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- Active learning (1)
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- Calorie counting (1)
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Articles 1 - 9 of 9
Full-Text Articles in Databases and Information Systems
Careermapper: An Automated Resume Evaluation Tool, Vivian Lai, Kyong Jin Shim, Richard J. Oentaryo, Philips K. Prasetyo, Casey Vu, Ee-Peng Lim, David Lo
Careermapper: An Automated Resume Evaluation Tool, Vivian Lai, Kyong Jin Shim, Richard J. Oentaryo, Philips K. Prasetyo, Casey Vu, Ee-Peng Lim, David Lo
Research Collection School Of Computing and Information Systems
The advent of the Web brought about major changes in the way people search for jobs and companies look for suitable candidates. As more employers and recruitment firms turn to the Web for job candidate search, an increasing number of people turn to the Web for uploading and creating their online resumes. Resumes are often the first source of information about candidates and also the first item of evaluation in candidate selection. Thus, it is imperative that resumes are complete, free of errors and well-organized. We present an automated resume evaluation tool called 'CareerMapper'. Our tool is designed to conduct …
Learning Sentence Embeddings With Auxiliary Tasks For Cross-Domain Sentiment Classification, Jianfei Yu, Jing Jiang
Learning Sentence Embeddings With Auxiliary Tasks For Cross-Domain Sentiment Classification, Jianfei Yu, Jing Jiang
Research Collection School Of Computing and Information Systems
In this paper, we study cross-domain sentiment classification with neural network architectures. We borrow the idea from Structural Correspondence Learning and use two auxiliary tasks to help induce a sentence embedding that supposedly works well across domains for sentiment classification. We also propose to jointly learn this sentence embedding together with the sentiment classifier itself. Experiment results demonstrate that our proposed joint model outperforms several state-of-the-art methods on five benchmark datasets.
Inferring Links Between Concerns And Methods With Multi-Abstraction Vector Space Model, Yun Zhang, David Lo, Xin Xia, Tien-Duy B. Le, Giuseppe Scanniello, Jianling Sun
Inferring Links Between Concerns And Methods With Multi-Abstraction Vector Space Model, Yun Zhang, David Lo, Xin Xia, Tien-Duy B. Le, Giuseppe Scanniello, Jianling Sun
Research Collection School Of Computing and Information Systems
Concern localization refers to the process of locating code units that match a particular textual description. It takes as input textual documents such as bug reports and feature requests and outputs a list of candidate code units that are relevant to the bug reports or feature requests. Many information retrieval (IR) based concern localization techniques have been proposed in the literature. These techniques typically represent code units and textual descriptions as a bag of tokens at one level of abstraction, e.g., each token is a word, or each token is a topic. In this work, we propose a multi-abstraction concern …
Satt: Tailoring Code Metric Thresholds For Different Software Architectures, Maurício Aniche, Christoph Treude, Andy Zaidman, Arie Van Deursen, Marco Aurélio Gerosa
Satt: Tailoring Code Metric Thresholds For Different Software Architectures, Maurício Aniche, Christoph Treude, Andy Zaidman, Arie Van Deursen, Marco Aurélio Gerosa
Research Collection School Of Computing and Information Systems
Code metric analysis is a well-known approach for assessing the quality of a software system. However, current tools and techniques do not take the system architecture (e.g., MVC, Android) into account. This means that all classes are assessed similarly, regardless of their specific responsibilities. In this paper, we propose SATT (Software Architecture Tailored Thresholds), an approach that detects whether an architectural role is considerably different from others in the system in terms of code metrics, and provides a specific threshold for that role. We evaluated our approach on 2 different architectures (MVC and Android) in more than 400 projects. We …
High Correlation Of Middle East Respiratory Syndrome Spread With Google Search And Twitter Trends In Korea, Soo-Yong Shin, Dong-Woo Seo, Jisun An, Haewoon Kwak, Sung-Han Kim, Jin Gwack, Min-Woo Jo
High Correlation Of Middle East Respiratory Syndrome Spread With Google Search And Twitter Trends In Korea, Soo-Yong Shin, Dong-Woo Seo, Jisun An, Haewoon Kwak, Sung-Han Kim, Jin Gwack, Min-Woo Jo
Research Collection School Of Computing and Information Systems
The Middle East respiratory syndrome coronavirus (MERS-CoV) was exported to Korea in 2015, resulting in a threat to neighboring nations. We evaluated the possibility of using a digital surveillance system based on web searches and social media data to monitor this MERS outbreak. We collected the number of daily laboratory-confirmed MERS cases and quarantined cases from May 11, 2015 to June 26, 2015 using the Korean government MERS portal. The daily trends observed via Google search and Twitter during the same time period were also ascertained using Google Trends and Topsy. Correlations among the data were then examined using Spearman …
Metaflow: A Scalable Metadata Lookup Service For Distributed File Systems In Data Centers, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Haiyong Xie
Metaflow: A Scalable Metadata Lookup Service For Distributed File Systems In Data Centers, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Haiyong Xie
Research Collection School Of Computing and Information Systems
In large-scale distributed file systems, efficient metadata operations are critical since most file operations have to interact with metadata servers first. In existing distributed hash table (DHT) based metadata management systems, the lookup service could be a performance bottleneck due to its significant CPU overhead. Our investigations showed that the lookup service could reduce system throughput by up to 70%, and increase system latency by a factor of up to 8 compared to ideal scenarios. In this paper, we present MetaFlow, a scalable metadata lookup service utilizing software-defined networking (SDN) techniques to distribute lookup workload over network components. MetaFlow tackles …
Efficient Multi-Class Selective Sampling On Graphs, Peng Yang, Peilin Zhao, Zhen Hai, Wei Liu, Hoi, Steven C. H., Xiao-Li Li
Efficient Multi-Class Selective Sampling On Graphs, Peng Yang, Peilin Zhao, Zhen Hai, Wei Liu, Hoi, Steven C. H., Xiao-Li Li
Research Collection School Of Computing and Information Systems
A graph-based multi-class classification problem is typically converted into a collection of binary classification tasks via the one-vs.-all strategy, and then tackled by applying proper binary classification algorithms. Unlike the one-vs.-all strategy, we suggest a unified framework which operates directly on the multi-class problem without reducing it to a collection of binary tasks. Moreover, this framework makes active learning practically feasible for multi-class problems, while the one-vs.-all strategy cannot. Specifically, we employ a novel randomized query technique to prioritize the informative instances. This query technique based on the hybrid criterion of "margin" and "uncertainty" can achieve a comparable mistake bound …
Semantic Memory Modeling And Memory Interaction In Learning Agents, Wenwen Wang, Ah-Hwee Tan, Loo-Nin Teow
Semantic Memory Modeling And Memory Interaction In Learning Agents, Wenwen Wang, Ah-Hwee Tan, Loo-Nin Teow
Research Collection School Of Computing and Information Systems
Semantic memory plays a critical role in reasoning and decision making. It enables an agent to abstract useful knowledge learned from its past experience. Based on an extension of fusion adaptive resonance theory network, this paper presents a novel self-organizing memory model to represent and learn various types of semantic knowledge in a unified manner. The proposed model, called fusion adaptive resonance theory for multimemory learning, incorporates a set of neural processes, through which it may transfer knowledge and cooperate with other long-term memory systems, including episodic memory and procedural memory. Specifically, we present a generic learning process, under which …
Insights From Machine-Learned Diet Success Prediction, Ingmar Weber, Palakorn Achananuparp
Insights From Machine-Learned Diet Success Prediction, Ingmar Weber, Palakorn Achananuparp
Research Collection School Of Computing and Information Systems
To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider “quantified self“ movement and many opt-in to publicly share their logged data. In this paper, we use public food diaries of more than 4,000 long-term active MyFitnessPal users to study the characteristics of a (un-)successful diet. Concretely, we train a machine learning model to predict repeatedly being over or under self-set daily calories goals and then look at which features contribute to the …