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Articles 2911 - 2940 of 7471
Full-Text Articles in Physical Sciences and Mathematics
Security Analysis Of A Large-Scale Concurrent Data Anonymous Batch Verification Scheme For Mobile Healthcare Crowd Sensing, Yinghui Zhang, Jiangang Shu, Ximeng Liu, Jin Li, Dong Zheng
Security Analysis Of A Large-Scale Concurrent Data Anonymous Batch Verification Scheme For Mobile Healthcare Crowd Sensing, Yinghui Zhang, Jiangang Shu, Ximeng Liu, Jin Li, Dong Zheng
Research Collection School Of Computing and Information Systems
As an important application of the Internet of Things (IoT) technologies, mobile healthcare crowd sensing (MHCS) still has challenging issues, such as privacy protection and efficiency. Quite recently in IEEE Internet of Things Journal (DOI: 10.1109/JIOT.2018.2828463), Liu et al. proposed a large-scale concurrent data anonymous batch verification scheme for mobile healthcare crowd sensing, claiming to provide batch authentication, non-repudiation, and anonymity. However, after a close look at the scheme, we point out that the scheme suffers two types of signature forgery attacks and hence fails to achieve the claimed security properties. In addition, a reasonable and rigorous probability analysis indicates …
Automatic Code Review By Learning The Revision Of Source Code, Shu-Ting Shi, Ming Li, David Lo, Ferdian Thung, Xuan Huo
Automatic Code Review By Learning The Revision Of Source Code, Shu-Ting Shi, Ming Li, David Lo, Ferdian Thung, Xuan Huo
Research Collection School Of Computing and Information Systems
Code review is the process of manual inspection on the revision of the source code in order to find out whether the revised source code eventually meets the revision requirements. However, manual code review is time-consuming, and automating such the code review process will alleviate the burden of code reviewers and speed up the software maintenance process. To construct the model for automatic code review, the characteristics of the revisions of source code (i.e., the difference between the two pieces of source code) should be properly captured and modeled. Unfortunately, most of the existing techniques can easily model the overall …
Discrete Social Recommendation, Chenghao Liu, Xin Wang, Tao Lu, Wenwu Zhu, Jianling Sun, Steven C. H. Hoi
Discrete Social Recommendation, Chenghao Liu, Xin Wang, Tao Lu, Wenwu Zhu, Jianling Sun, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Social recommendation, which aims at improving the performance of traditional recommender systems by considering social information, has attracted broad range of interests. As one of the most widely used methods, matrix factorization typically uses continuous vectors to represent user/item latent features. However, the large volume of user/item latent features results in expensive storage and computation cost, particularly on terminal user devices where the computation resource to operate model is very limited. Thus when taking extra social information into account, precisely extracting K most relevant items for a given user from massive candidates tends to consume even more time and memory, …
A Coordination Framework For Multi-Agent Persuasion And Adviser Systems, Budhitama Subagdja, Ah-Hwee Tan, Yilin Kang
A Coordination Framework For Multi-Agent Persuasion And Adviser Systems, Budhitama Subagdja, Ah-Hwee Tan, Yilin Kang
Research Collection School Of Computing and Information Systems
Assistive agents have been used to give advices to the users regarding activities in daily lives. Although adviser bots are getting smarter and gaining more popularity these days they are usually developed and deployed independent from each other. When several agents operate together in the same context, their advices may no longer be effective since they may instead overwhelm or confuse the user if not properly arranged. Only little attentions have been paid to coordinating different agents to give different advices to a user within the same environment. However, aligning the advices on-the-fly with the appropriate presentation timing at the …
Stock Market Prediction Analysis By Incorporating Social And News Opinion And Sentiment, Zhaoxia Wang, Seng-Beng Ho, Zhiping Lin
Stock Market Prediction Analysis By Incorporating Social And News Opinion And Sentiment, Zhaoxia Wang, Seng-Beng Ho, Zhiping Lin
Research Collection School Of Computing and Information Systems
The price of the stocks is an important indicator for a company and many factors can affect their values. Different events may affect public sentiments and emotions differently, which may have an effect on the trend of stock market prices. Because of dependency on various factors, the stock prices are not static, but are instead dynamic, highly noisy and nonlinear time series data. Due to its great learning capability for solving the nonlinear time series prediction problems, machine learning has been applied to this research area. Learning-based methods for stock price prediction are very popular and a lot of enhanced …
Manifold-Valued Image Generation With Wasserstein Generative Adversarial Nets, Zhiwu Huang, Wu J., G. L. Van
Manifold-Valued Image Generation With Wasserstein Generative Adversarial Nets, Zhiwu Huang, Wu J., G. L. Van
Research Collection School Of Computing and Information Systems
Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued images that are frequently encountered in real-world applications. To fill the gap, this paper first formulates the problem of generating manifold-valued images and exploits three typical instances: hue-saturation-value (HSV) color image generation, chromaticity-brightness (CB) color image generation, and diffusion-tensor (DT) image generation. For the proposed generative modeling problem, we then introduce a theorem of optimal transport to derive a new Wasserstein distance of data distributions on complete manifolds, enabling us to …
Explainable Reasoning Over Knowledge Graphs For Recommendation, Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, Tat-Seng Chua
Explainable Reasoning Over Knowledge Graphs For Recommendation, Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path. In this paper, we contribute a new model named Knowledgeaware Path Recurrent …
Transnfcm: Translation-Based Neural Fashion Compatibility Modeling, Xun Yang, Yunshan Ma, Lizi Liao, Meng Wang, Tat-Seng Chua
Transnfcm: Translation-Based Neural Fashion Compatibility Modeling, Xun Yang, Yunshan Ma, Lizi Liao, Meng Wang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Identifying mix-and-match relationships between fashion items is an urgent task in a fashion e-commerce recommender system. It will significantly enhance user experience and satisfaction. However, due to the challenges of inferring the rich yet complicated set of compatibility patterns in a large e-commerce corpus of fashion items, this task is still underexplored. Inspired by the recent advances in multi-relational knowledge representation learning and deep neural networks, this paper proposes a novel Translation-based Neural Fashion Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion item embeddings and category-specific complementary relations in a unified space via an end-to-end learning manner. TransNFCM places items …
Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and VTS authority as the regulatory agent. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale …
Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach, Chen Zhang, Steven C. H. Hoi
Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach, Chen Zhang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
This paper explores machine learning to address a problem of Partially Observable Multi-sensor Sequential Change Detection (POMSCD), where only a subset of sensors can be observed to monitor a target system for change-point detection at each online learning round. In contrast to traditional Multisensor Sequential Change Detection tasks where all the sensors are observable, POMSCD is much more challenging because the learner not only needs to detect on-the-fly whether a change occurs based on partially observed multi-sensor data streams, but also needs to cleverly choose a subset of informative sensors to be observed in the next learning round, in order …
Cryptocurrency Mining On Mobile As An Alternative Monetization Approach, Nguyen Phan Sinh Huynh, Kenny Choo, Rajesh Krishna Balan, Youngki Lee
Cryptocurrency Mining On Mobile As An Alternative Monetization Approach, Nguyen Phan Sinh Huynh, Kenny Choo, Rajesh Krishna Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
Can cryptocurrency mining (crypto-mining) be a practical ad-free monetization approach for mobile app developers? We conducted a lab experiment and a user study with 228 real Android users to investigate different aspects of mobile crypto-mining. In particular, we show that mobile devices have computational resources to spare and that these can be utilized for crypto-mining with minimal impact on the mobile user experience. We also examined the profitability of mobile crypto-mining and its stability as compared to mobile advertising. In many cases, the profit of mining can exceed mobile advertising's. Most importantly, our study shows that the majority (72%) of …
Verifiable Computation Using Re-Randomizable Garbled Circuits, Qingsong Zhao, Qingkai Zeng, Ximeng Liu, Huanliang Xu
Verifiable Computation Using Re-Randomizable Garbled Circuits, Qingsong Zhao, Qingkai Zeng, Ximeng Liu, Huanliang Xu
Research Collection School Of Computing and Information Systems
Yao's garbled circuit allows a client to outsource a function computation to a server with verifiablity. Unfortunately, the garbled circuit suffers from a one-time usage. The combination of fully homomorphic encryption (FHE) and garbled circuits enables the client and the server to reuse the garbled circuit with multiple inputs (Gennaro et al.). However, there still seems to be a long way to go for improving the efficiency of all known FHE schemes and it need much stronger security assumption. On the other hand, the construction is only proven to be secure in a weaker model where an adversary can not …
Recommending New Features From Mobile App Descriptions, He Jiang, Jingxuan Zhang, Xiaochen Li, Zhilei Ren, David Lo, Xindong Wu, Zhongxuan Luo
Recommending New Features From Mobile App Descriptions, He Jiang, Jingxuan Zhang, Xiaochen Li, Zhilei Ren, David Lo, Xindong Wu, Zhongxuan Luo
Research Collection School Of Computing and Information Systems
The rapidly evolving mobile applications (apps) have brought great demand for developers to identify new features by inspecting the descriptions of similar apps and acquire missing features for their apps. Unfortunately, due to the huge number of apps, this manual process is time-consuming and unscalable. To help developers identify new features, we propose a new approach named SAFER. In this study, we first develop a tool to automatically extract features from app descriptions. Then, given an app, we leverage the topic model to identify its similar apps based on the extracted features and API names of apps. Finally, we design …
An Attribute-Based Framework For Secure Communications In Vehicular Ad Hoc Networks, Hui Cui, Robert H. Deng, Guilin Wang
An Attribute-Based Framework For Secure Communications In Vehicular Ad Hoc Networks, Hui Cui, Robert H. Deng, Guilin Wang
Research Collection School Of Computing and Information Systems
In this paper, we introduce an attribute-based framework to achieve secure communications in vehicular ad hoc networks (VANETs), which enjoys several advantageous features. The proposed framework employs attribute-based signature (ABS) to achieve message authentication and integrity and protect vehicle privacy, which greatly mitigates the overhead caused by pseudonym/private key change or update in the existing solutions for VANETs based on symmetric key, asymmetric key, and identity-based cryptography and group signature. In addition, we extend a standard ABS scheme with traceability and revocation mechanisms and seamlessly integrate them into the proposed framework to support vehicle traceability and revocation by a trusted …
Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and VTS authority as the regulatory agent. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale …
Social Media Mining For Journalism, Arkaitz Zubiaga, Bahareh Heravi, Jisun An, Haewoon Kwak
Social Media Mining For Journalism, Arkaitz Zubiaga, Bahareh Heravi, Jisun An, Haewoon Kwak
Research Collection School Of Computing and Information Systems
The exponential growth of social media as a central communication practice, and its agility in capturing and announcing breaking news events more rapidly than traditional media, has changed the journalistic landscape: social media has been adopted as a significant source by professional journalists, and conversely, citizens are able to use social media as a form of direct reportage. This brings along new opportunities for newsrooms and journalists by providing new means for newsgathering through access to a wealth of citizen reportage and updates about current affairs, as well as an additional showcase for news dissemination.
Evolutionary Trends In The Collaborative Review Process Of A Large Software System, Subhajit Datta, Poulami Sarkar
Evolutionary Trends In The Collaborative Review Process Of A Large Software System, Subhajit Datta, Poulami Sarkar
Research Collection School Of Computing and Information Systems
In this paper, we study the evolutionary trends in the collaborative review process of a large open source software system. As expected, the number of reviews, the number of reviews commented on, as well as the number of reviewers, and the interactions between them show increasing trends over time. But unexpectedly, levels of clustering between developers in their interaction networks show a decreasing trend, even as connections between them increase. In the context of our study, clustering is an indicator of developer collaboration, whereas connection points to how intensely developers work together. Thus the trends we observe can inform how …
A Sampling Approach For Proactive Project Scheduling Under Generalized Time-Dependent Workability Uncertainty, Wen Song, Donghun Kang, Jie Zhang, Zhiguang Cao, Hui Xi
A Sampling Approach For Proactive Project Scheduling Under Generalized Time-Dependent Workability Uncertainty, Wen Song, Donghun Kang, Jie Zhang, Zhiguang Cao, Hui Xi
Research Collection School Of Computing and Information Systems
In real-world project scheduling applications, activity durations are often uncertain. Proactive scheduling can effectively cope with the duration uncertainties, by generating robust baseline solutions according to a priori stochastic knowledge. However, most of the existing proactive approaches assume that the duration uncertainty of an activity is not related to its scheduled start time, which may not hold in many real-world scenarios. In this paper, we relax this assumption by allowing the duration uncertainty to be time-dependent, which is caused by the uncertainty of whether the activity can be executed on each time slot. We propose a stochastic optimization model to …
Unveiling Exception Handling Guidelines Adopted By Java Developers, Hugo Melo, Roberta Coelho, Christoph Treude
Unveiling Exception Handling Guidelines Adopted By Java Developers, Hugo Melo, Roberta Coelho, Christoph Treude
Research Collection School Of Computing and Information Systems
Despite being an old language feature, Java exception handling code is one of the least understood parts of many systems. Several studies have analyzed the characteristics of exception handling code, trying to identify common practices or even link such practices to software bugs. Few works, however, have investigated exception handling issues from the point of view of developers. None of the works have focused on discovering exception handling guidelines adopted by current systems - which are likely to be a driver of common practices. In this work, we conducted a qualitative study based on semi-structured interviews and a survey whose …
Understanding Open Ports In Android Applications: Discovery, Diagnosis, And Security Assessment, Daoyuan Wu, Debin Gao, Rocky K. C. Chang, En He, Eric K. T. Cheng, Robert H. Deng
Understanding Open Ports In Android Applications: Discovery, Diagnosis, And Security Assessment, Daoyuan Wu, Debin Gao, Rocky K. C. Chang, En He, Eric K. T. Cheng, Robert H. Deng
Research Collection School Of Computing and Information Systems
Open TCP/UDP ports are traditionally used by servers to provide application services, but they are also found in many Android apps. In this paper, we present the first open-port analysis pipeline, covering the discovery, diagnosis, and security assessment, to systematically understand open ports in Android apps and their threats. We design and deploy a novel on-device crowdsourcing app and its server-side analytic engine to continuously monitor open ports in the wild. Over a period of ten months, we have collected over 40 million port monitoring records from 3,293 users in 136 countries worldwide, which allow us to observe the actual …
Grand Challenges In Accessible Maps, Jon E. Froehlich, Anke M. Brock, Anat Caspi, Joao Guerreiro, Kotaro Hara, Reuben Kirkham, Johannes Schoning, Benjamin Tannert
Grand Challenges In Accessible Maps, Jon E. Froehlich, Anke M. Brock, Anat Caspi, Joao Guerreiro, Kotaro Hara, Reuben Kirkham, Johannes Schoning, Benjamin Tannert
Research Collection School Of Computing and Information Systems
In this forum we celebrate research that helps to successfully bring the benefits of computing technologies to children, older adults, people with disabilities, and other populations that are often ignored in the design of mass-marketed products.
Deception In Finitely Repeated Security Games, Thanh H. Nguyen, Yongzhao Wang, Arunesh Sinha, Michael P. Wellman
Deception In Finitely Repeated Security Games, Thanh H. Nguyen, Yongzhao Wang, Arunesh Sinha, Michael P. Wellman
Research Collection School Of Computing and Information Systems
Allocating resources to defend targets from attack is often complicated by uncertainty about the attacker’s capabilities, objectives, or other underlying characteristics. In a repeated interaction setting, the defender can collect attack data over time to reduce this uncertainty and learn an effective defense. However, a clever attacker can manipulate the attack data to mislead the defender, influencing the learning process toward its own benefit. We investigate strategic deception on the part of an attacker with private type information, who interacts repeatedly with a defender. We present a detailed computation and analysis of both players’ optimal strategies given the attacker may …
Comparelda: A Topic Model For Document Comparison, Maksim Tkachenko, Hady Wirawan Lauw
Comparelda: A Topic Model For Document Comparison, Maksim Tkachenko, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
A number of real-world applications require comparison of entities based on their textual representations. In this work, we develop a topic model supervised by pairwise comparisons of documents. Such a model seeks to yield topics that help to differentiate entities along some dimension of interest, which may vary from one application to another. While previous supervised topic models consider document labels in an independent and pointwise manner, our proposed Comparative Latent Dirichlet Allocation (CompareLDA) learns predictive topic distributions that comply with the pairwise comparison observations. To fit the model, we derive a maximum likelihood estimation method via augmented variational approximation …
Risk Pooling, Supply Chain Hierarchy, And Analysts' Forecasts, Nan Hu, Jian-Yu Ke, Ling Liu, Yue Zhang
Risk Pooling, Supply Chain Hierarchy, And Analysts' Forecasts, Nan Hu, Jian-Yu Ke, Ling Liu, Yue Zhang
Research Collection School Of Computing and Information Systems
We investigate whether a firm's risk pooling affects its analysts' forecasts, specifically in terms of forecast accuracy and their use of public vs. private information, and how risk pooling interacts with a firm's position in the supply chain to affect analysts' forecasts. We use a social network analysis method to operationalize risk pooling and supply chain hierarchy, and find that risk pooling significantly reduces analysts' forecast errors and increases (decreases) their use of public (private) information. We also find that the positive (negative) relationships between risk pooling and analyst forecast accuracy and analysts' use of public (private) information are more …
Send Hardest Problems My Way: Probabilistic Path Prioritization For Hybrid Fuzzing, Lei Zhao, Yue Duan, Jifeng Xuan
Send Hardest Problems My Way: Probabilistic Path Prioritization For Hybrid Fuzzing, Lei Zhao, Yue Duan, Jifeng Xuan
Research Collection School Of Computing and Information Systems
Hybrid fuzzing which combines fuzzing and concolic execution has become an advanced technique for software vulnerability detection. Based on the observation that fuzzing and concolic execution are complementary in nature, the state-of-the-art hybrid fuzzing systems deploy ``demand launch'' and ``optimal switch'' strategies. Although these ideas sound intriguing, we point out several fundamental limitations in them, due to oversimplified assumptions. We then propose a novel ``discriminative dispatch'' strategy to better utilize the capability of concolic execution. We design a novel Monte Carlo based probabilistic path prioritization model to quantify each path's difficulty and prioritize them for concolic execution. This model treats …
Vistanet: Visual Aspect Attention Network For Multimodal Sentiment Analysis, Quoc Tuan Truong, Hady Wirawan Lauw
Vistanet: Visual Aspect Attention Network For Multimodal Sentiment Analysis, Quoc Tuan Truong, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Detecting the sentiment expressed by a document is a key task for many applications, e.g., modeling user preferences, monitoring consumer behaviors, assessing product quality. Traditionally, the sentiment analysis task primarily relies on textual content. Fueled by the rise of mobile phones that are often the only cameras on hand, documents on the Web (e.g., reviews, blog posts, tweets) are increasingly multimodal in nature, with photos in addition to textual content. A question arises whether the visual component could be useful for sentiment analysis as well. In this work, we propose Visual Aspect Attention Network or VistaNet, leveraging both textual and …
Preference-Aware Task Assignment In On-Demand Taxi Dispatching: An Online Stable Matching Approach, Boming Zhao, Pan Xu, Yexuan Shi, Yongxin Tong, Zimu Zhou, Yuxiang Zeng
Preference-Aware Task Assignment In On-Demand Taxi Dispatching: An Online Stable Matching Approach, Boming Zhao, Pan Xu, Yexuan Shi, Yongxin Tong, Zimu Zhou, Yuxiang Zeng
Research Collection School Of Computing and Information Systems
No abstract provided.
Topical Co-Attention Networks For Hashtag Recommendation On Microblogs, Yang Li, Ting Liu, Jingwen Hu, Jing Jiang
Topical Co-Attention Networks For Hashtag Recommendation On Microblogs, Yang Li, Ting Liu, Jingwen Hu, Jing Jiang
Research Collection School Of Computing and Information Systems
Hashtags provide a simple and natural way of organizing content in microblog services. Along with the fast growing of microblog services, the task of recommending hashtags for microblogs has been given increasing attention in recent years. However, much of the research depends on hand-crafted features. Motivated by the successful use of neural models for many natural language processing tasks, in this paper, we adopt an attention based neural network to learn the representation of a microblog post. Unlike previous works, which only focus on content attention of microblogs, we propose a novel Topical CoAttention Network (TCAN) that jointly models content …
Adaptive Cost-Sensitive Online Classification, Peilin Zhao, Yifan Zhang, Min Wu, Steven C. H. Hoi, Mingkui Tan, Junzhou Huang
Adaptive Cost-Sensitive Online Classification, Peilin Zhao, Yifan Zhang, Min Wu, Steven C. H. Hoi, Mingkui Tan, Junzhou Huang
Research Collection School Of Computing and Information Systems
Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost. However, previous existing methods only considered first-order information of data stream. It is insufficient in practice, since many recent studies have proved that incorporating second-order information enhances the prediction performance of classification models. Thus, we propose a family of cost-sensitive online classification algorithms with adaptive regularization in this paper. We theoretically analyze the proposed algorithms and empirically validate their effectiveness and properties in extensive experiments. Then, …
Multi-Task Learning With Multi-View Attention For Answer Selection And Knowledge Base Question Answering, Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Nan Du, Wei Fan, Kai Lei, Ying Shen
Multi-Task Learning With Multi-View Attention For Answer Selection And Knowledge Base Question Answering, Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Nan Du, Wei Fan, Kai Lei, Ying Shen
Research Collection School Of Computing and Information Systems
Answer selection and knowledge base question answering (KBQA) are two important tasks of question answering (QA) systems. Existing methods solve these two tasks separately, which requires large number of repetitive work and neglects the rich correlation information between tasks. In this paper, we tackle answer selection and KBQA tasks simultaneously via multi-task learning (MTL), motivated by the following motivations. First, both answer selection and KBQA can be regarded as a ranking problem, with one at text-level while the other at knowledge-level. Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), …