Open Access. Powered by Scholars. Published by Universities.®
Artificial Intelligence and Robotics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Deep learning (2)
- Test case generation (2)
- 3D Point Cloud (1)
- AI frameworks (1)
- AI libraries (1)
-
- AI model generation (1)
- Adversarial patch attack (1)
- Benchmark study (1)
- Bi-Objective (1)
- Bias (1)
- Conversational agents (1)
- Convolutional Neural (1)
- Cyber-physical systems (1)
- Data distribution (1)
- Deep Learning (1)
- Deep learning models (1)
- Deep learning testing (1)
- Disambiguation (1)
- Dynamic Vehicle Routing Problem (1)
- Fairness bug (1)
- Indoor human localization (1)
- Library vulnerabilities (1)
- Machine vision (1)
- Metamorphic testing (1)
- Model validation (1)
- Natural language processing (1)
- Networks (1)
- Neural networks (1)
- Object detection (1)
- Physical attacks (1)
Articles 1 - 12 of 12
Full-Text Articles in Artificial Intelligence and Robotics
Biasfinder: Metamorphic Test Generation To Uncover Bias For Sentiment Analysis Systems, Muhammad Hilmi Asyrofi, Zhou Yang, Imam Nur Bani Yusuf, Hong Jin Kang, Thung Ferdian, David Lo
Biasfinder: Metamorphic Test Generation To Uncover Bias For Sentiment Analysis Systems, Muhammad Hilmi Asyrofi, Zhou Yang, Imam Nur Bani Yusuf, Hong Jin Kang, Thung Ferdian, David Lo
Research Collection School Of Computing and Information Systems
Artificial intelligence systems, such as Sentiment Analysis (SA) systems, typically learn from large amounts of data that may reflect human bias. Consequently, such systems may exhibit unintended demographic bias against specific characteristics (e.g., gender, occupation, country-of-origin, etc.). Such bias manifests in an SA system when it predicts different sentiments for similar texts that differ only in the characteristic of individuals described. To automatically uncover bias in SA systems, this paper presents BiasFinder, an approach that can discover biased predictions in SA systems via metamorphic testing. A key feature of BiasFinder is the automatic curation of suitable templates from any given …
Recipegen++: An Automated Trigger Action Programs Generator, Imam Nur Bani Yusuf, Diyanah Abdul Jamal, Lingxiao Jiang, David Lo
Recipegen++: An Automated Trigger Action Programs Generator, Imam Nur Bani Yusuf, Diyanah Abdul Jamal, Lingxiao Jiang, David Lo
Research Collection School Of Computing and Information Systems
Trigger Action Programs (TAPs) are event-driven rules that allow users to automate smart-devices and internet services. Users can write TAPs by specifying triggers and actions from a set of predefined channels and functions. Despite its simplicity, composing TAPs can still be challenging for users due to the enormous search space of available triggers and actions. The growing popularity of TAPs is followed by the increasing number of supported devices and services, resulting in a huge number of possible combinations between triggers and actions. Motivated by such a fact, we improve our prior work and propose RecipeGen++, a deep-learning-based approach that …
Physical Adversarial Attack On A Robotic Arm, Yifan Jia, Christopher M. Poskitt, Jun Sun, Sudipta Chattopadhyay
Physical Adversarial Attack On A Robotic Arm, Yifan Jia, Christopher M. Poskitt, Jun Sun, Sudipta Chattopadhyay
Research Collection School Of Computing and Information Systems
Collaborative Robots (cobots) are regarded as highly safety-critical cyber-physical systems (CPSs) owing to their close physical interactions with humans. In settings such as smart factories, they are frequently augmented with AI. For example, in order to move materials, cobots utilize object detectors based on deep learning models. Deep learning, however, has been demonstrated as vulnerable to adversarial attacks: a minor change (noise) to benign input can fool the underlying neural networks and lead to a different result. While existing works have explored such attacks in the context of picture/object classification, less attention has been given to attacking neural networks used …
Answer Summarization For Technical Queries: Benchmark And New Approach, Chengran Yang, Bowen Xu, Ferdian Thung, Yucen Shi, Ting Zhang, Zhou Yang, Xin Zhou, Jieke Shi, Junda He, Donggyun Han, David Lo
Answer Summarization For Technical Queries: Benchmark And New Approach, Chengran Yang, Bowen Xu, Ferdian Thung, Yucen Shi, Ting Zhang, Zhou Yang, Xin Zhou, Jieke Shi, Junda He, Donggyun Han, David Lo
Research Collection School Of Computing and Information Systems
Prior studies have demonstrated that approaches to generate an answer summary for a given technical query in Software Question and Answer (SQA) sites are desired. We find that existing approaches are assessed solely through user studies. Hence, a new user study needs to be performed every time a new approach is introduced; this is time-consuming, slows down the development of the new approach, and results from different user studies may not be comparable to each other. There is a need for a benchmark with ground truth summaries as a complement assessment through user studies. Unfortunately, such a benchmark is non-existent …
An Empirical Study On Data Distribution-Aware Test Selection For Deep Learning Enhancement, Qiang Hu, Yuejun Guo, Maxime Cordy, Xiaofei Xie, Lei Ma, Mike Papadakis, Yves Le Traon
An Empirical Study On Data Distribution-Aware Test Selection For Deep Learning Enhancement, Qiang Hu, Yuejun Guo, Maxime Cordy, Xiaofei Xie, Lei Ma, Mike Papadakis, Yves Le Traon
Research Collection School Of Computing and Information Systems
Similar to traditional software that is constantly under evolution, deep neural networks need to evolve upon the rapid growth of test data for continuous enhancement (e.g., adapting to distribution shift in a new environment for deployment). However, it is labor intensive to manually label all of the collected test data. Test selection solves this problem by strategically choosing a small set to label. Via retraining with the selected set, deep neural networks will achieve competitive accuracy. Unfortunately, existing selection metrics involve three main limitations: (1) using different retraining processes, (2) ignoring data distribution shifts, and (3) being insufficiently evaluated. To …
Test Mimicry To Assess The Exploitability Of Library Vulnerabilities, Hong Jin Kang, Truong Giang Nguyen, Bach Le, Corina S. Pasareanu, David Lo
Test Mimicry To Assess The Exploitability Of Library Vulnerabilities, Hong Jin Kang, Truong Giang Nguyen, Bach Le, Corina S. Pasareanu, David Lo
Research Collection School Of Computing and Information Systems
Modern software engineering projects often depend on open-source software libraries, rendering them vulnerable to potential security issues in these libraries. Developers of client projects have to stay alert of security threats in the software dependencies. While there are existing tools that allow developers to assess if a library vulnerability is reachable from a project, they face limitations. Call graphonly approaches may produce false alarms as the client project may not use the vulnerable code in a way that triggers the vulnerability, while test generation-based approaches faces difficulties in overcoming the intrinsic complexity of exploiting a vulnerability, where extensive domain knowledge …
Reinforcement Learning Approach To Solve Dynamic Bi-Objective Police Patrol Dispatching And Rescheduling Problem, Waldy Joe, Hoong Chuin Lau, Jonathan Pan
Reinforcement Learning Approach To Solve Dynamic Bi-Objective Police Patrol Dispatching And Rescheduling Problem, Waldy Joe, Hoong Chuin Lau, Jonathan Pan
Research Collection School Of Computing and Information Systems
Police patrol aims to fulfill two main objectives namely to project presence and to respond to incidents in a timely manner. Incidents happen dynamically and can disrupt the initially-planned patrol schedules. The key decisions to be made will be which patrol agent to be dispatched to respond to an incident and subsequently how to adapt the patrol schedules in response to such dynamically-occurring incidents whilst still fulfilling both objectives; which sometimes can be conflicting. In this paper, we define this real-world problem as a Dynamic Bi-Objective Police Patrol Dispatching and Rescheduling Problem and propose a solution approach that combines Deep …
Feasibility Studies In Indoor Localization Through Intelligent Conversation, Sheshadri Smitha, Linus Cheng, Kotaro Hara
Feasibility Studies In Indoor Localization Through Intelligent Conversation, Sheshadri Smitha, Linus Cheng, Kotaro Hara
Research Collection School Of Computing and Information Systems
We propose a model to achieve human localization in indoor environments through intelligent conversation between users and an agent. We investigated the feasibility of conversational localization by conducting two studies. First, we conducted a Wizard-of-Oz study with N = 7 participants and studied the feasibility of localizing users through conversation. We identified challenges posed by users’ language and behavior. Second, we collected N = 800 user descriptions of virtual indoor locations from N = 80 Amazon Mechanical Turk participants to analyze user language. We explored the effects of conversational agent behavior and observed that people describe indoor locations differently based …
Exais: Executable Ai Semantics, Richard Schumi, Jun Sun
Exais: Executable Ai Semantics, Richard Schumi, Jun Sun
Research Collection School Of Computing and Information Systems
Neural networks can be regarded as a new programming paradigm, i.e., instead of building ever-more complex programs through (often informal) logical reasoning in the programmers' mind, complex 'AI' systems are built by optimising generic neural network models with big data. In this new paradigm, AI frameworks such as TensorFlow and PyTorch play a key role, which is as essential as the compiler for traditional programs. It is known that the lack of a proper semantics for programming languages (such as C), i.e., a correctness specification for compilers, has contributed to many problematic program behaviours and security issues. While it is …
Benchmarking Library Recognition In Tweets, Ting Zhang, Divya Prabha Chandrasekaran, Ferdian Thung, David Lo
Benchmarking Library Recognition In Tweets, Ting Zhang, Divya Prabha Chandrasekaran, Ferdian Thung, David Lo
Research Collection School Of Computing and Information Systems
Software developers often use social media (such as Twitter) to shareprogramming knowledge such as new tools, sample code snippets,and tips on programming. One of the topics they talk about is thesoftware library. The tweets may contain useful information abouta library. A good understanding of this information, e.g., on thedeveloper’s views regarding a library can be beneficial to weigh thepros and cons of using the library as well as the general sentimentstowards the library. However, it is not trivial to recognize whethera word actually refers to a library or other meanings. For example,a tweet mentioning the word “pandas" may refer to …
Comai: Enabling Lightweight, Collaborative Intelligence By Retrofitting Vision Dnns, Kasthuri Jayarajah, Dhanuja Wanniarachchige, Tarek Abdelzaher, Archan Misra
Comai: Enabling Lightweight, Collaborative Intelligence By Retrofitting Vision Dnns, Kasthuri Jayarajah, Dhanuja Wanniarachchige, Tarek Abdelzaher, Archan Misra
Research Collection School Of Computing and Information Systems
While Deep Neural Network (DNN) models have transformed machine vision capabilities, their extremely high computational complexity and model sizes present a formidable deployment roadblock for AIoT applications. We show that the complexity-vs-accuracy-vs-communication tradeoffs for such DNN models can be significantly addressed via a novel, lightweight form of “collaborative machine intelligence” that requires only runtime changes to the inference process. In our proposed approach, called ComAI, the DNN pipelines of different vision sensors share intermediate processing state with one another, effectively providing hints about objects located within their mutually-overlapping Field-of-Views (FoVs). CoMAI uses two novel techniques: (a) a secondary shallow ML …
Riconv++: Effective Rotation Invariant Convolutions For 3d Point Clouds Deep Learning, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung
Riconv++: Effective Rotation Invariant Convolutions For 3d Point Clouds Deep Learning, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung
Research Collection School Of Computing and Information Systems
3D point clouds deep learning is a promising field of research that allows a neural network to learn features of point clouds directly, making it a robust tool for solving 3D scene understanding tasks. While recent works show that point cloud convolutions can be invariant to translation and point permutation, investigations of the rotation invariance property for point cloud convolution has been so far scarce. Some existing methods perform point cloud convolutions with rotation-invariant features, existing methods generally do not perform as well as translation-invariant only counterpart. In this work, we argue that a key reason is that compared to …