Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (4216)
- Computer Sciences (4213)
- Databases and Information Systems (1558)
- Software Engineering (1254)
- Artificial Intelligence and Robotics (747)
-
- Information Security (730)
- Graphics and Human Computer Interfaces (490)
- Numerical Analysis and Scientific Computing (454)
- Engineering (345)
- Programming Languages and Compilers (269)
- Computer Engineering (232)
- Social and Behavioral Sciences (214)
- Theory and Algorithms (212)
- Business (202)
- OS and Networks (148)
- Communication (103)
- Operations Research, Systems Engineering and Industrial Engineering (101)
- Social Media (83)
- Data Storage Systems (81)
- Medicine and Health Sciences (79)
- Education (66)
- Public Affairs, Public Policy and Public Administration (55)
- Computer and Systems Architecture (52)
- Health Information Technology (51)
- Transportation (51)
- Management Information Systems (49)
- Digital Communications and Networking (47)
- E-Commerce (44)
- Finance and Financial Management (40)
- Higher Education (30)
- Keyword
-
- Deep learning (71)
- Machine learning (59)
- Privacy (41)
- Empirical study (40)
- Deep Learning (35)
-
- Training (34)
- Security (33)
- Task analysis (32)
- Cloud computing (31)
- Data mining (31)
- Artificial intelligence (29)
- Reinforcement learning (29)
- Social media (29)
- Software engineering (29)
- Neural networks (28)
- Android (27)
- Blockchain (27)
- Feature extraction (27)
- Large Language Models (26)
- Model Check (26)
- Access control (25)
- Natural language processing (25)
- Authentication (24)
- Large language models (24)
- Privacy-preserving (24)
- Servers (24)
- Anomaly detection (23)
- Information retrieval (23)
- Visualization (23)
- Classification (22)
- Publication Year
Articles 1381 - 1410 of 4266
Full-Text Articles in Entire DC Network
Aspect-Based Sentiment Analysis In Question Answering Forums, Wenxuan Zhang, Yang Deng, Xin Li, Lidong Bing, Wai Lam
Aspect-Based Sentiment Analysis In Question Answering Forums, Wenxuan Zhang, Yang Deng, Xin Li, Lidong Bing, Wai Lam
Research Collection School Of Computing and Information Systems
Aspect-based sentiment analysis (ABSA) typically focuses on extracting aspects and predicting their sentiments on individual sentences such as customer reviews. Recently, another kind of opinion sharing platform, namely question answering (QA) forum, has received increasing popularity, which accumulates a large number of user opinions towards various aspects. This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair. Unlike review sentences, a QA pair is composed of two parallel sentences, which requires interaction modeling to align the aspect mentioned in the question …
On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic
On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic
Research Collection School Of Computing and Information Systems
We consider the almost-sure (a.s.) termination problem for probabilistic programs, which are a stochastic extension of classical imperative programs. Lexicographic ranking functions provide a sound and practical approach for termination of non-probabilistic programs, and their extension to probabilistic programs is achieved via lexicographic ranking supermartingales (LexRSMs). However, LexRSMs introduced in the previous work have a limitation that impedes their automation: all of their components have to be non-negative in all reachable states. This might result in LexRSM not existing even for simple terminating programs. Our contributions are twofold: First, we introduce a generalization of LexRSMs which allows for some components …
A Bert-Based Two-Stage Model For Chinese Chengyu Recommendation, Minghuan Tan, Jing Jiang, Bingtian Dai
A Bert-Based Two-Stage Model For Chinese Chengyu Recommendation, Minghuan Tan, Jing Jiang, Bingtian Dai
Research Collection School Of Computing and Information Systems
In Chinese, Chengyu are fixed phrases consisting of four characters. As a type of idioms, their meanings usually cannot be derived from their component characters. In this paper, we study the task of recommending a Chengyu given a textual context. Observing some of the limitations with existing work, we propose a two-stage model, where during the first stage we re-train a Chinese BERT model by masking out Chengyu from a large Chinese corpus with a wide coverage of Chengyu. During the second stage, we fine-tune the retrained, Chengyu-oriented BERT on a specific Chengyu recommendation dataset. We evaluate this method on …
Automating Developer Chat Mining, Shengyi Pan, Lingfeng Bao, Xiaoxue Ren, Xin Xia, David Lo, Shanping Li
Automating Developer Chat Mining, Shengyi Pan, Lingfeng Bao, Xiaoxue Ren, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Online chatrooms are gaining popularity as a communication channel between widely distributed developers of Open Source Software (OSS) projects. Most discussion threads in chatrooms follow a Q&A format, with some developers (askers) raising an initial question and others (respondents) joining in to provide answers. These discussion threads are embedded with rich information that can satisfy the diverse needs of various OSS stakeholders. However, retrieving information from threads is challenging as it requires a thread-level analysis to understand the context. Moreover, the chat data is transient and unstructured, consisting of entangled informal conversations. In this paper, we address this challenge by …
Using Role Play To Develop An Empathetic Mindset In Executive Education, Anuradha Ravi, Kasun Gamlath, Siyan Hu, Archan Misra
Using Role Play To Develop An Empathetic Mindset In Executive Education, Anuradha Ravi, Kasun Gamlath, Siyan Hu, Archan Misra
Research Collection School Of Computing and Information Systems
We describe the practical development of a smart lighting control system, CS-Light, that uses a preexisting surveillance camera infrastructure as the sole sensing substrate. At a high level, the camera feeds are used to both (a) estimate the illuminance of individual, fine-grained (roughly 12m2) sub-regions, and (b) identify sub-regions that have non-transient human occupancy. Subsequently, these estimates are used to perform fine-grained (non-binary) power optimization of a set of LED luminaires, collectively minimizing energy consumption while assuring comfort to human occupants. The key to our approach is the ability to tackle the challenging problem of translating the luminance (pixel intensity) …
Factual Consistency Evaluation For Text Summarization Via Counterfactual Estimation, Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, Bolin Ding
Factual Consistency Evaluation For Text Summarization Via Counterfactual Estimation, Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, Bolin Ding
Research Collection School Of Computing and Information Systems
Despite significant progress has been achieved in text summarization, factual inconsistency in generated summaries still severely limits its practical applications. Among the key factors to ensure factual consistency, a reliable automatic evaluation metric is the first and the most crucial one. However, existing metrics either neglect the intrinsic cause of the factual inconsistency or rely on auxiliary tasks, leading to an unsatisfied correlation with human judgments or increasing the inconvenience of usage in practice. In light of these challenges, we propose a novel metric to evaluate the factual consistency in text summarization via counterfactual estimation, which formulates the causal relationship …
Efficient Online-Friendly Two-Party Ecdsa Signature, Haiyang Xue, Ho Man Au, Xiang Xie, Hon Tsz Yuen, Handong Cui
Efficient Online-Friendly Two-Party Ecdsa Signature, Haiyang Xue, Ho Man Au, Xiang Xie, Hon Tsz Yuen, Handong Cui
Research Collection School Of Computing and Information Systems
Two-party ECDSA signatures have received much attention due to their widespread deployment in cryptocurrencies. Depending on whether or not the message is required, we could divide two-party signing into two different phases, namely, offline and online. Ideally, the online phase should be made as lightweight as possible. At the same time, the cost of the offline phase should remain similar to that of a normal signature generation. However, the existing two-party protocols of ECDSA are not optimal: either their online phase requires decryption of a ciphertext, or their offline phase needs at least two executions of multiplicative-to-additive conversion which dominates …
Profiling Student Learning From Q&A Interactions In Online Discussion Forums, De Lin Ong, Kyong Jin Shim, Gottipati Swapna
Profiling Student Learning From Q&A Interactions In Online Discussion Forums, De Lin Ong, Kyong Jin Shim, Gottipati Swapna
Research Collection School Of Computing and Information Systems
The last two decades have witnessed an explosive growth in technology adoption in education. Proliferation of digital learning resources through Massive Open Online Courses (MOOCs) and social media platforms coupled with significantly lowered cost of learning has brought and is continuing to take education to every doorstep globally. In recent years, the use of asynchronous online discussion forums has become pervasive in tertiary education institutions. Online discussion forums are widely used for facilitating interactions both during the lesson time and beyond. Numerous prior studies have reported benefits of using online discussion forums including enhanced quality of learning, improved level of …
Towards Balancing Vr Immersion And Bystander Awareness, Yoshiki Kudo, Anthony Tang, Kazuyuki Fujita, Isamu Endo, Kazuki Takashima, Yoshifumi Kitamura
Towards Balancing Vr Immersion And Bystander Awareness, Yoshiki Kudo, Anthony Tang, Kazuyuki Fujita, Isamu Endo, Kazuki Takashima, Yoshifumi Kitamura
Research Collection School Of Computing and Information Systems
Head-mounted displays (HMDs) increase immersion into virtual worlds. The problem is that this limits headset users' awareness of bystanders: headset users cannot attend to bystanders' presence and activities. We call this the HMD boundary. We explore how to make the HMD boundary permeable by comparing different ways of providing informal awareness cues to the headset user about bystanders. We adapted and implemented three visualization techniques (Avatar View, Radar and Presence++) that share bystanders' location and orientation with headset users. We conducted a hybrid user and simulation study with three different types of VR content (high, medium, low interactivity) with twenty …
Towards Enriching Responses With Crowd-Sourced Knowledge For Task-Oriented Dialogue, Yingxu He, Lizi Liao, Zheng Zhang, Tat-Seng Chua
Towards Enriching Responses With Crowd-Sourced Knowledge For Task-Oriented Dialogue, Yingxu He, Lizi Liao, Zheng Zhang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Task-oriented dialogue agents are built to assist users in completing various tasks. Generating appropriate responses for satisfactory task completion is the ultimate goal. Hence, as a convenient and straightforward way, metrics such as success rate, inform rate etc., have been widely leveraged to evaluate the generated responses. However, beyond task completion, there are several other factors that largely affect user satisfaction, which remain under-explored. In this work, we focus on analyzing different agent behavior patterns that lead to higher user satisfaction scores. Based on the findings, we design a neural response generation model EnRG. It naturally combines the power of …
Probablistic Verification Of Neural Networks Against Group Fairness, Bing Sun, Jun Sun, Ting Dai, Lijun Zhang
Probablistic Verification Of Neural Networks Against Group Fairness, Bing Sun, Jun Sun, Ting Dai, Lijun Zhang
Research Collection School Of Computing and Information Systems
Fairness is crucial for neural networks which are used in applications with important societal implication. Recently, there have been multiple attempts on improving fairness of neural networks, with a focus on fairness testing (e.g., generating individual discriminatory instances) and fairness training (e.g., enhancing fairness through augmented training). In this work, we propose an approach to formally verify neural networks against fairness, with a focus on independence-based fairness such as group fairness. Our method is built upon an approach for learning Markov Chains from a user-provided neural network (i.e., a feed-forward neural network or a recurrent neural network) which is guaranteed …
Does Active Service Intervention Drive More Complaints On Social Media? The Roles Of Service Quality And Awareness, Shujing Sun, Yang Gao, Huaxia Rui
Does Active Service Intervention Drive More Complaints On Social Media? The Roles Of Service Quality And Awareness, Shujing Sun, Yang Gao, Huaxia Rui
Research Collection School Of Computing and Information Systems
Despite many advantages of social media as a customer service channel, there is a concern that active service intervention encourages excessive service complaints. Our paper casts doubt on this misconception by examining the dynamics between social media customer complaints and brand service interventions. We find service interventions indeed cause more complaints, yet this increase is driven by service awareness rather than chronic complaining. Due to the publicity and connectivity of social media, customers learn about the new service channel by observing customer service delivery to others – a mechanism that is unique to social media customer service and does not …
On Aggregating Salaries Of Occupations From Job Post And Review Data, Chih-Chieh Hung, Ee-Peng Lim
On Aggregating Salaries Of Occupations From Job Post And Review Data, Chih-Chieh Hung, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
The popularity of job websites has significantly changed the way people learn about different occupations. Among the insights offered by these websites are the statistics of occupation salaries which are useful information for job seekers, career coaches, graduating students, and labor related government agencies. Such statistics include the distribution of job salaries of each occupation, such as average or quantiles. However, significant variability in salary (and review salary) can be found among jobs of the same occupation as we gather job post and review data from job websites. Such variability shows the existence of biases, including salary competitiveness in job …
Leap: Leakage-Abuse Attack On Efficiently Deployable, Efficiently Searchable Encryption With Partially Known Dataset, Jianting Ning, Xinyi Huang, Geong Sen Poh, Jiaming Yuan, Yingjiu Li, Jian Weng, Robert H. Deng
Leap: Leakage-Abuse Attack On Efficiently Deployable, Efficiently Searchable Encryption With Partially Known Dataset, Jianting Ning, Xinyi Huang, Geong Sen Poh, Jiaming Yuan, Yingjiu Li, Jian Weng, Robert H. Deng
Research Collection School Of Computing and Information Systems
Searchable Encryption (SE) enables private queries on encrypted documents. Most existing SE schemes focus on constructing industrialready, practical solutions at the expense of information leakages that are considered acceptable. In particular, ShadowCrypt utilizes a cryptographic approach named “efficiently deployable, efficiently searchable encryption” (EDESE) that reveals the encrypted dataset and the query tokens among other information. However, recent attacks showed that such leakages can be exploited to (partially) recover the underlying keywords of query tokens under certain assumptions on the attacker’s background knowledge. We continue this line of work by presenting LEAP, a new leakageabuse attack on EDESE schemes that can …
Predicting Anti-Asian Hateful Users On Twitter During Covid-19, Jisun An, Haewoon Kwak, Claire Seungeun Lee, Bogang Jun, Yong-Yeol Ahn
Predicting Anti-Asian Hateful Users On Twitter During Covid-19, Jisun An, Haewoon Kwak, Claire Seungeun Lee, Bogang Jun, Yong-Yeol Ahn
Research Collection School Of Computing and Information Systems
We investigate predictors of anti-Asian hate among Twitter users throughout COVID-19. With the rise of xenophobia and polarization that has accompanied widespread social media usage in many nations, online hate has become a major social issue, attracting many researchers. Here, we apply natural language processing techniques to characterize social media users who began to post anti-Asian hate messages during COVID-19. We compare two user groups—those who posted anti-Asian slurs and those who did not—with respect to a rich set of features measured with data prior to COVID-19 and show that it is possible to predict who later publicly posted anti-Asian …
Automating User Notice Generation For Smart Contract Functions, Xing Hu, Zhipeng Gao, Xin Xia, David Lo, Xiaohu Yang
Automating User Notice Generation For Smart Contract Functions, Xing Hu, Zhipeng Gao, Xin Xia, David Lo, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Smart contracts have obtained much attention and are crucial for automatic financial and business transactions. For end-users who have never seen the source code, they can read the user notice shown in end-user client to understand what a transaction does of a smart contract function. However, due to time constraints or lack of motivation, user notice is often missing during the development of smart contracts. For endusers who lack the information of the user notices, there is no easy way for them to check the code semantics of the smart contracts. Thus, in this paper, we propose a new approach …
Incbl: Incremental Bug Localization, Zhou Yang, Jieke Shi, Wang Shaowei, David Lo
Incbl: Incremental Bug Localization, Zhou Yang, Jieke Shi, Wang Shaowei, David Lo
Research Collection School Of Computing and Information Systems
Numerous efforts have been invested in improving the effectiveness of bug localization techniques, whereas little attention is paid to making these tools run more efficiently in continuously evolving software repositories. This paper first analyzes the information retrieval model behind a classic bug localization tool, BugLocator, and builds a mathematical foundation illustrating that the model can be updated incrementally when codebase or bug reports evolve. Then, we present IncBL, a tool for Incremental Bug Localization in evolving software repositories. IncBL is evaluated on the Bugzbook dataset, and the results show that IncBL can significantly reduce the running time by 77.79% on …
Expediting The Accuracy-Improving Process Of Svms For Class Imbalance Learning, Bin Cao, Yuqi Liu, Chenyu Hou, Jing Fan, Baihua Zheng, Jianwei Jin
Expediting The Accuracy-Improving Process Of Svms For Class Imbalance Learning, Bin Cao, Yuqi Liu, Chenyu Hou, Jing Fan, Baihua Zheng, Jianwei Jin
Research Collection School Of Computing and Information Systems
To improve the classification performance of support vector machines (SVMs) on imbalanced datasets, cost-sensitive learning methods have been proposed, e.g., DEC (Different Error Costs) and FSVM-CIL (Fuzzy SVM for Class Imbalance Learning). They relocate the hyperplane by adjusting the costs associated with misclassifying samples. However, the error costs are determined either empirically or by performing an exhaustive search in the parameter space. Both strategies can not guarantee effectiveness and efficiency simultaneously. In this paper, we propose ATEC, a solution that can efficiently find a preferable hyperplane by automatically tuning the error cost for between-class samples. ATEC distinguishes itself from all …
Exploiting Reasoning Chains For Multi-Hop Science Question Answering, Weiwen Xu, Yang Deng, Huihui Zhang, Deng Cai, Wai Lam
Exploiting Reasoning Chains For Multi-Hop Science Question Answering, Weiwen Xu, Yang Deng, Huihui Zhang, Deng Cai, Wai Lam
Research Collection School Of Computing and Information Systems
We propose a novel Chain Guided Retriever reader (CGR) framework to model the reasoning chain for multi-hop Science Question Answering. Our framework is capable of performing explainable reasoning without the need of any corpus-specific annotations, such as the ground-truth reasoning chain, or human annotated entity mentions. Specifically, we first generate reasoning chains from a semantic graph constructed by Abstract Meaning Representation of retrieved evidence facts. A Chain-aware loss, concerning both local and global chain information, is also designed to enable the generated chains to serve as distant supervision signals for training the retriever, where reinforcement learning is also adopted to …
Aspect Sentiment Quad Prediction As Paraphrase Generation, Wenxuan Zhang, Yang Deng, Xin Li, Yifei Yuan, Lidong Bing, Wai Lam
Aspect Sentiment Quad Prediction As Paraphrase Generation, Wenxuan Zhang, Yang Deng, Xin Li, Yifei Yuan, Lidong Bing, Wai Lam
Research Collection School Of Computing and Information Systems
Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years, which typically involves four fundamental sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. Existing studies usually consider the detection of partial sentiment elements, instead of predicting the four elements in one shot. In this work, we introduce the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence, which can reveal a more comprehensive and complete aspect-level sentiment structure. We further propose a novel Paraphrase modeling paradigm to cast the ASQP task to a …
Causal Attention For Unbiased Visual Recognition, Tan Wang, Chang Zhou, Qianru Sun, Hanwang Zhang
Causal Attention For Unbiased Visual Recognition, Tan Wang, Chang Zhou, Qianru Sun, Hanwang Zhang
Research Collection School Of Computing and Information Systems
Attention module does not always help deep models learn causal features that are robust in any confounding context, e.g., a foreground object feature is invariant to different backgrounds. This is because the confounders trick the attention to capture spurious correlations that benefit the prediction when the training and testing data are IID (identical & independent distribution); while harm the prediction when the data are OOD (out-of-distribution). The sole fundamental solution to learn causal attention is by causal intervention, which requires additional annotations of the confounders, e.g., a “dog” model is learned within “grass+dog” and “road+dog” respectively, so the “grass” and …
Transporting Causal Mechanisms For Unsupervised Domain Adaptation, Zhongqi Yue, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang
Transporting Causal Mechanisms For Unsupervised Domain Adaptation, Zhongqi Yue, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang
Research Collection School Of Computing and Information Systems
Existing Unsupervised Domain Adaptation (UDA) literature adopts the covariate shift and conditional shift assumptions, which essentially encourage models to learn common features across domains. However, due to the lack of supervision in the target domain, they suffer from the semantic loss: the feature will inevitably lose nondiscriminative semantics in source domain, which is however discriminative in target domain. We use a causal view—transportability theory [41]—to identify that such loss is in fact a confounding effect, which can only be removed by causal intervention. However, the theoretical solution provided by transportability is far from practical for UDA, because it requires the …
Direct Differentiable Augmentation Search, Aoming Liu, Zehao Huang, Zhiwu Huang, Huang, Naiyan Wang
Direct Differentiable Augmentation Search, Aoming Liu, Zehao Huang, Zhiwu Huang, Huang, Naiyan Wang
Research Collection School Of Computing and Information Systems
Data augmentation has been an indispensable tool to improve the performance of deep neural networks, however the augmentation can hardly transfer among different tasks and datasets. Consequently, a recent trend is to adopt AutoML technique to learn proper augmentation policy without extensive hand-crafted tuning. In this paper, we propose an efficient differentiable search algorithm called Direct Differentiable Augmentation Search (DDAS). It exploits meta-learning with one-step gradient update and continuous relaxation to the expected training loss for efficient search. Our DDAS can achieve efficient augmentation search without relying on approximations such as Gumbel-Softmax or second order gradient approximation. To further reduce …
Disentangling Hate In Online Memes, Ka Wei, Roy Lee, Rui Cao, Ziqing Fan, Jing Jiang, Wen Haw Chong
Disentangling Hate In Online Memes, Ka Wei, Roy Lee, Rui Cao, Ziqing Fan, Jing Jiang, Wen Haw Chong
Research Collection School Of Computing and Information Systems
Hateful and offensive content detection has been extensively explored in a single modality such as text. However, such toxic information could also be communicated via multimodal content such as online memes. Therefore, detecting multimodal hateful content has recently garnered much attention in academic and industry research communities. This paper aims to contribute to this emerging research topic by proposing DisMultiHate, which is a novel framework that performed the classification of multimodal hateful content. Specifically, DisMultiHate is designed to disentangle target entities in multimodal memes to improve the hateful content classification and explainability. We conduct extensive experiments on two publicly available …
Differentiated Learning For Multi-Modal Domain Adaptation, Jianming Lv, Kaijie Liu, Shengfeng He
Differentiated Learning For Multi-Modal Domain Adaptation, Jianming Lv, Kaijie Liu, Shengfeng He
Research Collection School Of Computing and Information Systems
Directly deploying a trained multi-modal classifier to a new environment usually leads to poor performance due to the well-known domain shift problem. Existing multi-modal domain adaptation methods treated each modality equally and optimize the sub-models of different modalities synchronously. However, as observed in this paper, the degrees of domain shift in different modalities are usually diverse. We propose a novel Differentiated Learning framework to make use of the diversity between multiple modalities for more effective domain adaptation. Specifically, we model the classifiers of different modalities as a group of teacher/student sub-models, and a novel Prototype based Reliability Measurement is presented …
Visionary Caption: Improving The Accessibility Of Presentation Slides Through Highlighting Visualization, Carmen Ji Yan Yip, Jie Mi Chong, Sin Yee Kwek, Yong Wang, Kotaro Hara
Visionary Caption: Improving The Accessibility Of Presentation Slides Through Highlighting Visualization, Carmen Ji Yan Yip, Jie Mi Chong, Sin Yee Kwek, Yong Wang, Kotaro Hara
Research Collection School Of Computing and Information Systems
Presentation slides are widely used in occasions such as academic talks and business meetings. Captions placed on slides support deaf and hard of hearing (DHH) people to understand spoken contents, but simultaneously comprehending and associating visual contents on slides and caption text could be challenging. In this paper, we design and develop a visualization technique to highlight and associate chart on a slide and numerical data in caption. We first conduct a small formative study with people with and without hearing impairments to assess the value of the visualization technique using a lo-fidelity video prototype. We then develop Visionary Caption, …
Towards Source-Aligned Variational Models For Cross-Domain Recommendation, Aghiles Salah, Thanh-Binh Tran, Hady W. Lauw
Towards Source-Aligned Variational Models For Cross-Domain Recommendation, Aghiles Salah, Thanh-Binh Tran, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Data sparsity is a long-standing challenge in recommender systems. Among existing approaches to alleviate this problem, cross-domain recommendation consists in leveraging knowledge from a source domain or category (e.g., Movies) to improve item recommendation in a target domain (e.g., Books). In this work, we advocate a probabilistic approach to cross-domain recommendation and rely on variational autoencoders (VAEs) as our latent variable models. More precisely, we assume that we have access to a VAE trained on the source domain that we seek to leverage to improve preference modeling in the target domain. To this end, we propose a model which learns …
Burst-Induced Multi-Armed Bandit For Learning Recommendation, Rodrigo Alves, Antoine Ledent, Marius Kloft
Burst-Induced Multi-Armed Bandit For Learning Recommendation, Rodrigo Alves, Antoine Ledent, Marius Kloft
Research Collection School Of Computing and Information Systems
In this paper, we introduce a non-stationary and context-free Multi-Armed Bandit (MAB) problem and a novel algorithm (which we refer to as BMAB) to solve it. The problem is context-free in the sense that no side information about users or items is needed. We work in a continuous-time setting where each timestamp corresponds to a visit by a user and a corresponding decision regarding recommendation. The main novelty is that we model the reward distribution as a consequence of variations in the intensity of the activity, and thereby we assist the exploration/exploitation dilemma by exploring the temporal dynamics of the …
Holoboard: A Large-Format Immersive Teaching Board Based On Pseudo Holographics, Jiangtao Gong, Teng Han, Siling Guo, Jiannan Li, Siyu Zha, Liuxin Zhang, Feng Tian, Qianying Wang, Yong Rui
Holoboard: A Large-Format Immersive Teaching Board Based On Pseudo Holographics, Jiangtao Gong, Teng Han, Siling Guo, Jiannan Li, Siyu Zha, Liuxin Zhang, Feng Tian, Qianying Wang, Yong Rui
Research Collection School Of Computing and Information Systems
In this paper, we present HoloBoard, an interactive large-format pseduo-holographic display system for lecture based classes. With its unique properties of immersive visual display and transparent screen, we designed and implemented a rich set of novel interaction techniques like immersive presentation, role-play, and lecturing behind the scene that are potentially valuable for lecturing in class. We conducted a controlled experimental study to compare a HoloBoard class with a normal class through measuring students’ learning outcomes and three dimensions of engagement (i.e., behavioral, emotional, and cognitive engagement). We used pre-/post- knowledge tests and multimodal learning analytics to measure students’ learning outcomes …
Measuring Data Collection Diligence For Community Healthcare, Galawala Ramesha Samurdhi Karunasena, M. S. Ambiya, Arunesh Sinha, R. Nagar, S. Dalal, Abdullah. H., D. Thakkar, D. Narayanan, M. Tambe
Measuring Data Collection Diligence For Community Healthcare, Galawala Ramesha Samurdhi Karunasena, M. S. Ambiya, Arunesh Sinha, R. Nagar, S. Dalal, Abdullah. H., D. Thakkar, D. Narayanan, M. Tambe
Research Collection School Of Computing and Information Systems
Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of highquality public health data is a significant challenge in developing countries primarily due to non-diligent data collection by community health workers (CHWs). Our use of the word non-diligence here is to emphasize that poor data collection is often not a deliberate action by CHW but arises due to a myriad of factors, sometime beyond the control of the CHW. In this work, we define and test a data collection diligence score. This challenging unlabeled data problem is handled by building upon domain expert’s guidance …