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Articles 1711 - 1740 of 4266
Full-Text Articles in Entire DC Network
Extracting Deep Features From Short Ecg Signals For Early Atrial Fibrillation Detection, Xiaodan Wu, Yumeng Zheng, Chao-Hsien Chu, Zhen He
Extracting Deep Features From Short Ecg Signals For Early Atrial Fibrillation Detection, Xiaodan Wu, Yumeng Zheng, Chao-Hsien Chu, Zhen He
Research Collection School Of Computing and Information Systems
Atrial Fibrillation (AF) at an early stage has a short duration and is sometimes asymptomatic, making it difficult to detect. Although the use of mobile sensing devices has provided the possibility of real-time cardiac detection, it is highly susceptible to the noise signals generated by body movement. Therefore, it is of great importance to study early AF detection for mobile terminals with noise immunity. Extracting effective features is critical to AF detection, but most existing studies used shallow time, frequency or time-frequency energy (TFE) features with weak representation that need to rely on long ECG signals to capture the variation …
An Empirical Study Of The Dependency Networks Of Deep Learning Libraries, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia
An Empirical Study Of The Dependency Networks Of Deep Learning Libraries, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia
Research Collection School Of Computing and Information Systems
Deep Learning techniques have been prevalent in various domains, and more and more open source projects in GitHub rely on deep learning libraries to implement their algorithms. To that end, they should always keep pace with the latest versions of deep learning libraries to make the best use of deep learning libraries. Aptly managing the versions of deep learning libraries can help projects avoid crashes or security issues caused by deep learning libraries. Unfortunately, very few studies have been done on the dependency networks of deep learning libraries. In this paper, we take the first step to perform an exploratory …
Adaptive Task Sampling For Meta-Learning, Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi
Adaptive Task Sampling For Meta-Learning, Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks. The key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time by randomly sampling classes in meta-training data to construct fewshot tasks for episodic training. While a rich line of work focuses solely on how to extract meta-knowledge across tasks, we exploit the complementary problem on how to generate informative tasks. We argue that the randomly sampled tasks could be sub-optimal and uninformative (e.g., the task of classifying “dog” from “laptop” is often trivial) to the meta-learner. …
Commanding And Re-Dictation: Developing Eyes-Free Voice-Based Interaction For Editing Dictated Text, Debjyoti Ghosh, Can Liu, Shengdong Zhao, Kotaro Hara
Commanding And Re-Dictation: Developing Eyes-Free Voice-Based Interaction For Editing Dictated Text, Debjyoti Ghosh, Can Liu, Shengdong Zhao, Kotaro Hara
Research Collection School Of Computing and Information Systems
Existing voice-based interfaces have limited support for text editing, especially when seeing the text is difficult, e.g., while walking or cooking. This research develops voice interaction techniques for eyes-free text editing. First, with a Wizard-of-Oz study, we identified two primary user strategies: using commands, e.g., “replace go with goes” and re-dictating over an erroneous portion, e.g., correcting “he go there” by saying “he goes there.” To support these user strategies with an actual system implementation, we developed two eyes-free voice interaction techniques, Commanding and Re-dictation, and evaluated them with a controlled experiment. Results showed that while Re-dictation performs significantly better …
Accelerating Exact Constrained Shortest Paths On Gpus, Shengliang Lu, Bingsheng He, Yuchen Li, Hao Fu
Accelerating Exact Constrained Shortest Paths On Gpus, Shengliang Lu, Bingsheng He, Yuchen Li, Hao Fu
Research Collection School Of Computing and Information Systems
The recently emerging applications such as software-defined networks and autonomous vehicles require efficient and exact solutions for constrained shortest paths (CSP), which finds the shortest path in a graph while satisfying some user-defined constraints. Compared with the common shortest path problems without constraints, CSP queries have a significantly larger number of subproblems. The most widely used labeling algorithm becomes prohibitively slow and impractical. Other existing approaches tend to find approximate solutions and build costly indices on graphs for fast query processing, which are not suitable for emerging applications with the requirement of exact solutions. A natural question is whether and …
Aim 2020 Challenge On Video Extreme Super-Resolution: Methods And Results, D. Fuoli, Zhiwu Huang, S. Gu, R. Timofte, A. Raventos, A. Esfandiari, S. Karout, X. Xu, X. Li, X. Xiong, J. Wang, Michelini P. Navarrete, W. Zhang, D. Zhang, H. Zhu, D. Xia, H. Chen, J. Gu, Z. Zhang, T. Zhao
Aim 2020 Challenge On Video Extreme Super-Resolution: Methods And Results, D. Fuoli, Zhiwu Huang, S. Gu, R. Timofte, A. Raventos, A. Esfandiari, S. Karout, X. Xu, X. Li, X. Xiong, J. Wang, Michelini P. Navarrete, W. Zhang, D. Zhang, H. Zhu, D. Xia, H. Chen, J. Gu, Z. Zhang, T. Zhao
Research Collection School Of Computing and Information Systems
This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020. Common scaling factors for learned video super-resolution (VSR) do not go beyond factor 4. Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details. The task in this challenge is to upscale videos with an extreme factor of 16, which results in more serious degradations that also affect the structural integrity of the videos. A single pixel in the lowresolution (LR) domain corresponds to 256 pixels in the high-resolution (HR) domain. …
Prevalence, Contents And Automatic Detection Of Kl-Satd, Leevi Rantala, Mika Mantyla, David Lo
Prevalence, Contents And Automatic Detection Of Kl-Satd, Leevi Rantala, Mika Mantyla, David Lo
Research Collection School Of Computing and Information Systems
When developers use different keywords such as TODO and FIXME in source code comments to describe self-admitted technical debt (SATD), we refer it as Keyword-Labeled SATD (KL-SATD). We study KL-SATD from 33 software repositories with 13,588 KL-SATD comments. We find that the median percentage of KL-SATD comments among all comments is only 1,52%. We find that KL-SATD comment contents include words expressing code changes and uncertainty, such as remove, fix, maybe and probably. This makes them different compared to other comments. KL-SATD comment contents are similar to manually labeled SATD comments of prior work. Our machine learning classifier using logistic …
An Attention-Based Rumor Detection Model With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong
An Attention-Based Rumor Detection Model With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Rumor spread in social media severely jeopardizes the credibility of online content. Thus, automatic debunking of rumors is of great importance to keep social media a healthy environment. While facing a dubious claim, people often dispute its truthfulness sporadically in their posts containing various cues, which can form useful evidence with long-distance dependencies. In this work, we propose to learn discriminative features from microblog posts by following their non-sequential propagation structure and generate more powerful representations for identifying rumors. For modeling non-sequential structure, we first represent the diffusion of microblog posts with propagation trees, which provide valuable clues on how …
Rethinking Pruning For Accelerating Deep Inference At The Edge, Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Ke Xu, Lothar Thiele
Rethinking Pruning For Accelerating Deep Inference At The Edge, Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Ke Xu, Lothar Thiele
Research Collection School Of Computing and Information Systems
There is a growing trend to deploy deep neural networks at the edge for high-accuracy, real-time data mining and user interaction. Applications such as speech recognition and language understanding often apply a deep neural network to encode an input sequence and then use a decoder to generate the output sequence. A promising technique to accelerate these applications on resource-constrained devices is network pruning, which compresses the size of the deep neural network without severe drop in inference accuracy. However, we observe that although existing network pruning algorithms prove effective to speed up the prior deep neural network, they lead to …
Refactoring From 9 To 5? What And When Employees And Volunteers Contribute To Oss, Luiz Felipe Dias, Caio Barbosa, Gustavo Pinto, Igor Steinmacher, Baldoino Fonseca, Márcio Ribeiro, Christoph Treude, Daniel Alencar Da Costa
Refactoring From 9 To 5? What And When Employees And Volunteers Contribute To Oss, Luiz Felipe Dias, Caio Barbosa, Gustavo Pinto, Igor Steinmacher, Baldoino Fonseca, Márcio Ribeiro, Christoph Treude, Daniel Alencar Da Costa
Research Collection School Of Computing and Information Systems
In this paper we characterize the contributions made by employees (developers that work for GitHub, the company) and volunteers (developers that use GitHub, the platform) to OSS projects maintained by GitHub (the company) on GitHub (the platform). By mining activities performed in five well-known company-owned OSS projects, we investigate what they do and when they do it. We found that the majority of the volunteers' contributions are related to reengineering (e.g., refactoring), while employees focus more on management (e.g., documentation). When it comes to the working hours, we found that contributions are made mostly from 9am-5pm, even for the volunteers.
How Practitioners Perceive Automated Bug Report Management Techniques, Weiqin Zou, David Lo, Zhenyu Chen, Xin Xia, Yang Feng, Baowen Xu
How Practitioners Perceive Automated Bug Report Management Techniques, Weiqin Zou, David Lo, Zhenyu Chen, Xin Xia, Yang Feng, Baowen Xu
Research Collection School Of Computing and Information Systems
Bug reports play an important role in the process of debugging and fixing bugs. To reduce the burden of bug report managers and facilitate the process of bug fixing, a great amount of software engineering research has been invested into automated bug report management techniques. However, the verdict is still open whether such techniques are actually required and applicable outside of the theoretical research domain. To fill this gap, in this paper, we conducted a survey among 327 practitioners to gain their insights into various categories of automated bug report management techniques. Specifically, in the survey, we asked them to …
Skin-Mimo: Vibration-Based Mimo Communication Over Human Skin, Dong Ma, Yuezhong Wu, Ming Ding, Mahbub Hassan, Wen Hu
Skin-Mimo: Vibration-Based Mimo Communication Over Human Skin, Dong Ma, Yuezhong Wu, Ming Ding, Mahbub Hassan, Wen Hu
Research Collection School Of Computing and Information Systems
We explore the feasibility of Multiple-Input-Multiple-Output (MIMO) communication through vibrations over human skin. Using off-the-shelf motors and piezo transducers as vibration transmitters and receivers, respectively, we build a 2x2 MIMO testbed to collect and analyze vibration signals from real subjects. Our analysis reveals that there exist multiple independent vibration channels between a pair of transmitter and receiver, confirming the feasibility of MIMO. Unfortunately, the slow ramping of mechanical motors and rapidly changing skin channels make it impractical for conventional channel sounding based channel state information (CSI) acquisition, which is critical for achieving MIMO capacity gains. To solve this problem, we …
Towards An Optimal Outdoor Advertising Placement: When A Budget Constraint Meets Moving Trajectories, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng
Towards An Optimal Outdoor Advertising Placement: When A Budget Constraint Meets Moving Trajectories, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng
Research Collection School Of Computing and Information Systems
In this article, we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards U (each with a location and a cost), a database of trajectories T, and a budget L, we find a set of billboards within the budget to influence the largest number of trajectories. One core challenge is to identify and reduce the overlap of the influence from different billboards to the same trajectories, while keeping the budget constraint into consideration. We show that this problem is NP-hard and present an enumeration based algorithm with (1-1/e) approximation ratio. However, the enumeration would …
Improving Multimodal Named Entity Recognition Via Entity Span Detection With Unified Multimodal Transformer, Jianfei Yu, Jing Jiang, Li Yang, Rui Xia
Improving Multimodal Named Entity Recognition Via Entity Span Detection With Unified Multimodal Transformer, Jianfei Yu, Jing Jiang, Li Yang, Rui Xia
Research Collection School Of Computing and Information Systems
In this paper, we study Multimodal Named Entity Recognition (MNER) for social media posts. Existing approaches for MNER mainly suffer from two drawbacks: (1) despite generating word-aware visual representations, their word representations are insensitive to the visual context; (2) most of them ignore the bias brought by the visual context. To tackle the first issue, we propose a multimodal interaction module to obtain both image-aware word representations and word-aware visual representations. To alleviate the visual bias, we further propose to leverage purely text-based entity span detection as an auxiliary module, and design a Unified Multimodal Transformer to guide the final …
Graph-To-Tree Learning For Solving Math Word Problems, Jipeng Zhang, Lei Wang, Roy Ka-Wei Lee, Yi Bin, Yan Wang, Jie Shao, Ee-Peng Lim
Graph-To-Tree Learning For Solving Math Word Problems, Jipeng Zhang, Lei Wang, Roy Ka-Wei Lee, Yi Bin, Yan Wang, Jie Shao, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well. This results in poor quantity representations and incorrect solution expressions. In this paper, we propose Graph2Tree, a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. Included in our Graph2Tree framework are two graphs, namely the Quantity Cell Graph and Quantity Comparison Graph, which are designed to address limitations of existing methods by …
Automatic Android Deprecated-Api Usage Update By Learning From Single Updated Example, Stefanus A. Haryono, Ferdian Thung, Hong Jin Kang, Lucas Serrano, Gilles Muller, Julia Lawall, David Lo, Lingxiao Jiang
Automatic Android Deprecated-Api Usage Update By Learning From Single Updated Example, Stefanus A. Haryono, Ferdian Thung, Hong Jin Kang, Lucas Serrano, Gilles Muller, Julia Lawall, David Lo, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Due to the deprecation of APIs in the Android operating system, developers have to update usages of the APIs to ensure that their applications work for both the past and current versions of Android. Such updates may be widespread, non-trivial, and time-consuming. Therefore, automation of such updates will be of great benefit to developers. AppEvolve, which is the state-of-the-art tool for automating such updates, relies on having before- and after-update examples to learn from. In this work, we propose an approach named CocciEvolve that performs such updates using only a single after-update example. CocciEvolve learns edits by extracting the relevant …
Psc2code: Denoising Code Extraction From Programming Screencasts, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Minghui Wu, Xiaohu Yang
Psc2code: Denoising Code Extraction From Programming Screencasts, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Minghui Wu, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Programming screencasts have become a pervasive resource on the Internet, which help developers learn new programming technologies or skills. The source code in programming screencasts is an important and valuable information for developers. But the streaming nature of programming screencasts (i.e., a sequence of screen-captured images) limits the ways that developers can interact with the source code in the screencasts. Many studies use the Optical Character Recognition (OCR) technique to convert screen images (also referred to as video frames) into textual content, which can then be indexed and searched easily. However, noisy screen images significantly affect the quality of source …
Recovering Fitness Gradients For Interprocedural Boolean Flags In Search-Based Testing, Yun Lin, Jun Sun, Gordon Fraser, Ziheng Xiu, Ting Liu, Jin Song Dong
Recovering Fitness Gradients For Interprocedural Boolean Flags In Search-Based Testing, Yun Lin, Jun Sun, Gordon Fraser, Ziheng Xiu, Ting Liu, Jin Song Dong
Research Collection School Of Computing and Information Systems
In Search-based Software Testing (SBST), test generation is guided by fitness functions that estimate how close a test case is to reach an uncovered test goal (e.g., branch). A popular fitness function estimates how close conditional statements are to evaluating to true or false, i.e., the branch distance. However, when conditions read Boolean variables (e.g., if(x && y)), the branch distance provides no gradient for the search, since a Boolean can either be true or false. This flag problem can be addressed by transforming individual procedures such that Boolean flags are replaced with numeric comparisons that provide better guidance for …
Global Pac Bounds For Learning Discrete Time Markov Chains, Hugo Bazille, Blaise Genest, Cyrille Jegourel, Jun Sun
Global Pac Bounds For Learning Discrete Time Markov Chains, Hugo Bazille, Blaise Genest, Cyrille Jegourel, Jun Sun
Research Collection School Of Computing and Information Systems
Learning models from observations of a system is a powerful tool with many applications. In this paper, we consider learning Discrete Time Markov Chains (DTMC), with different methods such as frequency estimation or Laplace smoothing. While models learnt with such methods converge asymptotically towards the exact system, a more practical question in the realm of trusted machine learning is how accurate a model learnt with a limited time budget is. Existing approaches provide bounds on how close the model is to the original system, in terms of bounds on local (transition) probabilities, which has unclear implication on the global behavior. …
Improving Event Detection Via Open-Domain Event Trigger Knowledge, Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, Jun Xie
Improving Event Detection Via Open-Domain Event Trigger Knowledge, Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, Jun Xie
Research Collection School Of Computing and Information Systems
Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the small scale of training data, previous methods perform poorly on unseen/sparsely labeled trigger words and are prone to overfitting densely labeled trigger words. To address the issue, we propose a novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations. Experiments on benchmark ACE2005 show that our model outperforms nine strong baselines, is especially effective for unseen/sparsely labeled trigger words. The source code is released on https://github.com/shuaiwa16/ekd.git.
Spinfer: Inferring Semantic Patches For The Linux Kernel, Lucas Serrano, Van-Anh Nguyen, Ferdian Thung, Lingxiao Jiang, David Lo, Julia Lawall, Gilles Muller
Spinfer: Inferring Semantic Patches For The Linux Kernel, Lucas Serrano, Van-Anh Nguyen, Ferdian Thung, Lingxiao Jiang, David Lo, Julia Lawall, Gilles Muller
Research Collection School Of Computing and Information Systems
In a large software system such as the Linux kernel, there is a continual need for large-scale changes across many source files, triggered by new needs or refined design decisions. In this paper, we propose to ease such changes by suggesting transformation rules to developers, inferred automatically from a collection of examples. Our approach can help automate large-scale changes as well as help understand existing large-scale changes, by highlighting the various cases that the developer who performed the changes has taken into account. We have implemented our approach as a tool, Spinfer. We evaluate Spinfer on a range of challenging …
Video-Grounded Dialogues With Pretrained Generation Language Models, Hung Le, Steven C. H. Hoi
Video-Grounded Dialogues With Pretrained Generation Language Models, Hung Le, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Pre-trained language models have shown remarkable success in improving various downstream NLP tasks due to their ability to capture dependencies in textual data and generate natural responses. In this paper, we leverage the power of pre-trained language models for improving video-grounded dialogue, which is very challenging and involves complex features of different dynamics: (1) Video features which can extend across both spatial and temporal dimensions; and (2) Dialogue features which involve semantic dependencies over multiple dialogue turns. We propose a framework by extending GPT-2 models to tackle these challenges by formulating video-grounded dialogue tasks as a sequence-to-sequence task, combining both …
Optimising The Fit Of Stack Overflow Code Snippets Into Existing Code, Brittany Reid, Christoph Treude, Markus Wagner
Optimising The Fit Of Stack Overflow Code Snippets Into Existing Code, Brittany Reid, Christoph Treude, Markus Wagner
Research Collection School Of Computing and Information Systems
Software developers often reuse code from online sources such as Stack Overflow within their projects. However, the process of searching for code snippets and integrating them within existing source code can be tedious. In order to improve efficiency and reduce time spent on code reuse, we present an automated code reuse tool for the Eclipse IDE (Integrated Developer Environment), NLP2TestableCode. NLP2TestableCode can not only search for Java code snippets using natural language tasks, but also evaluate code snippets based on a user’s existing code, modify snippets to improve fit and correct errors, before presenting the user with the best snippet, …
Lightweight And Privacy-Aware Fine-Grained Access Control For Iot-Oriented Smart Health, Jianfei Sun, Hu Xiong, Ximeng Liu, Yinghui Zhang, Xuyun Nie, Robert H. Deng
Lightweight And Privacy-Aware Fine-Grained Access Control For Iot-Oriented Smart Health, Jianfei Sun, Hu Xiong, Ximeng Liu, Yinghui Zhang, Xuyun Nie, Robert H. Deng
Research Collection School Of Computing and Information Systems
With the booming of Internet of Things (IoT), smart health (s-health) is becoming an emerging and attractive paradigm. It can provide an accurate prediction of various diseases and improve the quality of healthcare. Nevertheless, data security and user privacy concerns still remain issues to be addressed. As a high potential and prospective solution to secure IoT-oriented s-health applications, ciphertext policy attribute-based encryption (CP-ABE) schemes raise challenges, such as heavy overhead and attribute privacy of the end users. To resolve these drawbacks, an optimized vector transformation approach is first proposed to efficiently transform the access policy and user attribute set into …
Mining And Predicting Micro-Process Patterns Of Issue Resolution For Open Source Software Projects, Yiran Wang, Jian Cao, David Lo
Mining And Predicting Micro-Process Patterns Of Issue Resolution For Open Source Software Projects, Yiran Wang, Jian Cao, David Lo
Research Collection School Of Computing and Information Systems
Addressing issue reports is an integral part of open source software (OSS) projects. Although several studies have attempted to discover the factors that affect issue resolution, few pay attention to the underlying micro-process patterns of resolution processes. Discovering these micro-patterns will help us understand the dynamics of issue resolution processes so that we can manage and improve them in better ways. Of the various types of issues, those relating to corrective maintenance account for nearly half hence resolving these issues efficiently is critical for the success of OSS projects. Therefore, we apply process mining techniques to discover the micro-patterns of …
The Prediction Of Delay Time At Intersection And Route Planning For Autonomous Vehicles, Genwang Gou, Yongxin Zhao, Jiawei Liang, Ling Shi
The Prediction Of Delay Time At Intersection And Route Planning For Autonomous Vehicles, Genwang Gou, Yongxin Zhao, Jiawei Liang, Ling Shi
Research Collection School Of Computing and Information Systems
Intelligent Intersections (roundabout and crossroads) management is considered as one of the challenges to significantly improve urban traffic efficiency. Recent researches in artificial intelligence suggest that autonomous vehicles have the possibility of forming intelligent intersection management, and likely to occupy the leading role in future urban traffic. If route planning method can be used for route decision of autonomous vehicle, the urban traffic efficiency can be further improved. In this paper, we propose an Intelligent Intersection Control Protocol (IICP) for controlling autonomous vehicles cross intersection, and recommend route for autonomous vehicles to reduce travel time and improve urban traffic efficiency. …
Tree-Augmented Cross-Modal Encoding For Complex-Query Video Retrieval, Xun Yang, Jianfeng Dong, Yixin Cao, Xun Wang, Meng Wang, Tat-Seng Chua
Tree-Augmented Cross-Modal Encoding For Complex-Query Video Retrieval, Xun Yang, Jianfeng Dong, Yixin Cao, Xun Wang, Meng Wang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
The rapid growth of user-generated videos on the Internet has intensified the need for text-based video retrieval systems. Traditional methods mainly favor the concept-based paradigm on retrieval with simple queries, which are usually ineffective for complex queries that carry far more complex semantics. Recently, embedding-based paradigm has emerged as a popular approach. It aims to map the queries and videos into a shared embedding space where semantically-similar texts and videos are much closer to each other. Despite its simplicity, it forgoes the exploitation of the syntactic structure of text queries, making it suboptimal to model the complex queries. To facilitate …
Bridging Hierarchical And Sequential Context Modeling For Question-Driven Extractive Answer Summarization, Yang Deng, Wenxuan Zhang, Yaliang Li, Min Yang, Wai Lam, Ying Shen
Bridging Hierarchical And Sequential Context Modeling For Question-Driven Extractive Answer Summarization, Yang Deng, Wenxuan Zhang, Yaliang Li, Min Yang, Wai Lam, Ying Shen
Research Collection School Of Computing and Information Systems
Non-factoid question answering (QA) is one of the most extensive yet challenging application and research areas of retrieval-based question answering. In particular, answers to non-factoid questions can often be too lengthy and redundant to comprehend, which leads to the great demand on answer sumamrization in non-factoid QA. However, the multi-level interactions between QA pairs and the interrelation among different answer sentences are usually modeled separately on current answer summarization studies. In this paper, we propose a unified model to bridge hierarchical and sequential context modeling for question-driven extractive answer summarization. Specifically, we design a hierarchical compare-aggregate method to integrate the …
Expertise Style Transfer: A New Task Towards Better Communication Between Experts And Laymen, Yixin Cao, Ruihao Shui, Liangming Pan, Min-Yen Kan, Zhiyuan Lu, Tat-Seng Chua
Expertise Style Transfer: A New Task Towards Better Communication Between Experts And Laymen, Yixin Cao, Ruihao Shui, Liangming Pan, Min-Yen Kan, Zhiyuan Lu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style …
Search Me In The Dark: Privacy-Preserving Boolean Range Query Over Encrypted Spatial Data, Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Robert H. Deng, Yinbin Miao, Dan Zhu, Zhuoran Ma
Search Me In The Dark: Privacy-Preserving Boolean Range Query Over Encrypted Spatial Data, Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Robert H. Deng, Yinbin Miao, Dan Zhu, Zhuoran Ma
Research Collection School Of Computing and Information Systems
With the increasing popularity of geo-positioning technologies and mobile Internet, spatial keyword data services have attracted growing interest from both the industrial and academic communities in recent years. Meanwhile, a massive amount of data is increasingly being outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing while without compromising data privacy. Most existing works primarily focus on the privacy-preserving schemes for either spatial or keyword queries, and they cannot be directly applied to solve the spatial keyword query problem over encrypted data. In this paper, we study the challenging problem of Privacy-preserving Boolean Range Query …