Trajectory Optimization For Safe Navigation In Maritime Traffic Using Historical Data,
2022
Singapore Management University
Trajectory Optimization For Safe Navigation In Maritime Traffic Using Historical Data, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, T. K. Satish Kumar
Research Collection School Of Computing and Information Systems
Increasing maritime trade often results in congestion in busy ports, thereby necessitating planning methods to avoid close quarter risky situations among vessels. Rapid digitization and automation of port operations and vessel navigation provide unique opportunities for significantly improving navigation safety. Our key contributions are as follows. First, given a set of future candidate trajectories for vessels in a traffic hotspot zone, we develop a multiagent trajectory optimization method to choose trajectories that result in the best overall close quarter risk reduction. Our novel MILP-based optimization method is more than an order-of-magnitude faster than a standard MILP for this problem, and …
Simple And Optimal Stochastic Gradient Methods For Nonsmooth Nonconvex Optimization,
2022
Singapore Management University
Simple And Optimal Stochastic Gradient Methods For Nonsmooth Nonconvex Optimization, Zhize Li, Jian Li
Research Collection School Of Computing and Information Systems
We propose and analyze several stochastic gradient algorithms for finding stationary points or local minimum in nonconvex, possibly with nonsmooth regularizer, finite-sum and online optimization problems. First, we propose a simple proximal stochastic gradient algorithm based on variance reduction called ProxSVRG+. We provide a clean and tight analysis of ProxSVRG+, which shows that it outperforms the deterministic proximal gradient descent (ProxGD) for a wide range of minibatch sizes, hence solves an open problem proposed in Reddi et al. (2016b). Also, ProxSVRG+ uses much less proximal oracle calls than ProxSVRG (Reddi et al., 2016b) and extends to the online setting by …
Efficient Resource Allocation With Fairness Constraints In Restless Multi-Armed Bandits,
2022
Singapore Management University
Efficient Resource Allocation With Fairness Constraints In Restless Multi-Armed Bandits, Dexun Li, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Restless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, personalized recommendations and many more. Existing research in RMAB has contributed mechanisms and theoretical results to a wide variety of settings, where the focus is on maximizing expected value. In this paper, we are interested in ensuring that RMAB decision making is also fair to different arms while maximizing expected value. In the context of public health settings, this would ensure that different people and/or communities are fairly represented while making public health intervention …
Systems And Methods For Contrastive Learning With Self-Labeling Refinement,
2022
Singapore Management University
Systems And Methods For Contrastive Learning With Self-Labeling Refinement, Pan Zhou, Caiming Xiong, Steven Hoi
Research Collection School Of Computing and Information Systems
Embodiments described herein provide a contrastive learning mechanism with self - labeling refinement , which iteratively employs the network and data themselves to generate more accurate and informative soft labels for contrastive learning . Specifically , the contrastive learning framework includes a self - labeling refinery module to explicitly generate accurate labels , and a momentum mix - up module to increase similarity between a query and its positive , which in turn implicitly improves label accuracy.
Evaluation Of Automation Techniques For Data Quality Assessment For Party And Product Master Data,
2022
University of Arkansas Little Rock
Evaluation Of Automation Techniques For Data Quality Assessment For Party And Product Master Data, Mahmood Mohammed
Theses and Dissertations
In an era, where data is being used by organizations in achieving their business goals and driving their business decisions, it is key to ensure the quality of data. For an organization, the most important data assets include master data assets such as product master data and customer master data, supplier master data, employee master data which can be generalized as party master data. There has been significant growth and variety in data in recent years because of which the traditional rules-based approach and dependency on data experts for assessing data quality is no longer working. This dissertation evaluates and …
A Low-Power Passive Uhf Tag With High-Precision Temperature Sensor For Human Body Application,
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 …
Machine Learning Approach For Classifying Power Outage In Secondary Electric Distribution Network,
2022
Department of Computer Science and Engineering, University of Dar es Salaam, P.O. Box 33335, Dar es Salaam
Machine Learning Approach For Classifying Power Outage In Secondary Electric Distribution Network, Stephan Mgaya, Hellen Maziku
Tanzania Journal of Engineering and Technology (TJET)
Power outage is the problem that hinders social and economic development especially for developing countries like Tanzania. Frequent power outages damage electric equipment, and negatively affect the industrial production process. Power outages cannot be completely eradicated due to uncontrolled cause like natural calamities but technical challenges can be managed and hence reducing power outages. The existing manual methods used to locate power outage like customer calls is inefficient and time consuming. On the other hand, modern method like the Advanced Metering Infrastructure (AMI) still faces a challenge in effectively classifying power line outage due to the nature of imbalanced datasets. …
Big Data Analytics Framework For Effective Higher Education Institutions,
2022
ICT Department, Moshi Co-operative University, P.O. Box 474, Moshi, Kilimanjaro
Big Data Analytics Framework For Effective Higher Education Institutions, George Matto
Tanzania Journal of Engineering and Technology (TJET)
There has been an increased dependency on Information and Communication Technologies (ICTs) in undertaking various activities in Higher Education Institutions (HEIs) ecosystems. Because of that, huge volumes of data have increasingly been generated. There have been, for instance, considerable amounts of data generated through electronic platforms involved in students’ admission and registration process, students’ academic records management, teaching and learning data, curriculum related data, and several other administrative data. Analysis of data generated from these platforms stands to give students, lecturers, HEIs Management, policy makers and implementers, and other stakeholders useful insights that would help in improving HEIs’ effectiveness. Unfortunately, …
An Iot Based Worker Safety Helmet Using Cloud Computing Technology,
2022
Department of Electronics and Telecommunication Engineering, Zanzibar University
An Iot Based Worker Safety Helmet Using Cloud Computing Technology, Yuthriya Iddi Khamis, Mahmoud Alawi, Waheed Muhammad Sanya
Tanzania Journal of Engineering and Technology (TJET)
Miner safety and security is a major challenge around the world due to the exposure to toxic gases that are frequently released in underground mines. Miners' health is adversely affected primarily by toxic gases, which endanger the workers' lives. Furthermore, human sensory abilities do not detect these dangerous gases. As a result, this paper proposes a safety monitoring system that includes a temperature sensor, humidity sensor, and gas sensors to detect harmful gases and alert miners to those harmful gases using the smart helmet they wear. These gases are transmitted to the control station via the cloud using Internet of …
Reflection As An Agile Course Evaluation Tool,
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 …
Using Constraint Programming And Graph Representation Learning For Generating Interpretable Cloud Security Policies,
2022
Singapore Management University
Using Constraint Programming And Graph Representation Learning For Generating Interpretable Cloud Security Policies, Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar
Research Collection School Of Computing and Information Systems
Modern software systems rely on mining insights from business sensitive data stored in public clouds. A data breach usually incurs signifcant (monetary) loss for a commercial organization. Conceptually, cloud security heavily relies on Identity Access Management (IAM) policies that IT admins need to properly confgure and periodically update. Security negligence and human errors often lead to misconfguring IAM policies which may open a backdoor for attackers. To address these challenges, frst, we develop a novel framework that encodes generating optimal IAM policies using constraint programming (CP). We identify reducing dormant permissions of cloud users as an optimality criterion, which intuitively …
Strategic Signaling For Utility Control In Audit Games,
2022
Purdue University
Strategic Signaling For Utility Control In Audit Games, Jianan Chen, Qin Hu, Honglu Jiang
Informatics and Engineering Systems Faculty Publications
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 …
Self-Guided Learning To Denoise For Robust Recommendation,
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 …
Lead1.0: A Large-Scale Annotated Dataset For Energy Anomaly Detection In Commercial Buildings,
2022
Singapore Management University
Lead1.0: A Large-Scale Annotated Dataset For Energy Anomaly Detection In Commercial Buildings, Manoj Gulati, Pandarasamy Arjunan
Research Collection School Of Computing and Information Systems
Modern buildings are densely equipped with smart energy meters, which periodically generate a massive amount of time-series data yielding a few million data points every day. This data can be leveraged to discover the underlying load and infer their energy consumption patterns, inter-dependencies on environmental factors, and the building’s operational properties. Furthermore, it allows us to simultaneously identify anomalies present in the electricity consumption profiles, which is a big step towards saving energy and achieving global sustainability. However, to date, the lack of large-scale annotated energy consumption datasets hinders the ongoing research in anomaly detection. We contribute to this effort …
Towards Aligning Slides And Video Snippets: Mitigating Sequence And Content Mismatches,
2022
Singapore Management University
Towards Aligning Slides And Video Snippets: Mitigating Sequence And Content Mismatches, Ziyuan Liu, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Slides are important form of teaching materials used in various courses at academic institutions. Due to their compactness, slides on their own may not stand as complete reference materials. To aid students’ understanding, it would be useful to supplement slides with other materials such as online videos. Given a deck of slides and a related video, we seek to align each slide in the deck to a relevant video snippet, if any. While this problem could be formulated as aligning two time series (each involving a sequence of text contents), we anticipate challenges in generating matches arising from differences in …
Declaration-Based Prompt Tuning For Visual Question Answering,
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 …
Multi-Level Cross-View Contrastive Learning For Knowledge-Aware Recommender System,
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 …
Finding Top-M Leading Records In Temporal Data,
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 …
Contours Of Virtual Enfreakment In Fighting Game Characters,
2022
Singapore Management University
Contours Of Virtual Enfreakment In Fighting Game Characters, Sercan Sengun, Peter Mawhorter, James Bowie-Wilson, Yusef Audeh, Haewoon Kwak, D. Fox Harrell
Research Collection School Of Computing and Information Systems
Characters in fighting videogames1 such as Street Fighter V and Tekken7 typically reveal a phenomenon that we define as virtual enfreakment: their bodies, costumes, and fighting styles are exaggerated (1) in a manner that emphasizes perceived exoticism and (2) to enable them to be easily visually and conceptually distinguishable from one another. Here, using both quantitative and qualitative methods, including crowd-sourced surveys and analyses of game mechanics, we report on the contours of virtual enfreakment in those games. We specifically examine differences in character design across gender, national-origin, and skin-color lines. Disappointingly but not surprisingly, we find racism and sexism …
Multi-Agent Reinforcement Learning For Traffic Signal Control Through Universal Communication Method,
2022
Singapore Management University
Multi-Agent Reinforcement Learning For Traffic Signal Control Through Universal Communication Method, Qize Jiang, Minhao Qin, Shengmin Shi, Weiwei Sun Sun, Baihua Zheng
Research Collection School Of Computing and Information Systems
How to coordinate the communication among intersections effectively in real complex traffic scenarios with multi-intersection is challenging. Existing approaches only enable the communication in a heuristic manner without considering the content/importance of information to be shared. In this paper, we propose a universal communication form UniComm between intersections. UniComm embeds massive observations collected at one agent into crucial predictions of their impact on its neighbors, which improves the communication efficiency and is universal across existing methods. We also propose a concise network UniLight to make full use of communications enabled by UniComm. Experimental results on real datasets demonstrate that UniComm …
