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Variational Graph Author Topic Modeling, Ce ZHANG, Hady Wirawan LAUW 2022 Singapore Management University

Variational Graph Author Topic Modeling, Ce Zhang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

While Variational Graph Auto-Encoder (VGAE) has presented promising ability to learn representations for documents, most existing VGAE methods do not model a latent topic structure and therefore lack semantic interpretability. Exploring hidden topics within documents and discovering key words associated with each topic allow us to develop a semantic interpretation of the corpus. Moreover, documents are usually associated with authors. For example, news reports have journalists specializing in writing certain type of events, academic papers have authors with expertise in certain research topics, etc. Modeling authorship information could benefit topic modeling, since documents by the same authors tend to reveal …


A Low-Power Passive Uhf Tag With High-Precision Temperature Sensor For Human Body Application, Liang-Hung Wang, Zheng Pan, Hao Jiang, Hua-Ling Lai, Qi-Peng Ran, Patricia Angela R. Abu 2022 Fuzhou University

A Low-Power Passive Uhf Tag With High-Precision Temperature Sensor For Human Body Application, Liang-Hung Wang, Zheng Pan, Hao Jiang, Hua-Ling Lai, Qi-Peng Ran, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Radio frequency identification (RFID) tags are widely used in various electronic devices due to their low cost, simple structure, and convenient data reading. This topic aims to study the key technologies of ultra-high frequency (UHF) RFID tags and high-precision temperature sensors, and how to reduce the power consumption of the temperature sensor and the overall circuits while maintaining minimal loss of performance. Combined with the biomedicine, an innovative high-precision human UHF RFID chip for body temperature monitoring is designed. In this study, a ring oscillator whose output frequency is linearly related to temperature is designed and proposed as a temperature-sensing …


A Detailed Review Work On The Existing Animal Detection System, Dechen Doma Bhutia Miss, Swarup Das Dr., Rakesh Kumar Mandal Dr. 2022 North Bengal University,West Bengal,India

A Detailed Review Work On The Existing Animal Detection System, Dechen Doma Bhutia Miss, Swarup Das Dr., Rakesh Kumar Mandal Dr.

International Journal of Computer and Communication Technology

Technology plays a very important part in today’s world, the simplest of tasks demands technology and we as humans crave every day for better technology to make our lives easier, with the help of technology that saves us valuable time and energy which can be utilized to do more productive work, amongst technological advances, expert system plays a very important role in every field and the major field where expert systems can be employed in, is animal detection, for the welfare of animals and the people who need to interact and at times avoid interaction with these animals. The review …


Strategic Signaling For Utility Control In Audit Games, Jianan Chen, Qin Hu, Honglu Jiang 2022 Purdue University

Strategic Signaling For Utility Control In Audit Games, Jianan Chen, Qin Hu, Honglu Jiang

Informatics and Engineering Systems Faculty Publications and Presentations

As an effective method to protect the daily access to sensitive data against malicious attacks, the audit mechanism has been widely deployed in various practical fields. In order to examine security vulnerabilities and prevent the leakage of sensitive data in a timely manner, the database logging system usually employs an online signaling scheme to issue an alert when suspicious access is detected. Defenders can audit alerts to reduce potential damage. This interaction process between a defender and an attacker can be modeled as an audit game. In previous studies, it was found that sending real-time signals in the audit …


Ai-Enabled Adaptive Learning Using Automated Topic Alignment And Doubt Detection, Kar Way TAN, Siaw Ling LO, Eng Lieh OUH, Wei Leng (LIANG Weilin) NEO 2022 Singapore Management University

Ai-Enabled Adaptive Learning Using Automated Topic Alignment And Doubt Detection, Kar Way Tan, Siaw Ling Lo, Eng Lieh Ouh, Wei Leng (Liang Weilin) Neo

Research Collection School Of Computing and Information Systems

Implementing adaptive learning is often a challenging task at higher learning institutions where the students come from diverse backgrounds and disciplines. In this work, we collected informal learning journals from learners. Using the journals, we trained two machine learning models, an automated topic alignment and a doubt detection model to identify areas of adjustment required for teaching and students who require additional attention. The models form the baseline for a quiz recommender tool to dynamically generate personalized quizzes for each learner as practices to reinforce learning. Our pilot deployment of our AI-enabled Adaptive Learning System showed that our approach delivers …


Data-Driven Retail Decision-Making Using Spatial Partitioning And Delineation Of Communities, Ming Hui TAN, Kar Way TAN 2022 Singapore Management University

Data-Driven Retail Decision-Making Using Spatial Partitioning And Delineation Of Communities, Ming Hui Tan, Kar Way Tan

Research Collection School Of Computing and Information Systems

Urbanisation is resulting in rapid growth in road networks within cities. The evolution of road networks can be indicative of a city's economic growth and it is a field of research gaining prominence in recent years. This paper proposes a framework for spatial partition of large scale road networks that produces appropriately sized geospatial units in order to identify the type of community they serve. To this end, we have developed a three-stage procedure which first partitions the road network using Louvain method, followed by outlining the boundary of each partition using Uber H3 grids before classifying each partition using …


Finding Top-M Leading Records In Temporal Data, Yiyi WANG 2022 Singapore Management University

Finding Top-M Leading Records In Temporal Data, Yiyi Wang

Dissertations and Theses Collection (Open Access)

A traditional top-k query retrieves the records that stand out at a certain point in time. On the other hand, a durable top-k query considers how long the records retain their supremacy, i.e., it reports those records that are consistently among the top-k in a given time interval. In this thesis, we introduce a new query to the family of durable top-k formulations. It finds the top-m leading records, i.e., those that rank among the top-k for the longest duration within the query interval. Practically, this query assesses the records based on how long …


Cross-Lingual Transfer Learning For Statistical Type Inference, Zhiming LI, Xiaofei XIE, Haoliang LI, Zhengzi XU, Yi LI, Yang LIU 2022 Singapore Management University

Cross-Lingual Transfer Learning For Statistical Type Inference, Zhiming Li, Xiaofei Xie, Haoliang Li, Zhengzi Xu, Yi Li, Yang Liu

Research Collection School Of Computing and Information Systems

Hitherto statistical type inference systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label large amounts of data. Most Turing-complete imperative languages share similar control- and data-flow structures, which make it possible to transfer knowledge learned from one language to another. In this paper, we propose a cross-lingual transfer learning framework, Plato, for statistical type inference, which allows us to leverage prior knowledge learned from the labeled dataset of one language and transfer it to the others, e.g., Python to JavaScript, Java to JavaScript, etc. Plato is powered by a novel kernelized attention mechanism …


Self-Guided Learning To Denoise For Robust Recommendation, Yunjun GAO, Yuntao DU, Yujia HU, Lu CHEN, Xinjun ZHU, Ziquan FANG, Baihua ZHENG 2022 Singapore Management University

Self-Guided Learning To Denoise For Robust Recommendation, Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang, Baihua Zheng

Research Collection School Of Computing and Information Systems

The ubiquity of implicit feedback makes them the default choice to build modern recommender systems. Generally speaking, observed interactions are considered as positive samples, while unobserved interactions are considered as negative ones. However, implicit feedback is inherently noisy because of the ubiquitous presence of noisy-positive and noisy-negative interactions. Recently, some studies have noticed the importance of denoising implicit feedback for recommendations, and enhanced the robustness of recommendation models to some extent. Nonetheless, they typically fail to (1) capture the hard yet clean interactions for learning comprehensive user preference, and (2) provide a universal denoising solution that can be applied to …


Quantum Machine Learning For Credit Scoring, N. SCHETAKIS, D. AGHAMALYAN, M. BOGUSLAVSKY, A. REES, Marc RAKOTOMALALA, Paul GRIFFIN 2022 Quantum Innovation, Greece

Quantum Machine Learning For Credit Scoring, N. Schetakis, D. Aghamalyan, M. Boguslavsky, A. Rees, Marc Rakotomalala, Paul Griffin

Research Collection School Of Computing and Information Systems

In this paper we explore the use of quantum machine learning (QML) applied to credit scoring for small and medium size businesses (SMEs). A quantum/classical hybrid approach has been used for two years of experimentation with several models, activation functions, epochs, other parameters. Results are shown from the best model, using two quantum classifiers and a classical neural network, applied to data for companies in Singapore. We observe significantly more efficient training for the quantum models over the classical models for comparable prediction performance. Practical issues are also explored including a quadratic computational slow down with the number of qubits …


Reflection As An Agile Course Evaluation Tool, Siaw Ling LO, Pei Hua CHER, Fernando BELLO 2022 Singapore Management University

Reflection As An Agile Course Evaluation Tool, Siaw Ling Lo, Pei Hua Cher, Fernando Bello

Research Collection School Of Computing and Information Systems

Reflection is often used as a tool to analyse student learning, be it for internalizing of acquired knowledge or as a form of seeking help through expression of doubts or misconceptions. However, it can be a challenge to extract relevant information from the free-form reflection text. Often times the workload of manually analyzing the reflection text can be a form of deterrence instead of providing insights in the course delivery for instructors, let alone improving the learning experience. In this paper, we review the current usage of reflection and propose an automated reflection framework, together with an end-to-end analysis of …


Open-Set Domain Adaptation By Deconfounding Domain Gaps, Xin ZHAO, Shengsheng WANG, Qianru SUN 2022 Singapore Management University

Open-Set Domain Adaptation By Deconfounding Domain Gaps, Xin Zhao, Shengsheng Wang, Qianru Sun

Research Collection School Of Computing and Information Systems

Open-Set Domain Adaptation (OSDA) aims to adapt the model trained on a source domain to the recognition tasks in a target domain while shielding any distractions caused by open-set classes, i.e., the classes “unknown” to the source model. Compared to standard DA, the key of OSDA lies in the separation between known and unknown classes. Existing OSDA methods often fail the separation because of overlooking the confounders (i.e., the domain gaps), which means their recognition of “unknown classes” is not because of class semantics but domain difference (e.g., styles and contexts). We address this issue by explicitly deconfounding domain gaps …


Structured And Natural Responses Co-Generation For Conversational Search, Chenchen YE, Lizi LIAO, Fuli FENG, Wei JI, Tat-Seng CHUA 2022 Singapore Management University

Structured And Natural Responses Co-Generation For Conversational Search, Chenchen Ye, Lizi Liao, Fuli Feng, Wei Ji, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Generating fluent and informative natural responses while main- taining representative internal states for search optimization is critical for conversational search systems. Existing approaches ei- ther 1) predict structured dialog acts first and then generate natural response; or 2) map conversation context to natural responses di- rectly in an end-to-end manner. Both kinds of approaches have shortcomings. The former suffers from error accumulation while the semantic associations between structured acts and natural re- sponses are confined in single direction. The latter emphasizes generating natural responses but fails to predict structured acts. Therefore, we propose a neural co-generation model that gener- ates …


Multi-Level Cross-View Contrastive Learning For Knowledge-Aware Recommender System, Ding ZOU, Wei WEI, Xian-Ling MAO, Ziyang WANG, Minghui QIU, Feida ZHU, Xin CAO 2022 Singapore Management University

Multi-Level Cross-View Contrastive Learning For Knowledge-Aware Recommender System, Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, Xin Cao

Research Collection School Of Computing and Information Systems

Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there is a natural deficiency for GNN-based KGR models, that is, the sparse supervised signal problem, which may make their actual performance drop to some extent. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring the contrastive learning in KG-aware recommendation and propose a novel multi-level cross-view contrastive learning mechanism, named MCCLK. Different from traditional contrastive learning methods which …


Docee: A Large-Scale And Fine-Grained Benchmark For Document-Level Event Extraction, Meihan TONG, Bin XU, Shuai WANG, Meihuan HAN, Yixin CAO, Jiangqi ZHU, Siyu CHEN, Lei HOU, Juanzi LI 2022 Singapore Management University

Docee: A Large-Scale And Fine-Grained Benchmark For Document-Level Event Extraction, Meihan Tong, Bin Xu, Shuai Wang, Meihuan Han, Yixin Cao, Jiangqi Zhu, Siyu Chen, Lei Hou, Juanzi Li

Research Collection School Of Computing and Information Systems

Event extraction aims to identify an event and then extract the arguments participating in the event. Despite the great success in sentencelevel event extraction, events are more naturally presented in the form of documents, with event arguments scattered in multiple sentences. However, a major barrier to promote documentlevel event extraction has been the lack of large-scale and practical training and evaluation datasets. In this paper, we present DocEE, a new document-level event extraction dataset including 27,000+ events, 180,000+ arguments. We highlight three features: largescale manual annotations, fine-grained argument types and application-oriented settings. Experiments show that there is still a big …


End-To-End Open-Set Semi-Supervised Node Classification With Out-Of-Distribution Detection, Tiancheng HUANG, Donglin WANG, Yuan FANG 2022 Singapore Management University

End-To-End Open-Set Semi-Supervised Node Classification With Out-Of-Distribution Detection, Tiancheng Huang, Donglin Wang, Yuan Fang

Research Collection School Of Computing and Information Systems

Out-Of-Distribution (OOD) samples are prevalent in real-world applications. The OOD issue becomes even more severe on graph data, as the effect of OOD nodes can be potentially amplified by propagation through the graph topology. Recent works have considered the OOD detection problem, which is critical for reducing the uncertainty in learning and improving the robustness. However, no prior work considers simultaneously OOD detection and node classification on graphs in an end-to-end manner. In this paper, we study a novel problem of end-to-end open-set semisupervised node classification (OSSNC) on graphs, which deals with node classification in the presence of OOD nodes. …


Early Rumor Detection Using Neural Hawkes Process With A New Benchmark Dataset, Fengzhu ZENG, Wei GAO 2022 Singapore Management University

Early Rumor Detection Using Neural Hawkes Process With A New Benchmark Dataset, Fengzhu Zeng, Wei Gao

Research Collection School Of Computing and Information Systems

Little attention has been paid on EArly Rumor Detection (EARD), and EARD performance was evaluated inappropriately on a few datasets where the actual early-stage information is largely missing. To reverse such situation, we construct BEARD, a new Benchmark dataset for EARD, based on claims from fact-checking websites by trying to gather as many early relevant posts as possible. We also propose HEARD, a novel model based on neural Hawkes process for EARD, which can guide a generic rumor detection model to make timely, accurate and stable predictions. Experiments show that HEARD achieves effective EARD performance on two commonly used general …


Test Mimicry To Assess The Exploitability Of Library Vulnerabilities, Hong Jin KANG, Truong Giang NGUYEN, Bach LE, Corina S. PASAREANU, David LO 2022 Singapore Management University

Test Mimicry To Assess The Exploitability Of Library Vulnerabilities, Hong Jin Kang, Truong Giang Nguyen, Bach Le, Corina S. Pasareanu, David Lo

Research Collection School Of Computing and Information Systems

Modern software engineering projects often depend on open-source software libraries, rendering them vulnerable to potential security issues in these libraries. Developers of client projects have to stay alert of security threats in the software dependencies. While there are existing tools that allow developers to assess if a library vulnerability is reachable from a project, they face limitations. Call graphonly approaches may produce false alarms as the client project may not use the vulnerable code in a way that triggers the vulnerability, while test generation-based approaches faces difficulties in overcoming the intrinsic complexity of exploiting a vulnerability, where extensive domain knowledge …


Real-Time Hierarchical Map Segmentation For Coordinating Multi-Robot Exploration, Tianze LUO, Zichen CHEN, Budhitama SUBAGDJA, Ah-hwee TAN 2022 Singapore Management University

Real-Time Hierarchical Map Segmentation For Coordinating Multi-Robot Exploration, Tianze Luo, Zichen Chen, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Coordinating a team of autonomous agents to explore an environment can be done by partitioning the map of the environment into segments and allocating the segments as targets for the individual agents to visit. However, given an unknown environment, map segmentation must be conducted in a continuous and incremental manner. In this paper, we propose a novel real-time hierarchical map segmentation method for supporting multi-agent exploration of indoor environments, wherein clusters of regions of segments are formed hierarchically from randomly sampled points in the environment. Each cluster is then assigned with a cost-utility value based on the minimum cost possible …


Declaration-Based Prompt Tuning For Visual Question Answering, Yuhang LIU, Wei WEI, Feida ZHU, Feida ZHU 2022 Singapore Management University

Declaration-Based Prompt Tuning For Visual Question Answering, Yuhang Liu, Wei Wei, Feida Zhu, Feida Zhu

Research Collection School Of Computing and Information Systems

In recent years, the pre-training-then-fine-tuning paradigm has yielded immense success on a wide spectrum of cross-modal tasks, such as visual question answering (VQA), in which a visual-language (VL) model is first optimized via self-supervised task objectives, e.g., masked language modeling (MLM) and image-text matching (ITM), and then fine-tuned to adapt to downstream task (e.g., VQA) via a brand-new objective function, e.g., answer prediction. However, the inconsistency of the objective forms not only severely limits the generalization of pre-trained VL models to downstream tasks, but also requires a large amount of labeled data for fine-tuning. To alleviate the problem, we propose …


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