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Finding A Rule-Based Solution To The Appellate Body Crisis: Looking Beyond The Multiparty Interim Appeal Arbitration Arrangement, Henry S. Gao Sep 2021

Finding A Rule-Based Solution To The Appellate Body Crisis: Looking Beyond The Multiparty Interim Appeal Arbitration Arrangement, Henry S. Gao

Research Collection Yong Pung How School Of Law

The WTO dispute settlement system is in crisis due to the persistent blockage of the appointment of Appellate Body members by the United States. This paper reviews the US criticisms against the Appellate Body and argues that its allegations are unfounded and its approach is wrong. To deal with the US blockage, various proposals have been made, with the most popular being the Multiparty Interim Appeal Arbitration Arrangement (MPIA) set up by several key Members including the European Union and China. After a thorough analysis of the key features of the MPIA from both theoretical and practical perspectives, this paper …

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‘Choice Of Law For Formation Of Contracts: Solomon Lew V Kaikhushru Shiavax Nargolwala, Adeline Chong Sep 2021

‘Choice Of Law For Formation Of Contracts: Solomon Lew V Kaikhushru Shiavax Nargolwala, Adeline Chong

Research Collection Yong Pung How School Of Law

The appropriate choice of law rule for the formation of a contract is an intractable question. Various solutions have been offered, with none enjoying universal approval. In Solomon Lew v Kaikhushru Shiavax Nargolwala, the Singapore Court of Appeal held in favour of the application of a nuanced version of the putative proper law of a contract. It further held that that there was no role for the lex fori in resolving this classic conflict of laws conundrum. While the SGCA emphasised that the reasonable expectations of the parties would be accommodated through its approach, this note argues that this would …

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Adversarial Attacks And Mitigation For Anomaly Detectors Of Cyber-Physical Systems, Yifan Jia, Jingyi Wang, Christopher M. Poskitt, Sudipta Chattopadhyay, Jun Sun, Yuqi Chen Sep 2021

Adversarial Attacks And Mitigation For Anomaly Detectors Of Cyber-Physical Systems, Yifan Jia, Jingyi Wang, Christopher M. Poskitt, Sudipta Chattopadhyay, Jun Sun, Yuqi Chen

Research Collection School Of Computing and Information Systems

The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models. The effectiveness of anomaly detectors can be assessed by subjecting them to test suites of attacks, but less consideration has been given to adversarial attackers that craft noise specifically designed to deceive them. While successfully applied in domains such as images and audio, adversarial attacks are much harder to implement in CPSs due to the presence of other built-in defence mechanisms such as rule checkers (or invariant checkers). In this work, we …

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Precision Public Health Campaign: Delivering Persuasive Messages To Relevant Segments Through Targeted Advertisements On Social Media, Jisun An, Haewoon Kwak, Hanya M. Qureshi, Ingmar Weber Sep 2021

Precision Public Health Campaign: Delivering Persuasive Messages To Relevant Segments Through Targeted Advertisements On Social Media, Jisun An, Haewoon Kwak, Hanya M. Qureshi, Ingmar Weber

Research Collection School Of Computing and Information Systems

Although established marketing techniques have been applied to design more effective health campaigns, more often than not, the same message is broadcasted to large populations, irrespective of unique characteristics. As individual digital device use has increased, so have individual digital footprints, creating potential opportunities for targeted digital health interventions. We propose a novel precision public health campaign framework to structure and standardize the process of designing and delivering tailored health messages to target particular population segments using social media–targeted advertising tools. Our framework consists of five stages: defining a campaign goal, priority audience, and evaluation metrics; splitting the target audience …

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Characterization And Automatic Updates Of Deprecated Machine-Learning Api Usages, Stefanus Agus Haryono, Thung Ferdian, David Lo, Julia Lawall, Lingxiao Jiang Sep 2021

Characterization And Automatic Updates Of Deprecated Machine-Learning Api Usages, Stefanus Agus Haryono, Thung Ferdian, David Lo, Julia Lawall, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Due to the rise of AI applications, machine learning (ML) libraries, often written in Python, have become far more accessible. ML libraries tend to be updated periodically, which may deprecate existing APIs, making it necessary for application developers to update their usages. In this paper, we build a tool to automate deprecated API usage updates. We first present an empirical study to better understand how updates of deprecated ML API usages in Python can be done. The study involves a dataset of 112 deprecated APIs from Scikit-Learn, TensorFlow, and PyTorch. Guided by the findings of our empirical study, we propose …

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Biasheal: On-The-Fly Black-Box Healing Of Bias In Sentiment Analysis Systems, Zhou Yang, Harshit Jain, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo Sep 2021

Biasheal: On-The-Fly Black-Box Healing Of Bias In Sentiment Analysis Systems, Zhou Yang, Harshit Jain, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo

Research Collection School Of Computing and Information Systems

Although Sentiment Analysis (SA) is widely applied in many domains, existing research has revealed that the unfairness in SA systems can be harmful to the welfare of less privileged people. Several works propose pre-processing and in-processing methods to eliminate bias in SA systems, but little attention is paid to utilizing post-processing methods to heal bias. Postprocessing methods are particularly important for systems that use third-party SA services. Systems that use such services have no access to the SA engine or its training data and thus cannot apply pre-processing nor in-processing methods. Therefore, this paper proposes a black-box post-processing method to …

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Learning And Evaluating Chinese Idiom Embeddings, Minghuan Tan, Jing Jiang Sep 2021

Learning And Evaluating Chinese Idiom Embeddings, Minghuan Tan, Jing Jiang

Research Collection School Of Computing and Information Systems

We study the task of learning and evaluating Chinese idiom embeddings. We first construct a new evaluation dataset that contains idiom synonyms and antonyms. Observing that existing Chinese word embedding methods may not be suitable for learning idiom embeddings, we further present a BERT-based method that directly learns embedding vectors for individual idioms. We empirically compare representative existing methods and our method. We find that our method substantially outperforms existing methods on the evaluation dataset we have constructed.

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Injecting Descriptive Meta-Information Into Pre-Trained Language Models With Hypernetworks, Wenying Duan, Xiaoxi He, Zimu Zhou, Hong Rao, Lothar Thiele Sep 2021

Injecting Descriptive Meta-Information Into Pre-Trained Language Models With Hypernetworks, Wenying Duan, Xiaoxi He, Zimu Zhou, Hong Rao, Lothar Thiele

Research Collection School Of Computing and Information Systems

Pre-trained language models have been widely adopted as backbones in various natural language processing tasks. However, existing pre-trained language models ignore the descriptive meta-information in the text such as the distinction between the title and the mainbody, leading to over-weighted attention to insignificant text. In this paper, we propose a hypernetwork-based architecture to model the descriptive meta-information and integrate it into pre-trained language models. Evaluations on three natural language processing tasks show that our method notably improves the performance of pre-trained language models and achieves the state-of-the-art results on keyphrase extraction.

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Celebrating A Milestone: 20th Anniversary Achievement Highlights [School Of Accountancy], Singapore Management University Aug 2021

Celebrating A Milestone: 20th Anniversary Achievement Highlights [School Of Accountancy], Singapore Management University

SMU Corporate Reports

The book “Celebrating a Milestone 20th Anniversary Achievement Highlights” was published in August 2021 to commemorate SoA’s 20th Anniversary as well as the 15th Anniversary of the Master of Professional Accounting Programme. The book documents the achievements and key milestones in SoA’s history.

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Different Strokes For Different Folks: Long Memory And Roughness, Shuping Shi, Jun Yu Aug 2021

Different Strokes For Different Folks: Long Memory And Roughness, Shuping Shi, Jun Yu

SMU Economics and Statistics Working Paper Series

The log realized volatility of financial assets is often modeled as an autoregressive fractionally integrated moving average model (ARFIMA) process, denoted by ARFIMA(p, d, q), with p = 1 and q = 0. Two conflicting results have been found in the literature regarding the dynamics. One stream shows that the data series has a long memory (i.e., the fractional parameter d > 0) with strong mean reversion (i.e., the autoregressive coefficient |α1| ≈ 0). The other stream suggests that the volatil-ity is rough (i.e., d < 0) with highly persistent dynamic (i.e., α1 → 1). To consolidate the findings, this paper first examines the finite sample properties of alternative estimation methods employed in the literature for the ARFIMA(1, d, 0) model and then applies the outperforming techniques to a wide range of financial assets. The candidate methods include two parametric maximum likeli-hood (ML) methods (the maximum time-domain modified profile likelihood (MPL) and maximum frequency-domain likelihood) and two semiparametric methods (the local Whittle method and log periodogram estimation method). The two parametric methods work well across all parameter set-tings, with the MPL method outperforming. In contrast, the two semiparametric methods have a very large upward bias for d and an equally large downward bias for α1 when α1 is close to unity. The poor performance of the semiparametric methods in the presence of a highly persistent dynamic might lead to a false conclusion of long memory. In the empirical applications, we find that the log realized volatilities of exchange rate futures over the past decade have a long memory, where the point estimate of d is between 0.4 and 0.5 and the estimate of α1 is near zero. For other finan-cial assets considered (including stock indices and industry indices), we find that they have rough volatility, with the point estimate of d being negative and the point estimates of α1 close to unity.

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Actions Define A Character: Assessment Centers As Behavior-Focused Personality Measures, Anna Luca Heimann, Pia V. Ingold, Filip Lievens, Klaus G. Melchers, Gert Keen, Martin Kleinmann Aug 2021

Actions Define A Character: Assessment Centers As Behavior-Focused Personality Measures, Anna Luca Heimann, Pia V. Ingold, Filip Lievens, Klaus G. Melchers, Gert Keen, Martin Kleinmann

Research Collection Lee Kong Chian School Of Business

To expand our knowledge of personality assessment, this study connects research and theory related to two common selection methods: assessment centers (ACs) and personality inventories. We examine the validity of personality-based AC ratings within a multi-method framework. Drawing from the self-other knowledge asymmetry model (Vazire, 2010), we propose that AC ratings are suited to capture personality traits that are observable in social interactions, whereas other methods (i.e., self-ratings) are useful to assess more internal traits. We obtained data from two personality-based ACs, self- and other-rated personality inventories, and supervisor ratings of job performance. Confirmatory factor analyses indicated that personality-based AC …

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An Empirical Study Of The Discreteness Prior In Low-Rank Matrix Completion, Rodrigo Alves, Antoine Ledent, Renato Assunção, Marius And Kloft Aug 2021

An Empirical Study Of The Discreteness Prior In Low-Rank Matrix Completion, Rodrigo Alves, Antoine Ledent, Renato Assunção, Marius And Kloft

Research Collection School Of Computing and Information Systems

A reasonable assumption in recommender systems is that the rows (users) and columns (items) of the rating matrix can be split into groups (communities) with the following property: each entry of the matrix is the sum of components corresponding to community behavior and a purely low-rank component corresponding to individual behavior. We investigate (1) whether such a structure is present in real-world datasets, (2) whether the knowledge of the existence of such structure alone can improve performance, without explicit information about the community memberships. To these ends, we formulate a joint optimization problem over all (completed matrix, set of communities) …

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Learning To Assign: Towards Fair Task Assignment In Large-Scale Ride Hailing, Dingyuan Shi, Yongxin Tong, Zimu Zhou, Bingchen Song, Weifeng Lv, Qiang Yang Aug 2021

Learning To Assign: Towards Fair Task Assignment In Large-Scale Ride Hailing, Dingyuan Shi, Yongxin Tong, Zimu Zhou, Bingchen Song, Weifeng Lv, Qiang Yang

Research Collection School Of Computing and Information Systems

Ride hailing is a widespread shared mobility application where the central issue is to assign taxi requests to drivers with various objectives. Despite extensive research on task assignment in ride hailing, the fairness of earnings among drivers is largely neglected. Pioneer studies on fair task assignment in ride hailing are ineffective and inefficient due to their myopic optimization perspective and timeconsuming assignment techniques. In this work, we propose LAF, an effective and efficient task assignment scheme that optimizes both utility and fairness. We adopt reinforcement learning to make assignments in a holistic manner and propose a set of acceleration techniques …

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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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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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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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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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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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The Impact Of Ceo/Cfo Outside Directorships On Auditor Selection And Audit Quality, Jaeyoon Yu, Byungjin Kwak, Myung Seok Park, Yoonseok Zang Aug 2021

The Impact Of Ceo/Cfo Outside Directorships On Auditor Selection And Audit Quality, Jaeyoon Yu, Byungjin Kwak, Myung Seok Park, Yoonseok Zang

Research Collection School Of Accountancy

We examine whether outside directorships of chief executive officer/chief financial officer (CEO/CFO) and resulting network ties to auditors affect auditor selection decisions and subsequent audit quality. The network ties arise when the CEO/CFO of a firm (home firm) serves as an outside director of another firm that hires an auditor (connected auditor). Using a sample of firms that switch auditors in the post-Sarbanes-Oxley Act period, we find that home firms are more likely to appoint connected auditors. We also find that home firms hiring connected auditors experience a significant decline in subsequent audit quality, compared to those hiring …

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Forecasting Interaction Order On Temporal Graphs, Wenwen Xia, Yuchen Li, Jianwei Tian, Shenghong Li Aug 2021

Forecasting Interaction Order On Temporal Graphs, Wenwen Xia, Yuchen Li, Jianwei Tian, Shenghong Li

Research Collection School Of Computing and Information Systems

Link prediction is a fundamental task for graph analysis and the topic has been studied extensively for static or dynamic graphs. Essentially, the link prediction is formulated as a binary classification problem about two nodes. However, for temporal graphs, links (or interactions) among node sets appear in sequential orders. And the orders may lead to interesting applications. While a binary link prediction formulation fails to handle such an order-sensitive case. In this paper, we focus on such an interaction order prediction (IOP) problem among a given node set on temporal graphs. For the technical aspect, we develop a graph neural …

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Distracted: Why Students Cannot Focus By James M. Lang, Joax Wong, Andree Hartanto Aug 2021

Distracted: Why Students Cannot Focus By James M. Lang, Joax Wong, Andree Hartanto

Research Collection School of Social Sciences

Humans are easily distracted creatures. Our attention seems to constantly waver, shifting every second to different objects, sounds or stimuli. As we transition into adolescence, we start to hear the all-too familiar phrase that technology – smartphones, laptops, televisions and gaming devices – is an obstacle preventing us from reaching full productivity and sapping our attention. In Distracted: Why Students Can’t Focus and What You Can Do About It, James Lang describes the oftentimes complicated and false conceptions about distraction and the part that technology plays in it. Currently, society places extremely high demands on students and expects them to …

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Inter-Retailer Channel Competition: Empirical Analyses Of Store Entry Effects On Online Purchases, Qian Tang, Mei Lin, Youngsoo Kim Aug 2021

Inter-Retailer Channel Competition: Empirical Analyses Of Store Entry Effects On Online Purchases, Qian Tang, Mei Lin, Youngsoo Kim

Research Collection School Of Computing and Information Systems

This study empirically examines the effect of offline store entry on a competing online retailer in the footwear industry and investigates how this effect depends on the relative product assortment and price between the offline store and the online retailer. Using transaction data from a large online footwear retailer and offline store entry data from 19 major shoe retail chains and 3 department store chains, we quantify the entry effect of offline stores. Categorizing offline stores by assortment and price, we find that the entry of regular-price narrow-assortment stores generates a complementary effect that increases online purchases, while the entry …

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Non-Precluded Measures Clauses: Regime, Trends And Practice, Dilini Pathirana, Mark Mclaughlin Aug 2021

Non-Precluded Measures Clauses: Regime, Trends And Practice, Dilini Pathirana, Mark Mclaughlin

Research Collection Yong Pung How School Of Law

“Non-precluded measures” (NPM) clauses have become a fixture of the modern international investment regime. As an integral aspect of attempts to recalibrate the public-private balance in investment treaties, these clauses are intended as a corrective to the pro-investor interpretations of early arbitral tribunals. They expressly provide for the primacy of public policy over investment protection standards under certain conditions. This contribution seeks to identify trends in the drafting of NPM clauses and identify their common components. It will categorize the conditions that must be satisfied in order that host States can have recourse to them, as well as the role …

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Context-Aware Outstanding Fact Mining From Knowledge Graphs, Yueji Yang, Yuchen Li, Panagiotis Karras, Anthony Tung Aug 2021

Context-Aware Outstanding Fact Mining From Knowledge Graphs, Yueji Yang, Yuchen Li, Panagiotis Karras, Anthony Tung

Research Collection School Of Computing and Information Systems

An Outstanding Fact (OF) is an attribute that makes a target entity stand out from its peers. The mining of OFs has important applications, especially in Computational Journalism, such as news promotion, fact-checking, and news story finding. However, existing approaches to OF mining: (i) disregard the context in which the target entity appears, hence may report facts irrelevant to that context; and (ii) require relational data, which are often unavailable or incomplete in many application domains. In this paper, we introduce the novel problem of mining Contextaware Outstanding Facts (COFs) for a target entity under a given context specified by …

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Estimating Homophily In Social Networks Using Dyadic Predictions, George Berry, Antonio Sirianni, Ingmar Weber, Jisun An, Michael Macy Aug 2021

Estimating Homophily In Social Networks Using Dyadic Predictions, George Berry, Antonio Sirianni, Ingmar Weber, Jisun An, Michael Macy

Research Collection School Of Computing and Information Systems

Predictions of node categories are commonly used to estimate homophily and other relational properties in networks. However, little is known about the validity of using predictions for this task. We show that estimating homophily in a network is a problem of predicting categories of dyads (edges) in the graph. Homophily estimates are unbiased when predictions of dyad categories are unbiased. Node-level prediction models, such as the use of names to classify ethnicity or gender, do not generally produce unbiased predictions of dyad categories and therefore produce biased homophily estimates. Bias comes from three sources: sampling bias, correlation between model errors …

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Effective Digital Learning Practices For Is Design Courses During Covid-19, Eng Lieh Ouh, Benjamin Gan Aug 2021

Effective Digital Learning Practices For Is Design Courses During Covid-19, Eng Lieh Ouh, Benjamin Gan

Research Collection School Of Computing and Information Systems

The COVID-19 pandemic has pushed educational institutions to adopt digital learning for an extended period. This research studies the effectiveness of digital learning practices based on student feedback data collected for two Information Systems design courses: human interaction design and solution architecture design. This paper leverages the data to analyze the effectiveness of a set of digital learning practices: ZOOM lectures, polling or Kahoot questions, self-reflection, virtual exercises and virtual mentorship. Our research questions are on the effectiveness of these learning practices to keep the student’s interest and learn the course materials. The research compares each learning practice and 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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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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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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Code Integrity Attestation For Plcs Using Black Box Neural Network Predictions, Yuqi Chen, Christopher M. Poskitt, Jun Sun Aug 2021

Code Integrity Attestation For Plcs Using Black Box Neural Network Predictions, Yuqi Chen, Christopher M. Poskitt, Jun Sun

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPSs) are widespread in critical domains, and significant damage can be caused if an attacker is able to modify the code of their programmable logic controllers (PLCs). Unfortunately, traditional techniques for attesting code integrity (i.e. verifying that it has not been modified) rely on firmware access or roots-of-trust, neither of which proprietary or legacy PLCs are likely to provide. In this paper, we propose a practical code integrity checking solution based on privacy-preserving black box models that instead attest the input/output behaviour of PLC programs. Using faithful offline copies of the PLC programs, we identify their most important …

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