning scheme, which enables the conventional confidence-weighted learning method to handle non-separable cases. Unlike the previous confidence-weighted learning algorithms, the proposed soft confidence-weighted learning method enjoys all the four salient properties: (i) large margin training, (ii) confidence weighting, (iii) capability to handle non-separable data, and (iv) adaptive margin. Our experimental results show that the proposed SCW algorithms significantly outperform the original CW algorithm. When comparing with a variety of state-of-the art algorithms (including AROW, NAROW and NHERD), we found that SCW generally achieves better or at least comparable predictive …
Fast Bounded Online Gradient Descent Algorithms For Scalable Kernel-Based Online Learning, Peilin Zhao, Jialei Wang, Pengcheng Wu, Rong Jin, Steven C. H. Hoi
Fast Bounded Online Gradient Descent Algorithms For Scalable Kernel-Based Online Learning, Peilin Zhao, Jialei Wang, Pengcheng Wu, Rong Jin, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded number of support vectors, making it non-scalable and unsuitable for applications with large-scale datasets. In this work, we study the problem of bounded kernel-based online learning that aims to constrain the number of support vectors by a predefined budget. Although several algorithms have been proposed in literature, they are neither computationally efficient due to their intensive budget maintenance strategy nor effective due to the use of simple Perceptron algorithm. To overcome these limitations, we propose a …
Online Kernel Selection: Algorithms And Evaluations, Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, Steven C. H. Hoi
Online Kernel Selection: Algorithms And Evaluations, Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Kernel methods have been successfully applied to many machine learning problems. Nevertheless, since the performance of kernel methods depends heavily on the type of kernels being used, identifying good kernels among a set of given kernels is important to the success of kernel methods. A straightforward approach to address this problem is cross-validation by training a separate classifier for each kernel and choosing the best kernel classifier out of them. Another approach is Multiple Kernel Learning (MKL), which aims to learn a single kernel classifier from an optimal combination of multiple kernels. However, both approaches suffer from a high computational …
Innovation Rules: A Method For Identifying Disruptive Innovation Opportunities?, Arcot Desai Narasimhalu
Innovation Rules: A Method For Identifying Disruptive Innovation Opportunities?, Arcot Desai Narasimhalu
Research Collection School Of Computing and Information Systems
Innovation Rules or innovation evolution paths are described in this paper as a mechanism for identifying disruptive innovation opportunities. A generic structure of an Innovation Rule is first introduced followed by a discussion using specific instance of an Innovation Rule. The application of Innovation Rules for the discovery of disruptive innovation opportunities is presented next. Two methods that use Innovation Rules as the basis for identifying disruptive innovation opportunities are described next, one for market pull and the other one for technology push. A framework for dealing with Innovation Rules that represent both big and small disruptions is discussed next. …
Mining Input Sanitization Patterns For Predicting Sql Injection And Cross Site Scripting Vulnerabilities, Lwin Khin Shar, Hee Beng Kuan Tan
Mining Input Sanitization Patterns For Predicting Sql Injection And Cross Site Scripting Vulnerabilities, Lwin Khin Shar, Hee Beng Kuan Tan
Research Collection School Of Computing and Information Systems
Static code attributes such as lines of code and cyclomatic complexity have been shown to be useful indicators of defects in software modules. As web applications adopt input sanitization routines to prevent web security risks, static code attributes that represent the characteristics of these routines may be useful for predicting web application vulnerabilities. In this paper, we classify various input sanitization methods into different types and propose a set of static code attributes that represent these types. Then we use data mining methods to predict SQL injection and cross site scripting vulnerabilities in web applications. Preliminary experiments show that our …
Analyzing Multi-Agent Systems With Probabilistic Model Checking Approach, Songzheng Song, Jianye Hao, Yang Liu, Jun Sun, Ho-Fung Leung, Jin Song Dong
Analyzing Multi-Agent Systems With Probabilistic Model Checking Approach, Songzheng Song, Jianye Hao, Yang Liu, Jun Sun, Ho-Fung Leung, Jin Song Dong
Research Collection School Of Computing and Information Systems
Multi-agent systems, which are composed of autonomous agents, have been successfully employed as a modeling paradigm in many scenarios. However, it is challenging to guarantee the correctness of their behaviors due to the complex nature of the autonomous agents, especially when they have stochastic characteristics. In this work, we propose to apply probabilistic model checking to analyze multi-agent systems. A modeling language called PMA is defined to specify such kind of systems, and LTL property and logic of knowledge combined with probabilistic requirements are supported to analyze system behaviors. Initial evaluation indicates the effectiveness of our current progress; meanwhile some …
Lagrangian Relaxation For Large-Scale Multi-Agent Planning, Geoff Gordon, Pradeep Reddy Varakantham, William Yeoh, Ajay Srinivasan, Hoong Chuin Lau, Shih-Fen Cheng
Lagrangian Relaxation For Large-Scale Multi-Agent Planning, Geoff Gordon, Pradeep Reddy Varakantham, William Yeoh, Ajay Srinivasan, Hoong Chuin Lau, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
Multi-agent planning is a well-studied problem with applications in various areas. Due to computational constraints, existing research typically focuses either on unstructured domains with many agents, where we are content with heuristic solutions, or domains with small numbers of agents or special structure, where we can find provably near-optimal solutions. In contrast, here we focus on provably near-optimal solutions in domains with many agents, by exploiting influence limits. To that end, we make two key contributions: (a) an algorithm, based on Lagrangian relaxation and randomized rounding, for solving multi-agent planning problems represented as large mixed-integer programs; (b) a proof of …
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 …
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.
Aligning Images In The Wild, Wen-Yan Lin, Linlin Liu, Yasuyuki Matsushita, Kok-Lim Low, Siying Liu
Aligning Images In The Wild, Wen-Yan Lin, Linlin Liu, Yasuyuki Matsushita, Kok-Lim Low, Siying Liu
Research Collection School Of Computing and Information Systems
Aligning image pairs with significant appearance change is a long standing computer vision challenge. Much of this problem stems from the local patch descriptors’ instability to appearance variation. In this paper we suggest this instability is due less to descriptor corruption and more the difficulty in utilizing local information to canonically define the orientation (scale and rotation) at which a patch’s descriptor should be computed. We address this issue by jointly estimating correspondence and relative patch orientation, within a hierarchical algorithm that utilizes a smoothly varying parameterization of geometric transformations. By collectively estimating the correspondence and orientation of all the …
Workitemexplorer: Visualizing Software Development Tasks Using An Interactive Exploration Environment, Christoph Treude, Patrick Gorman, Lars Grammel, Margaret-Anne Storey
Workitemexplorer: Visualizing Software Development Tasks Using An Interactive Exploration Environment, Christoph Treude, Patrick Gorman, Lars Grammel, Margaret-Anne Storey
Research Collection School Of Computing and Information Systems
This demo introduces WorkItemExplorer, an interactive environment to visually explore data from software development tasks. WorkItemExplorer enables developers and managers to investigate activity and correlations in their task management system by making data exploration flexible and interactive, and by utilizing multiple coordinated views. Our preliminary evaluation shows that WorkItemExplorer is able to answer questions that developers ask, while also enabling them to gain new insights through the free exploration of data.
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 Subagdja, Wenwen Wang, Ah-Hwee Tan, Yuan-Sin Tan, Loo-Nin Teow
Memory Formation, Consolidation, And Forgetting In Learning Agents, Budhitama Subagdja, 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 only …
A Biologically-Inspired Affective Model Based On Cognitive Situational Appraisal, Feng Shu, Ah-Hwee Tan
A Biologically-Inspired Affective Model Based On Cognitive Situational Appraisal, Feng Shu, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Although various emotion models have been proposed based on appraisal theories, most of them focus on designing specific appraisal rules and there is no unified framework for emotional appraisal. Moreover, few existing emotion models are biologically-inspired and are inadequate in imitating emotion process of human brain. This paper proposes a bio-inspired computational model called Cognitive Regulated Affective Architecture (CRAA), inspired by the cognitive regulated emotion theory and the network theory of emotion. This architecture is proposed by taking the following positions: (1) Cognition and emotion are not separated but interacted systems; (2) The appraisal of emotion depends on and should …
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 …
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 …