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Articles 1 - 12 of 12
Full-Text Articles in Physical Sciences and Mathematics
Efficient Unsupervised Video Hashing With Contextual Modeling And Structural Controlling, Jingru Duan, Yanbin Hao, Bin Zhu, Lechao Cheng, Pengyuan Zhou, Xiang Wang
Efficient Unsupervised Video Hashing With Contextual Modeling And Structural Controlling, Jingru Duan, Yanbin Hao, Bin Zhu, Lechao Cheng, Pengyuan Zhou, Xiang Wang
Research Collection School Of Computing and Information Systems
The most important effect of the video hashing technique is to support fast retrieval, which is benefiting from the high efficiency of binary calculation. Current video hash approaches are thus mainly targeted at learning compact binary codes to represent video content accurately. However, they may overlook the generation efficiency for hash codes, i.e., designing lightweight neural networks. This paper proposes an method, which is not only for computing compact hash codes but also for designing a lightweight deep model. Specifically, we present an MLP-based model, where the video tensor is split into several groups and multiple axial contexts are explored …
Exploiting Reuse For Gpu Subgraph Enumeration, Wentiao Guo, Yuchen Li, Kian-Lee Tan
Exploiting Reuse For Gpu Subgraph Enumeration, Wentiao Guo, Yuchen Li, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
Subgraph enumeration is important for many applications such as network motif discovery, community detection, and frequent subgraph mining. To accelerate the execution, recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration. The performances of these parallel schemes are dominated by the set intersection operations which account for up to $95\%$ of the total processing time. (Un)surprisingly, a significant portion (as high as $99\%$) of these operations is actually redundant, i.e., the same set of vertices is repeatedly encountered and evaluated. Therefore, in this paper, we seek to salvage and recycle the results of such operations to avoid repeated …
Topic-Guided Conversational Recommender In Multiple Domains, Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, Tat-Seng Chua
Topic-Guided Conversational Recommender In Multiple Domains, Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Conversational systems have recently attracted significant attention. Both the research community and industry believe that it will exert huge impact on human-computer interaction, and specifically, the IR/RecSys community has begun to explore Conversational Recommendation. In real-life scenarios, such systems are often urgently needed in helping users accomplishing different tasks under various situations. However, existing works still face several shortcomings: (1) Most efforts are largely confined in single task setting. They fall short of hands in handling tasks across domains. (2) Aside from soliciting user preference from dialogue history, a conversational recommender naturally has access to the back-end data structure which …
Joint Search By Social And Spatial Proximity [Extended Abstract], Kyriakos Mouratidis, Jing Li, Yu Tang, Nikos Mamoulis
Joint Search By Social And Spatial Proximity [Extended Abstract], Kyriakos Mouratidis, Jing Li, Yu Tang, Nikos Mamoulis
Research Collection School Of Computing and Information Systems
The diffusion of social networks introduces new challengesand opportunities for advanced services, especially so with their ongoingaddition of location-based features. We show how applications like company andfriend recommendation could significantly benefit from incorporating social andspatial proximity, and study a query type that captures these twofold semantics.We develop highly scalable algorithms for its processing, and use real socialnetwork data to empirically verify their efficiency and efficacy.
Joint Search By Social And Spatial Proximity, Kyriakos Mouratidis, Jing Li, Yu Tang, Nikos Mamoulis
Joint Search By Social And Spatial Proximity, Kyriakos Mouratidis, Jing Li, Yu Tang, Nikos Mamoulis
Research Collection School Of Computing and Information Systems
The diffusion of social networks introduces new challenges and opportunities for advanced services, especially so with their ongoing addition of location-based features. We show how applications like company and friend recommendation could significantly benefit from incorporating social and spatial proximity, and study a query type that captures these two-fold semantics. We develop highly scalable algorithms for its processing, and enhance them with elaborate optimizations. Finally, we use real social network data to empirically verify the efficiency and efficacy of our solutions.
On-Line Discovery Of Hot Motion Paths, Dimitris Sacharidis, Kostas Patroumpas, Manolis Terrovitis, Verena Kantere, Michalis Potamias, Kyriakos Mouratidis, Timos Sellis
On-Line Discovery Of Hot Motion Paths, Dimitris Sacharidis, Kostas Patroumpas, Manolis Terrovitis, Verena Kantere, Michalis Potamias, Kyriakos Mouratidis, Timos Sellis
Research Collection School Of Computing and Information Systems
We consider an environment of numerous moving objects, equipped with location-sensing devices and capable of communicating with a central coordinator. In this setting, we investigate the problem of maintaining hot motion paths, i.e., routes frequently followed by multiple objects over the recent past. Motion paths approximate portions of objects' movement within a tolerance margin that depends on the uncertainty inherent in positional measurements. Discovery of hot motion paths is important to applications requiring classification/profiling based on monitored movement patterns, such as targeted advertising, resource allocation, etc. To achieve this goal, we delegate part of the path extraction process to objects, …
Gprune: A Constraint Pushing Framework For Graph Pattern Mining, Feida Zhu, Xifeng Yan, Jiawei Han, Philip S. Yu
Gprune: A Constraint Pushing Framework For Graph Pattern Mining, Feida Zhu, Xifeng Yan, Jiawei Han, Philip S. Yu
Research Collection School Of Computing and Information Systems
In graph mining applications, there has been an increasingly strong urge for imposing user-specified constraints on the mining results. However, unlike most traditional itemset constraints, structural constraints, such as density and diameter of a graph, are very hard to be pushed deep into the mining process. In this paper, we give the first comprehensive study on the pruning properties of both traditional and structural constraints aiming to reduce not only the pattern search space but the data search space as well. A new general framework, called gPrune, is proposed to incorporate all the constraints in such a way that they …
Ontosearch: A Full-Text Search Engine For The Semantic Web, Xing Jiang, Ah-Hwee Tan
Ontosearch: A Full-Text Search Engine For The Semantic Web, Xing Jiang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
OntoSearch, a full-text search engine that exploits ontological knowledge for document retrieval, is presented in this paper. Different from other ontology based search engines, OntoSearch does not require a user to specify the associated concepts of his/her queries. Domain ontology in OntoSearch is in the form of a semantic network. Given a keyword based query, OntoSearch infers the related concepts through a spreading activation process in the domain ontology. To provide personalized information access, we further develop algorithms to learn and exploit user ontology model based on a customized view of the domain ontology. The proposed system has been applied …
Dsim: A Distance-Based Indexing Method For Genomic Sequences, Xia Cao, Beng-Chin Ooi, Hwee Hwa Pang, Kian-Lee Tan, Anthony K. H. Tung
Dsim: A Distance-Based Indexing Method For Genomic Sequences, Xia Cao, Beng-Chin Ooi, Hwee Hwa Pang, Kian-Lee Tan, Anthony K. H. Tung
Research Collection School Of Computing and Information Systems
In this paper, we propose a Distance-based Sequence Indexing Method (DSIM) for indexing and searching genome databases. Borrowing the idea of video compression, we compress the genomic sequence database around a set of automatically selected reference words, formed from high-frequency data substrings and substrings in past queries. The compression captures the distance of each non-reference word in the database to some reference word. At runtime, a query is processed by comparing its substrings with the compressed data strings, through their distances to the reference words. We also propose an efficient scheme to incrementally update the reference words and the compressed …
A Support-Ordered Trie For Fast Frequent Itemset Discovery, Ee Peng Lim, Yew-Kwong Woon, Wee-Keong Ng
A Support-Ordered Trie For Fast Frequent Itemset Discovery, Ee Peng Lim, Yew-Kwong Woon, Wee-Keong Ng
Research Collection School Of Computing and Information Systems
The importance of data mining is apparent with the advent of powerful data collection and storage tools; raw data is so abundant that manual analysis is no longer possible. Unfortunately, data mining problems are difficult to solve and this prompted the introduction of several novel data structures to improve mining efficiency. Here, we critically examine existing preprocessing data structures used in association rule mining for enhancing performance in an attempt to understand their strengths and weaknesses. Our analyses culminate in a practical structure called the SOTrielT (support-ordered trie itemset) and two synergistic algorithms to accompany it for the fast discovery …
Multiple Representation For Understanding Data Structures, Biffah Hancies, Venky Shankararaman, Jose Munoz
Multiple Representation For Understanding Data Structures, Biffah Hancies, Venky Shankararaman, Jose Munoz
Research Collection School Of Computing and Information Systems
In this paper an approach to enhance the learning of abstract computing concepts by novice students is presented. This approach is based on effective use of multiple visual representations, and it was applied within the domain of linear data structures: array, stack, queue and linked list. A prototype computer-based instructional system called MRUDS (Multiple Representation for Understanding Data Structures) was developed and evaluated. It was found from the evaluation that the three presentation modules namely, analogy, representation and algorithm contributed to the students' learning process, each contributing to and reinforcing the effect of the others.
Export Database Derivation Approach For Supporting Object-Oriented Wrapper Queries, Ee Peng Lim, Hon-Kuan Lee
Export Database Derivation Approach For Supporting Object-Oriented Wrapper Queries, Ee Peng Lim, Hon-Kuan Lee
Research Collection School Of Computing and Information Systems
Wrappers export the schema and data of existing heterogeneous databases and support queries on them. In the context of cooperative information systems, we present a flexible approach to specify the derivation of object-oriented (OO) export databases from local relational databases. Our export database derivation consists of a set of extent derivation structures (EDS) which defines the extent and deep extent of export classes. Having well-defined semantics, the EDS can be readily used in transforming wrapper queries to local queries. Based on the EDS, we developed a wrapper query evaluation strategy which handles OO queries on the export databases. The strategy …