Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

Singapore Management University

Discipline
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 3781 - 3810 of 9025

Full-Text Articles in Computer Sciences

Automated Deprecated-Api Usage Update For Android Apps: How Far Are We?, Ferdian Thung, Stefanus Agus Haryono, Lucas Serrano, Gilles Muller, Julia Lawall, David Lo, Lingxiao Jiang Feb 2020

Automated Deprecated-Api Usage Update For Android Apps: How Far Are We?, Ferdian Thung, Stefanus Agus Haryono, Lucas Serrano, Gilles Muller, Julia Lawall, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

As the Android API evolves, some API methods may be deprecated, to be eventually removed. App developers face the challenge of keeping their apps up-to-date, to ensure that the apps work in both older and newer Android versions. Currently, AppEvolve is the state-of-the-art approach to automate such updates, and it has been shown to be quite effective. Still, the number of experiments reported is moderate, involving only API usage updates in 41 usage locations. In this work, we replicate the evaluation of AppEvolve and assess whether its effectiveness is generalizable. Given the set of APIs on which AppEvolve has been …

Go to article

Ausearch: Accurate Api Usage Search In Github Repositories With Type Resolution, Muhammad Hilmi Asyrofi, Ferdian Thung, David Lo, Lingxiao Jiang Feb 2020

Ausearch: Accurate Api Usage Search In Github Repositories With Type Resolution, Muhammad Hilmi Asyrofi, Ferdian Thung, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Nowadays, developers use APIs to implement their applications. To know how to use an API, developers may search for code examples that use the API in repositories such as GitHub. Although code search engines have been developed to help developers perform such search, these engines typically only accept a query containing the description of the task that needs to be implemented or the names of the APIs that the developer wants to use without the capability for the developer to specify particular search constraints, such as the class and parameter types that the relevant API should take. These engines are …

Go to article

Essential Sentences For Navigating Stack Overflow Answers, Sarah Nadi, Christoph Treude Feb 2020

Essential Sentences For Navigating Stack Overflow Answers, Sarah Nadi, Christoph Treude

Research Collection School Of Computing and Information Systems

Stack Overflow (SO) has become an essential resource for software development. Despite its success and prevalence, navigating SO remains a challenge. Ideally, SO users could benefit from highlighted navigational cues that help them decide if an answer is relevant to their task and context. Such navigational cues could be in the form of essential sentences that help the searcher decide whether they want to read the answer or skip over it. In this paper, we compare four potential approaches for identifying essential sentences. We adopt two existing approaches and develop two new approaches based on the idea that contextual information …

Go to article

Joint Learning Of Answer Selection And Answer Summary Generation In Community Question Answering, Yang Deng, Wai Lam, Yuexiang Xie, Daoyuan Chen, Yaliang Li, Min Yang, Ying Shen Feb 2020

Joint Learning Of Answer Selection And Answer Summary Generation In Community Question Answering, Yang Deng, Wai Lam, Yuexiang Xie, Daoyuan Chen, Yaliang Li, Min Yang, Ying Shen

Research Collection School Of Computing and Information Systems

Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-Answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers …

Go to article

Analysis Of Blockchain Protocol Against Static Adversarial Miners Corrupted By Long Delay Attackers, Quan Yuan, Puwen Wei, Keting Jia, Haiyang Xue Feb 2020

Analysis Of Blockchain Protocol Against Static Adversarial Miners Corrupted By Long Delay Attackers, Quan Yuan, Puwen Wei, Keting Jia, Haiyang Xue

Research Collection School Of Computing and Information Systems

Bitcoin, which was initially introduced by Nakamoto, is the most disruptive and impactive cryptocurrency. The core Bitcoin technology is the so-called blockchain protocol. In recent years, several studies have focused on rigorous analyses of the security of Nakamoto’s blockchain protocol in an asynchronous network where network delay must be considered. Wei, Yuan, and Zheng investigated the effect of a long delay attack against Nakamoto’s blockchain protocol. However, their proof only holds in the honest miner setting. In this study, we improve Wei, Yuan and Zheng’s result using a stronger model where the adversary can perform long delay attacks and corrupt …

Go to article

Stealthy And Efficient Adversarial Attacks Against Deep Reinforcement Learning, Jianwen Sun, Tianwei Zhang, Xiaofei Xie, Lei Ma, Yan Zheng, Kangjie Chen, Yang Liu Feb 2020

Stealthy And Efficient Adversarial Attacks Against Deep Reinforcement Learning, Jianwen Sun, Tianwei Zhang, Xiaofei Xie, Lei Ma, Yan Zheng, Kangjie Chen, Yang Liu

Research Collection School Of Computing and Information Systems

Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed. However, the possibility and feasibility of such attacks against Deep Reinforcement Learning (DRL) are less explored. As DRL has achieved great success in various complex tasks, designing effective adversarial attacks is an indispensable prerequisite towards building robust DRL algorithms. In this paper, we introduce two novel adversarial attack techniques to stealthily and efficiently attack the DRL agents. These two techniques enable an adversary to inject adversarial samples in a minimal set of critical moments while causing the most severe damage to …

Go to article

Learning-Guided Network Fuzzing For Testing Cyber-Physical System Defences, Yuqi Chen, Christopher M. Poskitt, Jun Sun, Sridhar Adepu, Fan Zhang Jan 2020

Learning-Guided Network Fuzzing For Testing Cyber-Physical System Defences, Yuqi Chen, Christopher M. Poskitt, Jun Sun, Sridhar Adepu, Fan Zhang

Research Collection School Of Computing and Information Systems

The threat of attack faced by cyber-physical systems (CPSs), especially when they play a critical role in automating public infrastructure, has motivated research into a wide variety of attack defence mechanisms. Assessing their effectiveness is challenging, however, as realistic sets of attacks to test them against are not always available. In this paper, we propose smart fuzzing, an automated, machine learning guided technique for systematically finding 'test suites' of CPS network attacks, without requiring any knowledge of the system's control programs or physical processes. Our approach uses predictive machine learning models and metaheuristic search algorithms to guide the fuzzing of …

Go to article

Key Regeneration-Free Ciphertext-Policy Attribute-Based Encryption And Its Application, Hui Cui, Robert H. Deng, Baodong Qin, Jian Weng Jan 2020

Key Regeneration-Free Ciphertext-Policy Attribute-Based Encryption And Its Application, Hui Cui, Robert H. Deng, Baodong Qin, Jian Weng

Research Collection School Of Computing and Information Systems

Attribute-based encryption (ABE) provides a promising solution for enabling scalable access control over encrypted data stored in the untrusted servers (e.g., cloud) due to its ability to perform data encryption and decryption defined over descriptive attributes. In order to bind different components which correspond to different attributes in a user's attribute-based decryption key together, key randomization technique has been applied in most existing ABE schemes. This randomization method, however, also empowers a user the capability of regenerating a newly randomized decryption key over a subset of the attributes associated with the original decryption key. Because key randomization breaks the linkage …

Go to article

Game Theoretical Study On Client-Controlled Cloud Data Deduplication, Xueqin Liang, Zheng Yan, Robert H. Deng Jan 2020

Game Theoretical Study On Client-Controlled Cloud Data Deduplication, Xueqin Liang, Zheng Yan, Robert H. Deng

Research Collection School Of Computing and Information Systems

Data deduplication eliminates redundant data and is receiving increasing attention in cloud storage services due to the proliferation of big data and the demand for efficient storage. Data deduplication not only requires a consummate technological designing, but also involves multiple parties with conflict interests. Thus, how to design incentive mechanisms and study their acceptance by all relevant stakeholders remain important open issues. In this paper, we detail the payoff structure of a client-controlled deduplication scheme and analyze the feasibilities of unified discount and individualized discount under this structure. Through game theoretical study, a privacy-preserving individualized discount-based incentive mechanism is further …

Go to article

Migrating From Monoliths To Cloud-Based Microservices: A Banking Industry Example, Alan Megargel, Venky Shankararaman, David K. Walker Jan 2020

Migrating From Monoliths To Cloud-Based Microservices: A Banking Industry Example, Alan Megargel, Venky Shankararaman, David K. Walker

Research Collection School Of Computing and Information Systems

As more organizations are placing cloud computing at the heart of their digital transformation strategy, it is important that they adopt appropriate architectures and development methodologies to leverage the full benefits of the cloud. A mere “lift and move” approach, where traditional monolith applications are moved to the cloud will not support the demands of digital services. While, monolithic applications may be easier to develop and control, they are inflexible to change and lack the scalability needed for cloud environments. Microservices architecture, which adopts some of the concepts and principles from service-oriented architecture, provides a number of benefits when developing …

Go to article

The Future Of Work Now: Medical Coding With Ai, Thomas H. Davenport, Steven M. Miller Jan 2020

The Future Of Work Now: Medical Coding With Ai, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

The coding of medical diagnosis and treatment has always been a challenging issue. Translating a patient’s complex symptoms, and a clinician’s efforts to address them, into a clear and unambiguous classification code was difficult even in simpler times. Now, however, hospitals and health insurance companies want very detailed information on what was wrong with a patient and the steps taken to treat them— for clinical record-keeping, for hospital operations review and planning, and perhaps most importantly, for financial reimbursement purposes.

Go to article

Artificial Intelligence-Enhanced Decision Support For Informing Global Sustainable Development: A Human-Centric Ai-Thinking Approach, Meng-Leong How, Sin Mei Cheah, Yong-Jiet Chan, Aik Cheow Khor, Eunice Mei Ping Say Jan 2020

Artificial Intelligence-Enhanced Decision Support For Informing Global Sustainable Development: A Human-Centric Ai-Thinking Approach, Meng-Leong How, Sin Mei Cheah, Yong-Jiet Chan, Aik Cheow Khor, Eunice Mei Ping Say

CCX Research

Sustainable development is crucial to humanity. Utilization of primary socio-environmental data for analysis is essential for informing decision making by policy makers about sustainability in development. Artificial intelligence (AI)-based approaches are useful for analyzing data. However, it was not easy for people who are not trained in computer science to use AI. The significance and novelty of this paper is that it shows how the use of AI can be democratized via a user-friendly human-centric probabilistic reasoning approach. Using this approach, analysts who are not computer scientists can also use AI to analyze sustainability-related EPI data. Further, this human-centric probabilistic …

Go to article

Optimal Feature Selection For Learning-Based Algorithms For Sentiment Classification, Zhaoxia Wang, Zhiping Lin Jan 2020

Optimal Feature Selection For Learning-Based Algorithms For Sentiment Classification, Zhaoxia Wang, Zhiping Lin

Research Collection School Of Computing and Information Systems

Sentiment classification is an important branch of cognitive computation—thus the further studies of properties of sentiment analysis is important. Sentiment classification on text data has been an active topic for the last two decades and learning-based methods are very popular and widely used in various applications. For learning-based methods, a lot of enhanced technical strategies have been used to improve the performance of the methods. Feature selection is one of these strategies and it has been studied by many researchers. However, an existing unsolved difficult problem is the choice of a suitable number of features for obtaining the best sentiment …

Go to article

Pgas: Privacy-Preserving Graph Encryption For Accurate Constrained Shortest Distance Queries, Can Zhang, Liehuang Zhu, Kashif Sharif, Chuan Zhang, Ximeng Liu Jan 2020

Pgas: Privacy-Preserving Graph Encryption For Accurate Constrained Shortest Distance Queries, Can Zhang, Liehuang Zhu, Kashif Sharif, Chuan Zhang, Ximeng Liu

Research Collection School Of Computing and Information Systems

The constrained shortest distance (CSD) query is used to determine the shortest distance between two vertices of a graph while ensuring that the total cost remains lower than a given threshold. The virtually unlimited storage and processing capabilities of cloud computing have enabled the graph owners to outsource their graph data to cloud servers. However, it may introduce privacy challenges that are difficult to address. In recent years, some relevant schemes that support the shortest distance query on the encrypted graph have been proposed. Unfortunately, some of them have unacceptable query accuracy, and some of them leak sensitive information that …

Go to article

Synthesizing Aspect-Driven Recommendation Explanations From Reviews, Trung-Hoang Le, Hady W. Lauw Jan 2020

Synthesizing Aspect-Driven Recommendation Explanations From Reviews, Trung-Hoang Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Explanations help to make sense of recommendations, increasing the likelihood of adoption. However, existing approaches to explainable recommendations tend to rely on rigid, standardized templates, customized only via fill-in-the-blank aspect sentiments. For more flexible, literate, and varied explanations covering various aspects of interest, we synthesize an explanation by selecting snippets from reviews, while optimizing for representativeness and coherence. To fit target users' aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of several product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text …

Go to article

Aateam: Achieving The Ad Hoc Teamwork By Employing The Attention Mechanism, Shuo Chen, Ewa Andrejczuk, Zhiguang Cao, Jie Zhang Jan 2020

Aateam: Achieving The Ad Hoc Teamwork By Employing The Attention Mechanism, Shuo Chen, Ewa Andrejczuk, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

In the ad hoc teamwork setting, a team of agents needs to perform a task without prior coordination. The most advanced approach learns policies based on previous experiences and reuses one of the policies to interact with new teammates. However, the selected policy in many cases is sub-optimal. Switching between policies to adapt to new teammates' behaviour takes time, which threatens the successful performance of a task. In this paper, we propose AATEAM – a method that uses the attention-based neural networks to cope with new teammates' behaviour in real-time. We train one attention network per teammate type. The attention …

Go to article

Coupled Rain Streak And Background Estimation Via Separable Element-Wise Attention, Yinjie Tan, Qiang Wen, Jing Qin, Jianbo Jiao, Guoqiang Han, Shengfeng He Jan 2020

Coupled Rain Streak And Background Estimation Via Separable Element-Wise Attention, Yinjie Tan, Qiang Wen, Jing Qin, Jianbo Jiao, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Single image de-raining is challenging especially in the scenarios with dense rain streaks. Existing methods resolve this problem by predicting the rain streaks of the image, which constrains the network to focus on local rain streaks features. However, dense rain streaks are visually similar to mist or fog (with large intensities), in this case, the training objective should be shifted to image recovery instead of extracting rain streaks. In this paper, we propose a coupled rain streak and background estimation network that explores the intrinsic relations between two tasks. In particular, our network produces task-dependent feature maps, each part of …

Go to article

Vireo @ Video Browser Showdown 2020, Phuong Anh Nguyen, Jiaxin Wu, Chong-Wah Ngo, Danny Francis, Benoit Huet Jan 2020

Vireo @ Video Browser Showdown 2020, Phuong Anh Nguyen, Jiaxin Wu, Chong-Wah Ngo, Danny Francis, Benoit Huet

Research Collection School Of Computing and Information Systems

In this paper, we present the features implemented in the 4th version of the VIREO Video Search System (VIREO-VSS). In this version, we propose a sketch-based retrieval model, which allows the user to specify a video scene with objects and their basic properties, including color, size, and location. We further utilize the temporal relation between video frames to strengthen this retrieval model. For text-based retrieval module, we supply speech and on-screen text for free-text search and upgrade the concept bank for concept search. The search interface is also re-designed targeting the novice user. With the introduced system, we expect that …

Go to article

Systematic Classification Of Attackers Via Bounded Model Checking, Eric Rothstein-Morris, Jun Sun, Sudipta Chattopadyay Jan 2020

Systematic Classification Of Attackers Via Bounded Model Checking, Eric Rothstein-Morris, Jun Sun, Sudipta Chattopadyay

Research Collection School Of Computing and Information Systems

In this work, we study the problem of verification of systems in the presence of attackers using bounded model checking. Given a system and a set of security requirements, we present a methodology to generate and classify attackers, mapping them to the set of requirements that they can break. A naive approach suffers from the same shortcomings of any large model checking problem, i.e., memory shortage and exponential time. To cope with these shortcomings, we describe two sound heuristics based on cone-of-influence reduction and on learning, which we demonstrate empirically by applying our methodology to a set of hardware benchmark …

Go to article

Practical Server-Side Indoor Localization: Tackling Cardinality Outlier Challenges, Anuradha Ravi, Archan Misra Jan 2020

Practical Server-Side Indoor Localization: Tackling Cardinality Outlier Challenges, Anuradha Ravi, Archan Misra

Research Collection School Of Computing and Information Systems

In spite of many advances in indoor localization techniques, practical implementation of robust device independent, server-side Wi-Fi localization (i.e., without any active participation of client devices) remains a challenge. This work utilizes an operationally-deployed Wi-Fi based indoor location infrastructure, based on the classical RADAR algorithm, to tackle two such practical challenges: (a) low cardinality, whereby only the associated AP generates sufficient RSSI reports and (b) outlier identification, which requires explicit identification of mobile clients that are attached to the Wi-Fi network but outside the fingerprinted region. To tackle the low-cardinality problem, we present a technique that uses cardinality changes to …

Go to article

A Survey Of Spatial Crowdsourcing, Yongxin Tong, Zimu Zhou, Yuxiang Zeng, Lei Chen, Cyrus Shahabi Jan 2020

A Survey Of Spatial Crowdsourcing, Yongxin Tong, Zimu Zhou, Yuxiang Zeng, Lei Chen, Cyrus Shahabi

Research Collection School Of Computing and Information Systems

Crowdsourcing is a computing paradigm where humans are actively involved in a computing task, especially for tasks that are intrinsically easier for humans than for computers. Spatial crowdsourcing (SC) is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy, where tasks are spatiotemporal and must be completed at a specific location and time. In fact, spatial crowdsourcing has stimulated a series of recent industrial successes including sharing economy for urban services (Uber and Gigwalk) and spatiotemporal data collection (OpenStreetMap and Waze). This survey dives deep into the challenges and techniques brought by the unique …

Go to article

Scalable, Adaptable And Fast Estimation Of Transient Downtime In Virtual Infrastructures Using Convex Decomposition And Sample Path Randomization, Zhiling Guo, Jin Li, Ram Ramesh Jan 2020

Scalable, Adaptable And Fast Estimation Of Transient Downtime In Virtual Infrastructures Using Convex Decomposition And Sample Path Randomization, Zhiling Guo, Jin Li, Ram Ramesh

Research Collection School Of Computing and Information Systems

Network function virtualization enables efficient cloud-resource planning by virtualizing network services and applications into software running on commodity servers. A cloud-service provider needs to manage and ensure service availability of a network of concurrent virtualized network functions (VNFs). The downtime distribution of a network of VNFs can be estimated using sample-path randomization on the underlying birth–death process. An integrated modeling approach for this purpose is limited by its scalability and computational load because of the high dimensionality of the integrated birth–death process. We propose a generalized convex decomposition of the integrated birth-death process, which transforms the high-dimensional multi-VNF process into …

Go to article

Server-Aided Revocable Attribute-Based Encryption For Cloud Computing Services, Hui Cui, Tsz Hon Yuen, Robert H. Deng, Guilin Wang Jan 2020

Server-Aided Revocable Attribute-Based Encryption For Cloud Computing Services, Hui Cui, Tsz Hon Yuen, Robert H. Deng, Guilin Wang

Research Collection School Of Computing and Information Systems

Attribute-based encryption (ABE) has been regarded as a promising solution in cloud computing services to enable scalable access control without compromising the security. Despite of the advantages, efficient user revocation has been a challenge in ABE. One suggestion for user revocation is using the binary tree in the key generation phase of an ABE scheme, which enables a trusted key generation center to periodically distribute the key update information to all nonrevoked users over a public channel. This revocation approach reduces the size of key updates from linear to logarithmic in the number of users. But it requires each user …

Go to article

Spatial Multi-Objective Land Use Optimization Toward Livability Based On Boundary-Based Genetic Algorithm: A Case Study In Singapore, Kai Cao, Muyang Liu, Shu Wang, Mengqi Liu, Wenting Zhang, Qiang Meng, Bo Huang Jan 2020

Spatial Multi-Objective Land Use Optimization Toward Livability Based On Boundary-Based Genetic Algorithm: A Case Study In Singapore, Kai Cao, Muyang Liu, Shu Wang, Mengqi Liu, Wenting Zhang, Qiang Meng, Bo Huang

Research Collection School Of Computing and Information Systems

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have …

Go to article

Structure-Priority Image Restoration Through Genetic Algorithm Optimization, Zhaoxia Wang, Haibo Pen, Ting Yang, Quan Wang Jan 2020

Structure-Priority Image Restoration Through Genetic Algorithm Optimization, Zhaoxia Wang, Haibo Pen, Ting Yang, Quan Wang

Research Collection School Of Computing and Information Systems

With the significant increase in the use of image information, image restoration has been gaining much attention by researchers. Restoring the structural information as well as the textural information of a damaged image to produce visually plausible restorations is a challenging task. Genetic algorithm (GA) and its variants have been applied in many fields due to their global optimization capabilities. However, the applications of GA to the image restoration domain still remain an emerging discipline. It is still challenging and difficult to restore a damaged image by leveraging GA optimization. To address this problem, this paper proposes a novel GA-based …

Go to article

An Exact Single-Agent Task Selection Algorithm For The Crowdsourced Logistics, Chung-Kyun Han, Shih-Fen Cheng Jan 2020

An Exact Single-Agent Task Selection Algorithm For The Crowdsourced Logistics, Chung-Kyun Han, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

The trend of moving online in the retail industry has created great pressure for the logistics industry to catch up both in terms of volume and response time. On one hand, volume is fluctuating at greater magnitude, making peaks higher; on the other hand, customers are also expecting shorter response time. As a result, logistics service providers are pressured to expand and keep up with the demands. Expanding fleet capacity, however, is not sustainable as capacity built for the peak seasons would be mostly vacant during ordinary days. One promising solution is to engage crowdsourced workers, who are not employed …

Go to article

Lightweight Sharable And Traceable Secure Mobile Health System, Yang Yang, Ximeng Liu, Robert H. Deng, Yingjiu Li Jan 2020

Lightweight Sharable And Traceable Secure Mobile Health System, Yang Yang, Ximeng Liu, Robert H. Deng, Yingjiu Li

Research Collection School Of Computing and Information Systems

Mobile health (mHealth) has emerged as a new patient centric model which allows real-time collection of patient data via wearable sensors, aggregation and encryption of these data at mobile devices, and then uploading the encrypted data to the cloud for storage and access by healthcare staff and researchers. However, efficient and scalable sharing of encrypted data has been a very challenging problem. In this paper, we propose a Lightweight Sharable and Traceable (LiST) secure mobile health system in which patient data are encrypted end-to-end from a patient’s mobile device to data users. LiST enables efficient keyword search and finegrained access …

Go to article

Vietnamese Punctuation Prediction Using Deep Neural Networks, Thuy Pham, Nhu Nguyen, Hong Quang Pham, Han Cao, Binh Nguyen Jan 2020

Vietnamese Punctuation Prediction Using Deep Neural Networks, Thuy Pham, Nhu Nguyen, Hong Quang Pham, Han Cao, Binh Nguyen

Research Collection School Of Computing and Information Systems

Adding appropriate punctuation marks into text is an essential step in speech-to-text where such information is usually not available. While this has been extensively studied for English, there is no large-scale dataset and comprehensive study in the punctuation prediction problem for the Vietnamese language. In this paper, we collect two massive datasets and conduct a benchmark with both traditional methods and deep neural networks. We aim to publish both our data and all implementation codes to facilitate further research, not only in Vietnamese punctuation prediction but also in other related fields. Our project, including datasets and implementation details, is publicly …

Go to article

Remote Communication In Wilderness Search And Rescue: Implications For The Design Of Emergency Distributed-Collaboration Tools For Network-Sparse Environments, Brennan Jones, Anthony Tang, Carman Neustaedter Jan 2020

Remote Communication In Wilderness Search And Rescue: Implications For The Design Of Emergency Distributed-Collaboration Tools For Network-Sparse Environments, Brennan Jones, Anthony Tang, Carman Neustaedter

Research Collection School Of Computing and Information Systems

Wilderness search and rescue (WSAR) requires careful communication between workers in different locations. To understand the contexts from which WSAR workers communicate and the challenges they face, we interviewed WSAR workers and observed a mock-WSAR scenario. Our findings illustrate that WSAR workers face challenges in maintaining a shared mental model. This is primarily done through distributed communication using two-way radios and cell phones for text and photo messaging; yet both implicit and explicit communication suffer. WSAR workers send messages for various reasons and share different types of information with varying levels of urgency. This warrants the use of multiple communication …

Go to article

Entity-Sensitive Attention And Fusion Network For Entity-Level Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang Jan 2020

Entity-Sensitive Attention And Fusion Network For Entity-Level Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

Entity-level (aka target-dependent) sentiment analysis of social media posts has recently attracted increasing attention, and its goal is to predict the sentiment orientations over individual target entities mentioned in users' posts. Most existing approaches to this task primarily rely on the textual content, but fail to consider the other important data sources (e.g., images, videos, and user profiles), which can potentially enhance these text-based approaches. Motivated by the observation, we study entity-level multimodal sentiment classification in this article, and aim to explore the usefulness of images for entity-level sentiment detection in social media posts. Specifically, we propose an Entity-Sensitive Attention …

Go to article

First Prev Next Last