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Articles 4741 - 4770 of 8495
Full-Text Articles in Computer Sciences
Comparative Relation Generative Model, Maksim Tkachenko, Hady W. Lauw
Comparative Relation Generative Model, Maksim Tkachenko, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Online reviews are important decision aids to consumers. Other than helping users to evaluate individual products, reviews also support comparison shopping by comparing two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors, is not always equitable due to the different standards and preferences of authors. Therefore, we focus on comparative sentences, whereby two products are compared directly by a review author within a sentence. We study the problem of comparative relation mining. Given a set of comparative sentences, each relating a pair of entities, our objective is three-fold: …
Aspect Extraction From Product Reviews Using Category Hierarchy Information, Yifeng Yang, Chen Cen, Minghui Qiu, Forrest Sheng Bao
Aspect Extraction From Product Reviews Using Category Hierarchy Information, Yifeng Yang, Chen Cen, Minghui Qiu, Forrest Sheng Bao
Research Collection School Of Computing and Information Systems
Aspect extraction is a task to abstract the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those from parent categories. Hence, we propose an LDA-based generative topic model inducing the two-layer categorical information (CAT-LDA), to balance the aspects of both a parent category and its child categories. Our hypothesis is that child categories inherit aspects from parent categories, controlled by the hierarchy between them. Experimental results on 5 …
What You See Is Not What You Get: Leakage-Resilient Password Entry Schemes For Smart Glasses, Yan Li, Yao Cheng, Yingjiu Li, Robert H. Deng
What You See Is Not What You Get: Leakage-Resilient Password Entry Schemes For Smart Glasses, Yan Li, Yao Cheng, Yingjiu Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
Smart glasses are becoming popular for users to access various services such as email. To protect these services, password-based user authentication is widely used. Unfortunately, the password based user authentication has inherent vulnerability against password leakage. Many efforts have been put on designing leakage resilient password entry schemes on PCs and mobile phones with traditional input equipment including keyboards and touch screens. However, such traditional input equipment is not available on smart glasses. Existing password entry on smart glasses relies on additional PCs or mobile devices. Such solutions force users to switch between different systems, which causes interrupted experience and …
The Crowd Work Accessibility Problem, Saiganesh Swaminathan, Kotaro Hara, Jeffrey P. Bigham
The Crowd Work Accessibility Problem, Saiganesh Swaminathan, Kotaro Hara, Jeffrey P. Bigham
Research Collection School Of Computing and Information Systems
Crowd work is an increasingly prevalent and important kind of work. Because of its flexible nature, crowd work may offer benefits for people with disabilities. Unfortunately, people with disabilities currently lack access to much of this work because the tasks that are posted are often inaccessible. In this paper, we first characterize the accessibility of the tasks posted to a popular crowd marketplace, Amazon Mechanical Turk, by performing manual and automatic checks on 120 tasks from several common types. We then outline research directions that could have positive impact on this problem. Given ongoing and upcoming changes to the world …
A Secure And Efficient Id-Based Aggregate Signature Scheme For Wireless Sensor Networks, Limin Shen, Jianfeng Ma, Ximeng Liu, Fushan Wei, Meixia Miao
A Secure And Efficient Id-Based Aggregate Signature Scheme For Wireless Sensor Networks, Limin Shen, Jianfeng Ma, Ximeng Liu, Fushan Wei, Meixia Miao
Research Collection School Of Computing and Information Systems
Affording secure and efficient big data aggregation methods is very attractive in the field of wireless sensor networks (WSNs) research. In real settings, the WSNs have been broadly applied, such as target tracking and environment remote monitoring. However, data can be easily compromised by a vast of attacks, such as data interception and data tampering, etc. In this paper, we mainly focus on data integrity protection, give an identity-based aggregate signature (IBAS) scheme with a designated verifier for WSNs. According to the advantage of aggregate signatures, our scheme not only can keep data integrity, but also can reduce bandwidth and …
Online Growing Neural Gas For Anomaly Detection In Changing Surveillance Scenes, Qianru Sun, Hong Liu, Tatsuya Harada
Online Growing Neural Gas For Anomaly Detection In Changing Surveillance Scenes, Qianru Sun, Hong Liu, Tatsuya Harada
Research Collection School Of Computing and Information Systems
Anomaly detection is still a challenging task for video surveillance due to complex environments and unpredictable human behaviors. Most existing approaches train offline detectors using manually labeled data and predefined parameters, and are hard to model changing scenes. This paper introduces a neural network based model called online Growing Neural Gas (online GNG) to perform an unsupervised learning. Unlike a parameter-fixed GNG, our model updates learning parameters continuously, for which we propose several online neighbor-related strategies. Specific operations, namely neuron insertion, deletion, learning rate adaptation and stopping criteria selection, get upgraded to online modes. In the anomaly detection stage, the …
A Dynamic Programming Approach For Quickly Estimating Large Network-Based Mev Models, Tien Mai, Emma Frejinger, Mogens Fosgereau, Fabian Bastin
A Dynamic Programming Approach For Quickly Estimating Large Network-Based Mev Models, Tien Mai, Emma Frejinger, Mogens Fosgereau, Fabian Bastin
Research Collection School Of Computing and Information Systems
We propose a way to estimate a family of static Multivariate Extreme Value (MEV) models with large choice sets in short computational time. The resulting model is also straightforward and fast to use for prediction. Following Daly and Bierlaire (2006), the correlation structure is defined by a rooted, directed graph where each node without successor is an alternative. We formulate a family of MEV models as dynamic discrete choice models on graphs of correlation structures and show that the dynamic models are consistent with MEV theory and generalize the network MEV model (Daly and Bierlaire, 2006). Moreover, we show that …
Should We Learn Probabilistic Models For Model Checking? A New Approach And An Empirical Study, Jingyi Wang, Jun Sun, Qixia Yuan, Jun Pang
Should We Learn Probabilistic Models For Model Checking? A New Approach And An Empirical Study, Jingyi Wang, Jun Sun, Qixia Yuan, Jun Pang
Research Collection School Of Computing and Information Systems
Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done manually, which is often considered as a hindrance to adopt model-based system analysis and development techniques. To overcome this problem, researchers have proposed to automatically “learn” models based on sample system executions and shown that the learned models can be useful sometimes. There are however many questions to be answered. For instance, how much shall we generalize from the observed samples and how fast would learning converge? Or, would the analysis result based on …
Clustering Classes In Packages For Program Comprehension, Xiaobing Sun, Xiangyue Liu, Bin Li, Bixin Li, David Lo, Lingzhi Liao
Clustering Classes In Packages For Program Comprehension, Xiaobing Sun, Xiangyue Liu, Bin Li, Bixin Li, David Lo, Lingzhi Liao
Research Collection School Of Computing and Information Systems
During software maintenance and evolution, one of the important tasks faced by developers is to understand a system quickly and accurately. With the increasing size and complexity of an evolving system, program comprehension becomes an increasingly difficult activity. Given a target system for comprehension, developers may first focus on the package comprehension. The packages in the system are of different sizes. For small-sized packages in the system, developers can easily comprehend them. However, for large-sized packages, they are difficult to understand. In this article, we focus on understanding these large-sized packages and propose a novel program comprehension approach for large-sized …
Discovering Anomalous Events From Urban Informatics Data, Kasthuri Jayarajah, Vigneshwaran Subbaraju, Dulanga Kaveesha Weerakoon Mudiyanselage, Archan Misra, La Thanh Tam, Noel Athaide
Discovering Anomalous Events From Urban Informatics Data, Kasthuri Jayarajah, Vigneshwaran Subbaraju, Dulanga Kaveesha Weerakoon Mudiyanselage, Archan Misra, La Thanh Tam, Noel Athaide
Research Collection School Of Computing and Information Systems
Singapore's "smart city" agenda is driving the government to provide public access to a broader variety of urban informatics sources, such as images from traffic cameras and information about buses servicing different bus stops. Such informatics data serves as probes of evolving conditions at different spatiotemporal scales. This paper explores how such multi-modal informatics data can be used to establish the normal operating conditions at different city locations, and then apply appropriate outlier-based analysis techniques to identify anomalous events at these selected locations. We will introduce the overall architecture of sociophysical analytics, where such infrastructural data sources can be combined …
Now You See It, Now You Don't! A Study Of Content Modification Behavior In Facebook, Fuxiang Chen, Ee-Peng Lim
Now You See It, Now You Don't! A Study Of Content Modification Behavior In Facebook, Fuxiang Chen, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Social media, as a major platform to disseminate information, has changed the way users and communities contribute content. In this paper, we aim to study content modifications on public Facebook pages operated by news media, community groups, and bloggers. We also study the possible reasons behind them, and their effects on user interaction. We conducted a detailed study of Content Censorship (CC) and Content Edit (CE) in Facebook using a detailed longitudinal dataset consisting of 57 public Facebook pages over 3 weeks covering 145,955 posts and 9,379,200 comments. We detected many CC and CE activities between 28% and 56% of …
Modeling Topics And Behavior Of Microbloggers: An Integrated Approach, Tuan Anh Hoang, Ee-Peng Lim
Modeling Topics And Behavior Of Microbloggers: An Integrated Approach, Tuan Anh Hoang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Microblogging encompasses both user-generated content and behavior. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behavior. In this article, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users' topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and the user's dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content …
Empirical Study Of Usage And Performance Of Java Collections, Diego Costa, Artur Andrzejak, Janos Seboek, David Lo
Empirical Study Of Usage And Performance Of Java Collections, Diego Costa, Artur Andrzejak, Janos Seboek, David Lo
Research Collection School Of Computing and Information Systems
Collection data structures have a major impact on the performance of applications, especially in languages such as Java, C#, or C++. This requires a developer to select an appropriate collection from a large set of possibilities, including different abstractions (e.g. list, map, set, queue), and multiple implementations. In Java, the default implementation of collections is provided by the standard Java Collection Framework (JCF). However, there exist a large variety of less known third-party collection libraries which can provide substantial performance benefits with minimal code changes.
Understanding The Information-Based Transformation Of Strategy And Society, Eric K. Clemons, Rajiv M. Dewan, Robert J. Kauffman, Thomas A. Weber
Understanding The Information-Based Transformation Of Strategy And Society, Eric K. Clemons, Rajiv M. Dewan, Robert J. Kauffman, Thomas A. Weber
Research Collection School Of Computing and Information Systems
The world economy is undergoing dramatic changes, largely driven by the new availability of fine-grained information. Innovative ways of using data—large and small—have also prompted a rethinking of the boundaries for the combination and use of knowledge. The strategic design of information flows in the economy has the upside of higher economic rents and competitive advantage, as well as the downsides of wealth inequality and abuse of power. This has brought a wide range of regulatory challenges. To understand the nature of these sweeping changes, it is important to examine the new ways information is used, and how information flows …
Assessing The Language Of Chat For Teamwork Dialogue, Antonette Shibani, Elizabeth Koh, Vivian Lai, Kyong Jin Shim
Assessing The Language Of Chat For Teamwork Dialogue, Antonette Shibani, Elizabeth Koh, Vivian Lai, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
In technology enhanced language learning, many pedagogical activities involve students in online discussion such as synchronous chat, in order to help them practice their language skills. Besides developing the language competency of students, it is also crucial to nurture their teamwork competencies for today's global and complex environment. Language communication is an important glue of teamwork. In order to assess the language of chat for teamwork dimensions, several text mining methods are pos sible. However, difficulties arise such as pre-processing being a black box and classification approaches and algorithms being dependent on the context. To address these issues, the study …
Vulnerabilities, Attacks, And Countermeasures In Balise-Based Train Control Systems, Yongdong Wu, Jian Weng, Zhe Tang, Xin Li, Robert H. Deng
Vulnerabilities, Attacks, And Countermeasures In Balise-Based Train Control Systems, Yongdong Wu, Jian Weng, Zhe Tang, Xin Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
In modern rail transport systems, balises are widely used to exchange track-train information via air-gap interface. In this paper, we first present the vulnerabilities on the standard balise air-gap interface, and then conduct vulnerability simulations using the system parameters that were specified in the European Train Control System. The simulation results show that the vulnerabilities can be exploited to launch effective and practical attacks, which could lead to catastrophic consequences, such as train derailment or collision. To mitigate the vulnerabilities and attacks, we propose to implement a challenge-response authentication process in the air-gap interface in the existing transport infrastructure.
Achievement And Friends: Key Factors Of Player Retention Vary Across Player Levels In Online Multiplayer Games, Korea Advanced Institute Of Science & Technology, Qatar Computing Research Institute, Haewoon Kwak
Achievement And Friends: Key Factors Of Player Retention Vary Across Player Levels In Online Multiplayer Games, Korea Advanced Institute Of Science & Technology, Qatar Computing Research Institute, Haewoon Kwak
Research Collection School Of Computing and Information Systems
Retaining players over an extended period of time is a long-standing challenge in game industry. Significant effort has been paid to understanding what motivates players enjoy games. While individuals may have varying reasons to play or abandon a game at different stages within the game, previous studies have looked at the retention problem from a snapshot view. This study, by analyzing in-game logs of 51,104 distinct individuals in an online multiplayer game, uniquely offers a multifaceted view of the retention problem over the players' virtual life phases. We find that key indicators of longevity change with the game level. Achievement …
Neural Collaborative Filtering, Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
Neural Collaborative Filtering, Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback.Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in …
Inferring User Consumption Preferences From Social Media, Yang Li, Jing Jiang, Ting Liu
Inferring User Consumption Preferences From Social Media, Yang Li, Jing Jiang, Ting Liu
Research Collection School Of Computing and Information Systems
Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly …
Mining Sandboxes For Linux Containers, Zhiyuan Wan, David Lo, Xin Xia, Liang Cai, Shanping Li
Mining Sandboxes For Linux Containers, Zhiyuan Wan, David Lo, Xin Xia, Liang Cai, Shanping Li
Research Collection School Of Computing and Information Systems
A container is a group of processes isolated from other groups via distinct kernel namespaces and resource allocation quota. Attacks against containers often leverage kernel exploits through system call interface. In this paper, we present an approach that mines sandboxes for containers. We first explore the behaviors of a container by leveraging automatic testing, and extract the set of system calls accessed during testing. The set of system calls then results as a sandbox of the container. The mined sandbox restricts the container's access to system calls which are not seen during testing and thus reduces the attack surface. In …
The Wonders Of The Spreadsheet Tool For Data Management And Insights, Michelle L. F. Cheong
The Wonders Of The Spreadsheet Tool For Data Management And Insights, Michelle L. F. Cheong
Research Collection School Of Computing and Information Systems
Ask any student at the Singapore Management University (SMU) toname one of the most practical and useful courses offered by theuniversity. The answer would inevitably include CAT. CAT stands forthe "Computer as an Analysis Tool" course. Originally based on acourse of the same title offered by the Wharton Business School, thefocus of CAT was shifted to provide business students the essentialpractical skills and necessary “real-world” exposure to better usepersonal computers for resolving business problems. The course isbasically centred on using the Excel spreadsheet to work onambiguous ill-defined problems (Leong & Cheong, 2009). Over theyears, three editions of a textbook have …
Efficient Motif Discovery In Spatial Trajectories Using Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Kai Wang
Efficient Motif Discovery In Spatial Trajectories Using Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Kai Wang
Research Collection School Of Computing and Information Systems
The discrete Fréchet distance (DFD) captures perceptual and geographical similarity between discrete trajectories. It has been successfully adopted in a multitude of applications, such as signature and handwriting recognition, computer graphics, as well as geographic applications. Spatial applications, e.g., sports analysis, traffic analysis, etc. require discovering the pair of most similar subtrajectories, be them parts of the same or of different input trajectories.The identified pair of subtrajectories is called a motif.The adoption of DFD as the similarity measure in motif discovery,although semantically ideal, is hindered by the high computational complexity of DFD calculation. In this paper, we propose a suite …
Social Tag Relevance Learning Via Ranking-Oriented Neighbor Voting, Chaoran Cui, Jialie Shen, Jun Ma, Tao Lian
Social Tag Relevance Learning Via Ranking-Oriented Neighbor Voting, Chaoran Cui, Jialie Shen, Jun Ma, Tao Lian
Research Collection School Of Computing and Information Systems
High quality tags play a critical role in applications involving online multimedia search, such as social image annotation, sharing and browsing. However, user-generated tags in real world are often imprecise and incomplete to describe the image contents, which severely degrades the performance of current search systems. To improve the descriptive powers of social tags, a fundamental issue is tag relevance learning, which concerns how to interpret the relevance of a tag with respect to the contents of an image effectively. In this paper, we investigate the problem from a new perspective of learning to rank, and develop a novel approach …
Dark Hazard: Large-Scale Discovery Of Unknown Hidden Sensitive Operations In Android Apps, Xiaorui Pan, Xueqiang Wang, Yue Duan, Xiaofeng Wang, Heng Yin
Dark Hazard: Large-Scale Discovery Of Unknown Hidden Sensitive Operations In Android Apps, Xiaorui Pan, Xueqiang Wang, Yue Duan, Xiaofeng Wang, Heng Yin
Research Collection School Of Computing and Information Systems
Hidden sensitive operations (HSO) such as stealing privacy user data upon receiving an SMS message are increasingly utilized by mobile malware and other potentially-harmful apps (PHAs) to evade detection. Identification of such behaviors is hard, due to the challenge in triggering them during an app’s runtime. Current static approaches rely on the trigger conditions or hidden behaviors known beforehand and therefore cannot capture previously unknown HSO activities. Also these techniques tend to be computationally intensive and therefore less suitable for analyzing a large number of apps. As a result, our understanding of real-world HSO today is still limited, not to …
Metric Similarity Joins Using Mapreduce, Yunjun Gao, Keyu Yang, Lu Chen, Baihua Zheng, Gang Chen, Chun Chen
Metric Similarity Joins Using Mapreduce, Yunjun Gao, Keyu Yang, Lu Chen, Baihua Zheng, Gang Chen, Chun Chen
Research Collection School Of Computing and Information Systems
Given two object sets Q and O , a metric similarity join finds similar object pairs according to a certain criterion. This operation has a wide variety of applications in data cleaning, data mining, to name but a few. However, the rapidly growing volume of data nowadays challenges traditional metric similarity join methods, and thus, a distributed method is required. In this paper, we adopt a popular distributed framework, namely, MapReduce, to support scalable metric similarity joins. To ensure the load balancing, we present two sampling based partition methods. One utilizes the pivot and the space-filling curve mappings to cluster …
Version-Sensitive Mobile App Recommendation, Da Cao, Liqiang Nie, Xiangnan He, Xiaochi Wei, Jialie Shen, Shunxiang Wu, Tat-Seng Chua
Version-Sensitive Mobile App Recommendation, Da Cao, Liqiang Nie, Xiangnan He, Xiaochi Wei, Jialie Shen, Shunxiang Wu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Being part and parcel of the daily life for billions of people all over the globe, the domain of mobile Applications (Apps) is the fastest growing sector of mobile market today. Users, however, are frequently overwhelmed by the vast number of released Apps and frequently updated versions. Towards this end, we propose a novel version-sensitive mobile App recommendation framework. It is able to recommend appropriate Apps to right users by jointly exploring the version progression and dual-heterogeneous data. It is helpful for alleviating the data sparsity problem caused by version division. As a byproduct, it can be utilized to solve …
Probabilistic Public Key Encryption For Controlled Equijoin In Relational Databases, Yujue Wang, Hwee Hwa Pang
Probabilistic Public Key Encryption For Controlled Equijoin In Relational Databases, Yujue Wang, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
We present a public key encryption scheme for relational databases (PKDE) that allows the owner to control the execution of cross-relation joins on an outsourced server. The scheme allows anyone to deposit encrypted records in a database on the server. Thereafter, the database owner may authorize the server to join any two relations to identify matching records across them, while preventing self-joins that would reveal information on records that are unmatched in the join. The security of our construction is formally proved in the random oracle model based on the computational bilinear Diffie-Hellman assumption. Specifically, before a relation is joined, …
Effective K-Vertex Connected Component Detection In Large-Scale Networks, Yuan Li, Yuha Zhao, Guoren Wang, Feida Zhu, Yubao Wu, Shenglei Shi
Effective K-Vertex Connected Component Detection In Large-Scale Networks, Yuan Li, Yuha Zhao, Guoren Wang, Feida Zhu, Yubao Wu, Shenglei Shi
Research Collection School Of Computing and Information Systems
Finding components with high connectivity is an important problem in component detection with a wide range of applications, e.g., social network analysis, web-page research and bioinformatics. In particular, k-edge connected component (k-ECC) has recently been extensively studied to discover disjoint components. Yet many real applications present needs and challenges for overlapping components. In this paper, we propose a k-vertex connected component (k-VCC) model, which is much more cohesive and therefore allows overlapping between components. To find k-VCCs, a top-down framework is first developed to find the exact k-VCCs. To further reduce the high computational cost for input networks of large …
Improving Automated Bug Triaging With Specialized Topic Model, Xin Xia, David Lo, Ying Ding, Jafar M. Al-Kofahi, Tien N. Nguyen, Xinyu Wang
Improving Automated Bug Triaging With Specialized Topic Model, Xin Xia, David Lo, Ying Ding, Jafar M. Al-Kofahi, Tien N. Nguyen, Xinyu Wang
Research Collection School Of Computing and Information Systems
Bug triaging refers to the process of assigning a bug to the most appropriate developer to fix. It becomes more and more difficult and complicated as the size of software and the number of developers increase. In this paper, we propose a new framework for bug triaging, which maps the words in the bug reports (i.e., the term space) to their corresponding topics (i.e., the topic space). We propose a specialized topic modeling algorithm named multi-feature topic model (MTM) which extends Latent Dirichlet Allocation (LDA) for bug triaging. MTM considers product and component information of bug reports to map the …
Feature Learning Via Partial Differential Equation With Applications To Face Recognition, Cong Fang, Zhenyu Zhao, Pan Zhou, Zhouchen Lin
Feature Learning Via Partial Differential Equation With Applications To Face Recognition, Cong Fang, Zhenyu Zhao, Pan Zhou, Zhouchen Lin
Research Collection School Of Computing and Information Systems
Feature learning is a critical step in pattern recognition, such as image classification. However, most of the existing methods cannot extract features that are discriminative and at the same time invariant under some transforms. This limits the classification performance, especially in the case of small training sets. To address this issue, in this paper we propose a novel Partial Differential Equation (PDE) based method for feature learning. The feature learned by our PDE is discriminative, also translationally and rotationally invariant, and robust to illumination variation. To our best knowledge, this is the first work that applies PDE to feature learning …