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Articles 1 - 13 of 13
Full-Text Articles in Theory and Algorithms
Snap-And-Ask: Answering Multimodal Question By Naming Visual Instance, Wei Zhang, Lei Pang, Chong-Wah Ngo
Snap-And-Ask: Answering Multimodal Question By Naming Visual Instance, Wei Zhang, Lei Pang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
In real-life, it is easier to provide a visual cue when asking a question about a possibly unfamiliar topic, for example, asking the question, “Where was this crop circle found?”. Providing an image of the instance is far more convenient than texting a verbose description of the visual properties, especially when the name of the query instance is not known. Nevertheless, having to identify the visual instance before processing the question and eventually returning the answer makes multimodal question-answering technically challenging. This paper addresses the problem of visual-totext naming through the paradigm of answering-by-search in a two-stage computational framework, which …
Microblog Search And Filtering With Time Sensitive Feedback And Thresholding Based On Bm25, Wei Gao, Zhongyu Wei, Kam-Fai Wong
Microblog Search And Filtering With Time Sensitive Feedback And Thresholding Based On Bm25, Wei Gao, Zhongyu Wei, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Microblogs such as Twitter are considered faster first-hand sources of information with many real-time fashions. We report our work in the real-time adhoc search and filtering tasks of TREC 2012 microblog track. Our system is built based on the traditional BM25 relevance model, in which specific techniques are tried out to respond to the ne.ed of frnding relevant tweets, ln thc real-time adhoc task, we applied a peak detection algorithm for the process of blind feedback, We also tried to automatically combine the search results of multiple retrieval techniques. In the real-time filtering pilot task, we examine the effectiveness of …
Verifying Total Correctness Of Graph Programs, Christopher M. Poskitt, Detlef Plump
Verifying Total Correctness Of Graph Programs, Christopher M. Poskitt, Detlef Plump
Research Collection School Of Computing and Information Systems
GP 2 is an experimental nondeterministic programming language based on graph transformation rules, allowing for visual programming and the solving of graph problems at a high-level of abstraction. In previous work we demonstrated how to verify graph programs using a Hoare-style proof calculus, but only partial correctness was considered. In this paper, we add new proof rules and termination functions, which allow for proofs to additionally guarantee that program executions always terminate (weak total correctness), or that programs always terminate and do so without failure (total correctness). We show that the new proof rules are sound with respect to the …
Verification Of Graph Programs, Christopher M. Poskitt
Verification Of Graph Programs, Christopher M. Poskitt
Research Collection School Of Computing and Information Systems
GP (for Graph Programs) is an experimental nondeterministic programming language which allows for the manipulation of graphs at a high level of abstraction. The program states of GP are directed labelled graphs. These are manipulated directly via the application of (conditional) rule schemata, which generalise double-pushout rules with expressions over labels and relabelling. In contrast with graph grammars, the application of these rule schemata is directed by a number of simple control constructs including sequential composition, conditionals, and as-long-as-possible iteration. GP shields programmers at all times from low-level implementation issues (e.g. graph representation), and with its nondeterministic semantics, allows one …
Measurement-Driven Performance Analysis Of Indoor Femtocellular Networks, Trung-Tuan Luong, Vigneshwaran Subbaraju, Archan Misra, Srinivasan Seshan
Measurement-Driven Performance Analysis Of Indoor Femtocellular Networks, Trung-Tuan Luong, Vigneshwaran Subbaraju, Archan Misra, Srinivasan Seshan
Research Collection School Of Computing and Information Systems
This paper describes initial empirical studies, performed on a 6-node 3G indoor femtocellular testbed, that investigate the impact of pedestrian mobility on network parameters, such as handoff behavior and data throughput. The studies establish that, owing to the small radii of cells, even modest changes in movement speed can have disproportionately large impact on handoff patterns and network throughput. By also revealing a strong temporal dependency effect, the studies motivate the need for algorithms to accurately predict RF signal strength distributions in dynamic indoor environments. We present such an RF prediction algorithm, based on crowd-sourced signal strength readings, and show …
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 …
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 …
Distributed Incomplete Pattern Matching Via A Novelweighted Bloom Filter, Siyuan Liu, Lei Kang, Lei Chen, Lionel Ni
Distributed Incomplete Pattern Matching Via A Novelweighted Bloom Filter, Siyuan Liu, Lei Kang, Lei Chen, Lionel Ni
Research Collection School Of Computing and Information Systems
In this paper, we first propose a very interesting and practical problem, pattern matching in a distributed mobile environment. Pattern matching is a well-known problem and extensive research has been conducted for performing effective and efficient search. However, previous proposed approaches assume that data are centrally stored, which is not the case in a mobile environment (e.g., mobile phone networks), where one person’s pattern could be separately stored in a number of different stations, and such a local pattern is incomplete compared with the global pattern. A simple solution to pattern matching over a mobile environment is to collect all …
Extreme Learning Machine Terrain-Based Navigation For Unmanned Aerial Vehicles, Ee May Kan, Meng Hiot Lim, Yew Soon Ong, Ah-Hwee Tan, Swee Ping Yeo
Extreme Learning Machine Terrain-Based Navigation For Unmanned Aerial Vehicles, Ee May Kan, Meng Hiot Lim, Yew Soon Ong, Ah-Hwee Tan, Swee Ping Yeo
Research Collection School Of Computing and Information Systems
Unmanned aerial vehicles (UAVs) rely on global positioning system (GPS) information to ascertain its position for navigation during mission execution. In the absence of GPS information, the capability of a UAV to carry out its intended mission is hindered. In this paper, we learn alternative means for UAVs to derive real-time positional reference information so as to ensure the continuity of the mission. We present extreme learning machine as a mechanism for learning the stored digital elevation information so as to aid UAVs to navigate through terrain without the need for GPS. The proposed algorithm accommodates the need of the …
An Improved K-Nearest-Neighbor Algorithm For Text Categorization, Shengyi Jiang, Guansong Pang, Meiling Wu, Limin Kuang
An Improved K-Nearest-Neighbor Algorithm For Text Categorization, Shengyi Jiang, Guansong Pang, Meiling Wu, Limin Kuang
Research Collection School Of Computing and Information Systems
Text categorization is a significant tool to manage and organize the surging text data. Many text categorization algorithms have been explored in previous literatures, such as KNN, Naive Bayes and Support Vector Machine. KNN text categorization is an effective but less efficient classification method. In this paper, we propose an improved KNN algorithm for text categorization, which builds the classification model by combining constrained one pass clustering algorithm and KNN text categorization. Empirical results on three benchmark corpora show that our algorithm can reduce the text similarity computation substantially and outperform the-state-of-the-art KNN, Naive Bayes and Support Vector Machine classifiers. …