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Testing Automated Driving Systems By Breaking Many Laws Efficiently, Xiaodong Zhang, Wei Zhao, Yang Sun, Jun Sun, Yulong Shen, Xuewen Dong, Zijiang Yang Jul 2023

Testing Automated Driving Systems By Breaking Many Laws Efficiently, Xiaodong Zhang, Wei Zhao, Yang Sun, Jun Sun, Yulong Shen, Xuewen Dong, Zijiang Yang

Research Collection School Of Computing and Information Systems

An automated driving system (ADS), as the brain of an autonomous vehicle (AV), should be tested thoroughly ahead of deployment. ADS must satisfy a complex set of rules to ensure road safety, e.g., the existing traffic laws and possibly future laws that are dedicated to AVs. To comprehensively test an ADS, we would like to systematically discover diverse scenarios in which certain traffic law is violated. The challenge is that (1) there are many traffic laws (e.g., 13 testable articles in Chinese traffic laws and 16 testable articles in Singapore traffic laws, with 81 and 43 violation situations respectively); and …

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Cheer: Centrality-Aware High-Order Event Reasoning Network For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Yan Zhang, Zhiwei Liu Jul 2023

Cheer: Centrality-Aware High-Order Event Reasoning Network For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Yan Zhang, Zhiwei Liu

Research Collection School Of Computing and Information Systems

Document-level Event Causality Identification (DECI) aims to recognize causal relations between events within a document. Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures — there are one or two “central” events that prevail throughout the document, with most other events serving as either their cause or consequence. In this paper, we manually annotate central events for a systematical investigation and propose a novel DECI model, CHEER, which performs high-order reasoning while considering event centrality. First, we summarize a general GNN-based DECI model and provide a unified view for better understanding. Second, we …

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Cone: An Efficient Coarse-To-Fine Alignment Framework For Long Video Temporal Grounding, Zhijian Hou, Wanjun Zhong, Lei Ji, Difei Gao, Kun Yan, Wing-Kwong Chan, Chong-Wah Ngo, Mike Z. Shou, Nan. Duan Jul 2023

Cone: An Efficient Coarse-To-Fine Alignment Framework For Long Video Temporal Grounding, Zhijian Hou, Wanjun Zhong, Lei Ji, Difei Gao, Kun Yan, Wing-Kwong Chan, Chong-Wah Ngo, Mike Z. Shou, Nan. Duan

Research Collection School Of Computing and Information Systems

This paper tackles an emerging and challenging problem of long video temporal grounding (VTG) that localizes video moments related to a natural language (NL) query. Compared with short videos, long videos are also highly demanded but less explored, which brings new challenges in higher inference computation cost and weaker multi-modal alignment. To address these challenges, we propose CONE, an efficient COarse-to-fiNE alignment framework. CONE is a plug-and-play framework on top of existing VTG models to handle long videos through a sliding window mechanism. Specifically, CONE (1) introduces a query-guided window selection strategy to speed up inference, and (2) proposes a …

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Fine-Grained Domain Adaptive Crowd Counting Via Point-Derived Segmentation, Yongtuo Liu, Dan Xu, Sucheng Ren, Hanjie Wu, Hongmin Cai, Shengfeng He Jul 2023

Fine-Grained Domain Adaptive Crowd Counting Via Point-Derived Segmentation, Yongtuo Liu, Dan Xu, Sucheng Ren, Hanjie Wu, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

Due to domain shift, a large performance drop is usually observed when a trained crowd counting model is deployed in the wild. While existing domain-adaptive crowd counting methods achieve promising results, they typically regard each crowd image as a whole and reduce domain discrepancies in a holistic manner, thus limiting further improvement of domain adaptation performance. To this end, we propose to untangle domain-invariant crowd and domain-specific background from crowd images and design a fine-grained domain adaption method for crowd counting. Specifically, to disentangle crowd from background, we propose to learn crowd segmentation from point-level crowd counting annotations in a …

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Diaasq: A Benchmark Of Conversational Aspect-Based Sentiment Quadruple Analysis, Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, Donghong Ji Jul 2023

Diaasq: A Benchmark Of Conversational Aspect-Based Sentiment Quadruple Analysis, Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, Donghong Ji

Research Collection School Of Computing and Information Systems

The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. To bridge the gap between fine-grained sentiment analysis and conversational opinion mining, in this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We deliberately develop a neural model to benchmark …

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Recognizing Hand Gestures Using Solar Cells, Dong Ma, Guohao Lan, Mahbub Hassan, Wen Hu, B. Mushfika Upama, Ashraf Uddin, Youseef, Moustafa Jul 2023

Recognizing Hand Gestures Using Solar Cells, Dong Ma, Guohao Lan, Mahbub Hassan, Wen Hu, B. Mushfika Upama, Ashraf Uddin, Youseef, Moustafa

Research Collection School Of Computing and Information Systems

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its discernible signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for …

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Singapore's Ai Applications In The Public Sector: Six Examples, Steven M. Miller Jul 2023

Singapore's Ai Applications In The Public Sector: Six Examples, Steven M. Miller

Research Collection School Of Computing and Information Systems

Steven M. Miller describes six instances in which Singapore has applied AI in the public sector, illustrating different ways of improving its engagement with the public by making government services more accessible, anywhere, anytime, and speeding its responses to public processes and feedback. He illustrates how its leaders made the city a living lab for AI use, and what they learned.

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Imitation Improvement Learning For Large-Scale Capacitated Vehicle Routing Problems, The Viet Bui, Tien Mai Jul 2023

Imitation Improvement Learning For Large-Scale Capacitated Vehicle Routing Problems, The Viet Bui, Tien Mai

Research Collection School Of Computing and Information Systems

Recent works using deep reinforcement learning (RL) to solve routing problems such as the capacitated vehicle routing problem (CVRP) have focused on improvement learning-based methods, which involve improving a given solution until it becomes near-optimal. Although adequate solutions can be achieved for small problem instances, their efficiency degrades for large-scale ones. In this work, we propose a newimprovement learning-based framework based on imitation learning where classical heuristics serve as experts to encourage the policy model to mimic and produce similar or better solutions. Moreover, to improve scalability, we propose Clockwise Clustering, a novel augmented framework for decomposing large-scale CVRP into …

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Seed Selection For Testing Deep Neural Networks, Yuhan Zhi, Xiaofei Xie, Chao Shen, Jun Sun, Xiaoyu Zhang, Xiaohong Guan Jul 2023

Seed Selection For Testing Deep Neural Networks, Yuhan Zhi, Xiaofei Xie, Chao Shen, Jun Sun, Xiaoyu Zhang, Xiaohong Guan

Research Collection School Of Computing and Information Systems

Deep learning (DL) has been applied in many applications. Meanwhile, the quality of DL systems is becoming a big concern. To evaluate the quality of DL systems, a number of DL testing techniques have been proposed. To generate test cases, a set of initial seed inputs are required. Existing testing techniques usually construct seed corpus by randomly selecting inputs from training or test dataset. Till now, there is no study on how initial seed inputs affect the performance of DL testing and how to construct an optimal one. To fill this gap, we conduct the first systematic study to evaluate …

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Adaptive Split-Fusion Transformer, Zixuan Su, Jingjing Chen, Lei Pang, Chong-Wah Ngo, Yu-Gang Jiang Jul 2023

Adaptive Split-Fusion Transformer, Zixuan Su, Jingjing Chen, Lei Pang, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Neural networks for visual content understanding have recently evolved from convolutional ones to transformers. The prior (CNN) relies on small-windowed kernels to capture the regional clues, demonstrating solid local expressiveness. On the contrary, the latter (transformer) establishes long-range global connections between localities for holistic learning. Inspired by this complementary nature, there is a growing interest in designing hybrid models which utilize both techniques. Current hybrids merely replace convolutions as simple approximations of linear projection or juxtapose a convolution branch with attention without considering the importance of local/global modeling. To tackle this, we propose a new hybrid named Adaptive Split-Fusion Transformer …

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Take A Break In The Middle: Investigating Subgoals Towards Hierarhical Script Generation, Xinze Li, Yixin Cao, Muhao Chen, Aixin Sun Jul 2023

Take A Break In The Middle: Investigating Subgoals Towards Hierarhical Script Generation, Xinze Li, Yixin Cao, Muhao Chen, Aixin Sun

Research Collection School Of Computing and Information Systems

Goal-oriented Script Generation is a new task of generating a list of steps that can fulfill the given goal. In this paper, we propose to extend the task from the perspective of cognitive theory. Instead of a simple flat structure, the steps are typically organized hierarchically — Human often decompose a complex task into subgoals, where each subgoal can be further decomposed into steps. To establish the benchmark, we contribute a new dataset, propose several baseline methods, and set up evaluation metrics. Both automatic and human evaluation verify the high-quality of dataset, as well as the effectiveness of incorporating subgoals …

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Synthesizing Speech Test Cases With Text-To-Speech? An Empirical Study On The False Alarms In Automated Speech Recognition Testing, Julia Kaiwen Lau, Kelvin Kai Wen Kong, Julian Hao Yong, Per Hoong Tan, Zhou Yang, Zi Qian Yong, Joshua Chern Wey Low, Chun Yong Chong, Mei Kuan Lim, David Lo Jul 2023

Synthesizing Speech Test Cases With Text-To-Speech? An Empirical Study On The False Alarms In Automated Speech Recognition Testing, Julia Kaiwen Lau, Kelvin Kai Wen Kong, Julian Hao Yong, Per Hoong Tan, Zhou Yang, Zi Qian Yong, Joshua Chern Wey Low, Chun Yong Chong, Mei Kuan Lim, David Lo

Research Collection School Of Computing and Information Systems

Recent studies have proposed the use of Text-To-Speech (TTS) systems to automatically synthesise speech test cases on a scale and uncover a large number of failures in ASR systems. However, the failures uncovered by synthetic test cases may not reflect the actual performance of an ASR system when it transcribes human audio, which we refer to as false alarms. Given a failed test case synthesised from TTS systems, which consists of TTS-generated audio and the corresponding ground truth text, we feed the human audio stating the same text to an ASR system. If human audio can be correctly transcribed, an …

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Mitigating Adversarial Attacks On Data-Driven Invariant Checkers For Cyber-Physical Systems, Rajib Ranjan Maiti, Cheah Huei Yoong, Venkata Reddy Palleti, Arlindo Silva, Christopher M. Poskitt Jul 2023

Mitigating Adversarial Attacks On Data-Driven Invariant Checkers For Cyber-Physical Systems, Rajib Ranjan Maiti, Cheah Huei Yoong, Venkata Reddy Palleti, Arlindo Silva, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

The use of invariants in developing security mechanisms has become an attractive research area because of their potential to both prevent attacks and detect attacks in Cyber-Physical Systems (CPS). In general, an invariant is a property that is expressed using design parameters along with Boolean operators and which always holds in normal operation of a system, in particular, a CPS. Invariants can be derived by analysing operational data of various design parameters in a running CPS, or by analysing the system's requirements/design documents, with both of the approaches demonstrating significant potential to detect and prevent cyber-attacks on a CPS. While …

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Transfer-Once-For-All: Ai Model Optimization For Edge, Achintya Kundu, Laura Wynter, Rhui Dih Lee, Luis Angel Bathen Jul 2023

Transfer-Once-For-All: Ai Model Optimization For Edge, Achintya Kundu, Laura Wynter, Rhui Dih Lee, Luis Angel Bathen

Research Collection School Of Computing and Information Systems

Weight-sharing neural architecture search aims to optimize a configurable neural network model (supernet) for a variety of deployment scenarios across many devices with different resource constraints. Existing approaches use evolutionary search to extract models of different sizes from a supernet trained on a very large data set, and then fine-tune the extracted models on the typically small, real-world data set of interest. The computational cost of training thus grows linearly with the number of different model deployment scenarios. Hence, we propose Transfer-Once-For-All (TOFA) for supernet-style training on small data sets with constant computational training cost over any number of edge …

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A Review On The Effects Of Chanting And Solfeggio Frequencies On Well-Being, Xuyu Yang, Fiona Fui-Hoon Nah, Fen Lin Jul 2023

A Review On The Effects Of Chanting And Solfeggio Frequencies On Well-Being, Xuyu Yang, Fiona Fui-Hoon Nah, Fen Lin

Research Collection School Of Computing and Information Systems

This paper presents our literature review on how chanting and solfeggio frequencies affect brain activity, enhance well-being, and inform the design of sound therapy. We call for more scientific research to investigate and better understand the effects of chanting and solfeggio frequencies on well-being.

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Understanding The Role Of External Pull Requests In The Npm Ecosystem, Vittunyuta Maeprasart, Supatsara Wattanakriengkrai, Raula Gaikovina Kula, Christoph Treude, Kenichi Matsumoto Jul 2023

Understanding The Role Of External Pull Requests In The Npm Ecosystem, Vittunyuta Maeprasart, Supatsara Wattanakriengkrai, Raula Gaikovina Kula, Christoph Treude, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

The risk to using third-party libraries in a software application is that much needed maintenance is solely carried out by library maintainers. These libraries may rely on a core team of maintainers (who might be a single maintainer that is unpaid and overworked) to serve a massive client user-base. On the other hand, being open source has the benefit of receiving contributions (in the form of External PRs) to help fix bugs and add new features. In this paper, we investigate the role by which External PRs (contributions from outside the core team of maintainers) contribute to a library. Through …

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The Impact Of A Continuous Integration Service On The Delivery Time Of Merged Pull Requests, João Helis Bernardo, Daniel Alencar Da Costa, Uirá Kulesza, Christoph Treude Jul 2023

The Impact Of A Continuous Integration Service On The Delivery Time Of Merged Pull Requests, João Helis Bernardo, Daniel Alencar Da Costa, Uirá Kulesza, Christoph Treude

Research Collection School Of Computing and Information Systems

Continuous Integration (CI) is a software development practice that builds and tests software frequently (e.g., at every push). One main motivator to adopt CI is the potential to deliver software functionalities more quickly than not using CI. However, there is little empirical evidence to support that CI helps projects deliver software functionalities more quickly. Through the analysis of 162,653 pull requests (PRs) of 87 GitHub projects, we empirically study whether adopting a CI service (TRAVISCI) can quicken the time to deliver merged PRs. We complement our quantitative study by analyzing 450 survey responses from participants of 73 software projects. Our …

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Socialz: Multi-Feature Social Fuzz Testing, Francisco Zanartu, Christoph Treude, Markus Wagner Jul 2023

Socialz: Multi-Feature Social Fuzz Testing, Francisco Zanartu, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Online social networks have become an integral aspect of our daily lives and play a crucial role in shaping our relationships with others. However, bugs and glitches, even minor ones, can cause anything from frustrating problems to serious data leaks that can have farreaching impacts on millions of users. To mitigate these risks, fuzz testing, a method of testing with randomised inputs, can provide increased confidence in the correct functioning of a social network. However, implementing traditional fuzz testing methods can be prohibitively difficult or impractical for programmers outside of the network’s development team. To tackle this challenge, we present …

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Learning To Ask Clarification Questions With Spatial Reasoning, Yang Deng, Shuaiyi Li, Wai Lam Jul 2023

Learning To Ask Clarification Questions With Spatial Reasoning, Yang Deng, Shuaiyi Li, Wai Lam

Research Collection School Of Computing and Information Systems

Asking clarifying questions has become a key element of various conversational systems, allowing for an effective resolution of ambiguity and uncertainty through natural language questions. Despite the extensive applications of spatial information grounded dialogues, it remains an understudied area on learning to ask clarification questions with the capability of spatial reasoning. In this work, we propose a novel method, named SpatialCQ, for this problem. Specifically, we first align the representation space between textual and spatial information by encoding spatial states with textual descriptions. Then a multi-relational graph is constructed to capture the spatial relations and enable spatial reasoning with relational …

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Towards Robust Personalized Dialogue Generation Via Order-Insensitive Representation Regularization, Liang Chen, Hongru Wang, Yang Deng, Wai-Chung Kwan, Zezhong Wang, Kam-Fai Wong Jul 2023

Towards Robust Personalized Dialogue Generation Via Order-Insensitive Representation Regularization, Liang Chen, Hongru Wang, Yang Deng, Wai-Chung Kwan, Zezhong Wang, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Generating persona consistent dialogue response is important for developing an intelligent conversational agent. Recent works typically fine-tune large-scale pre-trained models on this task by concatenating persona texts and dialogue history as a single input sequence to generate the target response. While simple and effective, our analysis shows that this popular practice is seriously affected by order sensitivity where different input orders of persona sentences significantly impact the quality and consistency of generated response, resulting in severe performance fluctuations (i.e., 29.4% on GPT2 and 83.2% on BART). To mitigate the order sensitivity problem, we propose a model-agnostic framework, ORder Insensitive Generation …

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Semantic-Based Neural Network Repair, Richard Schumi, Jun Sun Jul 2023

Semantic-Based Neural Network Repair, Richard Schumi, Jun Sun

Research Collection School Of Computing and Information Systems

Recently, neural networks have spread into numerous fields including many safety-critical systems. Neural networks are built (and trained) by programming in frameworks such as TensorFlow and PyTorch. Developers apply a rich set of pre-defined layers to manually program neural networks or to automatically generate them (e.g., through AutoML). Composing neural networks with different layers is error-prone due to the non-trivial constraints that must be satisfied in order to use those layers. In this work, we propose an approach to automatically repair erroneous neural networks. The challenge is in identifying a minimal modification to the network so that it becomes valid. …

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Proactive Conversational Agents In The Post-Chatgpt World, Lizi Liao, Grace Hui Yang, Chirag Shah Jul 2023

Proactive Conversational Agents In The Post-Chatgpt World, Lizi Liao, Grace Hui Yang, Chirag Shah

Research Collection School Of Computing and Information Systems

ChatGPT and similar large language model (LLM) based conversational agents have brought shock waves to the research world. Although astonished by their human-like performance, we find they share a significant weakness with many other existing conversational agents in that they all take a passive approach in responding to user queries. This limits their capacity to understand the users and the task better and to offer recommendations based on a broader context than a given conversation. Proactiveness is still missing in these agents, including their ability to initiate a conversation, shift topics, or offer recommendations that take into account a more …

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Information Screening Whilst Exploiting! Multimodal Relation Extraction With Feature Denoising And Multimodal Topic Modeling, Shengqiong Wu, Hao Fei, Yixin Cao, Lidong Bing, Tat-Seng Chua Jul 2023

Information Screening Whilst Exploiting! Multimodal Relation Extraction With Feature Denoising And Multimodal Topic Modeling, Shengqiong Wu, Hao Fei, Yixin Cao, Lidong Bing, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Existing research on multimodal relation extraction (MRE) faces two co-existing challenges, internal-information over-utilization and external-information under-exploitation. To combat that, we propose a novel framework that simultaneously implements the idea of internal-information screening and external-information exploiting. First, we represent the fine-grained semantic structures of the input image and text with the visual and textual scene graphs, which are further fused into a unified cross-modal graph (CMG). Based on CMG, we perform structure refinement with the guidance of the graph information bottleneck principle, actively denoising the less-informative features. Next, we perform topic modeling over the input image and text, incorporating latent multimodal …

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Few-Shot Event Detection: An Empirical Study And A Unified View, Yubo Ma, Zehao Wang, Yixin Cao, Aixin Sun Jul 2023

Few-Shot Event Detection: An Empirical Study And A Unified View, Yubo Ma, Zehao Wang, Yixin Cao, Aixin Sun

Research Collection School Of Computing and Information Systems

Few-shot event detection (ED) has been widely studied, while this brings noticeable discrepancies, e.g., various motivations, tasks, and experimental settings, that hinder the understanding of models for future progress. This paper presents a thorough empirical study, a unified view of ED models, and a better unified baseline. For fair evaluation, we compare 12 representative methods on three datasets, which are roughly grouped into prompt-based and prototype-based models for detailed analysis. Experiments consistently demonstrate that prompt-based methods, including ChatGPT, still significantly trail prototype-based methods in terms of overall performance. To investigate their superior performance, we break down their design elements along …

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Context-Aware Neural Fault Localization, Zhuo Zhang, Xiaoguang Mao, Meng Yan, Xin Xia, David Lo, David Lo Jul 2023

Context-Aware Neural Fault Localization, Zhuo Zhang, Xiaoguang Mao, Meng Yan, Xin Xia, David Lo, David Lo

Research Collection School Of Computing and Information Systems

Numerous fault localization techniques identify suspicious statements potentially responsible for program failures by discovering the statistical correlation between test results (i.e., failing or passing) and the executions of the different statements of a program (i.e., covered or not covered). They rarely incorporate a failure context into their suspiciousness evaluation despite the fact that a failure context showing how a failure is produced is useful for analyzing and locating faults. Since a failure context usually contains the transitive relationships among the statements of causing a failure, its relationship complexity becomes one major obstacle for the context incorporation in suspiciousness evaluation of …

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Balancing Privacy And Flexibility Of Cloud-Based Personal Health Records Sharing System, Yudi Zhang, Fuchun Guo, Willy Susilo, Guomin Yang Jul 2023

Balancing Privacy And Flexibility Of Cloud-Based Personal Health Records Sharing System, Yudi Zhang, Fuchun Guo, Willy Susilo, Guomin Yang

Research Collection School Of Computing and Information Systems

The Internet of Things and cloud services have been widely adopted in many applications, and personal health records (PHR) can provide tailored medical care. The PHR data is usually stored on cloud servers for sharing. Weighted attribute-based encryption (ABE) is a practical and flexible technique to protect PHR data. Under a weighted ABE policy, the data user's attributes will be “scored”, if and only if the score reaches the threshold value, he/she can access the data. However, while this approach offers a flexible access policy, the data owners have difficulty controlling their privacy, especially sharing PHR data in collaborative e-health …

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Binalign: Alignment Padding Based Compiler Provenance Recovery, Maliha Ismail, Yan Lin, Donggyun Han, Debin Gao Jul 2023

Binalign: Alignment Padding Based Compiler Provenance Recovery, Maliha Ismail, Yan Lin, Donggyun Han, Debin Gao

Research Collection School Of Computing and Information Systems

Compiler provenance is significant in investigating the source-level indicators of binary code, like development-environment, source compiler, and optimization settings. Not only does compiler provenance analysis have important security applications in malware and vulnerability analysis, but it is also very challenging to extract useful artifacts from binary when high-level language constructs are missing. Previous works applied machine-learning techniques to predict the source compiler of binaries. However, most of the work is done on the binaries compiled on Linux operating system. We highlight the importance and need to explore Windows compilers and the complicated binaries compiled on the latest versions of these …

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Reducing Spatial Labeling Redundancy For Active Semi-Supervised Crowd Counting, Yongtuo Liu, Sucheng Ren, Liangyu Chai, Hanjie Wu, Dan Xu, Jing Qin, Shengfeng He Jul 2023

Reducing Spatial Labeling Redundancy For Active Semi-Supervised Crowd Counting, Yongtuo Liu, Sucheng Ren, Liangyu Chai, Hanjie Wu, Dan Xu, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Labeling is onerous for crowd counting as it should annotate each individual in crowd images. Recently, several methods have been proposed for semi-supervised crowd counting to reduce the labeling efforts. Given a limited labeling budget, they typically select a few crowd images and densely label all individuals in each of them. Despite the promising results, we argue the None-or-All labeling strategy is suboptimal as the densely labeled individuals in each crowd image usually appear similar while the massive unlabeled crowd images may contain entirely diverse individuals. To this end, we propose to break the labeling chain of previous methods and …

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Prompt To Be Consistent Is Better Than Self-Consistent? Few-Shot And Zero-Shot Fact Verification With Pre-Trained Language Models, Fengzhu Zeng, Wei Gao Jul 2023

Prompt To Be Consistent Is Better Than Self-Consistent? Few-Shot And Zero-Shot Fact Verification With Pre-Trained Language Models, Fengzhu Zeng, Wei Gao

Research Collection School Of Computing and Information Systems

Few-shot or zero-shot fact verification only relies on a few or no labeled training examples. In this paper, we propose a novel method called ProToCo, to Prompt pre-trained language models (PLMs) To be Consistent, for improving the factuality assessment capability of PLMs in the few-shot and zero-shot settings. Given a claim-evidence pair, ProToCo generates multiple variants of the claim with different relations and frames a simple consistency mechanism as constraints for making compatible predictions across these variants. We update PLMs by using parameter-efficient fine-tuning (PEFT), leading to more accurate predictions in few-shot and zero-shot fact verification tasks. Our experiments on …

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Safe Mdp Planning By Learning Temporal Patterns Of Undesirable Trajectories And Averting Negative Side Effects, Siow Meng Low, Akshat Kumar, Scott Sanner Jul 2023

Safe Mdp Planning By Learning Temporal Patterns Of Undesirable Trajectories And Averting Negative Side Effects, Siow Meng Low, Akshat Kumar, Scott Sanner

Research Collection School Of Computing and Information Systems

In safe MDP planning, a cost function based on the current state and action is often used to specify safety aspects. In real world, often the state representation used may lack sufficient fidelity to specify such safety constraints. Operating based on an incomplete model can often produce unintended negative side effects (NSEs). To address these challenges, first, we associate safety signals with state-action trajectories (rather than just immediate state-action). This makes our safety model highly general. We also assume categorical safety labels are given for different trajectories, rather than a numerical cost function, which is harder to specify by the …

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