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Curiosity-Driven And Victim-Aware Adversarial Policies, Chen Gong, Zhou Yang, Yunpeng Bai, Jieke Shi, Arunesh Sinha, Bowen Xu, David Lo, Xinwen Hou, Guoliang Fan Dec 2022

Curiosity-Driven And Victim-Aware Adversarial Policies, Chen Gong, Zhou Yang, Yunpeng Bai, Jieke Shi, Arunesh Sinha, Bowen Xu, David Lo, Xinwen Hou, Guoliang Fan

Research Collection School Of Computing and Information Systems

Recent years have witnessed great potential in applying Deep Reinforcement Learning (DRL) in various challenging applications, such as autonomous driving, nuclear fusion control, complex game playing, etc. However, recently researchers have revealed that deep reinforcement learning models are vulnerable to adversarial attacks: malicious attackers can train adversarial policies to tamper with the observations of a well-trained victim agent, the latter of which fails dramatically when faced with such an attack. Understanding and improving the adversarial robustness of deep reinforcement learning is of great importance in enhancing the quality and reliability of a wide range of DRL-enabled systems. In this paper, …

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A Unified Dialogue User Simulator For Few-Shot Data Augmentation, Dazhen Wan, Zheng Zhang, Qi Zhu, Lizi Liao, Minlie Huang Dec 2022

A Unified Dialogue User Simulator For Few-Shot Data Augmentation, Dazhen Wan, Zheng Zhang, Qi Zhu, Lizi Liao, Minlie Huang

Research Collection School Of Computing and Information Systems

Pre-trained language models have shown superior performance in task-oriented dialogues. However, existing datasets are on limited scales, which cannot support large-scale pre-training. Fortunately, various data augmentation methods have been developed to augment largescale task-oriented dialogue corpora. However, they heavily rely on annotated data in the target domain, which require a tremendous amount of data collection and human labeling work. In this paper, we build a unified dialogue user simulation model by pre-training on several publicly available datasets. The model can then be tuned on a target domain with fewshot data. The experiments on a target dataset across multiple domains show …

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Supply Regulation Under The Exclusion Policy In A Ride-Sourcing Market, Xiaonan Li, Xiangyong Li, Hai Wang, Junxin Shi, Yash P. Aneja Dec 2022

Supply Regulation Under The Exclusion Policy In A Ride-Sourcing Market, Xiaonan Li, Xiangyong Li, Hai Wang, Junxin Shi, Yash P. Aneja

Research Collection School Of Computing and Information Systems

On-demand ride-sourcing platforms have quickly emerged and become ubiquitous in our daily lives. Motivated by the rising public concern about service quality in the ride-sourcing market, this paper aims to examine the impact of exclusion policy that can serve as both quality management and supply regulation strategy. With an exclusion policy, the platform excludes low-quality service providers/drivers from the ride-sourcing market by setting a quality threshold of admission (QTA). We propose a model to describe and analyze the market equilibrium under the exclusion policy and present our analytical and numerical results – some of which are non-intuitive and intriguing. Considering …

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Compressed Federated Learning Based On Adaptive Local Differential Privacy, Yinbin Miao, Rongpeng Xie, Xinghua Li, Ximeng Liu, Zhuo Ma, Robert H. Deng Dec 2022

Compressed Federated Learning Based On Adaptive Local Differential Privacy, Yinbin Miao, Rongpeng Xie, Xinghua Li, Ximeng Liu, Zhuo Ma, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated learning (FL) was once considered secure for keeping clients’ raw data locally without relaying on a central server. However, the transmitted model weights or gradients still reveal private information, which can be exploited to launch various inference attacks. Moreover, FL based on deep neural networks is prone to the curse of dimensionality. In this paper, we propose a compressed and privacy-preserving FL scheme in DNN architecture by using Compressive sensing and Adaptive local differential privacy (called as CAFL). Specifically, we first compress the local models by using Compressive Sensing (CS), then adaptively perturb the remaining weights according to their …

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Multi-Relation Extraction Via A Global-Local Graph Convolutional Network, Harry Cheng, Lizi Liao, Linmei Hu, Liqiang Nie Dec 2022

Multi-Relation Extraction Via A Global-Local Graph Convolutional Network, Harry Cheng, Lizi Liao, Linmei Hu, Liqiang Nie

Research Collection School Of Computing and Information Systems

Relation extraction (RE) extracts the semantic relations among entities in a sentence, which converts the unstructured text into structured and easy-to-understand information. Although RE has been studied over decades, it still faces two kinds of research challenges that are not well addressed thus far: 1) joint consideration of the global sentence structure and the local entity interaction, and 2) effective solution to the overlapping triplets within the same sentence. To tackle these issues, in this paper, we present globallocal graph-based convolutional network towards multi-relation extraction, GAME for short. In particular, we devise two layers of graph convolutional network (GCN) with …

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R2f: A General Retrieval, Reading And Fusion Framework For Document-Level Natural Language Inference, Hao Wang, Yixin Cao, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao Dec 2022

R2f: A General Retrieval, Reading And Fusion Framework For Document-Level Natural Language Inference, Hao Wang, Yixin Cao, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao

Research Collection School Of Computing and Information Systems

Document-level natural language inference (DocNLI) is a new challenging task in natural language processing, aiming at judging the entailment relationship between a pair of hypothesis and premise documents. Current datasets and baselines largely follow sentence-level settings, but fail to address the issues raised by longer documents. In this paper, we establish a general solution, named Retrieval, Reading and Fusion (R2F) framework, and a new setting, by analyzing the main challenges of DocNLI: interpretability, long-range dependency, and cross-sentence inference. The basic idea of the framework is to simplify document-level task into a set of sentence-level tasks, and improve both performance and …

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Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu Dec 2022

Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu

Research Collection School Of Computing and Information Systems

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient …

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Differentiated Security Architecture For Secure And Efficient Infotainment Data Communication In Iov Networks, Jiani Fan, Lwin Khin Shar, Jiale Guo, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam Dec 2022

Differentiated Security Architecture For Secure And Efficient Infotainment Data Communication In Iov Networks, Jiani Fan, Lwin Khin Shar, Jiale Guo, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

This paper aims to provide differentiated security protection for infotainment data commu- nication in Internet-of-Vehicle (IoV) networks. The IoV is a network of vehicles that uses various sensors, software, built-in hardware, and communication technologies to enable information exchange between pedestrians, cars, and urban infrastructure. Negligence on the security of infotainment data commu- nication in IoV networks can unintentionally open an easy access point for social engineering attacks. The attacker can spread false information about traffic conditions, mislead drivers in their directions, and interfere with traffic management. Such attacks can also cause distractions to the driver, which has a potential implication …

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Conversation Disentanglement With Bi-Level Contrastive Learning, Chengyu Huang, Zheng Zhang, Hao Fei, Lizi Liao Dec 2022

Conversation Disentanglement With Bi-Level Contrastive Learning, Chengyu Huang, Zheng Zhang, Hao Fei, Lizi Liao

Research Collection School Of Computing and Information Systems

Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations. Existing methods have two main drawbacks. First, they overemphasize pairwise utterance relations but pay inadequate attention to the utterance-to-context relation modeling. Second, a huge amount of human annotated data is required for training, which is expensive to obtain in practice. To address these issues, we propose a general disentangle model based on bi-level contrastive learning. It brings closer utterances in the same session while encourages each utterance to be near its clustered session prototypes in the representation space. Unlike existing approaches, our …

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Gamified Online Industry Learning Platform For Teaching Of Foundational Computing Skills, Yi Meng Lau, Rafael Jose Barros Barrios, Gottipati Swapna, Kyong Jin Shim Dec 2022

Gamified Online Industry Learning Platform For Teaching Of Foundational Computing Skills, Yi Meng Lau, Rafael Jose Barros Barrios, Gottipati Swapna, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

Online industry learning platforms are widely used by organizations for employee training and upskilling. Courses or lessons offered by these platforms can be generic or specific to an enterprise application. The increased demand of new hires to learn these platforms or who are already certified in some of these courses has led universities to look at the opportunities for integrating online industry learning platforms into their curricula. Universities hope to use these platforms to aid students in their learning of concepts and theories. At the same time, these platforms can equip students with industryrecognized certifications or digital badges. This paper …

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Authentic Assessments For Digital Education: Learning Technologies Shaping Assessment Practices, Tristan Lim, Swapna Gottipati, Michelle L. F. Cheong Dec 2022

Authentic Assessments For Digital Education: Learning Technologies Shaping Assessment Practices, Tristan Lim, Swapna Gottipati, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

Assessment is a powerful lever that affects learning. To better inform educators on authentic assessment practices within digital education in the higher education landscape, this paper takes us through a meta-analysis of existing literature between 2011 to 2021. The study evaluates the following research question: “How are emerging technologies shaping authentic assessment practices within digital education for higher education for the period between 2011 and 2023”. To aid with the forecasting, we utilize the EDUCAUSE Horizon Reports, which provide the predictions of emerging technology. This study affirms the importance of immersive learning technologies, followed by ubiquitous and adaptive learning technologies …

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Segment-Wise Time-Varying Dynamic Bayesian Network With Graph Regularization, Xing Yang, Chen Zhang, Baihua Zheng Dec 2022

Segment-Wise Time-Varying Dynamic Bayesian Network With Graph Regularization, Xing Yang, Chen Zhang, Baihua Zheng

Research Collection School Of Computing and Information Systems

Time-varying dynamic Bayesian network (TVDBN) is essential for describing time-evolving directed conditional dependence structures in complex multivariate systems. In this article, we construct a TVDBN model, together with a score-based method for its structure learning. The model adopts a vector autoregressive (VAR) model to describe inter-slice and intra-slice relations between variables. By allowing VAR parameters to change segment-wisely over time, the time-varying dynamics of the network structure can be described. Furthermore, considering some external information can provide additional similarity information of variables. Graph Laplacian is further imposed to regularize similar nodes to have similar network structures. The regularized maximum a …

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A Logistic Regression And Linear Programming Approach For Multi-Skill Staffing Optimization In Call Centers, Thuy Anh Ta, Tien Mai, Fabian Bastin, Pierre L'Ecuyer Dec 2022

A Logistic Regression And Linear Programming Approach For Multi-Skill Staffing Optimization In Call Centers, Thuy Anh Ta, Tien Mai, Fabian Bastin, Pierre L'Ecuyer

Research Collection School Of Computing and Information Systems

We study a staffing optimization problem in multi-skill call centers. The objective is to minimize the total cost of agents under some quality of service (QoS) constraints. The key challenge lies in the fact that the QoS functions have no closed-form and need to be approximated by simulation. In this paper we propose a new way to approximate the QoS functions by logistic functions and design a new algorithm that combines logistic regression, cut generations and logistic-based local search to efficiently find good staffing solutions. We report computational results using examples up to 65 call types and 89 agent groups …

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Pickup And Multi-Delivery Problem With Time Windows, Pham Tuan Anh, Aldy Gunawan, Vincent F. Yu, Tuan C. Chau Dec 2022

Pickup And Multi-Delivery Problem With Time Windows, Pham Tuan Anh, Aldy Gunawan, Vincent F. Yu, Tuan C. Chau

Research Collection School Of Computing and Information Systems

This paper addresses a new variant of Pickup and Delivery Problem with Time Windows (PDPTW) for enhancing customer satisfaction. In particular, a huge number of requests is served in the system, where each request includes a pickup node and several delivery nodes instead of a pair of pickup and delivery nodes. It is named Pickup and Multi-Delivery Problem with Time Windows (PMDPTW). A mixed-integer programming model is formulated with the objective of minimizing total travel costs. Computational experiments are conducted to test the correctness of the model with a newly generated benchmark based on the PDPTW benchmark instances. Results show …

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Cold Calls To Enhance Class Participation And Student Engagement, Manoj Thulasidas, Aldy Gunawan Dec 2022

Cold Calls To Enhance Class Participation And Student Engagement, Manoj Thulasidas, Aldy Gunawan

Research Collection School Of Computing and Information Systems

The question whether cold calls increase student engagement in the classroom has not been conclusively answered in the literature. This study describes the automated system to implement unbiased, randomized cold calling by posing a question, allowing all students to think first and then calling on a particular student to respond. Since we already have a measure of the level of student engagement as the self-reported classparticipation entries from the students, its correlation to cold calling is also further studied. The results show that there is a statistically significant increase in the class participation reported, and therefore in student engagement, in …

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S-Prompts Learning With Pre-Trained Transformers: An Occam's Razor For Domain Incremental Learning, Yabin Wang, Zhiwu Huang, Xiaopeng. Hong Dec 2022

S-Prompts Learning With Pre-Trained Transformers: An Occam's Razor For Domain Incremental Learning, Yabin Wang, Zhiwu Huang, Xiaopeng. Hong

Research Collection School Of Computing and Information Systems

State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL). The key idea of the paradigm is to learn prompts independently across domains with pre-trained transformers, avoiding the use of exemplars that commonly appear in conventional methods. This results in a win-win game where the prompting can achieve the best for each domain. The independent prompting across domains only …

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Conreader: Exploring Implicit Relations In Contracts For Contract Clause Extraction, Weiwen Xu, Yang Deng, Wenqiang Lei, Wenlong Zhao, Tat-Seng Chua, Wai Lam Dec 2022

Conreader: Exploring Implicit Relations In Contracts For Contract Clause Extraction, Weiwen Xu, Yang Deng, Wenqiang Lei, Wenlong Zhao, Tat-Seng Chua, Wai Lam

Research Collection School Of Computing and Information Systems

We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts. Existing CCE methods mostly treat contracts as plain text, creating a substantial barrier to understanding contracts of high complexity. In this work, we first comprehensively analyze the complexity issues of contracts and distill out three implicit relations commonly found in contracts, namely, 1) Long-range Context Relation that captures the correlations of distant clauses; 2) Term-Definition Relation that captures the relation between important terms with their corresponding definitions; and 3) Similar Clause Relation that captures the similarities between clauses of the same type. Then we propose a …

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Pacific: Towards Proactive Conversational Question Answering Over Tabular And Textual Data In Finance, Yang Deng, Wenqiang Lei, Wenxuan Zhang, Wai Lam, Tat-Seng Chua Dec 2022

Pacific: Towards Proactive Conversational Question Answering Over Tabular And Textual Data In Finance, Yang Deng, Wenqiang Lei, Wenxuan Zhang, Wai Lam, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

To facilitate conversational question answering (CQA) over hybrid contexts in finance, we present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numerical reasoning, and (iii) hybrid context of tables and text. A new task is defined accordingly to study Proactive Conversational Question Answering (PCQA), which combines clarification question generation and CQA. In addition, we propose a novel method, namely UniPCQA, to adapt a hybrid format of input and output content in PCQA into the Seq2Seq problem, including the reformulation of the numerical reasoning process as code generation. UniPCQA performs multi-task …

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Mining Competitively-Priced Bundle Configurations, Ezekiel Ong Young, Hady W. Lauw Dec 2022

Mining Competitively-Priced Bundle Configurations, Ezekiel Ong Young, Hady W. Lauw

Research Collection School Of Computing and Information Systems

We examine the bundle configuration problem in the presence of competition. Given a competitor's bundle configuration and pricing, we determine what to bundle together, and at what prices, to maximize the target firm's revenue. We highlight the difficulty in pricing bundles and propose a scalable alternative and an efficient search heuristic to refine the approximate prices. Furthermore, we extend the heuristics proposed by previous work to accommodate the presence of a competitor. We analyze the effectiveness of our proposed models through experimentation on real-life ratings-based preference data.

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Vr Computing Lab: An Immersive Classroom For Computing Learning, Shawn Pang, Kyong Jin Shim, Yi Meng Lau, Swapna Gottipati Dec 2022

Vr Computing Lab: An Immersive Classroom For Computing Learning, Shawn Pang, Kyong Jin Shim, Yi Meng Lau, Swapna Gottipati

Research Collection School Of Computing and Information Systems

In recent years, virtual reality (VR) is gaining popularity amongst educators and learners. If a picture is worth a thousand words, a VR session is worth a trillion words. VR technology completely immerses users with an experience that transports them into a simulated world. Universities across the United States, United Kingdom, and other countries have already started using VR for higher education in areas such as medicine, business, architecture, vocational training, social work, virtual field trips, virtual campuses, helping students with special needs, and many more. In this paper, we propose a novel VR platform learning framework which maps elements …

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Prompting For Multimodal Hateful Meme Classification, Rui Cao, Roy Ka-Wei Lee, Wen-Haw Chong, Jing Jiang Dec 2022

Prompting For Multimodal Hateful Meme Classification, Rui Cao, Roy Ka-Wei Lee, Wen-Haw Chong, Jing Jiang

Research Collection School Of Computing and Information Systems

Hateful meme classification is a challenging multimodal task that requires complex reasoning and contextual background knowledge. Ideally, we could leverage an explicit external knowledge base to supplement contextual and cultural information in hateful memes. However, there is no known explicit external knowledge base that could provide such hate speech contextual information. To address this gap, we propose PromptHate, a simple yet effective prompt-based model that prompts pre-trained language models (PLMs) for hateful meme classification. Specifically, we construct simple prompts and provide a few in-context examples to exploit the implicit knowledge in the pretrained RoBERTa language model for hateful meme classification. …

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Bank Error In Whose Favor? A Case Study Of Decentralized Finance Misgovernance, Ping Fan Ke, Ka Chung Boris Ng Dec 2022

Bank Error In Whose Favor? A Case Study Of Decentralized Finance Misgovernance, Ping Fan Ke, Ka Chung Boris Ng

Research Collection School Of Computing and Information Systems

Decentralized Finance (DeFi) emerged rapidly in recent years and provided open and transparent financial services to the public. Due to its popularity, it is not uncommon to see cybersecurity incidents in the DeFi landscape, yet the impact of such incidents is under-studied. In this paper, we examine two incidents in DeFi protocol that are mainly caused by misgovernance and mistake in the smart contract. By using the synthetic control method, we found that the incident in Alchemix did not have a significant effect on the total value locked (TVL) in the protocol, whereas the incident in Compound caused a 6.13% …

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Distilled Siamese Networks For Visual Tracking, Jianbing Shen, Yuanpei Liu, Xingping Dong, Xiankai Lu, Fahad Shahbaz Khan, Steven Hoi Dec 2022

Distilled Siamese Networks For Visual Tracking, Jianbing Shen, Yuanpei Liu, Xingping Dong, Xiankai Lu, Fahad Shahbaz Khan, Steven Hoi

Research Collection School Of Computing and Information Systems

In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from high memory costs, which restrict their applicability to mobile devices with tight memory budgets. To address this issue, we propose a distilled Siamese tracking framework to learn small, fast and accurate trackers (students), which capture critical knowledge from large Siamese trackers (teachers) by a teacher-students knowledge distillation model. This model is intuitively inspired by the one teacher versus multiple students learning method typically employed in schools. In particular, our model contains a single teacher-student distillation module …

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Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan Bose, Arunesh Sinha, Tien Mai Dec 2022

Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan Bose, Arunesh Sinha, Tien Mai

Research Collection School Of Computing and Information Systems

Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of fractionals, non-convex optimization which is used for decision making in prominent areas such as facility location and security games. In contrast to previous work, we find it more tractable to optimize the equivalent variance regularized form of DRO rather than the minimax form. We transform the variance regularized form to a mixed-integer second order cone program (MISOCP), which, while guaranteeing near global optimality, does not scale enough …

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Interventional Training For Out-Of-Distribution Natural Language Understanding, Sicheng Yu, Jing Jiang, Hao Zhang, Yulei Niu, Qianru Sun, Lidong Bing Dec 2022

Interventional Training For Out-Of-Distribution Natural Language Understanding, Sicheng Yu, Jing Jiang, Hao Zhang, Yulei Niu, Qianru Sun, Lidong Bing

Research Collection School Of Computing and Information Systems

Out-of-distribution (OOD) settings are used to measure a model’s performance when the distribution of the test data is different from that of the training data. NLU models are known to suffer in OOD settings (Utama et al., 2020b). We study this issue from the perspective of causality, which sees confounding bias as the reason for models to learn spurious correlations. While a common solution is to perform intervention, existing methods handle only known and single confounder, but in many NLU tasks the confounders can be both unknown and multifactorial. In this paper, we propose a novel interventional training method called …

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Innovative And Effective Spreadsheet Tool For Learning Sentiment Analysis And Prediction, Michelle L. F. Cheong, Jean Yun-Chen Chen Dec 2022

Innovative And Effective Spreadsheet Tool For Learning Sentiment Analysis And Prediction, Michelle L. F. Cheong, Jean Yun-Chen Chen

Research Collection School Of Computing and Information Systems

In this paper, we describe a spreadsheet tool which was developed and implemented to teach sentiment analysis and prediction to university students who are less technically inclined. We adopted the “Use-Modify-Create” cycle to ensure that the students learn the computing concepts and practices in a progressive and active learning manner, and we evaluated the effectiveness of the tool using the Learning Object Evaluation Scale for Students (LOES-S). The evaluation shows that our tool performed well in terms of quality, learn and engagement constructs, and our paired T-test shows strong evidence that, on average, the course module with the tool does …

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Dialogconv: A Lightweight Fully Convolutional Network For Multi-View Response Selection, Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang Dec 2022

Dialogconv: A Lightweight Fully Convolutional Network For Multi-View Response Selection, Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

Research Collection School Of Computing and Information Systems

Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms. Although promising results have been achieved, these models often suffer from slow inference or huge number of parameters. In this paper, we propose a novel lightweight fully convolutional architecture, called DialogConv, for response selection. DialogConv is exclusively built on top of convolution to extract matching features of context and response. Dialogues are modeled in 3D views, where DialogConv performs convolution operations on embedding view, word view and utterance view to capture richer semantic information from multiple contextual views. On the four benchmark datasets, …

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Opportunities And Challenges In Code Search Tools, Chao Liu, Xin Xia, David Lo, Cuiying Gao, Xiaohu Yang, John Grundy Dec 2022

Opportunities And Challenges In Code Search Tools, Chao Liu, Xin Xia, David Lo, Cuiying Gao, Xiaohu Yang, John Grundy

Research Collection School Of Computing and Information Systems

Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique …

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Soteriafl: A Unified Framework For Private Federated Learning With Communication Compression, Zhize Li, Haoyu Zhao, Boyue Li, Yuejie Chi Dec 2022

Soteriafl: A Unified Framework For Private Federated Learning With Communication Compression, Zhize Li, Haoyu Zhao, Boyue Li, Yuejie Chi

Research Collection School Of Computing and Information Systems

To enable large-scale machine learning in bandwidth-hungry environments such as wireless networks, significant progress has been made recently in designing communication-efficient federated learning algorithms with the aid of communication compression. On the other end, privacy-preserving, especially at the client level, is another important desideratum that has not been addressed simultaneously in the presence of advanced communication compression techniques yet. In this paper, we propose a unified framework that enhances the communication efficiency of private federated learning with communication compression. Exploiting both general compression operators and local differential privacy, we first examine a simple algorithm that applies compression directly to differentially-private …

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On The Merge Of K-Nn Graph, Wan-Lei Zhao, Hui Wang, Peng-Cheng Lin, Chong-Wah Ngo Dec 2022

On The Merge Of K-Nn Graph, Wan-Lei Zhao, Hui Wang, Peng-Cheng Lin, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

k-nearest neighbor graph is a fundamental data structure in many disciplines such as information retrieval, data-mining, pattern recognition, and machine learning, etc. In the literature, considerable research has been focusing on how to efficiently build an approximate k-nearest neighbor graph (k-NN graph) for a fixed dataset. Unfortunately, a closely related issue of how to merge two existing k-NN graphs has been overlooked. In this paper, we address the issue of k-NN graph merging in two different scenarios. In the first scenario, a symmetric merge algorithm is proposed to combine two approximate k-NN graphs. The algorithm facilitates large-scale processing by the …

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