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Articles 60931 - 60960 of 294438

Full-Text Articles in Physical Sciences and Mathematics

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 May 2022

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 …

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Efficient Encrypted Data Search With Expressive Queries And Flexible Update, Jianting Ning, Jiageng Chen, Kaitai Liang, Joseph K. Liu, Chunhua Su, Qianhong Wu May 2022

Efficient Encrypted Data Search With Expressive Queries And Flexible Update, Jianting Ning, Jiageng Chen, Kaitai Liang, Joseph K. Liu, Chunhua Su, Qianhong Wu

Research Collection School Of Computing and Information Systems

Outsourcing encrypted data to cloud servers that has become a prevalent trend among Internet users to date. There is a long list of advantages on data outsourcing, such as the reduction cost of local data management. How to securely operate encrypted data (remotely), however, is the top-rank concern over data owner. Liang et al. proposed a novel encrypted cloud-based data share and search system without loss of privacy. The system allows users to flexibly search and share encrypted data as well as updating keyword field. However, the search complexity of the system is of extreme inefficiency, O(nd), where d is …

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Tourgether360: Exploring 360° Tour Videos With Others, Kartikaeya Kumar, Lev Poretski, Jiannan Li, Anthony Tang May 2022

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 …

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Graphcode2vec: Generic Code Embedding Via Lexical And Program Dependence Analyses, Wei Ma, Mengjie Zhao, Ezekiel Soremekun, Qiang Hu, Jie M. Zhang, Mike Papadakis, Maxime Cordy, Xiaofei Xie, Yves Le Traon May 2022

Graphcode2vec: Generic Code Embedding Via Lexical And Program Dependence Analyses, Wei Ma, Mengjie Zhao, Ezekiel Soremekun, Qiang Hu, Jie M. Zhang, Mike Papadakis, Maxime Cordy, Xiaofei Xie, Yves Le Traon

Research Collection School Of Computing and Information Systems

Code embedding is a keystone in the application of machine learning on several Software Engineering (SE) tasks. To effectively support a plethora of SE tasks, the embedding needs to capture program syntax and semantics in a way that is generic. To this end, we propose the first self-supervised pre-training approach (called Graphcode2vec) which produces task-agnostic embedding of lexical and program dependence features. Graphcode2vec achieves this via a synergistic combination of code analysis and Graph Neural Networks. Graphcode2vec is generic, it allows pre-training, and it is applicable to several SE downstream tasks. We evaluate the effectiveness of Graphcode2vec on four (4) …

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Learning Transferable Perturbations For Image Captioning, Hanjie Wu, Yongtuo Liu, Hongmin Cai, Shengfeng He May 2022

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 …

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Causality-Based Neural Network Repair, Bing Sun, Jun Sun, Long H. Pham, Jie Shi May 2022

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 …

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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 May 2022

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 …

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Itss: Interactive Web-Based Authoring And Playback Integrated Environment For Programming Tutorials, Eng Lieh Ouh, Benjamin Gan, David Lo May 2022

Itss: Interactive Web-Based Authoring And Playback Integrated Environment For Programming Tutorials, Eng Lieh Ouh, Benjamin Gan, David Lo

Research Collection School Of Computing and Information Systems

Video-based programming tutorials are a popular form of tutorial used by authors to guide learners to code. Still, the interactivity of these videos is limited primarily to control video flow. There are existing works with increased interactivity that are shown to improve the learning experience. Still, these solutions require setting up a custom recording environment and are not well-integrated with the playback environment. This paper describes our integrated ITSS environment and evaluates the ease of authoring and playback of our interactive programming tutorials. Our environment is designed to run within the browser sandbox and is less intrusive to record interactivity …

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Unified Route Planning For Shared Mobility: An Insertion-Based Framework, Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Lei Chen, Ke. Xu May 2022

Unified Route Planning For Shared Mobility: An Insertion-Based Framework, Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Lei Chen, Ke. Xu

Research Collection School Of Computing and Information Systems

There has been a dramatic growth of shared mobility applications such as ride-sharing, food delivery, and crowdsourced parcel delivery. Shared mobility refers to transportation services that are shared among users, where a central issue is route planning. Given a set of workers and requests, route planning finds for each worker a route, i.e., a sequence of locations to pick up and drop off passengers/parcels that arrive from time to time, with different optimization objectives. Previous studies lack practicability due to their conflicted objectives and inefficiency in inserting a new request into a route, a basic operation called insertion. In addition, …

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Adaptive Task Planning For Large-Scale Robotized Warehouses, Dingyuan Shi, Yongxin Tong, Zimu Zhou, Ke Xu, Wenzhe Tan, Hongbo Li May 2022

Adaptive Task Planning For Large-Scale Robotized Warehouses, Dingyuan Shi, Yongxin Tong, Zimu Zhou, Ke Xu, Wenzhe Tan, Hongbo Li

Research Collection School Of Computing and Information Systems

Robotized warehouses are deployed to automatically distribute millions of items brought by the massive logistic orders from e-commerce. A key to automated item distribution is to plan paths for robots, also known as task planning, where each task is to deliver racks with items to pickers for processing and then return the rack back. Prior solutions are unfit for large-scale robotized warehouses due to the inflexibility to time-varying item arrivals and the low efficiency for high throughput. In this paper, we propose a new task planning problem called TPRW, which aims to minimize the end-to-end makespan that incorporates the entire …

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Guided Attention Multimodal Multitask Financial Forecasting With Inter-Company Relationships And Global And Local News, Meng Kiat Gary Ang, Ee-Peng Lim May 2022

Guided Attention Multimodal Multitask Financial Forecasting With Inter-Company Relationships And Global And Local News, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Most works on financial forecasting use information directly associated with individual companies (e.g., stock prices, news on the company) to predict stock returns for trading. We refer to such company-specific information as local information. Stock returns may also be influenced by global information (e.g., news on the economy in general), and inter-company relationships. Capturing such diverse information is challenging due to the low signal-to-noise ratios, different time-scales, sparsity and distributions of global and local information from different modalities. In this paper, we propose a model that captures both global and local multimodal information for investment and risk management-related forecasting tasks. …

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Indoor Localization Using Solar Cells, Hamada Rizk, Dong Ma, Mahbub Hassan, Moustafa Youssef May 2022

Indoor Localization Using Solar Cells, Hamada Rizk, Dong Ma, Mahbub Hassan, Moustafa Youssef

Research Collection School Of Computing and Information Systems

The development of the Internet of Things (IoT) opens the doors for innovative solutions in indoor positioning systems. Recently, light-based positioning has attracted much attention due to the dense and pervasive nature of light sources (e.g., Light-emitting Diode lighting) in indoor environments. Nevertheless, most existing solutions necessitate carrying a high-end phone at hand in a specific orientation to detect the light intensity with the phone's light sensing capability (i.e., light sensor or camera). This limits the ease of deployment of these solutions and leads to drainage of the phone battery. We propose PVDeepLoc, a device-free light-based indoor localization system that …

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Message-Locked Searchable Encryption: A New Versatile Tool For Secure Cloud Storage, Xueqiao Liu, Guomin Yang, Willy Susilo, Joseph Tonien, Rongmao Chen, Xixiang Lv May 2022

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 …

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Uipdroid: Unrooted Dynamic Monitor Of Android App Uis For Fine-Grained Permission Control, Mulin Duan, Lingxiao Jiang, Lwin Khin Shar, Debin Gao May 2022

Uipdroid: Unrooted Dynamic Monitor Of Android App Uis For Fine-Grained Permission Control, Mulin Duan, Lingxiao Jiang, Lwin Khin Shar, Debin Gao

Research Collection School Of Computing and Information Systems

Proper permission controls in Android systems are important for protecting users' private data when running applications installed on the devices. Currently Android systems require apps to obtain authorization from users at the first time when they try to access users' sensitive data, but every permission is only managed at the application level, allowing apps to (mis)use permissions granted by users at the beginning for different purposes subsequently without informing users. Based on privacy-by-design principles, this paper develops a new permission manager, named UIPDroid, that (1) enforces the users' basic right-to-know through user interfaces whenever an app uses permissions, and (2) …

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Feasibility Studies In Indoor Localization Through Intelligent Conversation, Sheshadri Smitha, Linus Cheng, Kotaro Hara May 2022

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 …

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Do Pre-Trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation And A Reasonable Approach, Xin Lv, Yankai Lin, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou May 2022

Do Pre-Trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation And A Reasonable Approach, Xin Lv, Yankai Lin, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou

Research Collection School Of Computing and Information Systems

In recent years, pre-trained language models (PLMs) have been shown to capture factual knowledge from massive texts, which encourages the proposal of PLM-based knowledge graph completion (KGC) models. However, these models are still quite behind the SOTA KGC models in terms of performance. In this work, we find two main reasons for the weak performance: (1) Inaccurate evaluation setting. The evaluation setting under the closed-world assumption (CWA) may underestimate the PLM-based KGC models since they introduce more external knowledge; (2) Inappropriate utilization of PLMs. Most PLM-based KGC models simply splice the labels of entities and relations as inputs, leading to …

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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 May 2022

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 …

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Exploring And Adapting Chinese Gpt To Pinyin Input Method, Minghuan Tan, Yong Dai, Duyu Tang, Zhangyin Feng, Guoping Huang, Jing Jiang, Jiwei Li, Shuming Shi May 2022

Exploring And Adapting Chinese Gpt To Pinyin Input Method, Minghuan Tan, Yong Dai, Duyu Tang, Zhangyin Feng, Guoping Huang, Jing Jiang, Jiwei Li, Shuming Shi

Research Collection School Of Computing and Information Systems

While GPT has become the de-facto method for text generation tasks, its application to pinyin input method remains unexplored. In this work, we make the first exploration to leverage Chinese GPT for pinyin input method. We find that a frozen GPT achieves state-of-the-art performance on perfect pinyin. However, the performance drops dramatically when the input includes abbreviated pinyin. A reason is that an abbreviated pinyin can be mapped to many perfect pinyin, which links to even larger number of Chinese characters. We mitigate this issue with two strategies, including enriching the context with pinyin and optimizing the training process to …

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Translate-Train Embracing Translationese Artifacts, Sicheng Yu, Qianru Sun, Hao Zhang, Jing Jiang May 2022

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 …

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Unified And Incremental Simrank: Index-Free Approximation With Scheduled Principle (Extended Abstract), Fanwei Zhu, Yuan Fang, Kai Zhang, Kevin Chen-Chuan Chang, Hongtai Cao, Zhen Jiang, Minghui Wu May 2022

Unified And Incremental Simrank: Index-Free Approximation With Scheduled Principle (Extended Abstract), Fanwei Zhu, Yuan Fang, Kai Zhang, Kevin Chen-Chuan Chang, Hongtai Cao, Zhen Jiang, Minghui Wu

Research Collection School Of Computing and Information Systems

SimRank is a popular link-based similarity measure on graphs. It enables a variety of applications with different modes of querying. In this paper, we propose UISim, a unified and incremental framework for all SimRank modes based on a scheduled approximation principle. UISim processes queries with incremental and prioritized exploration of the entire computation space, and thus allows flexible tradeoff of time and accuracy. On the other hand, it creates and shares common “building blocks” for online computation without relying on indexes, and thus is efficient to handle both static and dynamic graphs. Our experiments on various real-world graphs show that …

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Neighbor-Anchoring Adversarial Graph Neural Networks (Extended Abstract), Zemin Liu, Yuan Fang, Yong Liu, Vincent W. Zheng May 2022

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, …

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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 May 2022

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 …

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Context Modeling With Evidence Filter For Multiple Choice Question Answering, Sicheng Yu, Hao Zhang, Wei Jing, Jing Jiang May 2022

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 …

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Smile: Secure Memory Introspection For Live Enclave, Lei Zhou, Xuhua Ding, Zhang Fengwei May 2022

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 …

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Benchmarking Library Recognition In Tweets, Ting Zhang, Divya Prabha Chandrasekaran, Ferdian Thung, David Lo May 2022

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 …

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Xai4fl: Enhancing Spectrum-Based Fault Localization With Explainable Artificial Intelligence, Ratnadira Widyasari, Gede Artha Azriadi Prana, Stefanus Agus Haryono, Yuan Tian, Hafil Noer Zachiary, David Lo May 2022

Xai4fl: Enhancing Spectrum-Based Fault Localization With Explainable Artificial Intelligence, Ratnadira Widyasari, Gede Artha Azriadi Prana, Stefanus Agus Haryono, Yuan Tian, Hafil Noer Zachiary, David Lo

Research Collection School Of Computing and Information Systems

Manually finding the program unit (e.g., class, method, or statement) responsible for a fault is tedious and time-consuming. To mitigate this problem, many fault localization techniques have been proposed. A popular family of such techniques is spectrum-based fault localization (SBFL), which takes program execution traces (spectra) of failed and passed test cases as input and applies a ranking formula to compute a suspiciousness score for each program unit. However, most existing SBFL techniques fail to consider two facts: 1) not all failed test cases contribute equally to a considered fault(s), and 2) program units collaboratively contribute to the failure/pass of …

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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 May 2022

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 …

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Active Warden Attack: On The (In)Effectiveness Of Android App Repackage-Proofing, Haoyu Ma, Shijia Li, Debin Gao, Daoyuan Wu, Qiaowen Jia, Chunfu Jia May 2022

Active Warden Attack: On The (In)Effectiveness Of Android App Repackage-Proofing, Haoyu Ma, Shijia Li, Debin Gao, Daoyuan Wu, Qiaowen Jia, Chunfu Jia

Research Collection School Of Computing and Information Systems

App repackaging has raised serious concerns to the Android ecosystem with the repackage-proofing technology attracting attention in the Android research community. In this paper, we first show that existing repackage-proofing schemes rely on a flawed security assumption, and then propose a new class of active warden attack that intercepts and falsifies the metrics used by repackage-proofing for detecting the integrity violations during repackaging. We develop a proof-of-concept toolkit to demonstrate that all the existing repackage-proofing schemes can be bypassed by our attack toolkit. On the positive side, our analysis further identifies a new integrity metric in the Android ART runtime …

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Competition And Third-Party Platform-Integration In Ride-Sourcing Markets, Yaqian Zhou, Hai Yang, Jintao Ke, Hai Wang, Xinwei Li May 2022

Competition And Third-Party Platform-Integration In Ride-Sourcing Markets, Yaqian Zhou, Hai Yang, Jintao Ke, Hai Wang, Xinwei Li

Research Collection School Of Computing and Information Systems

Recently, some third-party integrators attempt to integrate the ride services offered by multiple independent ride-sourcing platforms. Accordingly, passengers can request ride through the integrators and receive ride service from any one of the ride-sourcing platforms. This novel business model, termed as third-party platform-integration in this work, has potentials to alleviate market fragmentation cost resulting from demand splitting among multiple platforms. Although most existing studies focus on operation strategies for one single monopolist platform, much less is known about the competition and platform-integration and their implications on operation strategy and system efficiency. In this work, we propose mathematical models to describe …

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Optimal In‐Place Suffix Sorting, Zhize Li, Jian Li, Hongwei Huo May 2022

Optimal In‐Place Suffix Sorting, Zhize Li, Jian Li, Hongwei Huo

Research Collection School Of Computing and Information Systems

The suffix array is a fundamental data structure for many applications that involve string searching and data compression. Designing time/space-efficient suffix array construction algorithms has attracted significant attention and considerable advances have been made for the past 20 years. We obtain the \emph{first} in-place suffix array construction algorithms that are optimal both in time and space for (read-only) integer alphabets. Concretely, we make the following contributions: 1. For integer alphabets, we obtain the first suffix sorting algorithm which takes linear time and uses only $O(1)$ workspace (the workspace is the total space needed beyond the input string and the output …

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