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Full-Text Articles in Entire DC Network
Simc 2.0: Improved Secure Ml Inference Against Malicious Clients, Guowen Xu, Xingshuo Han, Tianwei Zhang, Shengmin Xu, Jianting Ning, Xinyi Huang, Hongwei Li, Deng, Robert H.
Simc 2.0: Improved Secure Ml Inference Against Malicious Clients, Guowen Xu, Xingshuo Han, Tianwei Zhang, Shengmin Xu, Jianting Ning, Xinyi Huang, Hongwei Li, Deng, Robert H.
Research Collection School Of Computing and Information Systems
In this paper, we study the problem of secure ML inference against a malicious client and a semi-trusted server such that the client only learns the inference output while the server learns nothing. This problem is first formulated by Lehmkuhl et al. with a solution (MUSE, Usenix Security’21), whose performance is then substantially improved by Chandran et al.'s work (SIMC, USENIX Security’22). However, there still exists a nontrivial gap in these efforts towards practicality, giving the challenges of overhead reduction and secure inference acceleration in an all-round way. Based on this, we propose SIMC 2.0, which complies with the underlying …
Gamification In Erp Systems: A Study On Intrinsic Motivation And User Behavioral Intentions, Esi Adeborna, Fiona Fui-Hoon Nah, Luvai Motiwalla
Gamification In Erp Systems: A Study On Intrinsic Motivation And User Behavioral Intentions, Esi Adeborna, Fiona Fui-Hoon Nah, Luvai Motiwalla
Research Collection School Of Computing and Information Systems
In the evolving landscape of Enterprise Resource Planning (ERP) systems, integrating gamification shows promise for enhancing user training and education. However, empirical research on gamification's effect on intrinsic motivation in using ERP is limited. Hence, we examined the intrinsic motivational effects of a gamified ERP environment in this research. We created a Gamified Web Application linked to SAP ERP to investigate how gamification affects users' behavioral intention. Leveraging self-determination theory, we examined the influence of perceived competence, autonomy, and relatedness on usage intent. In a controlled experiment with 63 participants, data collected via survey revealed that gamification enhances perceived competence, …
Style: Improving Domain Transferability Of Asking Clarification Questions In Large Language Model Powered Conversational Agents, Yue Chen, Chen Huang, Yang Deng, Wenqiang Lei, Dingnan Jin, Jia Liu, Tat-Seng Chua
Style: Improving Domain Transferability Of Asking Clarification Questions In Large Language Model Powered Conversational Agents, Yue Chen, Chen Huang, Yang Deng, Wenqiang Lei, Dingnan Jin, Jia Liu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Equipping a conversational search engine with strategies regarding when to ask clarification questions is becoming increasingly important across various domains. Attributing to the context understanding capability of LLMs and their access to domain-specific sources of knowledge, LLM-based clarification strategies feature rapid transfer to various domains in a posthoc manner. However, they still struggle to deliver promising performance on unseen domains, struggling to achieve effective domain transferability. We take the first step to investigate this issue and existing methods tend to produce one-size-fits-all strategies across diverse domains, limiting their search effectiveness. In response, we introduce a novel method, called STYLE, to …
Chain-Of-Exemplar: Enhancing Distractor Generation For Multimodal Educational Question Generation, Haohao Luo, Yang Deng, Ying Shen, See-Kiong Ng, Tat-Seng Chua
Chain-Of-Exemplar: Enhancing Distractor Generation For Multimodal Educational Question Generation, Haohao Luo, Yang Deng, Ying Shen, See-Kiong Ng, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Multiple-choice questions (MCQs) are important in enhancing concept learning and student engagement for educational purposes. Despite the multimodal nature of educational content, current methods focus mainly on text-based inputs and often neglect the integration of visual information. In this work, we study the problem of multimodal educational question generation, which aims at generating subject-specific educational questions with plausible yet incorrect distractors based on multimodal educational content. To tackle this problem, we introduce a novel framework, named Chain-of-Exemplar (CoE), which utilizes multimodal large language models (MLLMs) with Chain-of-Thought reasoning to improve the generation of challenging distractors. Furthermore, CoE leverages three-stage contextualized …
Self-Chats From Large Language Models Make Small Emotional Support Chatbot Better, Zhonghua Zheng, Lizi Liao, Yang Deng, Libo Qin, Liqiang Nie
Self-Chats From Large Language Models Make Small Emotional Support Chatbot Better, Zhonghua Zheng, Lizi Liao, Yang Deng, Libo Qin, Liqiang Nie
Research Collection School Of Computing and Information Systems
Large Language Models (LLMs) have shown strong generalization abilities to excel in various tasks, including emotion support conversations. However, deploying such LLMs like GPT-3 (175B parameters) is resource-intensive and challenging at scale. In this study, we utilize LLMs as “Counseling Teacher” to enhance smaller models’ emotion support response abilities, significantly reducing the necessity of scaling up model size. To this end, we first introduce an iterative expansion framework, aiming to prompt the large teacher model to curate an expansive emotion support dialogue dataset. This curated dataset, termed ExTES, encompasses a broad spectrum of scenarios and is crafted with meticulous strategies …
Watme: Towards Lossless Watermarking Through Lexical Redundancy, Liang Chen, Yatao Bian, Yang Deng, Deng Cai, Shuaiyi Li, Peilin Zhao, Kam-Fai Wong
Watme: Towards Lossless Watermarking Through Lexical Redundancy, Liang Chen, Yatao Bian, Yang Deng, Deng Cai, Shuaiyi Li, Peilin Zhao, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Text watermarking has emerged as a pivotal technique for identifying machine-generated text. However, existing methods often rely on arbitrary vocabulary partitioning during decoding to embed watermarks, which compromises the availability of suitable tokens and significantly degrades the quality of responses. This study assesses the impact of watermarking on different capabilities of large language models (LLMs) from a cognitive science lens. Our finding highlights a significant disparity; knowledge recall and logical reasoning are more adversely affected than language generation. These results suggest a more profound effect of watermarking on LLMs than previously understood. To address these challenges, we introduce Watermarking with …
Enabling Sustainable Freight Forwarding Network Via Collaborative Games, Pang Jin Tan, Shih-Fen Cheng, Richard Chen
Enabling Sustainable Freight Forwarding Network Via Collaborative Games, Pang Jin Tan, Shih-Fen Cheng, Richard Chen
Research Collection School Of Computing and Information Systems
Freight forwarding plays a crucial role in facilitating global trade and logistics. However, as the freight forwarding market is extremely fragmented, freight forwarders often face the issue of not being able to fill the available shipping capacity. This recurrent issue motivates the creation of various freight forwarding networks that aim at exchanging capacities and demands so that the resource utilization of individual freight forwarders can be maximized. In this paper, we focus on how to design such a collaborative network based on collaborative game theory, with the Shapley value representing a fair scheme for profit sharing. Noting that the exact …
A New Hope: Contextual Privacy Policies For Mobile Applications And An Approach Toward Automated Generation, Shidong Pan, Zhen Tao, Thong Hoang, Dawen Zhang, Tianshi Li, Zhenchang Xing, Xiwei Xu, Mark Staples, Thierry Rakotoarivelo, David Lo
A New Hope: Contextual Privacy Policies For Mobile Applications And An Approach Toward Automated Generation, Shidong Pan, Zhen Tao, Thong Hoang, Dawen Zhang, Tianshi Li, Zhenchang Xing, Xiwei Xu, Mark Staples, Thierry Rakotoarivelo, David Lo
Research Collection School Of Computing and Information Systems
Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, the concept of contextual privacy policies (CPPs) has been proposed by researchers. The aim of CPPs is to fragment privacy policies into concise snippets, displaying them only within the corresponding contexts within the application’s graphical user interfaces (GUIs). In this paper, we first formulate CPP in mobile application scenario, and then present a novel multimodal framework, named SEEPRIVACY, specifically designed to automatically generate CPPs for mobile applications. This method uniquely integrates vision-based GUI understanding …
G2face: High-Fidelity Reversible Face Anonymization Via Generative And Geometric Priors, Haoxin Yang, Xuemiao Xu, Cheng Xu, Huaidong Zhang, Jing Qin, Yi Wang, Pheng-Ann Heng, Shengfeng He
G2face: High-Fidelity Reversible Face Anonymization Via Generative And Geometric Priors, Haoxin Yang, Xuemiao Xu, Cheng Xu, Huaidong Zhang, Jing Qin, Yi Wang, Pheng-Ann Heng, Shengfeng He
Research Collection School Of Computing and Information Systems
Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as encoder-decoder networks, often result in significant loss of facial details due to their limited learning capacity. Additionally, relying on latent manipulation in pre-trained GANs can lead to changes in ID-irrelevant attributes, adversely affecting data utility due to GAN inversion inaccuracies. This paper introduces G 2 Face, which leverages both generative and geometric priors to enhance identity manipulation, achieving high-quality reversible face anonymization without compromising data utility. We utilize a 3D face …
Exponential Qubit Reduction In Optimization For Financial Transaction Settlement, Elias X. Huber, Benjamin Y. L. Tan, Paul Robert Griffin, Dimitris G. Angelakis
Exponential Qubit Reduction In Optimization For Financial Transaction Settlement, Elias X. Huber, Benjamin Y. L. Tan, Paul Robert Griffin, Dimitris G. Angelakis
Research Collection School Of Computing and Information Systems
We extend the qubit-efficient encoding presented in (Tan et al. in Quantum 5:454, 2021) and apply it to instances of the financial transaction settlement problem constructed from data provided by a regulated financial exchange. Our methods are directly applicable to any QUBO problem with linear inequality constraints. Our extension of previously proposed methods consists of a simplification in varying the number of qubits used to encode correlations as well as a new class of variational circuits which incorporate symmetries thereby reducing sampling overhead, improving numerical stability and recovering the expression of the cost objective as a Hermitian observable. We also …
Decoding Gpt Mania In Chinese Stock Market, Yan Ma, Nan Hu, Shuyang Jia
Decoding Gpt Mania In Chinese Stock Market, Yan Ma, Nan Hu, Shuyang Jia
Research Collection School Of Computing and Information Systems
This study investigates the impact of investor attention on stock market reactions to ChatGPT using dialogues on the Chinese interactive investor platforms (IIPs). We measure investor attention by the number of investors’ questions toward ChatGPT on the IIPs and categorize the firms’ answers as Investing, Speculative, and Absent. The research reveals positive and statistically significant market reactions surrounding the initial questions that occur before firm responses. Positive abnormal returns are also observed around the initial answer dates, with Investing firms evoking the highest market response, followed by Speculative firms, and Absent firms exhibiting the lowest reactions. Our results suggest that …
Self-Adaptive Psro : Towards An Automatic Population-Based Game Solver, Pengdeng Li, Shuxin Li, Chang Yang, Xinrun Wang, Xiao Huang, Hau Chan, Bo An
Self-Adaptive Psro : Towards An Automatic Population-Based Game Solver, Pengdeng Li, Shuxin Li, Chang Yang, Xinrun Wang, Xiao Huang, Hau Chan, Bo An
Research Collection School Of Computing and Information Systems
Policy-Space Response Oracles (PSRO) as a general algorithmic framework has achieved state-of-the-art performance in learning equilibrium policies of two-player zero-sum games. However, the hand-crafted hyperparameter value selection in most of the existing works requires extensive domain knowledge, forming the main barrier to applying PSRO to different games. In this work, we make the first attempt to investigate the possibility of self-adaptively determining the optimal hyperparameter values in the PSRO framework. Our contributions are three-fold: (1) Using several hyperparameters, we propose a parametric PSRO that unifies the gradient descent ascent (GDA) and different PSRO variants. (2) We propose the self-adaptive PSRO …
A Multimodal Foundation Agent For Financial Trading : Tool-Augmented, Diversified, And Generalist, Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun, Jiaze Sun, Molei Qin, Xinyi Li, Yuqing Zhao, Yilei Zhao, Xinyu Cai, Longtao Zheng, Xinrun Wang, Bo An
A Multimodal Foundation Agent For Financial Trading : Tool-Augmented, Diversified, And Generalist, Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun, Jiaze Sun, Molei Qin, Xinyi Li, Yuqing Zhao, Yilei Zhao, Xinyu Cai, Longtao Zheng, Xinrun Wang, Bo An
Research Collection School Of Computing and Information Systems
Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high-frequency trading with various assets. While advanced AI techniques like deep learning and reinforcement learning are extensively utilized in finance, their application in financial trading tasks often faces challenges due to inadequate handling of multimodal data and limited generalizability across various tasks. To address these challenges, we present FinAgent, a multimodal foundational agent with tool augmentation for financial trading. FinAgent's market intelligence module processes a diverse range of data-numerical, textual, …
Reinforcement Tuning For Detecting Stances And Debunking Rumors Jointly With Large Language Models, Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Ling, Bo Wang
Reinforcement Tuning For Detecting Stances And Debunking Rumors Jointly With Large Language Models, Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Ling, Bo Wang
Research Collection School Of Computing and Information Systems
Learning multi-task models for jointly detecting stance and verifying rumors poses challenges due to the need for training data of stance at post level and rumor veracity at claim level, which are difficult to obtain. To address this issue, we leverage large language models (LLMs) as the foundation annotators for the joint stance detection (SD) and rumor verification (RV) tasks, dubbed as JSDRV. We introduce a novel reinforcement tuning framework to enhance the joint predictive capabilities of LLM-based SD and RV components. Specifically, we devise a policy for selecting LLM-annotated data at the two levels, employing a hybrid reward mechanism …
The Fourth International Workshop On Smart Data For Blockchain And Distributed Ledger (Sdbd '24), Feida Zhu, Jian Pei, Michael Zeller, Bingxue Zhang
The Fourth International Workshop On Smart Data For Blockchain And Distributed Ledger (Sdbd '24), Feida Zhu, Jian Pei, Michael Zeller, Bingxue Zhang
Research Collection School Of Computing and Information Systems
With the advent of Bitcoin, a cryptographically-enabled peer-to-peer digital payment system, blockchain together with a whole package of distributed ledger technologies, which serve as the underlying foundation of all the crypto-currencies, have been gaining attention from both academia and industry in the last fifteen years. The recent years have witnessed tremendous momentum in the development of blockchain and distributed ledger technologies, largely due to the impressive rise in the market capital of these digital tokens. More and more industries, from banking and insurance, to supply chain and e-commerce, are quickly realizing the great potential in blockchain technology in efficiency boost, …
Segac: Sample Efficient Generalized Actor Critic For The Stochastic On-Time Arrival Problem, Honglian Guo, Zhi He, Wenda Sheng, Zhiguang Cao, Yingjie Zhou, Weinan Gao
Segac: Sample Efficient Generalized Actor Critic For The Stochastic On-Time Arrival Problem, Honglian Guo, Zhi He, Wenda Sheng, Zhiguang Cao, Yingjie Zhou, Weinan Gao
Research Collection School Of Computing and Information Systems
This paper studies the problem in transportation networks and introduces a novel reinforcement learning-based algorithm, namely. Different from almost all canonical sota solutions, which are usually computationally expensive and lack generalizability to unforeseen destination nodes, segac offers the following appealing characteristics. segac updates the ego vehicle’s navigation policy in a sample efficient manner, reduces the variance of both value network and policy network during training, and is automatically adaptive to new destinations. Furthermore, the pre-trained segac policy network enables its real-time decision-making ability within seconds, outperforming state-of-the-art sota algorithms in simulations across various transportation networks. We also successfully deploy segac …
How To Avoid Jumping To Conclusions: Measuring The Robustness Of Outstanding Facts In Knowledge Graphs, Hanhua Xiao, Yuchen Li, Yanhao Wang, Panagiotis Karras, Kyriakos Mouratidis, Natalia Rozalia Avlona
How To Avoid Jumping To Conclusions: Measuring The Robustness Of Outstanding Facts In Knowledge Graphs, Hanhua Xiao, Yuchen Li, Yanhao Wang, Panagiotis Karras, Kyriakos Mouratidis, Natalia Rozalia Avlona
Research Collection School Of Computing and Information Systems
An outstanding fact (OF) is a striking claim by which some entities stand out from their peers on someattribute. OFs serve data journalism, fact checking, and recommendation. However, one could jump to conclusions by selecting truthful OFs while intentionally or inadvertently ignoring lateral contexts and data that render them less striking. This jumping conclusion bias from unstable OFs may disorient the public, including voters and consumers, raising concerns about fairness and transparency in political and business competition. It is thus ethically imperative for several stakeholders to measure the robustness of OFs with respect to lateral contexts and data. Unfortunately, a …
Heterogeneous Graph Transformer With Poly-Tokenization, Zhiyuan Lu, Yuan Fang, Cheng Yang, Chuan Shi
Heterogeneous Graph Transformer With Poly-Tokenization, Zhiyuan Lu, Yuan Fang, Cheng Yang, Chuan Shi
Research Collection School Of Computing and Information Systems
Graph neural networks have shown widespread success for learning on graphs, but they still face fundamental drawbacks, such as limited expressive power, over-smoothing, and over-squashing. Meanwhile, the transformer architecture offers a potential solution to these issues. However, existing graph transformers primarily cater to homogeneous graphs and are unable to model the intricate semantics of heterogeneous graphs. Moreover, unlike small molecular graphs where the entire graph can be considered as the receptive field in graph transformers, real-world heterogeneous graphs comprise a significantly larger number of nodes and cannot be entirely treated as such. Consequently, existing graph transformers struggle to capture the …
Robust Asynchronous Federated Learning With Time-Weighted And Stale Model Aggregation, Yinbin Miao, Ziteng Liu, Xinghua Li, Meng Li, Hongwei Li, Kim-Kwang Raymond Choo, Robert H. Deng
Robust Asynchronous Federated Learning With Time-Weighted And Stale Model Aggregation, Yinbin Miao, Ziteng Liu, Xinghua Li, Meng Li, Hongwei Li, Kim-Kwang Raymond Choo, Robert H. Deng
Research Collection School Of Computing and Information Systems
Federated Learning (FL) ensures collaborative learning among multiple clients while maintaining data locally. However, the traditional synchronous FL solutions have lower accuracy and require more communication time in scenarios where most devices drop out during learning. Therefore, we propose an Asynchronous Federated Learning (AsyFL) scheme using time-weighted and stale model aggregation, which effectively solves the problem of poor model performance due to the heterogeneity of devices. Then, we integrate Symmetric Homomorphic Encryption (SHE) into AsyFL to propose Asynchronous Privacy-Preserving Federated Learning (Asy-PPFL), which protects the privacy of clients and achieves lightweight computing. Privacy analysis shows that Asy-PPFL is indistinguishable under …
Empathyear : An Open-Source Avatar Multimodal Empathetic Chatbot, Hao Fei, Han Zhang, Bin Wang, Lizi Liao, Qian Liu, Erik Cambria
Empathyear : An Open-Source Avatar Multimodal Empathetic Chatbot, Hao Fei, Han Zhang, Bin Wang, Lizi Liao, Qian Liu, Erik Cambria
Research Collection School Of Computing and Information Systems
This paper introduces EmpathyEar, a pioneering open-source, avatar-based multimodal empathetic chatbot, to fill the gap in traditional text-only empathetic response generation (ERG) systems. Leveraging the advancements of a large language model, combined with multimodal encoders and generators, EmpathyEar supports user inputs in any combination of text, sound, and vision, and produces multimodal empathetic responses, offering users, not just textual responses but also digital avatars with talking faces and synchronized speeches. A series of emotion-aware instruction-tuning is performed for comprehensive emotional understanding and generation capabilities. In this way, EmpathyEar provides users with responses that achieve a deeper emotional resonance, closely emulating …
Exploring A Multimodal Fusion-Based Deep Learning Network For Detecting Facial Palsy, Heng Yim Nicole Oo, Min Hun Lee, J. H. Lim
Exploring A Multimodal Fusion-Based Deep Learning Network For Detecting Facial Palsy, Heng Yim Nicole Oo, Min Hun Lee, J. H. Lim
Research Collection School Of Computing and Information Systems
Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessment by clinicians. In this paper, we present a multimodal fusion-based deep learning model that utilizes unstructured data (i.e. an image frame with facial line segments) and structured data (i.e. features of facial expressions) to detect facial palsy. We then contribute to a study to analyze the effect of different data modalities and the benefits of a multimodal fusion-based approach using videos of 21 facial palsy patients. Our experimental results show that among various data modalities (i.e. unstructured data - RGB images …
Topic Modeling On Document Networks With Dirichlet Optimal Transport Barycenter, Ce Zhang, Hady Wirawan Lauw
Topic Modeling On Document Networks With Dirichlet Optimal Transport Barycenter, Ce Zhang, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Text documents are often interconnected in a network structure, e.g., academic papers via citations, Web pages via hyperlinks. On the one hand, though Graph Neural Networks (GNNs) have shown promising ability to derive effective embeddings for such networked documents, they do not assume a latent topic structure and result in uninterpretable embeddings. On the other hand, topic models can infer semantically interpretable topic distributions for documents by associating each topic with a group of understandable key words. However, most topic models mainly focus on plain text within documents and fail to leverage network structure across documents. Network connectivity reveals topic …
Interpretable Tensor Fusion, Saurabh Varshneya, Antoine Ledent, Philipp Liznerski, Andriy Balinskyy, Purvanshi Mehta, Waleed Mustafa, Marius Kloft
Interpretable Tensor Fusion, Saurabh Varshneya, Antoine Ledent, Philipp Liznerski, Andriy Balinskyy, Purvanshi Mehta, Waleed Mustafa, Marius Kloft
Research Collection School Of Computing and Information Systems
Conventional machine learning methods are predominantly designed to predict outcomes based on a single data type. However, practical applications may encompass data of diverse types, such as text, images, and audio. We introduce interpretable tensor fusion (InTense), a multimodal learning method training a neural network to simultaneously learn multiple data representations and their interpretable fusion. InTense can separately capture both linear combinations and multiplicative interactions of the data types, thereby disentangling higher-order interactions from the individual effects of each modality. InTense provides interpretability out of the box by assigning relevance scores to modalities and their associations, respectively. The approach is …
Task Scheduling Strategy For 3dpcp Considering Multidynamic Information Perturbation In Green Scene, Jianjia He, Jian Wu, Keng Siau
Task Scheduling Strategy For 3dpcp Considering Multidynamic Information Perturbation In Green Scene, Jianjia He, Jian Wu, Keng Siau
Research Collection School Of Computing and Information Systems
The 3D printing cloud platform (3DPCP) plays a pivotal role in breaking down the information silos between supply and demand, effectively reducing waste through information integration and intelligent production. However, due to the complexity of 3DPCP scheduling in green scenes and the multidynamic information perturbations, unveils problems in traditional task scheduling methods in 3DPCP. These issues manifest as incomplete considerations, subpar green performance, and weak adaptability to dynamic changes. There is an urgent need to design practical methods to realize the multidynamic information perturbations in green scenes within 3DPCP. Therefore, this article first defines the 3DPCP task scheduling problem for …
Peep With A Mirror: Breaking The Integrity Of Android App Sandboxing Via Unprivileged Cache Side Channel, Yan Lin, Joshua Wong, Xiang Li, Haoyu Ma, Debin Gao
Peep With A Mirror: Breaking The Integrity Of Android App Sandboxing Via Unprivileged Cache Side Channel, Yan Lin, Joshua Wong, Xiang Li, Haoyu Ma, Debin Gao
Research Collection School Of Computing and Information Systems
Application sandboxing is a well-established security principle employed in the Android platform to safeguard sensitive information. However, hardware resources, specifically the CPU caches, are beyond the protection of this software-based mechanism, leaving room for potential side-channel attacks. Existing attacks against this particular weakness of app sandboxing mainly target shared components among apps, hence can only observe system-level program dynamics (such as UI tracing). In this work, we advance cache side-channel attacks by demonstrating the viability of non-intrusive and fine-grained probing across different app sandboxes, which have the potential to uncover app-specific and private program behaviors, thereby highlighting the importance of …
Optimization Of Customer Service And Driver Dispatch Areas For On-Demand Food Delivery, Jingfeng Yang, Hoong Chuin Lau, Hai Wang
Optimization Of Customer Service And Driver Dispatch Areas For On-Demand Food Delivery, Jingfeng Yang, Hoong Chuin Lau, Hai Wang
Research Collection School Of Computing and Information Systems
With the rapid development and popularization of mobile and wireless communication technologies, on-demand food delivery (OFD) platforms have been able to connect restaurants, customers, and drivers in real time, drastically changing dining and food delivery services. Motivated by the critical need for supply and demand management in the on-demand food delivery market, we focus on the optimization of customer service area and driver dispatch area for on-demand food delivery services. Specifically, for each restaurant, the platform needs to decide the (1) customer service area (CSA), i.e., the surrounding area within which customers can see the restaurant’s information and order food …
Anopay: Anonymous Payment For Vehicle Parking With Updatable Credential, Yang Yang, Wenyi Xue, Yonghua Zhan, Minming Huang, Yingjiu Li, Robert H. Deng
Anopay: Anonymous Payment For Vehicle Parking With Updatable Credential, Yang Yang, Wenyi Xue, Yonghua Zhan, Minming Huang, Yingjiu Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
Many existing anonymous parking payment schemes lack high efficiency and flexibility. For instance, the calculation and communication costs involved in payment may linearly increase with the payment amount. In this paper, we propose an anonymous payment system (dubbed AnoPay) for vehicle parking, which leverages updatable attribute-based anonymous credentials and efficient zero-knowledge proof (ZKP) to achieve user anonymity and constant overhead for parking fee payment. To further improve the efficiency, we design a secure parking fee aggregation protocol based on linear homomorphic encryption to aggregate parking transactions, where the amount of each parking transaction is hidden and the privacy of the …
Is Aggregation The Only Choice? Federated Learning Via Layer-Wise Model Recombination, Ming Hu, Zhihao Yue, Xiaofei Xie, Cheng Chen Chen
Is Aggregation The Only Choice? Federated Learning Via Layer-Wise Model Recombination, Ming Hu, Zhihao Yue, Xiaofei Xie, Cheng Chen Chen
Research Collection School Of Computing and Information Systems
Although Federated Learning (FL) enables global model training Xiaofei Xie [email protected] Singapore Management University Singapore, Singapore Xian Wei [email protected] East China Normal University Shanghai, China Mingsong Chen∗ [email protected] East China Normal University Shanghai, China • Computing methodologies → Distributed artificial intelligence. across clients without compromising their raw data, due to the unevenly distributed data among clients, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance. Specifically, different data distributions among clients lead to various optimization directions of local models. Aggregating local models usually results in a low-generalized global model, which performs worse on most of the …
Sibo : A Simple Booster For Parameter-Efficient Fine-Tuning, Zhihao Wen, Jie Zhang, Yuan Fang
Sibo : A Simple Booster For Parameter-Efficient Fine-Tuning, Zhihao Wen, Jie Zhang, Yuan Fang
Research Collection School Of Computing and Information Systems
Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques, such as Adapter tuning and LoRA, allow for adjustments to only a minor fraction of the parameters of these LLMs. Concurrently, it has been noted that the issue of over-smoothing diminishes the effectiveness of these Transformer-based LLMs, resulting in suboptimal performances in downstream tasks. In this paper, we present SIBO, which is a SImple BOoster to enhance PEFT, by injecting an initial residual. SIBO is straightforward and readily extensible to a range of state-of-the-art PEFT techniques to alleviate …
Tackling Stackelberg Network Interdiction Against A Boundedly Rational Adversary, Tien Mai, Avinandan Bose, Arunesh Sinha, Thanh Nguyen, Ayushman Kumar Singh
Tackling Stackelberg Network Interdiction Against A Boundedly Rational Adversary, Tien Mai, Avinandan Bose, Arunesh Sinha, Thanh Nguyen, Ayushman Kumar Singh
Research Collection School Of Computing and Information Systems
This work studies Stackelberg network interdiction games --- an important class of games in which a defender first allocates (randomized) defense resources to a set of critical nodes on a graph while an adversary chooses its path to attack these nodes accordingly. We consider a boundedly rational adversary in which the adversary's response model is based on a dynamic form of classic logit-based (quantal response) discrete choice models. The resulting optimization is non-convex and additionally, involves complex terms that sum over exponentially many paths. We tackle these computational challenges by presenting new efficient algorithms with solution guarantees. First, we present …