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The Next 'Deep' Thing In X To Z Marketing: An Artificial Intelligence Driven Approach, Vincent Charles, Nripendra Rana, Ilias O. Pappas, Morten Kamphaug, Keng Siau, Kenth Engo-Monsen
The Next 'Deep' Thing In X To Z Marketing: An Artificial Intelligence Driven Approach, Vincent Charles, Nripendra Rana, Ilias O. Pappas, Morten Kamphaug, Keng Siau, Kenth Engo-Monsen
Research Collection School Of Computing and Information Systems
The existing body of literature indicates a growing interest in research pertaining to the influence of artificial intelligence (AI) on marketing strategies, processes, and practices. However, further studies are required to fully unravel its complete potential and the implications it holds for practical application. The aim of this special issue on “The Next ‘Deep’ Thing in X to Z Marketing: An Artificial Intelligence-Driven Approach” is to explore the next frontiers and delve into the various facets of AI-driven marketing, shedding light on cutting-edge research and practical insights that can shape the future of the field. It also focuses on novel …
Learning Transferable Negative Prompts For Out-Of-Distribution Detection, Tianqi Li, Guansong Pang, Xiao Bai, Wenjun Miao, Jin Zheng
Learning Transferable Negative Prompts For Out-Of-Distribution Detection, Tianqi Li, Guansong Pang, Xiao Bai, Wenjun Miao, Jin Zheng
Research Collection School Of Computing and Information Systems
Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate. To address this issue, we introduce a novel OOD detection method, named ‘NegPrompt’, to learn a set of negative prompts, each representing a negative connotation of a given class label, for delineating the boundaries between ID and OOD images. It learns such negative prompts with ID data only, without any reliance on external out-lier data. Further, current …
Toward Generalist Anomaly Detection Via In-Context Residual Learning With Few-Shot Sample Prompts, Jiawen Zhu, Guansong Pang
Toward Generalist Anomaly Detection Via In-Context Residual Learning With Few-Shot Sample Prompts, Jiawen Zhu, Guansong Pang
Research Collection School Of Computing and Information Systems
This paper explores the problem of Generalist Anomaly Detection (GAD), aiming to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without any further training on the target data. Some recent studies have showed that large pre-trained Visual-Language Models (VLMs) like CLIP have strong generalization capabilities on detecting industrial defects from various datasets, but their methods rely heavily on handcrafted text prompts about defects, making them difficult to generalize to anomalies in other applications, e.g., medical image anomalies or semantic anomalies in natural images. In this work, we propose to train …
Learning With Unreliability : Fast Few-Shot Voxel Radiance Fields With Relative Geometric Consistency, Yingjie Xu, Bangzhen Liu, Hao Tang, Bailin Deng, Shengfeng He
Learning With Unreliability : Fast Few-Shot Voxel Radiance Fields With Relative Geometric Consistency, Yingjie Xu, Bangzhen Liu, Hao Tang, Bailin Deng, Shengfeng He
Research Collection School Of Computing and Information Systems
We propose a voxel-based optimization framework, Re VoRF, for few-shot radiance fields that strategically ad-dress the unreliability in pseudo novel view synthesis. Our method pivots on the insight that relative depth relationships within neighboring regions are more reliable than the ab-solute color values in disoccluded areas. Consequently, we devise a bilateral geometric consistency loss that carefully navigates the trade-off between color fidelity and geometric accuracy in the context of depth consistency for uncertain regions. Moreover, we present a reliability-guided learning strategy to discern and utilize the variable quality across syn-thesized views, complemented by a reliability-aware voxel smoothing algorithm that smoothens …
Physician-Patient Interactions In Online Healthcare Communities: The Effects Of Preconsultation On Service Delivery And Patient Satisfaction, Qian Tang, Anqi Zhao
Physician-Patient Interactions In Online Healthcare Communities: The Effects Of Preconsultation On Service Delivery And Patient Satisfaction, Qian Tang, Anqi Zhao
Research Collection School Of Computing and Information Systems
Preconsultation by medical professionals is a common practice in offline healthcare services to improve consultation efficiency but is rarely adopted for online healthcare services. In a noteworthy departure from this trend, a Chinese online healthcare community (OHC) has instituted preconsultation by assistant physicians prior to online consultations. Using comprehensive service data from this OHC, this study scrutinizes the effects of preconsultation on online healthcare services from both the physician and patient perspectives. The findings reveal that preconsultation by the assistant physician can significantly increase the attending physician’s response speed, length, and provision of informational support, while maintaining a consistent level …
Efficient Cross-Modal Video Retrieval With Meta-Optimized Frames, Ning Han, Xun Yang, Ee-Peng Lim, Hao Chen, Qianru Sun
Efficient Cross-Modal Video Retrieval With Meta-Optimized Frames, Ning Han, Xun Yang, Ee-Peng Lim, Hao Chen, Qianru Sun
Research Collection School Of Computing and Information Systems
Cross-modal video retrieval aims to retrieve semantically relevant videos when given a textual query, and is one of the fundamental multimedia tasks. Most top-performing methods primarily leverage Vision Transformer (ViT) to extract video features [1]-[3]. However, they suffer from the high computational complexity of ViT, especially when encoding long videos. A common and simple solution is to uniformly sample a small number (e.g., 4 or 8) of frames from the target video (instead of using the whole video) as ViT inputs. The number of frames has a strong influence on the performance of ViT, e.g., using 8 frames yields better …
Violet: Visual Analytics For Explainable Quantum Neural Networks, Shaolun Ruan, Zhiding Liang, Qiang Guan, Paul Robert Griffin, Xiaolin Wen, Yanna Lin, Yong Wang
Violet: Visual Analytics For Explainable Quantum Neural Networks, Shaolun Ruan, Zhiding Liang, Qiang Guan, Paul Robert Griffin, Xiaolin Wen, Yanna Lin, Yong Wang
Research Collection School Of Computing and Information Systems
With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to understand, due to their unique quantum-specific layers (e.g., data encoding and measurement) in their architecture. It prevents QNN users and researchers from effectively understanding its inner workings and exploring the model training status. To fill the research gap, we propose VIOLET , a novel visual analytics approach to improve the explainability …
Friendly Sharpness-Aware Minimization, Tao Li, Pan Zhou, Zhengbao He, Xinwen Cheng, Xiaolin Huang
Friendly Sharpness-Aware Minimization, Tao Li, Pan Zhou, Zhengbao He, Xinwen Cheng, Xiaolin Huang
Research Collection School Of Computing and Information Systems
Sharpness-Aware Minimization (SAM) has been instrumental in improving deep neural network training by minimizing both training loss and loss sharpness. Despite the practical success, the mechanisms behind SAM’s generalization enhancements remain elusive, limiting its progress in deep learning optimization. In this work, we investigate SAM’s core components for generalization improvement and introduce “Friendly-SAM” (F-SAM) to further enhance SAM’s generalization. Our investigation reveals the key role of batch-specific stochastic gradient noise within the adversarial perturbation, i.e., the current minibatch gradient, which significantly influences SAM’s generalization performance. By decomposing the adversarial perturbation in SAM into full gradient and stochastic gradient noise components, …
Fully Automated Selfish Mining Analysis In Efficient Proof Systems Blockchains, Krishnendu Chatterjee, Amirali Ebrahimzadeh, Mehrdad Karrabi, Krzysztof Pietrzak, Michelle Yeo, Dorde Zikelic
Fully Automated Selfish Mining Analysis In Efficient Proof Systems Blockchains, Krishnendu Chatterjee, Amirali Ebrahimzadeh, Mehrdad Karrabi, Krzysztof Pietrzak, Michelle Yeo, Dorde Zikelic
Research Collection School Of Computing and Information Systems
We study selfish mining attacks in longest-chain blockchains like Bitcoin, but where the proof of work is replaced with efficient proof systems - like proofs of stake or proofs of space - and consider the problem of computing an optimal selfish mining attack which maximizes expected relative revenue of the adversary, thus minimizing the chain quality. To this end, we propose a novel selfish mining attack that aims to maximize this objective and formally model the attack as a Markov decision process (MDP). We then present a formal analysis procedure which computes an ϵ-tight lower bound on the optimal expected …
Smart Fitting Room: A One‑Stop Framework For Matching‑Aware Virtual Try‑On, Mingzhe Yu, Yunshan Ma, Lei Wu, Kai Cheng, Xue Li, Lei Meng, Tat-Seng Chua
Smart Fitting Room: A One‑Stop Framework For Matching‑Aware Virtual Try‑On, Mingzhe Yu, Yunshan Ma, Lei Wu, Kai Cheng, Xue Li, Lei Meng, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
The development of virtual try-on has revolutionized online shopping by allowing customers to visualize themselves in various fashion items, thus extending the in-store try-on experience to the cyber space. Although virtual try-on has attracted considerable research initiatives, existing systems only focus on the quality of image generation, overlooking whether the fashion item is a good match to the given person and clothes. Recognizing this gap, we propose to design a one-stop Smart Fitting Room, with the novel formulation of matching-aware virtual try-on. Following this formulation, we design a Hybrid Matching-aware Virtual Try-On Framework (HMaVTON), which combines retrieval-based and generative methods …
Locality-Aware Tail Node Embeddings On Homogeneous And Heterogeneous Networks, Zemin Liu, Yuan Fang, Wentao Zhang, Xinming Zhang, Steven C. H. Hoi
Locality-Aware Tail Node Embeddings On Homogeneous And Heterogeneous Networks, Zemin Liu, Yuan Fang, Wentao Zhang, Xinming Zhang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
While the state-of-the-art network embedding approaches often learn high-quality embeddings for high-degree nodes with abundant structural connectivity, the quality of the embeddings for low-degree or nodes is often suboptimal due to their limited structural connectivity. While many real-world networks are long-tailed, to date little effort has been devoted to tail node embeddings. In this article, we formulate the goal of learning tail node embeddings as a problem, given the few links on each tail node. In particular, since each node resides in its own local context, we personalize the regression model for each tail node. To reduce overfitting in the …
Imitating Cost-Constrained Behaviors In Reinforcement Learning, Qian Shao, Pradeep Varakantham, Shih-Fen Cheng
Imitating Cost-Constrained Behaviors In Reinforcement Learning, Qian Shao, Pradeep Varakantham, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
Complex planning and scheduling problems have long been solved using various optimization or heuristic approaches. In recent years, imitation learning that aims to learn from expert demonstrations has been proposed as a viable alternative to solving these problems. Generally speaking, imitation learning is designed to learn either the reward (or preference) model or directly the behavioral policy by observing the behavior of an expert. Existing work in imitation learning and inverse reinforcement learning has focused on imitation primarily in unconstrained settings (e.g., no limit on fuel consumed by the vehicle). However, in many real-world domains, the behavior of an expert …
Pkt-Sin: A Secure Communication Protocol For Space Information Networks With Periodic K-Time Anonymous Authentication, Yang Yang, Wenyi Xue, Jianfei Sun, Guomin Yang, Yingjiu Li, Hwee Hwa Pang, Robert H. Deng
Pkt-Sin: A Secure Communication Protocol For Space Information Networks With Periodic K-Time Anonymous Authentication, Yang Yang, Wenyi Xue, Jianfei Sun, Guomin Yang, Yingjiu Li, Hwee Hwa Pang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Space Information Network (SIN) enables universal Internet connectivity for any object, even in remote and extreme environments where deploying a cellular network is difficult. Access authentication is crucial for ensuring user access control in SIN and preventing unauthorized entities from gaining access to network services. However, due to the complex communication environment in SIN, including exposed links and higher signal delay, designing a secure and efficient authentication scheme presents a significant challenge. In this paper, we propose a secure communication protocol for SIN with periodic k-time anonymous authentication (named PkT-SIN) that allows satellite users to anonymously authenticate to ground stations …
Sgsh : Stimulate Large Language Models With Skeleton Heuristics For Knowledge Base Question Generation, Shasha Guo, Lizi Liao, Jing Zhang, Yanling Wang, Cuiping Li, Hong Chen
Sgsh : Stimulate Large Language Models With Skeleton Heuristics For Knowledge Base Question Generation, Shasha Guo, Lizi Liao, Jing Zhang, Yanling Wang, Cuiping Li, Hong Chen
Research Collection School Of Computing and Information Systems
Knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the performance of KBQG via pre-trained language models (PLMs) thanks to the richly endowed semantic knowledge. With the advance of pre-training techniques, large language models (LLMs) (e.g., GPT-3.5) undoubtedly possess much more semantic knowledge. Therefore, how to effectively organize and exploit the abundant knowledge for KBQG becomes the focus of our study. In this work, we propose SGSH — a simple and effective framework to Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG. The framework …
Generalized Graph Prompt: Toward A Unification Of Pre-Training And Downstream Tasks On Graphs, Xingtong Yu, Zhenghao Liu, Yuan Fang, Et Al.
Generalized Graph Prompt: Toward A Unification Of Pre-Training And Downstream Tasks On Graphs, Xingtong Yu, Zhenghao Liu, Yuan Fang, Et Al.
Research Collection School Of Computing and Information Systems
Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the 'pre-train, fine-tune' and 'pre-train, prompt' paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of …
D3still : Decoupled Differential Distillation For Asymmetric Image Retrieval, Yi Xie, Yihong Lin, Wenjie Cai, Xuemiao Xu, Huaidong Zhang, Yong Du, Shengfeng He
D3still : Decoupled Differential Distillation For Asymmetric Image Retrieval, Yi Xie, Yihong Lin, Wenjie Cai, Xuemiao Xu, Huaidong Zhang, Yong Du, Shengfeng He
Research Collection School Of Computing and Information Systems
Existing methods for asymmetric image retrieval employ a rigid pairwise similarity constraint between the query network and the larger gallery network. However, these oneto-one constraint approaches often fail to maintain retrieval order consistency, especially when the query network has limited representational capacity. To overcome this problem, we introduce the Decoupled Differential Distillation (D3still) framework. This framework shifts from absolute one-to-one supervision to optimizing the relational differences in pairwise similarities produced by the query and gallery networks, thereby preserving a consistent retrieval order across both networks. Our method involves computing a pairwise similarity differential matrix within the gallery domain, which is …
The Whole Is Better Than The Sum : Using Aggregated Demonstrations In In-Context Learning For Sequential Recommendation, Wang Lei, Ee-Peng Lim
The Whole Is Better Than The Sum : Using Aggregated Demonstrations In In-Context Learning For Sequential Recommendation, Wang Lei, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Large language models (LLMs) have shown excellent performance on various NLP tasks. To use LLMs as strong sequential recommenders, we explore the in-context learning approach to sequential recommendation. We investigate the effects of instruction format, task consistency, demonstration selection, and number of demonstrations. As increasing the number of demonstrations in ICL does not improve accuracy despite using a long prompt, we propose a novel method called LLMSRec-Syn that incorporates multiple demonstration users into one aggregated demonstration. Our experiments on three recommendation datasets show that LLMSRec-Syn outperforms state-of-the-art LLM-based sequential recommendation methods. In some cases, LLMSRec-Syn can perform on par with …
Factors Affecting Online Consumers' Cultural Presence And Cultural Immersion Experiences In Live Streaming Shopping, Lifu Li, Kyeong Kang, Yafei Feng, Anqi Zhao
Factors Affecting Online Consumers' Cultural Presence And Cultural Immersion Experiences In Live Streaming Shopping, Lifu Li, Kyeong Kang, Yafei Feng, Anqi Zhao
Research Collection School Of Computing and Information Systems
This paper conducts research on the interaction between ethnic minority group (EMG) live streamers and online consumers, aiming to present how EMG live streamers’ appearance and interaction factors affect online consumers’ cultural experience during live streaming shopping. Based on the social presence theory and the immersion theory, this paper explores how EMG live streamers impact online consumers’ cultural experience, and it designs personal EMG knowledge as a moderating variable under specific social and cultural backgrounds. Through analysing 325 online questionnaires based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the study results support that both …
On Efficient Object-Detection Nas For Adas On Edge Devices, Diksha Gupta, Rhui Dih Lee, Laura Wynter
On Efficient Object-Detection Nas For Adas On Edge Devices, Diksha Gupta, Rhui Dih Lee, Laura Wynter
Research Collection School Of Computing and Information Systems
Object detection is a crucial building block for Advanced Driving Assistance Systems (ADAS). These systems require real-time accurate detection on resource-constrained edge devices. Deep learning models are emerging as popular techniques over traditional methods with superior performance. A hurdle in deploying these models is the inference time and computational cost of these models, in addition to training challenges for specialized tasks.We address this using supernet training-based neural architecture search (NAS) to obtain a variety of object detection models at a scale specific to the ADAS application. To this end, we consider a convolutional neural network-based object detection model. We produce …
Network-Based Representations And Dynamic Discrete Choice Models For Multiple Discrete Choice Analysis, Huy Hung Tran, Tien Mai
Network-Based Representations And Dynamic Discrete Choice Models For Multiple Discrete Choice Analysis, Huy Hung Tran, Tien Mai
Research Collection School Of Computing and Information Systems
In many choice modeling applications, consumer demand is frequently characterized as multiple discrete, which means that consumer choose multiple items simultaneously. The analysis and prediction of consumer behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application …
Unmasking The Lurking: Malicious Behavior Detection For Iot Malware With Multi-Label Classification, Ruitao Feng, Sen Li, Sen Chen, Mengmeng Ge, Xuewei Li, Xiaohong Li
Unmasking The Lurking: Malicious Behavior Detection For Iot Malware With Multi-Label Classification, Ruitao Feng, Sen Li, Sen Chen, Mengmeng Ge, Xuewei Li, Xiaohong Li
Research Collection School Of Computing and Information Systems
Current methods for classifying IoT malware predominantly utilize binary and family classifications. However, these outcomes lack the detailed granularity to describe malicious behavior comprehensively. This limitation poses challenges for security analysts, failing to support further analysis and timely preventive actions. To achieve fine-grained malicious behavior identification in the lurking stage of IoT malware, we propose MaGraMal. This approach, leveraging masked graph representation, supplements traditional classification methodology, empowering analysts with critical insights for rapid responses. Through the empirical study, which took three person-months, we identify and summarize four fine-grained malicious behaviors during the lurking stage, constructing an annotated dataset. Our evaluation …
Inceptionnext: When Inception Meets Convnext, Weihao Yu, Pan Zhou, Shuicheng Yan, Xinchao Wang
Inceptionnext: When Inception Meets Convnext, Weihao Yu, Pan Zhou, Shuicheng Yan, Xinchao Wang
Research Collection School Of Computing and Information Systems
Inspired by the long-range modeling ability of ViTs, large-kernel convolutions are widely studied and adopted recently to enlarge the receptive field and improve model performance, like the remarkable work ConvNeXt which employs 7×7 depthwise convolution. Although such depthwise operator only consumes a few FLOPs, it largely harms the model efficiency on powerful computing devices due to the high memory access costs. For example, ConvNeXtT has similar FLOPs with ResNet-50 but only achieves ∼ 60% throughputs when trained on A100 GPUs with full precision. Although reducing the kernel size of ConvNeXt can improve speed, it results in significant performance degradation, which …
Applicability And Challenges Of Indoor Localization Using One-Sided Round Trip Time Measurements, Quang Hai Truong, Xi Kai Justin Lam, Guru Anand Anish, Rajesh Krishna Balan
Applicability And Challenges Of Indoor Localization Using One-Sided Round Trip Time Measurements, Quang Hai Truong, Xi Kai Justin Lam, Guru Anand Anish, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Radio Frequency fingerprinting, based on WiFi or cellular signals, has been a popular approach for localization. However, adoptions in real-world applications have confronted with challenges due to low accuracy, especially in crowded environments. The received signal strength (RSS) could be easily interfered by a large number of other devices or strictly depends on physical surrounding environments, which may cause localization errors of a few meters. On the other hand, the fine time measurement (FTM) round-trip time (RTT) has shown compelling improvement in indoor localization with ~1-2 meter accuracy in both 2D and 3D environments [13]. This method relies on the …
How Is Our Mobility Affected As We Age? Findings From A 934 Users Field Study Of Older Adults Conducted In An Urban Asian City, Yi Zhen Tan, Ngoc Doan Thu Tran, Sapphire Lin, Fang Zhao, Yee Sien Ng, Dong Ma, Jeonggil Ko, Rajesh Krishna Balan
How Is Our Mobility Affected As We Age? Findings From A 934 Users Field Study Of Older Adults Conducted In An Urban Asian City, Yi Zhen Tan, Ngoc Doan Thu Tran, Sapphire Lin, Fang Zhao, Yee Sien Ng, Dong Ma, Jeonggil Ko, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
In this paper, we analyze the results of a large study involving 934 older adults living in an urban Asian city that collected their mobility patterns, in the form of logged GPS data, along with a multitude of demographic and health data. We show that mobility, in terms of average distance travelled per day, is greatly affected by age and by employment status. In addition, other factors such as type of day, household size, physical and financial conditions and the onset of retirement also play a significant role in determining the mobility of an individual. These results will have high …
Criticality Aware Canvas-Based Visual Perception At The Edge, Ila Gokarn
Criticality Aware Canvas-Based Visual Perception At The Edge, Ila Gokarn
Research Collection School Of Computing and Information Systems
Efficient and effective machine perception remains a formidable challenge in sustaining high fidelity and high throughput of perception tasks on affordable edge devices. This is especially due to the continuing increase in resolution of sensor streams (e.g., video input streams generated by 4K/8K cameras and neuromorphic event cameras that produce ≥ 10 MEvents/second) and computational complexity of Deep Neural Network (DNN) models, which overwhelms edge platforms, adversely impacting machine perception efficiency. Given the insufficiency of the available computation resources, a question then arises on whether selected regions/components of the perception task can be prioritized (and executed preferentially) to achieve highest …
Refining Chatgpt-Generated Code: Characterizing And Mitigating Code Quality Issues, Yue Liu, Thanh Le-Cong, Ratnadira Widyasari, Chakkrit Kla Tantihamthavorn, Li Li, Xuan-Bach Dinh Le, David Lo
Refining Chatgpt-Generated Code: Characterizing And Mitigating Code Quality Issues, Yue Liu, Thanh Le-Cong, Ratnadira Widyasari, Chakkrit Kla Tantihamthavorn, Li Li, Xuan-Bach Dinh Le, David Lo
Research Collection School Of Computing and Information Systems
Since its introduction in November 2022, ChatGPT has rapidly gained popularity due to its remarkable ability in language understanding and human-like responses. ChatGPT, based on GPT-3.5 architecture, has shown great promise for revolutionizing various research fields, including code generation. However, the reliability and quality of code generated by ChatGPT remain unexplored, raising concerns about potential risks associated with the widespread use of ChatGPT-driven code generation.In this article, we systematically study the quality of 4,066 ChatGPT-generated programs of code implemented in two popular programming languages, i.e., Java and Python, for 2,033 programming tasks. The goal of this work is threefold. First, …
Learning Dynamic Multimodal Network Slot Concepts From The Web For Forecasting Environmental, Social And Governance Ratings, Gary Ang, Ee-Peng Lim
Learning Dynamic Multimodal Network Slot Concepts From The Web For Forecasting Environmental, Social And Governance Ratings, Gary Ang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Dynamic multimodal networks are networks with node attributes from different modalities where the at- tributes and network relationships evolve across time, i.e., both networks and multimodal attributes are dynamic; for example, dynamic relationship networks between companies that evolve across time due to changes in business strategies and alliances, which are associated with dynamic company attributes from multiple modalities such as textual online news, categorical events, and numerical financial-related data. Such information can be useful in predictive tasks involving companies. Environmental, social, and gov- ernance (ESG) ratings of companies are important for assessing the sustainability risks of companies. The process of …
Improving Interpretable Embeddings For Ad-Hoc Video Search With Generative Captions And Multi-Word Concept Bank, Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan
Improving Interpretable Embeddings For Ad-Hoc Video Search With Generative Captions And Multi-Word Concept Bank, Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan
Research Collection School Of Computing and Information Systems
Aligning a user query and video clips in cross-modal latent space and that with semantic concepts are two mainstream approaches for ad-hoc video search (AVS). However, the effectiveness of existing approaches is bottlenecked by the small sizes of available video-text datasets and the low quality of concept banks, which results in the failures of unseen queries and the out-of-vocabulary problem. This paper addresses these two problems by constructing a new dataset and developing a multi-word concept bank. Specifically, capitalizing on a generative model, we construct a new dataset consisting of 7 million generated text and video pairs for pre-training. To …
Cmd: Co-Analyzed Iot Malware Detection And Forensics Via Network And Hardware Domains, Ziming Zhao, Zhaoxuan Li, Jiongchi Yu, Fan Zhang, Xiaofei Xie, Haitao Xu, Binbin Chen
Cmd: Co-Analyzed Iot Malware Detection And Forensics Via Network And Hardware Domains, Ziming Zhao, Zhaoxuan Li, Jiongchi Yu, Fan Zhang, Xiaofei Xie, Haitao Xu, Binbin Chen
Research Collection School Of Computing and Information Systems
With the widespread use of Internet of Things (IoT) devices, malware detection has become a hot spot for both academic and industrial communities. Existing approaches can be roughly categorized into network-side and host-side. However, existing network-side methods are difficult to capture contextual semantics from cross-source traffic, and previous host-side methods could be adversary-perceived and expose risks for tampering. More importantly, a single perspective cannot comprehensively track the multi-stage lifecycle of IoT malware. In this paper, we present CMD, a co-analyzed IoT malware detection and forensics system by combining hardware and network domains. For the network part, CMD proposes a tailored …
Enhancing Visual Grounding In Vision-Language Pre-Training With Position-Guided Text Prompts, Alex Jinpeng Wang, Pan Zhou, Mike Zheng Shou, Shuicheng Yan
Enhancing Visual Grounding In Vision-Language Pre-Training With Position-Guided Text Prompts, Alex Jinpeng Wang, Pan Zhou, Mike Zheng Shou, Shuicheng Yan
Research Collection School Of Computing and Information Systems
Vision-Language Pre-Training (VLP) has demonstrated remarkable potential in aligning image and text pairs, paving the way for a wide range of cross-modal learning tasks. Nevertheless, we have observed that VLP models often fall short in terms of visual grounding and localization capabilities, which are crucial for many downstream tasks, such as visual reasoning. In response, we introduce a novel Position-guided Text Prompt ( PTP ) paradigm to bolster the visual grounding abilities of cross-modal models trained with VLP. In the VLP phase, PTP divides an image into N x N blocks and employs a widely-used object detector to identify objects …