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Full-Text Articles in Physical Sciences and Mathematics

An Improved Learnable Evolution Model For Solving Multi-Objective Vehicle Routing Problem With Stochastic Demand, Yunyun Niu, Detian Kong, Rong Wen, Zhiguang Cao, Jianhua Xiao Aug 2021

An Improved Learnable Evolution Model For Solving Multi-Objective Vehicle Routing Problem With Stochastic Demand, Yunyun Niu, Detian Kong, Rong Wen, Zhiguang Cao, Jianhua Xiao

Research Collection School Of Computing and Information Systems

The multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is much harder to tackle than other traditional vehicle routing problems (VRPs), due to the uncertainty in customer demands and potentially conflicted objectives. In this paper, we present an improved multi-objective learnable evolution model (IMOLEM) to solve MO-VRPSD with three objectives of travel distance, driver remuneration and number of vehicles. In our method, a machine learning algorithm, i.e., decision tree, is exploited to help find and guide the desirable direction of evolution process. To cope with the key issue of "route failure" caused due to stochastic customer demands, we propose a …

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Gp3: Gaussian Process Path Planning For Reliable Shortest Path In Transportation Networks, Hongliang Guo, Xuejie Hou, Zhiguang Cao, Jie Zhang Aug 2021

Gp3: Gaussian Process Path Planning For Reliable Shortest Path In Transportation Networks, Hongliang Guo, Xuejie Hou, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

This paper investigates the reliable shortest path (RSP) problem in Gaussian process (GP) regulated transportation networks. Specifically, the RSP problem that we are targeting at is to minimize the (weighted) linear combination of mean and standard deviation of the path's travel time. With the reasonable assumption that the travel times of the underlying transportation network follow a multi-variate Gaussian distribution, we propose a Gaussian process path planning (GP3) algorithm to calculate the a priori optimal path as the RSP solution. With a series of equivalent RSP problem transformations, we are able to reach a polynomial time complexity algorithm with guaranteed …

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Linear Algebra For Computer Science, M. Thulasidas Aug 2021

Linear Algebra For Computer Science, M. Thulasidas

Research Collection School Of Computing and Information Systems

This book has its origin in my experience teaching Linear Algebra to Computer Science students at Singapore Management University. Traditionally, Linear Algebra is taught as a pure mathematics course, almost as an afterthought, not fully integrated with any other applied curriculum. It certainly was taught that way to me. The course I was teaching, however, had a definite pedagogical objective of bringing out the applicability and the usefulness of Linear Algebra in Computer Science, which is nothing but applied mathematics. In today’s age of machine learning and artificial intelligence, Linear Algebra is the branch of mathematics that holds the most …

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Pruning-Aware Merging For Efficient Multitask Inference, Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Lothar Thiele Aug 2021

Pruning-Aware Merging For Efficient Multitask Inference, Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Lothar Thiele

Research Collection School Of Computing and Information Systems

Many mobile applications demand selective execution of multiple correlated deep learning inference tasks on resource-constrained platforms. Given a set of deep neural networks, each pre-trained for a single task, it is desired that executing arbitrary combinations of tasks yields minimal computation cost. Pruning each network separately yields suboptimal computation cost due to task relatedness. A promising remedy is to merge the networks into a multitask network to eliminate redundancy across tasks before network pruning. However, pruning a multitask network combined by existing network merging schemes cannot minimise the computation cost of every task combination because they do not consider such …

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Characterizing Search Activities On Stack Overflow, Jiakun Liu, Sebastian Baltes, Christoph Treude, David Lo, Yun Zhang, Xin Xia Aug 2021

Characterizing Search Activities On Stack Overflow, Jiakun Liu, Sebastian Baltes, Christoph Treude, David Lo, Yun Zhang, Xin Xia

Research Collection School Of Computing and Information Systems

To solve programming issues, developers commonly search on Stack Overflow to seek potential solutions. However, there is a gap between the knowledge developers are interested in and the knowledge they are able to retrieve using search engines. To help developers efficiently retrieve relevant knowledge on Stack Overflow, prior studies proposed several techniques to reformulate queries and generate summarized answers. However, few studies performed a large-scale analysis using real-world search logs. In this paper, we characterize how developers search on Stack Overflow using such logs. By doing so, we identify the challenges developers face when searching on Stack Overflow and seek …

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Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau Aug 2021

Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Many real world systems involve interaction among large number of agents to achieve a common goal, for example, air traffic control. Several model-free RL algorithms have been proposed for such settings. A key limitation is that the empirical reward signal in model-free case is not very effective in addressing the multiagent credit assignment problem, which determines an agent's contribution to the team's success. This results in lower solution quality and high sample complexity. To address this, we contribute (a) an approach to learn a differentiable reward model for both continuous and discrete action setting by exploiting the collective nature of …

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Data Pricing And Data Asset Governance In The Ai Era, Jian Pei, Feida Zhu, Zicun Cong, Luo Xuan, Liu Huiwen, Xin Mu Aug 2021

Data Pricing And Data Asset Governance In The Ai Era, Jian Pei, Feida Zhu, Zicun Cong, Luo Xuan, Liu Huiwen, Xin Mu

Research Collection School Of Computing and Information Systems

Data is one of the most critical resources in the AI Era. While substantial research has been dedicated to training machine learning models using various types of data, much less efforts have been invested in the exploration of assessing and governing data assets in end-to-end processes of machine learning and data science, that is, the pipeline where data is collected and processed, and then machine learning models are produced, requested, deployed, shared and evolved. To provide a state-of-the-art overall picture of this important and novel area and advocate the related research and development, we present a tutorial addressing two essential …

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Independent Reinforcement Learning For Weakly Cooperative Multiagent Traffic Control Problem, Chengwei Zhang, Shan Jin, Wanli Xue, Xiaofei Xie, Shengyong Chen, Rong Chen Aug 2021

Independent Reinforcement Learning For Weakly Cooperative Multiagent Traffic Control Problem, Chengwei Zhang, Shan Jin, Wanli Xue, Xiaofei Xie, Shengyong Chen, Rong Chen

Research Collection School Of Computing and Information Systems

The adaptive traffic signal control (ATSC) problem can be modeled as a multiagent cooperative game among urban intersections, where intersections cooperate to counter the city's traffic conditions. Recently, reinforcement learning (RL) has achieved marked successes in managing sequential decision making problems, which motivates us to apply RL in the ATSC problem. One of the largest challenges of this problem is that the observation of intersection is typically partially observable, which limits the learning performance of RL algorithms. Considering the large scale of intersections in an urban traffic environment, we use independent RL to solve ATSC problem in this study. We …

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Anomaly And Novelty Detection, Explanation, And Accommodation (Andea), Guansong Pang, Jundong Li, Anton Van Den Hengel, Longbing Cao, Thomas G. Dietterich Aug 2021

Anomaly And Novelty Detection, Explanation, And Accommodation (Andea), Guansong Pang, Jundong Li, Anton Van Den Hengel, Longbing Cao, Thomas G. Dietterich

Research Collection School Of Computing and Information Systems

The detection of, explanation of, and accommodation to anomalies and novelties are active research areas in multiple communities, including data mining, machine learning, and computer vision. They are applied in various guises including anomaly detection, out-of-distribution example detection, adversarial example recognition and detection, curiosity-driven reinforcement learning, and open-set recognition and adaptation, all of which are of great interest to the SIGKDD community. The techniques developed have been applied in a wide range of domains including fraud detection and anti-money laundering in fintech, early disease detection, intrusion detection in large-scale computer networks and data centers, defending AI systems from adversarial attacks, …

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An Empirical Study Of Gui Widget Detection For Industrial Mobile Games, Jiaming Ye, Ke Chen, Xiaofei Xie, Lei Ma, Ruochen Huang, Yingfeng Chen, Yinxing Xue, Jianjun Zhao Aug 2021

An Empirical Study Of Gui Widget Detection For Industrial Mobile Games, Jiaming Ye, Ke Chen, Xiaofei Xie, Lei Ma, Ruochen Huang, Yingfeng Chen, Yinxing Xue, Jianjun Zhao

Research Collection School Of Computing and Information Systems

With the widespread adoption of smartphones in our daily life, mobile games experienced increasing demand over the past years. Meanwhile, the quality of mobile games has been continuously drawing more and more attention, which can greatly affect the player experience. For better quality assurance, general-purpose testing has been extensively studied for mobile apps. However, due to the unique characteristic of mobile games, existing mobile testing techniques may not be directly suitable and applicable. To better understand the challenges in mobile game testing, in this paper, we first initiate an early step to conduct an empirical study towards understanding the challenges …

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Learning Interpretable Concept Groups In Cnns, Saurabh Varshneya, Antoine Ledent, Rob Vandermeulen, Yunwen Lei, Matthias Enders, Damian Borth, Marius Kloft Aug 2021

Learning Interpretable Concept Groups In Cnns, Saurabh Varshneya, Antoine Ledent, Rob Vandermeulen, Yunwen Lei, Matthias Enders, Damian Borth, Marius Kloft

Research Collection School Of Computing and Information Systems

We propose a novel training methodology---Concept Group Learning (CGL)---that encourages training of interpretable CNN filters by partitioning filters in each layer into concept groups, each of which is trained to learn a single visual concept. We achieve this through a novel regularization strategy that forces filters in the same group to be active in similar image regions for a given layer. We additionally use a regularizer to encourage a sparse weighting of the concept groups in each layer so that a few concept groups can have greater importance than others. We quantitatively evaluate CGL's model interpretability using standard interpretability evaluation …

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Fine-Grained Analysis Of Structured Output Prediction, Waleed Mustafa, Yunwen Lei, Antoine Ledent, Marius And Kloft Aug 2021

Fine-Grained Analysis Of Structured Output Prediction, Waleed Mustafa, Yunwen Lei, Antoine Ledent, Marius And Kloft

Research Collection School Of Computing and Information Systems

In machine learning we often encounter structured output prediction problems (SOPPs), i.e. problems where the output space admits a rich internal structure. Application domains where SOPPs naturally occur include natural language processing, speech recognition, and computer vision. Typical SOPPs have an extremely large label set, which grows exponentially as a function of the size of the output. Existing generalization analysis implies generalization bounds with at least a square-root dependency on the cardinality d of the label set, which can be vacuous in practice. In this paper, we significantly improve the state of the art by developing novel high-probability bounds with …

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Receiver-Anonymity In Rerandomizable Rcca-Secure Cryptosystems Resolved, Yi Wang, Rongmao Chen, Guomin Yang, Xinyi Huang, Baosheng Wang, Moti Yung Aug 2021

Receiver-Anonymity In Rerandomizable Rcca-Secure Cryptosystems Resolved, Yi Wang, Rongmao Chen, Guomin Yang, Xinyi Huang, Baosheng Wang, Moti Yung

Research Collection School Of Computing and Information Systems

In this work we resolve the open problem raised by Prabhakaran and Rosulek at CRYPTO 2007, and present the first anonymous, rerandomizable, Replayable-CCA (RCCA) secure public-key encryption scheme. This solution opens the door to numerous privacy-oriented applications with a highly desired RCCA security level. At the core of our construction is a non-trivial extension of smooth projective hash functions (Cramer and Shoup, EUROCRYPT 2002), and a modular generic framework developed for constructing rerandomizable RCCA-secure encryption schemes with receiver-anonymity. The framework gives an enhanced abstraction of the original Prabhakaran and Rosulek’s scheme (which was the first construction of rerandomizable RCCA-secure encryption …

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How Knowledge Graph And Attention Help? A Qualitative Analysis Into Bag-Level Relation Extraction, Zikun Hu, Yixin Cao, Lifu Huang, Tat-Seng Chua Aug 2021

How Knowledge Graph And Attention Help? A Qualitative Analysis Into Bag-Level Relation Extraction, Zikun Hu, Yixin Cao, Lifu Huang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Knowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. However, only qualitative analysis and ablation study are provided as evidence. In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE). We find that (1) higher attention accuracy may lead to worse performance as it may harm the model’s ability to extract entity mention features; (2) the performance of attention is largely influenced by various noise distribution patterns, which is closely related to real-world datasets; …

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Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau Aug 2021

Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Many real world systems involve interaction among large number of agents to achieve a common goal, for example, air traffic control. Several model-free RL algorithms have been proposed for such settings. A key limitation is that the empirical reward signal in model-free case is not very effective in addressing the multiagent credit assignment problem, which determines an agent's contribution to the team's success. This results in lower solution quality and high sample complexity. To address this, we contribute (a) an approach to learn a differentiable reward model for both continuous and discrete action setting by exploiting the collective nature of …

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Dynamic Lane Traffic Signal Control With Group Attention And Multi-Timescale Reinforcement Learning, Qize Jiang, Jingze Li, Weiwei Sun, Baihua Zheng Aug 2021

Dynamic Lane Traffic Signal Control With Group Attention And Multi-Timescale Reinforcement Learning, Qize Jiang, Jingze Li, Weiwei Sun, Baihua Zheng

Research Collection School Of Computing and Information Systems

Traffic signal control has achieved significant success with the development of reinforcement learning. However, existing works mainly focus on intersections with normal lanes with fixed outgoing directions. It is noticed that some intersections actually implement dynamic lanes, in addition to normal lanes, to adjust the outgoing directions dynamically. Existing methods fail to coordinate the control of traffic signal and that of dynamic lanes effectively. In addition, they lack proper structures and learning algorithms to make full use of traffic flow prediction, which is essential to set the proper directions for dynamic lanes. Motivated by the ineffectiveness of existing approaches when …

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Thunderrw: An In-Memory Graph Random Walk Engine, Shixuan Sun, Yuhang Chen, Shengliang Lu, Bingsheng He, Yuchen Li Aug 2021

Thunderrw: An In-Memory Graph Random Walk Engine, Shixuan Sun, Yuhang Chen, Shengliang Lu, Bingsheng He, Yuchen Li

Research Collection School Of Computing and Information Systems

As random walk is a powerful tool in many graph processing, mining and learning applications, this paper proposes an efficient inmemory random walk engine named ThunderRW. Compared with existing parallel systems on improving the performance of a single graph operation, ThunderRW supports massive parallel random walks. The core design of ThunderRW is motivated by our profiling results: common RW algorithms have as high as 73.1% CPU pipeline slots stalled due to irregular memory access, which suffers significantly more memory stalls than the conventional graph workloads such as BFS and SSSP. To improve the memory efficiency, we first design a generic …

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Automated Taxi Queue Management At High-Demand Venues, Mengyu Ji, Shih-Fen Cheng Aug 2021

Automated Taxi Queue Management At High-Demand Venues, Mengyu Ji, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

In this paper, we seek to identify an effective management policy that could reduce supply-demand gaps at taxi queues serving high-density locations where demand surges frequently happen. Unlike current industry practice, which relies on broadcasting to attract taxis to come and serve the queue, we propose more proactive and adaptive approaches to handle demand surges. Our design objective is to reduce the cumulative supply-demand gaps as much as we could by sending notifications to individual taxis. To address this problem, we first propose a highly effective passenger demand prediction system that is based on the real-time flight arrival information. By …

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Boundary Detection With Bert For Span-Level Emotion Cause Analysis, Xiangju Li, Wei Gao, Shi Feng, Yifei Zhang, Daling Wang Aug 2021

Boundary Detection With Bert For Span-Level Emotion Cause Analysis, Xiangju Li, Wei Gao, Shi Feng, Yifei Zhang, Daling Wang

Research Collection School Of Computing and Information Systems

Emotion cause analysis (ECA) has been anemerging topic in natural language processing,which aims to identify the reasons behind acertain emotion expressed in the text. MostECA methods intend to identify the clausewhich contains the cause of a given emotion,but such clause-level ECA (CECA) can be ambiguous and imprecise. In this paper, we aimat span-level ECA (SECA) by detecting theprecise boundaries of text spans conveying accurate emotion causes from the given context.We formulate this task as sequence labelingand position identification problems and design two neural methods to solve them. Experiments on two benchmark ECA datasets showthat the proposed methods substantially outperform the …

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Learning-Based Extraction Of First-Order Logic Representations Of Api Directives, Mingwei Liu, Xin Peng, Andrian Marcus, Christoph Treude, Xuefang Bai, Gang Lyu, Jiazhen Xie, Xiaoxin Zhang Aug 2021

Learning-Based Extraction Of First-Order Logic Representations Of Api Directives, Mingwei Liu, Xin Peng, Andrian Marcus, Christoph Treude, Xuefang Bai, Gang Lyu, Jiazhen Xie, Xiaoxin Zhang

Research Collection School Of Computing and Information Systems

Developers often rely on API documentation to learn API directives, i.e., constraints and guidelines related to API usage. Failing to follow API directives may cause defects or improper implementations. Since there are no industry-wide standards on how to document API directives, they take many forms and are often hard to understand by developers or challenging to parse with tools. In this paper, we propose a learning based approach for extracting first-order logic representations of API directives (FOL directives for short). The approach, called LeadFOL, uses a joint learning method to extract atomic formulas by identifying the predicates and arguments involved …

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Solving Large-Scale Extensive-Form Network Security Games Via Neural Fictitious Self-Play, Wanqi Xue, Youzhi Zhang, Shuxin Li, Xinrun Wang, Bo An, Chai Kiat Yeo Aug 2021

Solving Large-Scale Extensive-Form Network Security Games Via Neural Fictitious Self-Play, Wanqi Xue, Youzhi Zhang, Shuxin Li, Xinrun Wang, Bo An, Chai Kiat Yeo

Research Collection School Of Computing and Information Systems

Securing networked infrastructures is important in the real world. The problem of deploying security resources to protect against an attacker in networked domains can be modeled as Network Security Games (NSGs). Unfortunately, existing approaches, including the deep learning-based approaches, are inefficient to solve large-scale extensive-form NSGs. In this paper, we propose a novel learning paradigm, NSG-NFSP, to solve large-scale extensive-form NSGs based on Neural Fictitious Self-Play (NFSP). Our main contributions include: i) reforming the best response (BR) policy network in NFSP to be a mapping from action-state pair to action-value, to make the calculation of BR possible in NSGs; ii) …

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Bidding Mechanisms In Graph Games, Guy Avni, Thomas A. Henzinger, Dorde Zikelic Aug 2021

Bidding Mechanisms In Graph Games, Guy Avni, Thomas A. Henzinger, Dorde Zikelic

Research Collection School Of Computing and Information Systems

A graph game proceeds as follows: two players move a token through a graph to produce a finite or infinite path, which determines the payoff of the game. We study bidding games in which in each turn, an auction determines which player moves the token. Bidding games were largely studied in combination with two variants of first-price auctions called “Richman” and “poorman” bidding. We study taxman bidding, which span the spectrum between the two. The game is parameterized by a constant τ∈[0,1]: portion τ of the winning bid is paid to the other player, and portion 1−τ to the bank. …

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Towards Generative Aspect-Based Sentiment Analysis, Wenxuan Zhang, Xin Li, Yang Deng, Lidong Bing, Wai Lam Aug 2021

Towards Generative Aspect-Based Sentiment Analysis, Wenxuan Zhang, Xin Li, Yang Deng, Lidong Bing, Wai Lam

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) has received increasing attention recently. Most existing work tackles ABSA in a discriminative manner, designing various task-specific classification networks for the prediction. Despite their effectiveness, these methods ignore the rich label semantics in ABSA problems and require extensive task-specific designs. In this paper, we propose to tackle various ABSA tasks in a unified generative framework. Two types of paradigms, namely annotation-style and extraction-style modeling, are designed to enable the training process by formulating each ABSA task as a text generation problem. We conduct experiments on four ABSA tasks across multiple benchmark datasets where our proposed generative …

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Crossasr++: A Modular Differential Testing Framework For Automatic Speech Recognition, Muhammad Hilmi Asyrofi, Zhou Yang, David Lo Aug 2021

Crossasr++: A Modular Differential Testing Framework For Automatic Speech Recognition, Muhammad Hilmi Asyrofi, Zhou Yang, David Lo

Research Collection School Of Computing and Information Systems

Developers need to perform adequate testing to ensure the quality of Automatic Speech Recognition (ASR) systems. However, manually collecting required test cases is tedious and time-consuming. Our recent work proposes CrossASR, a differential testing method for ASR systems. This method first utilizes Text-to-Speech (TTS) to generate audios from texts automatically and then feed these audios into different ASR systems for cross-referencing to uncover failed test cases. It also leverages a failure estimator to find failing test cases more efficiently. Such a method is inherently self-improvable: the performance can increase by leveraging more advanced TTS and ASR systems. So, in this …

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Cfr-Mix: Solving Imperfect Information Extensive-Form Games With Combinatorial Action Space, Shuxin Li, Youzhi Zhang, Xinrun Wang, Wanqi Xue, Bo An Aug 2021

Cfr-Mix: Solving Imperfect Information Extensive-Form Games With Combinatorial Action Space, Shuxin Li, Youzhi Zhang, Xinrun Wang, Wanqi Xue, Bo An

Research Collection School Of Computing and Information Systems

In many real-world scenarios, a team of agents must coordinate with each other to compete against an opponent. The challenge of solving this type of game is that the team's joint action space grows exponentially with the number of agents, which results in the inefficiency of the existing algorithms, e.g., Counterfactual Regret Minimization (CFR). To address this problem, we propose a new framework of CFR: CFR-MIX. Firstly, we propose a new strategy representation that represents a joint action strategy using individual strategies of all agents and a consistency relationship to maintain the cooperation between agents. To compute the equilibrium with …

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Toward Explainable Deep Anomaly Detection, Guansong Pang, Charu Aggarwal Aug 2021

Toward Explainable Deep Anomaly Detection, Guansong Pang, Charu Aggarwal

Research Collection School Of Computing and Information Systems

Anomaly explanation, also known as anomaly localization, is as important as, if not more than, anomaly detection in many realworld applications. However, it is challenging to build explainable detection models due to the lack of anomaly-supervisory information and the unbounded nature of anomaly; most existing studies exclusively focus on the detection task only, including the recently emerging deep learning-based anomaly detection that leverages neural networks to learn expressive low-dimensional representations or anomaly scores for the detection task. Deep learning models, including deep anomaly detection models, are often constructed as black boxes, which have been criticized for the lack of explainability …

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Unlinkable And Revocable Secret Handshake, Yangguang Tian, Yingliu Li, Guomin Yang, Guomin Yang Aug 2021

Unlinkable And Revocable Secret Handshake, Yangguang Tian, Yingliu Li, Guomin Yang, Guomin Yang

Research Collection School Of Computing and Information Systems

In this paper, we introduce a new construction for unlinkable secret handshake that allows a group of users to perform handshakes anonymously. We define formal security models for the proposed construction and prove that it can achieve session key security, anonymity and affiliation hiding. In particular, the proposed construction ensures that (i) anonymity against protocol participants (including group authority) is achieved since a hierarchical identity-based signature is used in generating group user's pseudonym-credential pairs and (ii) revocation is achieved using a secret sharing-based revocation mechanism.

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Biasrv: Uncovering Biased Sentiment Predictions At Runtime, Zhou Yang, Muhammad Hilmi Asyrofi, David Lo Aug 2021

Biasrv: Uncovering Biased Sentiment Predictions At Runtime, Zhou Yang, Muhammad Hilmi Asyrofi, David Lo

Research Collection School Of Computing and Information Systems

Sentiment analysis (SA) systems, though widely applied in many domains, have been demonstrated to produce biased results. Some research works have been done in automatically generating test cases to reveal unfairness in SA systems, but the community still lacks tools that can monitor and uncover biased predictions at runtime. This paper fills this gap by proposing BiasRV, the first tool to raise an alarm when a deployed SA system makes a biased prediction on a given input text. To implement this feature, BiasRV dynamically extracts a template from an input text and from the template generates gender-discriminatory mutants (semanticallyequivalent texts …

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Modeling Transitions Of Focal Entities For Conversational Knowledge Base Question Answering, Yunshi Lan, Jing Jiang Aug 2021

Modeling Transitions Of Focal Entities For Conversational Knowledge Base Question Answering, Yunshi Lan, Jing Jiang

Research Collection School Of Computing and Information Systems

Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation. We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking. Our experiments on two datasets demonstrate the …

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Ava: Adversarial Vignetting Attack Against Visual Recognition, Binyu Tian, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Xiaohong Li, Yang Liu Aug 2021

Ava: Adversarial Vignetting Attack Against Visual Recognition, Binyu Tian, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Xiaohong Li, Yang Liu

Research Collection School Of Computing and Information Systems

Vignetting is an inherent imaging phenomenon within almost all optical systems, showing as a radial intensity darkening toward the corners of an image. Since it is a common effect for photography and usually appears as a slight intensity variation, people usually regard it as a part of a photo and would not even want to post-process it. Due to this natural advantage, in this work, we study the vignetting from a new viewpoint, i.e., adversarial vignetting attack (AVA), which aims to embed intentionally misleading information into the vignetting and produce a natural adversarial example without noise patterns. This example can …

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