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Articles 2161 - 2190 of 10509
Full-Text Articles in Entire DC Network
Singapore's Hospital To Home Program: Raising Patient Engagement Through Ai, John Abisheganaden, Kheng Hock Lee, Lian Leng Low, Eugene Shum, Han Leong Goh, Christine Gian Lee Ang, Andy Wee An Ta, Steven M. Miller
Singapore's Hospital To Home Program: Raising Patient Engagement Through Ai, John Abisheganaden, Kheng Hock Lee, Lian Leng Low, Eugene Shum, Han Leong Goh, Christine Gian Lee Ang, Andy Wee An Ta, Steven M. Miller
Research Collection School Of Computing and Information Systems
Because of their complex care needs, many elderly patients are discharged from hospitals only to be readmitted for multiple stays within the following twelve months. John Abisheganaden and his fellow authors describe Singapore’s Hospital to Home program, a community care initiative fueled by artificial intelligence.
Estimation Of Recursive Route Choice Models With Incomplete Trip Observations, Tien Mai, The Viet Bui, Quoc Phong Nguyen, Tho V. Le
Estimation Of Recursive Route Choice Models With Incomplete Trip Observations, Tien Mai, The Viet Bui, Quoc Phong Nguyen, Tho V. Le
Research Collection School Of Computing and Information Systems
This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the observations. A direct approach to handle this issue could be intractable because enumerating all paths between unconnected links (or nodes) in a real network is typically not possible. We exploit an expectation–maximization (EM) method that allows dealing with the missing-data issue by alternatively performing two steps of sampling the missing segments in the observations and solving maximum likelihood estimation problems. Moreover, observing that the EM method could be expensive, we propose a …
A Lightweight Privacy-Preserving Path Selection Scheme In Vanets, Guojun Wang, Huijie Yang
A Lightweight Privacy-Preserving Path Selection Scheme In Vanets, Guojun Wang, Huijie Yang
Research Collection School Of Computing and Information Systems
With the rapid development of edge computing, artificial intelligence and other technologies, intelligent transportation services in the vehicular ad hoc networks (VANETs) such as in-vehicle navigation and distress alert are increasingly being widely used in life. Currently, road navigation is an essential service in the vehicle network. However, when a user employs the road navigation service, his private data maybe exposed to roadside nodes. Meanwhile, when the trusted authorization sends the navigation route data to the user, the user can obtain all the road data. Especially, other unrequested data might be related to the military. Therefore, how to achieve secure …
Multi-View Hypergraph Contrastive Policy Learning For Conversational Recommendation, Sen Zhao, Wei Wei, Xian-Ling Mao, Shuai: Yang Zhu, Zujie Wen, Dangyang Chen, Feida Zhu, Feida Zhu
Multi-View Hypergraph Contrastive Policy Learning For Conversational Recommendation, Sen Zhao, Wei Wei, Xian-Ling Mao, Shuai: Yang Zhu, Zujie Wen, Dangyang Chen, Feida Zhu, Feida Zhu
Research Collection School Of Computing and Information Systems
Conversational recommendation systems (CRS) aim to interactively acquire user preferences and accordingly recommend items to users. Accurately learning the dynamic user preferences is of crucial importance for CRS. Previous works learn the user preferences with pairwise relations from the interactive conversation and item knowledge, while largely ignoring the fact that factors for a relationship in CRS are multiplex. Specifically, the user likes/dislikes the items that satisfy some attributes (Like/Dislike view). Moreover social influence is another important factor that affects user preference towards the item (Social view), while is largely ignored by previous works in CRS. The user preferences from these …
Generative Ai And Chatgpt: Applications, Challenges, And Ai-Human Collaboration, Fiona Nah, Ruilin Zheng, Jingyuan Cai, Keng Siau, Langtao Chen
Generative Ai And Chatgpt: Applications, Challenges, And Ai-Human Collaboration, Fiona Nah, Ruilin Zheng, Jingyuan Cai, Keng Siau, Langtao Chen
Research Collection School Of Computing and Information Systems
Artificial intelligence (AI) has elicited much attention across disciplines and industries (Hyder et al., Citation2019). AI has been defined as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” (Kaplan & Haenlein, Citation2019, p. 15). AI has gone through several development stages and AI winters. In the first two decades (i.e., 1950s and 1960s), AI demonstrated success which included programs such as General Problem Solver (Newell et al., Citation1959) and ELIZA (Weizenbaum, Citation1966). However, limitations in processing capacity and reduced spending on …
Outdoor Performance Of The Black Globe Temperature Sensor On A Hot And Humid Tropical Region, Juan A. Acero, Angela Dissegna, Yon Sun Tan, Adrian Tan, Leslie K. Norford
Outdoor Performance Of The Black Globe Temperature Sensor On A Hot And Humid Tropical Region, Juan A. Acero, Angela Dissegna, Yon Sun Tan, Adrian Tan, Leslie K. Norford
Research Collection School of Social Sciences
A crucial variable to evaluate thermal comfort is the mean radiant temperature (Tmrt). In this paper we evaluate the performance of the 150 mm black globe thermometer to provide reliable Tmrt values for outdoor settings in Singapore. Accurate Tmrt values are calculated by the method of integral radiation measurements. Based on these, the mean convection coefficient of the black globe has been re-calibrated. Results show an improvement in the estimation of Tmrt with the new coefficient in comparison with the default version suggested in ISO7726:1998. Increasing the averaging periods of the measured variables improved the performance of the derived mean …
Analyzing Taxi Drivers’ Decision-Making And Recommending Strategies For Enhanced Performance: A Data-Driven Approach, Mengyu Ji
Dissertations and Theses Collection (Open Access)
This thesis focuses on analyzing the decision-making process of taxi drivers and providing data-driven strategies to enhance their performance. By examin- ing comprehensive historical data encompassing passenger demand patterns, drivers’ spatial dynamics, and fare structures, valuable insights are gained into drivers’ choices regarding optimal routes, timing, and areas with high demand. Integrating real-time information sources, such as GPS data and passenger updates, allows drivers to adapt their strategies dynamically to changing traffic conditions and emerging demand patterns. Predictive analytics models, includ- ing ARIMA, XGBoost, and Linear Regression, are utilized to forecast demand flow at key locations, enabling proactive decision-making and …
Reinforcement Learning For Sequential Decision Making With Constraints, Jiajing Ling
Reinforcement Learning For Sequential Decision Making With Constraints, Jiajing Ling
Dissertations and Theses Collection (Open Access)
Reinforcement learning is a widely used approach to tackle problems in sequential decision making where an agent learns from rewards or penalties. However, in decision-making problems that involve safety or limited resources, the agent's exploration is often limited by constraints. To model such problems, constrained Markov decision processes and constrained decentralized partially observable Markov decision processes have been proposed for single-agent and multi-agent settings, respectively. A significant challenge in solving constrained Dec-POMDP is determining the contribution of each agent to the primary objective and constraint violations. To address this issue, we propose a fictitious play-based method that uses Lagrangian Relaxation …
Investment And The Long Swings Of Unemployment, Hian Teck Hoon, Margarita Katsimi, Gylfi Zoega
Investment And The Long Swings Of Unemployment, Hian Teck Hoon, Margarita Katsimi, Gylfi Zoega
Research Collection School Of Economics
We estimate the medium-term relationship between investment and unemployment over the period 1960–2017 in 20 OECD countries, a period that includes the years of the Great Recession. While neoclassical growth theory typically assumes full employment—with no effect of investment on unemployment—and New-Keynesian models assume that such a relationship only exists in the short term at business cycle frequencies, we find that over our sample period covering more than 5 decades, a statistically significant negative relationship does exist: in decades when investment fell, unemployment increased. Panel estimation, using a measure of stock market volatility as an instrument for investment, confirms the …
A Hierarchical Optimization Approach For Dynamic Pickup And Delivery Problem With Lifo Constraints, Jianhui Du, Zhiqin Zhang, Xu Wang, Hoong Chuin Lau
A Hierarchical Optimization Approach For Dynamic Pickup And Delivery Problem With Lifo Constraints, Jianhui Du, Zhiqin Zhang, Xu Wang, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We consider a dynamic pickup and delivery problem (DPDP) where loading and unloading operations must follow a last in first out (LIFO) sequence. A fleet of vehicles will pick up orders in pickup points and deliver them to destinations. The objective is to minimize the total over-time (that is the amount of time that exceeds the committed delivery time) and total travel distance. Given the dynamics of orders and vehicles, this paper proposes a hierarchical optimization approach based on multiple intuitive yet often-neglected strategies, namely what we term as the urgent strategy, hitchhike strategy and packing-bags strategy. These multiple strategies …
Barriers And Self-Efficacy: A Large-Scale Study On The Impact Of Oss Courses On Student Perceptions, Larissa Salerno, Simone De França Tonhão, Igor Steinmacher, Christoph Treude
Barriers And Self-Efficacy: A Large-Scale Study On The Impact Of Oss Courses On Student Perceptions, Larissa Salerno, Simone De França Tonhão, Igor Steinmacher, Christoph Treude
Research Collection School Of Computing and Information Systems
Open source software (OSS) development offers a unique opportunity for students in Software Engineering to experience and participate in large-scale software development, however, the impact of such courses on students’ self-efficacy and the challenges faced by students are not well understood. This paper aims to address this gap by analyzing data from multiple instances of OSS development courses at universities in different countries and reporting on how students’ self-efficacy changed as a result of taking the course, as well as the barriers and challenges faced by students
Pursue Sustainability, Stay Paranoid In A Post-Covid World, Ho Kwon Ping, Havovi Joshi
Pursue Sustainability, Stay Paranoid In A Post-Covid World, Ho Kwon Ping, Havovi Joshi
Asian Management Insights
Ho Kwon Ping, Founder and Executive Chairman of Banyan Tree Holdings, speaks to Havovi Joshi about making sure sustainability is more than just a buzzword, his optimism regarding Asia’s growth in the future, and the need for youths to think differently about their careers.
Atmosphere, Environment, Society: The Typhoon Vulnerability Nexus In Early Twentieth-Century Hong Kong, Fiona Williamson
Atmosphere, Environment, Society: The Typhoon Vulnerability Nexus In Early Twentieth-Century Hong Kong, Fiona Williamson
Research Collection College of Integrative Studies
By looking at how typhoon risk was managed in early twentieth-century colonial Hong Kong, this article argues that bringing a historical lens to the discussion of the human-climate-environment nexus is an essential part of studies of resilience and vulnerability. It interprets physical evidence of the strength of a natural event against human factors, including structural and social vulnerabilities and governance. Investigating two of the deadliest typhoons to have hit Hong Kong's shores (1906 and 1937), this article notes that social vulnerabilities played a major part in turning the typhoon of 1937 into a worse disaster than 1906, despite advances in …
Vulnerability To Climatic And Environmental Disaster And Change In The Indian Ocean World, Philip Gooding, Fiona Williamson, Julie Babin
Vulnerability To Climatic And Environmental Disaster And Change In The Indian Ocean World, Philip Gooding, Fiona Williamson, Julie Babin
Research Collection College of Integrative Studies
At the time of writing (Autumn 2022), extreme weather is devastating large swathes of the Indian Ocean World (IOW). In Pakistan, especially around the Indus River in Balochistan and Sindh, floods are causing severe distress. So far, more than 1,300 people have been killed, half a million more have been left homeless and there has been around $30 billion worth of damage to infrastructure. Nevertheless, even as the floods are projected to worsen during the second half of the south-west monsoon season, there are already fights on multiple spatial scales to shape the narratives about the root causes of the …
Toys As Tools For Good Science, Joshua Luczak
Toys As Tools For Good Science, Joshua Luczak
Research Collection College of Integrative Studies
The primary aim of this paper is to elevate the status of toy models, toy model based reasoning, and to encourage more focused discussions of them. This paper attempts to satisfy this aim by achieving two, secondary goals. First, it attempts elevate the status of toy models by making clear and elaborating on the distinction between them and representational models, and by discussing the connection between user intentionality and scientific representation (i.e. when a model represents something). And second, this paper attempts to elevate the status of toy models by highlighting, through some important historical examples, how they have been …
Finding Causally Different Tests For An Industrial Control System, Christopher M. Poskitt, Yuqi Chen, Jun Sun, Yu Jiang
Finding Causally Different Tests For An Industrial Control System, Christopher M. Poskitt, Yuqi Chen, Jun Sun, Yu Jiang
Research Collection School Of Computing and Information Systems
Industrial control systems (ICSs) are types of cyber-physical systems in which programs, written in languages such as ladder logic or structured text, control industrial processes through sensing and actuating. Given the use of ICSs in critical infrastructure, it is important to test their resilience against manipulations of sensor/actuator inputs. Unfortunately, existing methods fail to test them comprehensively, as they typically focus on finding the simplest-to-craft manipulations for a testing goal, and are also unable to determine when a test is simply a minor permutation of another, i.e. based on the same causal events. In this work, we propose a guided …
Impact Of Difficult Negatives On Twitter Crisis Detection, Yuhao Zhang, Siaw Ling Lo, Phyo Yi Win Myint
Impact Of Difficult Negatives On Twitter Crisis Detection, Yuhao Zhang, Siaw Ling Lo, Phyo Yi Win Myint
Research Collection School Of Computing and Information Systems
Twitter has become an alternative information source during a crisis. However, the short, noisy nature of tweets hinders information extraction. While models trained with standard Twitter crisis datasets accomplished decent performance, it remained a challenge to generalize to unseen crisis events. Thus, we proposed adding “difficult” negative examples during training to improve model generalization for Twitter crisis detection. Although adding random noise is a common practice, the impact of difficult negatives, i.e., negative data semantically similar to true examples, was never examined in NLP. Most of existing research focuses on the classification task, without considering the primary information need of …
Plan-And-Solve Prompting: Improving Zero-Shot Chain-Of-Thought Reasoning By Large Language Models, Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim
Plan-And-Solve Prompting: Improving Zero-Shot Chain-Of-Thought Reasoning By Large Language Models, Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, few-shot chain-of-thought (CoT) prompting includes a few manually crafted step-by-step reasoning demonstrations which enable LLMs to explicitly generate reasoning steps and improve their reasoning task accuracy. To eliminate the manual effort, Zeroshot-CoT concatenates the target problem statement with “Let’s think step by step” as an input prompt to LLMs. Despite the success of Zero-shot-CoT, it still suffers from three pitfalls: calculation errors, missing-step errors, and semantic misunderstanding errors. To address the missing-step errors, we propose Planand-Solve (PS) Prompting. It …
Machine-Learning Approach To Automated Doubt Identification On Stack Overflow Comments To Guide Programming Learners, Tianhao Chen, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo
Machine-Learning Approach To Automated Doubt Identification On Stack Overflow Comments To Guide Programming Learners, Tianhao Chen, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo
Research Collection School Of Computing and Information Systems
Stack Overflow is a popular Q&A platform for developers to find solutions to programming problems. However, due to the varying quality of user-generated answers, there is a need for ways to help users find high-quality answers. While Stack Overflow's community-based approach can be effective, important technical aspects of the answer need to be captured, and users’ comments might contain doubts regarding these aspects. In this paper, we showed the feasibility of using a machine learning model to identify doubts and conducted data analysis. We found that highly reputed users tend to raise more doubts; most answers have doubt in the …
Chatgpt, Can You Generate Solutions For My Coding Exercises? An Evaluation On Its Effectiveness In An Undergraduate Java Programming Course, Eng Lieh Ouh, Benjamin Gan, Kyong Jin Shim, Swavek Wlodkowski
Chatgpt, Can You Generate Solutions For My Coding Exercises? An Evaluation On Its Effectiveness In An Undergraduate Java Programming Course, Eng Lieh Ouh, Benjamin Gan, Kyong Jin Shim, Swavek Wlodkowski
Research Collection School Of Computing and Information Systems
In this study, we assess the efficacy of employing the ChatGPT language model to generate solutions for coding exercises within an undergraduate Java programming course. ChatGPT, a large-scale, deep learning-driven natural language processing model, is capable of producing programming code based on textual input. Our evaluation involves analyzing ChatGPT-generated solutions for 80 diverse programming exercises and comparing them to the correct solutions. Our findings indicate that ChatGPT accurately generates Java programming solutions, which are characterized by high readability and well-structured organization. Additionally, the model can produce alternative, memory-efficient solutions. However, as a natural language processing model, ChatGPT struggles with coding …
Conference Report On 2022 Ieee Symposium Series On Computational Intelligence (Ieee Ssci 2022), Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao
Conference Report On 2022 Ieee Symposium Series On Computational Intelligence (Ieee Ssci 2022), Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao
Research Collection School Of Computing and Information Systems
On behalf of the organizing committee, we are delighted to deliver this conference report for the 2022 IEEE Symposium Series on Computational Intelligence (SSCI 2022), which was held in Singapore from 4th to 7th December 2022. IEEE SSCI is an established flagship annual international series of symposia on computational intelligence (CI) sponsored by the IEEE Computational Intelligence Society (CIS) to promote and stimulate discussions on the latest theory, algorithms, applications, and emerging topics on computational intelligence. After two years of virtual conferences due to the global pandemic, IEEE SSCI returned as an in-person meeting with online elements in 2022.
Nodemedic: End-To-End Analysis Of Node.Js Vulnerabilities With Provenance Graphs, Darion Cassel, Wai Tuck Wong, Limin Jia
Nodemedic: End-To-End Analysis Of Node.Js Vulnerabilities With Provenance Graphs, Darion Cassel, Wai Tuck Wong, Limin Jia
Research Collection School Of Computing and Information Systems
Packages in the Node.js ecosystem often suffer from serious vulnerabilities such as arbitrary command injection and code execution. Existing taint analysis tools fall short in providing an end-to-end infrastructure for automatically detecting and triaging these vulnerabilities.We develop NodeMedic, an end-to-end analysis infrastructure that automates test driver creation, performs precise yet scalable dynamic taint propagation via algorithmically tuned propagation policies, and exposes taint provenance information as a provenance graph. Using provenance graphs we develop two post-detection analyses: automated constraint-based exploit synthesis to confirm vulnerabilities; Attack-defense-tree-based rating of flow exploitability.We demonstrate the effectiveness of NodeMedic through a large-scale evaluation of 10,000 Node.js …
Qebverif: Quantization Error Bound Verification Of Neural Networks, Yedi Zhang, Fu Song, Jun Sun
Qebverif: Quantization Error Bound Verification Of Neural Networks, Yedi Zhang, Fu Song, Jun Sun
Research Collection School Of Computing and Information Systems
To alleviate the practical constraints for deploying deep neural networks (DNNs) on edge devices, quantization is widely regarded as one promising technique. It reduces the resource requirements for computational power and storage space by quantizing the weights and/or activation tensors of a DNN into lower bit-width fixed-point numbers, resulting in quantized neural networks (QNNs). While it has been empirically shown to introduce minor accuracy loss, critical verified properties of a DNN might become invalid once quantized. Existing verification methods focus on either individual neural networks (DNNs or QNNs) or quantization error bound for partial quantization. In this work, we propose …
Augmenting Low-Resource Text Classification With Graph-Grounded Pre-Training And Prompting, Zhihao Wen, Yuan Fang
Augmenting Low-Resource Text Classification With Graph-Grounded Pre-Training And Prompting, Zhihao Wen, Yuan Fang
Research Collection School Of Computing and Information Systems
ext classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification, with few or no labeled samples, poses a serious concern for supervised learning. Meanwhile, many text data are inherently grounded on a network structure, such as a hyperlink/citation network for online articles, and a user-item purchase network for e-commerce products. These graph structures capture rich semantic relationships, which can potentially augment low-resource text classification. In this paper, we propose a novel model called Graph-Grounded Pre-training and Prompting (G2P2) …
Do-Good: Towards Distribution Shift Evaluation For Pre-Trained Visual Document Understanding Models, Jiabang He, Yi Hu, Lei Wang, Xing Xu, Ning Liu, Hui Liu
Do-Good: Towards Distribution Shift Evaluation For Pre-Trained Visual Document Understanding Models, Jiabang He, Yi Hu, Lei Wang, Xing Xu, Ning Liu, Hui Liu
Research Collection School Of Computing and Information Systems
Numerous pre-training techniques for visual document understanding (VDU) have recently shown substantial improvements in performance across a wide range of document tasks. However, these pre-trained VDU models cannot guarantee continued success when the distribution of test data differs from the distribution of training data. In this paper, to investigate how robust existing pre-trained VDU models are to various distribution shifts, we first develop an out-of-distribution (OOD) benchmark termed Do-GOOD for the fine-Grained analysis on Document image-related tasks specifically. The Do-GOOD benchmark defines the underlying mechanisms that result in different distribution shifts and contains 9 OOD datasets covering 3 VDU related …
Silent Compiler Bug De-Duplication Via Three-Dimensional Analysis, Chen Yang, Junjie Chen, Xingyu Fan, Jiajun Jiang, Jun Sun
Silent Compiler Bug De-Duplication Via Three-Dimensional Analysis, Chen Yang, Junjie Chen, Xingyu Fan, Jiajun Jiang, Jun Sun
Research Collection School Of Computing and Information Systems
Compiler testing is an important task for assuring the quality of compilers, but investigating test failures is very time-consuming. This is because many test failures are caused by the same compiler bug (known as bug duplication problem). In particular, this problem becomes much more challenging on silent compiler bugs (also called wrong code bugs), since these bugs can provide little information (unlike crash bugs that can produce error messages) for bug de-duplication. In this work, we propose a novel technique (called D3) to solve the duplication problem on silent compiler bugs. Its key insight is to characterize the silent bugs …
Towards Omni-Generalizable Neural Methods For Vehicle Routing Problems, Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
Towards Omni-Generalizable Neural Methods For Vehicle Routing Problems, Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This paper studies a challenging yet realistic setting, which considers generalization across both size and distribution in VRPs. We propose a generic meta-learning framework, which enables effective training of an initialized model with the capability of fast adaptation to new tasks during inference. We further develop a simple yet efficient approximation method to reduce …
Forward/Backward And Content Private Dsse For Spatial Keyword Queries, Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Yinbin Miao, Yang Liu, Robert H. Deng
Forward/Backward And Content Private Dsse For Spatial Keyword Queries, Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Yinbin Miao, Yang Liu, Robert H. Deng
Research Collection School Of Computing and Information Systems
Spatial keyword queries are attractive techniques that have been widely deployed in real-life applications in recent years, such as social networks and location-based services. However, existing solutions neither support dynamic update nor satisfy the privacy requirements in real applications. In this article, we investigate the problem of Dynamic Searchable Symmetric Encryption (DSSE) for spatial keyword queries. First, we formulate the definition of DSSE for spatial keyword queries (namely, DSSESKQ) and extend the DSSE leakage functions to capture the leakages in DSSESKQ. Then, we present a practical DSSESKQ construction based on geometric prefix encoding inverted-index and encrypted bitmap. Rigorous security analysis …
Fesa: Automatic Federated Swarm Attestation On Dynamic Large-Scale Iot Devices, Boyu Kuang, Anmin Fu, Yansong Gao, Yuqing Zhang, Jianying Zhou, Robert H. Deng
Fesa: Automatic Federated Swarm Attestation On Dynamic Large-Scale Iot Devices, Boyu Kuang, Anmin Fu, Yansong Gao, Yuqing Zhang, Jianying Zhou, Robert H. Deng
Research Collection School Of Computing and Information Systems
Swarm attestation, as an important branch of Remote Attestation (RA), enables a trusted party (verifier) to verify the security states of multiple devices (provers) in a large network (swarm) simultaneously via a challenge-response mechanism. However, swarm attestation suffers from significant redundancy overhead since all devices in the swarm need to be attested in each attestation round. Besides, it faces challenges such as verifier-impersonation Denial of Service (DoS) attacks, highly dynamic networks, transient & self-relocating malware, and Time-Of-Check-Time-Of-Use (TOCTOU) attacks. In this paper, considering not only the detection accuracy but also the privacy of swarm owners in real Internet of Things …
An Efficient Hybrid Genetic Algorithm For The Quadratic Traveling Salesman Problem, Quang Anh Pham, Hoong Chuin Lau, Minh Hoang Ha, Lam Vu
An Efficient Hybrid Genetic Algorithm For The Quadratic Traveling Salesman Problem, Quang Anh Pham, Hoong Chuin Lau, Minh Hoang Ha, Lam Vu
Research Collection School Of Computing and Information Systems
The traveling salesman problem (TSP) is the most well-known problem in combinatorial optimization which hasbeen studied for many decades. This paper focuses on dealing with one of the most difficult TSP variants named thequadratic traveling salesman problem (QTSP) that has numerous planning applications in robotics and bioinformatics.The goal of QTSP is similar to TSP which finds a cycle visiting all nodes exactly once with minimum total costs. However, the costs in QTSP are associated with three vertices traversed in succession (instead of two like in TSP). This leadsto a quadratic objective function that is much harder to solve.To efficiently solve …