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Full-Text Articles in Entire DC Network
Reinforcement Learning-Based Interactive Video Search, Zhixin Ma, Jiaxin Wu, Zhijian Hou, Chong-Wah Ngo
Reinforcement Learning-Based Interactive Video Search, Zhixin Ma, Jiaxin Wu, Zhijian Hou, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Despite the rapid progress in text-to-video search due to the advancement of cross-modal representation learning, the existing techniques still fall short in helping users to rapidly identify the search targets. Particularly, in the situation that a system suggests a long list of similar candidates, the user needs to painstakingly inspect every search result. The experience is frustrated with repeated watching of similar clips, and more frustratingly, the search targets may be overlooked due to mental tiredness. This paper explores reinforcement learning-based (RL) searching to relieve the user from the burden of brute force inspection. Specifically, the system maintains a graph …
Learnings From A Pilot Hybrid Question Answering System: Cqas: Case Study Based On A Singapore Government Agency's Customer Service Centre, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh
Learnings From A Pilot Hybrid Question Answering System: Cqas: Case Study Based On A Singapore Government Agency's Customer Service Centre, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh
Research Collection School Of Computing and Information Systems
The Singapore Government first released their digital government blueprint in 2018 with the key message for all their agencies to be "digital to the core and served with heart". With this push, agencies are moving towards human-centric digital services, especially for individual citizens. During COVID-19, Singapore government agencies introduced many COVID-19 digital initiatives resulting in more incoming inquiries from citizens to respective agencies. This surge in inquiries created the challenge on the agencies' end to meet service level agreements. One widely adopted solution is the use of chatbot technology that directly interfaces with the customer. However, several organisations have faced …
Generative Flows With Invertible Attentions, Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Radu Timofte, Luc Van Gool
Generative Flows With Invertible Attentions, Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Radu Timofte, Luc Van Gool
Research Collection School Of Computing and Information Systems
Flow-based generative models have shown an excellent ability to explicitly learn the probability density function of data via a sequence of invertible transformations. Yet, learning attentions in generative flows remains understudied, while it has made breakthroughs in other domains. To fill the gap, this paper introduces two types of invertible attention mechanisms, i.e., map-based and transformer-based attentions, for both unconditional and conditional generative flows. The key idea is to exploit a masked scheme of these two attentions to learn long-range data dependencies in the context of generative flows. The masked scheme allows for invertible attention modules with tractable Jacobian determinants, …
Officers: Operational Framework For Intelligent Crime-And-Emergency Response Scheduling, Jonathan David Chase, Siong Thye Goh, Tran Phong, Hoong Chuin Lau
Officers: Operational Framework For Intelligent Crime-And-Emergency Response Scheduling, Jonathan David Chase, Siong Thye Goh, Tran Phong, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In the quest to achieve better response times in dense urban environments, law enforcement agencies are seeking AI-driven planning systems to inform their patrol strategies. In this paper, we present a framework, OFFICERS, for deployment planning that learns from historical data to generate deployment schedules on a daily basis. We accurately predict incidents using ST-ResNet, a deep learning technique that captures wide-ranging spatio-temporal dependencies, and solve a large-scale optimization problem to schedule deployment, significantly improving its scalability through a simulated annealing solver. Methodologically, our approach outperforms our previous works where prediction was done using Generative Adversarial Networks, and optimization was …
Shunted Self-Attention Via Multi-Scale Token Aggregation, Sucheng Ren, Daquan Zhou, Shengfeng He, Jiashi Feng, Xinchao Wang
Shunted Self-Attention Via Multi-Scale Token Aggregation, Sucheng Ren, Daquan Zhou, Shengfeng He, Jiashi Feng, Xinchao Wang
Research Collection School Of Computing and Information Systems
Recent Vision Transformer (ViT) models have demonstrated encouraging results across various computer vision tasks, thanks to its competence in modeling long-range dependencies of image patches or tokens via self-attention. These models, however, usually designate the similar receptive fields of each token feature within each layer. Such a constraint inevitably limits the ability of each self-attention layer in capturing multi-scale features, thereby leading to performance degradation in handling images with multiple objects of different scales. To address this issue, we propose a novel and generic strategy, termed shunted selfattention (SSA), that allows ViTs to model the attentions at hybrid scales per …
Who Will Support My Project? Interactive Search Of Potential Crowdfunding Investors Through Insearch., Songheng Zhang, Yong Wang, Haotian Li, Wanyu Zhang
Who Will Support My Project? Interactive Search Of Potential Crowdfunding Investors Through Insearch., Songheng Zhang, Yong Wang, Haotian Li, Wanyu Zhang
Research Collection School Of Computing and Information Systems
Crowdfunding provides project founders with a convenient way to reach online investors. However, it is challenging for founders to find the most potential investors and successfully raise money for their projects on crowdfunding platforms. A few machine learning based methods have been proposed to recommend investors’ interest in a specific crowdfunding project, but they fail to provide project founders with explanations in detail for these recommendations, thereby leading to an erosion of trust in predicted investors. To help crowdfunding founders find truly interested investors, we conducted semi-structured interviews with four crowdfunding experts and presentsinSearch, a visual analytic system. inSearch allows …
Causality-Based Neural Network Repair, Bing Sun, Jun Sun, Long H. Pham, Jie Shi
Causality-Based Neural Network Repair, Bing Sun, Jun Sun, Long H. Pham, Jie Shi
Research Collection School Of Computing and Information Systems
Neural networks have had discernible achievements in a wide range of applications. The wide-spread adoption also raises the concern of their dependability and reliability. Similar to traditional decision-making programs, neural networks can have defects that need to be repaired. The defects may cause unsafe behaviors, raise security concerns or unjust societal impacts. In this work, we address the problem of repairing a neural network for desirable properties such as fairness and the absence of backdoor. The goal is to construct a neural network that satisfies the property by (minimally) adjusting the given neural network's parameters (i.e., weights). Specifically, we propose …
Managing The Phaseout Of Coal Power: A Comparison Of Power Decarbonization Pathways In Jilin Province, Weirong Zhang, Zhixu Meng, Jiongjun Yang, Yan Song, Yiou Zhou, Changhong Zhao, Jiahai Yuan
Managing The Phaseout Of Coal Power: A Comparison Of Power Decarbonization Pathways In Jilin Province, Weirong Zhang, Zhixu Meng, Jiongjun Yang, Yan Song, Yiou Zhou, Changhong Zhao, Jiahai Yuan
Research Collection School Of Computing and Information Systems
With the periodic goals of reaching carbon emission peak before 2030 and achieving carbon neutrality before 2060 (“dual carbon” goals), China shows its unprecedented determination to coal power phaseout. This research takes Jilin Province to showcase possible pathways of coal power units’ phaseout on provincial level. We set up four different coal power phaseout scenarios, under which their transition cost and effectiveness would be calculated, respectively. In terms of natural resource endowment and electricity demand, Jilin Province would achieve a complete coal power phaseout by 2045 or even by 2040. However, after assessing the effectiveness of power transition under the …
Tatl: Task Agnostic Transfer Learning For Skin Attributes Detection, Duy M.H. Nguyen, Thu T. Nguyen, Huong Vu, Hong Quang Pham, Manh-Duy Nguyen, Binh T. Nguyen, Daniel Sonntag
Tatl: Task Agnostic Transfer Learning For Skin Attributes Detection, Duy M.H. Nguyen, Thu T. Nguyen, Huong Vu, Hong Quang Pham, Manh-Duy Nguyen, Binh T. Nguyen, Daniel Sonntag
Research Collection School Of Computing and Information Systems
Existing skin attributes detection methods usually initialize with a pre-trained Imagenet network and then fine-tune on a medical target task. However, we argue that such approaches are suboptimal because medical datasets are largely different from ImageNet and often contain limited training samples. In this work, we propose Task Agnostic Transfer Learning (TATL), a novel framework motivated by dermatologists’ behaviors in the skincare context. TATL learns an attribute-agnostic segmenter that detects lesion skin regions and then transfers this knowledge to a set of attribute-specific classifiers to detect each particular attribute. Since TATL’s attribute-agnostic segmenter only detects skin attribute regions, it enjoys …
Designing Visuo-Haptic Illusions With Proxies In Virtual Reality: Exploration Of Grasp, Movement Trajectory And Object Mass, Martin Feick, Kora Persephone Regitz, Anthony Tang, Antonio Kruger
Designing Visuo-Haptic Illusions With Proxies In Virtual Reality: Exploration Of Grasp, Movement Trajectory And Object Mass, Martin Feick, Kora Persephone Regitz, Anthony Tang, Antonio Kruger
Research Collection School Of Computing and Information Systems
Visuo-haptic illusions are a method to expand proxy-based interactions in VR by introducing unnoticeable discrepancies between the virtual and real world. Yet how different design variables affect the illusions with proxies is still unclear. To unpack a subset of variables, we conducted two user studies with 48 participants to explore the impact of (1) different grasping types and movement trajectories, and (2) different grasping types and object masses on the discrepancy which may be introduced. Our Bayes analysis suggests that grasping types and object masses (≤ 500 g) did not noticeably affect the discrepancy, but for movement trajectory, results were …
Tourgether360: Exploring 360° Tour Videos With Others, Kartikaeya Kumar, Lev Poretski, Jiannan Li, Anthony Tang
Tourgether360: Exploring 360° Tour Videos With Others, Kartikaeya Kumar, Lev Poretski, Jiannan Li, Anthony Tang
Research Collection School Of Computing and Information Systems
Contemporary 360° video players do not provide ways to let people explore the videos together. Tourgether360 addresses this problem for 360° tour videos using a pseudo-spatial navigation technique that provides both an overhead “context” view of the environment as a minimap, as well as a shared pseudo-3D environment for exploring the video. Collaborators appear as avatars along a track depending on their position in the video timeline and can point and synchronize their playback. In this work, we describe the intellectual precedents for this work, our design goals, and our implementation approach of Tourgether360. Finally, we discuss future work based …
Probabilistic Path Prioritization For Hybrid Fuzzing, Lei Zhao, Pengcheng Cao, Yue Duan, Heng Yin, Jifeng Xuan
Probabilistic Path Prioritization For Hybrid Fuzzing, Lei Zhao, Pengcheng Cao, Yue Duan, Heng Yin, Jifeng Xuan
Research Collection School Of Computing and Information Systems
Hybrid fuzzing that combines fuzzing and concolic execution has become an advanced technique for software vulnerability detection. Based on the observation that fuzzing and concolic execution are complementary in nature, state-of-the-art hybrid fuzzing systems deploy “optimal concolic testing” and “demand launch” strategies. Although these ideas sound intriguing, we point out several fundamental limitations in them, due to unrealistic or oversimplified assumptions. Further, we propose a novel “discriminative dispatch” strategy and design a probabilistic hybrid fuzzing system to better utilize the capability of concolic execution. Specifically, we design a Monte Carlo-based probabilistic path prioritization model to quantify each path’s difficulty, and …
Learning Transferable Perturbations For Image Captioning, Hanjie Wu, Yongtuo Liu, Hongmin Cai, Shengfeng He
Learning Transferable Perturbations For Image Captioning, Hanjie Wu, Yongtuo Liu, Hongmin Cai, Shengfeng He
Research Collection School Of Computing and Information Systems
Present studies have discovered that state-of-the-art deep learning models can be attacked by small but well-designed perturbations. Existing attack algorithms for the image captioning task is time-consuming, and their generated adversarial examples cannot transfer well to other models. To generate adversarial examples faster and stronger, we propose to learn the perturbations by a generative model that is governed by three novel loss functions. Image feature distortion loss is designed to maximize the encoded image feature distance between original images and the corresponding adversarial examples at the image domain, and local-global mismatching loss is introduced to separate the mapping encoding representation …
Context Modeling With Evidence Filter For Multiple Choice Question Answering, Sicheng Yu, Hao Zhang, Wei Jing, Jing Jiang
Context Modeling With Evidence Filter For Multiple Choice Question Answering, Sicheng Yu, Hao Zhang, Wei Jing, Jing Jiang
Research Collection School Of Computing and Information Systems
Multiple-Choice Question Answering (MCQA) is one of the challenging tasks in machine reading comprehension. The main challenge in MCQA is to extract "evidence" from the given context that supports the correct answer. In OpenbookQA dataset [1], the requirement of extracting "evidence" is particularly important due to the mutual independence of sentences in the context. Existing work tackles this problem by annotated evidence or distant supervision with rules which overly rely on human efforts. To address the challenge, we propose a simple yet effective approach termed evidence filtering to model the relationships between the encoded contexts with respect to different options …
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 …
On The Effectiveness Of Pretrained Models For Api Learning, Mohammad Abdul Hadi, Imam Nur Bani Yusuf, Thung Ferdian, Gia Kien Luong, Lingxiao Jiang, Fatemeh H. Fard, David Lo
On The Effectiveness Of Pretrained Models For Api Learning, Mohammad Abdul Hadi, Imam Nur Bani Yusuf, Thung Ferdian, Gia Kien Luong, Lingxiao Jiang, Fatemeh H. Fard, David Lo
Research Collection School Of Computing and Information Systems
Developers frequently use APIs to implement certain functionalities, such as parsing Excel Files, reading and writing text files line by line, etc. Developers can greatly benefit from automatic API usage sequence generation based on natural language queries for building applications in a faster and cleaner manner. Existing approaches utilize information retrieval models to search for matching API sequences given a query or use RNN-based encoder-decoder to generate API sequences. As it stands, the first approach treats queries and API names as bags of words. It lacks deep comprehension of the semantics of the queries. The latter approach adapts a neural …
Structure-Aware Visualization Retrieval, Haotian Li, Yong Wang, Aoyu Wu, Huan Wei, Huamin. Qu
Structure-Aware Visualization Retrieval, Haotian Li, Yong Wang, Aoyu Wu, Huan Wei, Huamin. Qu
Research Collection School Of Computing and Information Systems
With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can benefit various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. …
Natural Attack For Pre-Trained Models Of Code, Zhou Yang, Jieke Shi, Junda He, David Lo
Natural Attack For Pre-Trained Models Of Code, Zhou Yang, Jieke Shi, Junda He, David Lo
Research Collection School Of Computing and Information Systems
Pre-trained models of code have achieved success in many important software engineering tasks. However, these powerful models are vulnerable to adversarial attacks that slightly perturb model inputs to make a victim model produce wrong outputs. Current works mainly attack models of code with examples that preserve operational program semantics but ignore a fundamental requirement for adversarial example generation: perturbations should be natural to human judges, which we refer to as naturalness requirement. In this paper, we propose ALERT (Naturalness Aware Attack), a black-box attack that adversarially transforms inputs to make victim models produce wrong outputs. Different from prior works, this …
Detecting False Alarms From Automatic Static Analysis Tools: How Far Are We?, Hong Jin Kang, Khai Loong Aw, David Lo
Detecting False Alarms From Automatic Static Analysis Tools: How Far Are We?, Hong Jin Kang, Khai Loong Aw, David Lo
Research Collection School Of Computing and Information Systems
Automatic static analysis tools (ASATs), such as Findbugs, have a high false alarm rate. The large number of false alarms produced poses a barrier to adoption. Researchers have proposed the use of machine learning to prune false alarms and present only actionable warnings to developers. The state-of-the-art study has identified a set of “Golden Features” based on metrics computed over the characteristics and history of the file, code, and warning. Recent studies show that machine learning using these features is extremely effective and that they achieve almost perfect performance. We perform a detailed analysis to better understand the strong performance …
Static Inference Meets Deep Learning: A Hybrid Type Inference Approach For Python, Yun Peng, Cuiyun Gao, Zongjie Li, Bowei Gao, David Lo, Qirun Zhang, Michael R. Lyu
Static Inference Meets Deep Learning: A Hybrid Type Inference Approach For Python, Yun Peng, Cuiyun Gao, Zongjie Li, Bowei Gao, David Lo, Qirun Zhang, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Type inference for dynamic programming languages such as Python is an important yet challenging task. Static type inference techniques can precisely infer variables with enough static constraints but are unable to handle variables with dynamic features. Deep learning (DL) based approaches are feature-agnostic, but they cannot guarantee the correctness of the predicted types. Their performance significantly depends on the quality of the training data (i.e., DL models perform poorly on some common types that rarely appear in the training dataset). It is interesting to note that the static and DL-based approaches offer complementary benefits. Unfortunately, to our knowledge, precise type …
Automated Identification Of Libraries From Vulnerability Data: Can We Do Better?, Stefanus A. Haryono, Hong Jin Kang, Abhishek Sharma, Asankhaya Sharma, Andrew E. Santosa, Ming Yi Ang, David Lo
Automated Identification Of Libraries From Vulnerability Data: Can We Do Better?, Stefanus A. Haryono, Hong Jin Kang, Abhishek Sharma, Asankhaya Sharma, Andrew E. Santosa, Ming Yi Ang, David Lo
Research Collection School Of Computing and Information Systems
Software engineers depend heavily on software libraries and have to update their dependencies once vulnerabilities are found in them. Software Composition Analysis (SCA) helps developers identify vulnerable libraries used by an application. A key challenge is the identification of libraries related to a given reported vulnerability in the National Vulnerability Database (NVD), which may not explicitly indicate the affected libraries. Recently, researchers have tried to address the problem of identifying the libraries from an NVD report by treating it as an extreme multi-label learning (XML) problem, characterized by its large number of possible labels and severe data sparsity. As input, …
Mmekg: Multi-Modal Event Knowledge Graph Towards Universal Representation Across Modalities, Yubo Ma, Zehao Wang, Mukai Li, Yixin Cao, Meiqi Chen, Xinze Li, Wenqi Sun, Kunquan Deng, Kun Wang, Aixin Sun, Jing Shao
Mmekg: Multi-Modal Event Knowledge Graph Towards Universal Representation Across Modalities, Yubo Ma, Zehao Wang, Mukai Li, Yixin Cao, Meiqi Chen, Xinze Li, Wenqi Sun, Kunquan Deng, Kun Wang, Aixin Sun, Jing Shao
Research Collection School Of Computing and Information Systems
Events are fundamental building blocks of realworld happenings. In this paper, we present a large-scale, multi-modal event knowledge graph named MMEKG. MMEKG unifies different modalities of knowledge via events, which complement and disambiguate each other. Specifically, MMEKG incorporates (i) over 990 thousand concept events with 644 relation types to cover most types of happenings, and (ii) over 863 million instance events connected through 934 million relations, which provide rich contextual information in texts and/or images. To collect billion-scale instance events and relations among them, we additionally develop an efficient yet effective pipeline for textual/visual knowledge extraction system. We also develop …
Prompt For Extraction? Paie: Prompting Argument Interaction For Event Argument Extraction, Yubo Ma, Zehao Wang, Yixin Cao, Mukai Li, Meiqi Chen, Kun Wang, Jing Shao
Prompt For Extraction? Paie: Prompting Argument Interaction For Event Argument Extraction, Yubo Ma, Zehao Wang, Yixin Cao, Mukai Li, Meiqi Chen, Kun Wang, Jing Shao
Research Collection School Of Computing and Information Systems
In this paper, we propose an effective yet efficient model PAIE for both sentence-level and document-level Event Argument Extraction (EAE), which also generalizes well when there is a lack of training data. On the one hand, PAIE utilizes prompt tuning for extractive objectives to take the best advantages of Pre-trained Language Models (PLMs). It introduces two span selectors based on the prompt to select start/end tokens among input texts for each role. On the other hand, it captures argument interactions via multi-role prompts and conducts joint optimization with optimal span assignments via a bipartite matching loss. Also, with a flexible …
Neighbor-Anchoring Adversarial Graph Neural Networks (Extended Abstract), Zemin Liu, Yuan Fang, Yong Liu, Vincent W. Zheng
Neighbor-Anchoring Adversarial Graph Neural Networks (Extended Abstract), Zemin Liu, Yuan Fang, Yong Liu, Vincent W. Zheng
Research Collection School Of Computing and Information Systems
While graph neural networks (GNNs) exhibit strong discriminative power, they often fall short of learning the underlying node distribution for increased robustness. To deal with this, inspired by generative adversarial networks (GANs), we investigate the problem of adversarial learning on graph neural networks, and propose a novel framework named NAGNN (i.e., Neighbor-anchoring Adversarial Graph Neural Networks) for graph representation learning, which trains not only a discriminator but also a generator that compete with each other. In particular, we propose a novel neighbor-anchoring strategy, where the generator produces samples with explicit features and neighborhood structures anchored on a reference real node, …
Message-Locked Searchable Encryption: A New Versatile Tool For Secure Cloud Storage, Xueqiao Liu, Guomin Yang, Willy Susilo, Joseph Tonien, Rongmao Chen, Xixiang Lv
Message-Locked Searchable Encryption: A New Versatile Tool For Secure Cloud Storage, Xueqiao Liu, Guomin Yang, Willy Susilo, Joseph Tonien, Rongmao Chen, Xixiang Lv
Research Collection School Of Computing and Information Systems
Message-Locked Encryption (MLE) is a useful tool to enable deduplication over encrypted data in cloud storage. It can significantly improve the cloud service quality by eliminating redundancy to save storage resources, and hence user cost, and also providing defense against different types of attacks, such as duplicate faking attack and brute-force attack. A typical MLE scheme only focuses on deduplication. On the other hand, supporting search operations on stored content is another essential requirement for cloud storage. In this article, we present a message-locked searchable encryption (MLSE) scheme in a dual-server setting, which achieves simultaneously the desirable features of supporting …
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 …
Does This Apply To Me? An Empirical Study Of Technical Context In Stack Overflow, Akalanka Galappaththi, Sarah Nadi, Christoph Treude
Does This Apply To Me? An Empirical Study Of Technical Context In Stack Overflow, Akalanka Galappaththi, Sarah Nadi, Christoph Treude
Research Collection School Of Computing and Information Systems
Stack Overflow has become an essential technical resource for developers. However, given the vast amount of knowledge available on Stack Overflow, finding the right information that is relevant for a given task is still challenging, especially when a developer is looking for a solution that applies to their specific requirements or technology stack. Clearly marking answers with their technical context, i.e., the information that characterizes the technologies and assumptions needed for this answer, is potentially one way to improve navigation. However, there is no information about how often such context is mentioned, and what kind of information it might offer. …
Translate-Train Embracing Translationese Artifacts, Sicheng Yu, Qianru Sun, Hao Zhang, Jing Jiang
Translate-Train Embracing Translationese Artifacts, Sicheng Yu, Qianru Sun, Hao Zhang, Jing Jiang
Research Collection School Of Computing and Information Systems
Translate-train is a general training approach to multilingual tasks. The key idea is to use the translator of the target language to generate training data to mitigate the gap between the source and target languages. However, its performance is often hampered by the artifacts in the translated texts (translationese). We discover that such artifacts have common patterns in different languages and can be modeled by deep learning, and subsequently propose an approach to conduct translate-train using Translationese Embracing the effect of Artifacts (TEA). TEA learns to mitigate such effect on the training data of a source language (whose original and …
Deep Depression Prediction On Longitudinal Data Via Joint Anomaly Ranking And Classification, Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley, Anton Van Den Hengel
Deep Depression Prediction On Longitudinal Data Via Joint Anomaly Ranking And Classification, Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley, Anton Van Den Hengel
Research Collection School Of Computing and Information Systems
A wide variety of methods have been developed for identifying depression, but they focus primarily on measuring the degree to which individuals are suffering from depression currently. In this work we explore the possibility of predicting future depression using machine learning applied to longitudinal socio-demographic data. In doing so we show that data such as housing status, and the details of the family environment, can provide cues for predicting future psychiatric disorders. To this end, we introduce a novel deep multi-task recurrent neural network to learn time-dependent depression cues. The depression prediction task is jointly optimized with two auxiliary anomaly …
Smile: Secure Memory Introspection For Live Enclave, Lei Zhou, Xuhua Ding, Zhang Fengwei
Smile: Secure Memory Introspection For Live Enclave, Lei Zhou, Xuhua Ding, Zhang Fengwei
Research Collection School Of Computing and Information Systems
SGX enclaves prevent external software from accessing their memory. This feature conflicts with legitimate needs for enclave memory introspection, e.g., runtime stack collection on an enclave under a return-oriented-programming attack. We propose SMILE for enclave owners to acquire live enclave contents with the assistance of a semi-trusted agent installed by the host platform’s vendor as a plug-in of the System Management Interrupt handler. SMILE authenticates the enclave under introspection without trusting the kernel nor depending on the SGX attestation facility. SMILE is enclave security preserving as breaking of SMILE does not undermine enclave security. It allows a cloud server to …