Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Adaptive large neighborhood search (3)
- Model Representation And Learning Domain Models For Planning (2)
- Multi-agent Planning And Learning (2)
- Reinforcement learning (2)
- Vehicle routing problem (2)
-
- ADL sequence (1)
- API call (1)
- Acoustic attack (1)
- Activity pattern (1)
- Ad hoc video search (1)
- Adaptive System (1)
- Aircraft allocation (1)
- Airport management; taxi; demand prediction (1)
- Alliances (1)
- Android fragmentation (1)
- App analysis (1)
- Application programs (1)
- Authentication (1)
- Automatic search (1)
- B2B Last Mile Logistics (1)
- Best Response Planning (1)
- Boundary precedence (BP) (1)
- Bus service deployment (1)
- COVID-19 (1)
- City logistics (1)
- Cloud services (1)
- Collaboration (1)
- Column generation (1)
- Computational modeling (1)
- Computer aided software engineering (1)
Articles 1 - 30 of 46
Full-Text Articles in Engineering
Efficient Server-Aided Secure Two-Party Computation In Heterogeneous Mobile Cloud Computing, Yulin Wu, Xuan Wang, Willy Susilo, Guomin Yang, Zoe L. Jiang, Qian Chen, Peng Xu
Efficient Server-Aided Secure Two-Party Computation In Heterogeneous Mobile Cloud Computing, Yulin Wu, Xuan Wang, Willy Susilo, Guomin Yang, Zoe L. Jiang, Qian Chen, Peng Xu
Research Collection School Of Computing and Information Systems
With the ubiquity of mobile devices and rapid development of cloud computing, mobile cloud computing (MCC) has been considered as an essential computation setting to support complicated, scalable and flexible mobile applications by overcoming the physical limitations of mobile devices with the aid of cloud. In the MCC setting, since many mobile applications (e.g., map apps) interacting with cloud server and application server need to perform computation with the private data of users, it is important to realize secure computation for MCC. In this article, we propose an efficient server-aided secure two-party computation (2PC) protocol for MCC. This is the …
Quantum-Inspired Algorithm For Vehicle Sharing Problem, Whei Yeap Suen, Chun Yat Lee, Hoong Chuin Lau
Quantum-Inspired Algorithm For Vehicle Sharing Problem, Whei Yeap Suen, Chun Yat Lee, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Recent hardware developments in quantum technologies have inspired a myriad of special-purpose hardware devices tasked to solve optimization problems. In this paper, we explore the application of Fujitsu’s quantum-inspired CMOS-based Digital Annealer (DA) in solving constrained routing problems arising in transportation and logistics. More precisely in this paper, we study the vehicle sharing problem and show that the DA as a QUBO solver can potentially fill the gap between two common methods: exact solvers like Cplex and heuristics. We benchmark the scalability and quality of solutions obtained by DA with Cplex and with a greedy heuristic. Our results show that …
Design Of A Two-Echelon Freight Distribution System In Last-Mile Logistics Considering Covering Locations And Occasional Drivers, Vincent F. Yu, Panca Jodiawan, Ming-Lu Hou, Aldy Gunawan
Design Of A Two-Echelon Freight Distribution System In Last-Mile Logistics Considering Covering Locations And Occasional Drivers, Vincent F. Yu, Panca Jodiawan, Ming-Lu Hou, Aldy Gunawan
Research Collection School Of Computing and Information Systems
This research addresses a new variant of the vehicle routing problem, called the two-echelon vehicle routing problem with time windows, covering options, and occasional drivers (2E-VRPTW-CO-OD). In this problem, two types of fleets are available to serve customers, city freighters and occasional drivers (ODs), while two delivery options are available to customers, home delivery and alternative delivery. For customers choosing the alternative delivery, their demands are delivered to one of the available covering locations for them to pick up. The objective of 2E-VRPTW-CO-OD is to minimize the total cost consisting of routing costs, connection costs, and compensations paid to ODs …
Routing Policy Choice Prediction In A Stochastic Network: Recursive Model And Solution Algorithm, Tien Mai, Xinlian Yu, Song Gao, Emma Frejinger
Routing Policy Choice Prediction In A Stochastic Network: Recursive Model And Solution Algorithm, Tien Mai, Xinlian Yu, Song Gao, Emma Frejinger
Research Collection School Of Computing and Information Systems
We propose a Recursive Logit (STD-RL) model for routing policy choice in a stochastic time-dependent (STD) network, where a routing policy is a mapping from states to actions on which link to take next, and a state is defined by node, time and information. A routing policy encapsulates travelers’ adaptation to revealed traffic conditions when making route choices. The STD-RL model circumvents choice set generation, a procedure with known issues related to estimation and prediction. In a given state, travelers make their link choice maximizing the sum of the utility of the outgoing link and the expected maximum utility until …
A Learning And Optimization Framework For Collaborative Urban Delivery Problems With Alliances, Jingfeng Yang, Hoong Chuin Lau
A Learning And Optimization Framework For Collaborative Urban Delivery Problems With Alliances, Jingfeng Yang, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
The emergence of e-Commerce imposes a tremendous strain on urban logistics which in turn raises concerns on environmental sustainability if not performed efficiently. While large logistics service providers (LSPs) can perform fulfillment sustainably as they operate extensive logistic networks, last-mile logistics are typically performed by small LSPs who need to form alliances to reduce delivery costs and improve efficiency, and to compete with large players. In this paper, we consider a multi-alliance multi-depot pickup and delivery problem with time windows (MAD-PDPTW) and formulate it as a mixed-integer programming (MIP) model. To cope with large-scale problem instances, we propose a two-stage …
The Empathetic Car: Exploring Emotion Inference Via Driver Behaviour And Traffic Context, Shu Liu, Kevin Koch, Zimu Zhou, Simon Foll, Xiaoxi He, Tina Menke, Elgar Fleisch, Felix Wortmann
The Empathetic Car: Exploring Emotion Inference Via Driver Behaviour And Traffic Context, Shu Liu, Kevin Koch, Zimu Zhou, Simon Foll, Xiaoxi He, Tina Menke, Elgar Fleisch, Felix Wortmann
Research Collection School Of Computing and Information Systems
An empathetic car that is capable of reading the driver’s emotions has been envisioned by many car manufacturers. Emotion inference enables in-vehicle applications to improve driver comfort, well-being, and safety. Available emotion inference approaches use physiological, facial, and speech-related data to infer emotions during driving trips. However, existing solutions have two major limitations: Relying on sensors that are not built into the vehicle restricts emotion inference to those people leveraging corresponding devices (e.g., smartwatches). Relying on modalities such as facial expressions and speech raises privacy concerns. By contrast, researchers in mobile health have been able to infer affective states (e.g., …
Which Variables Should I Log?, Zhongxin Liu, Xin Xia, David Lo, Zhenchang Xing, Ahmed E. Hassan, Shanping Li
Which Variables Should I Log?, Zhongxin Liu, Xin Xia, David Lo, Zhenchang Xing, Ahmed E. Hassan, Shanping Li
Research Collection School Of Computing and Information Systems
Developers usually depend on inserting logging statements into the source code to collect system runtime information. Such logged information is valuable for software maintenance. A logging statement usually prints one or more variables to record vital system status. However, due to the lack of rigorous logging guidance and the requirement of domain-specific knowledge, it is not easy for developers to make proper decisions about which variables to log. To address this need, in this work, we propose an approach to recommend logging variables for developers during development by learning from existing logging statements. Different from other prediction tasks in software …
Vehicle Routing: Review Of Benchmark Datasets, Aldy Gunawan, Graham Kendall, Barry Mccollum, Hsin-Vonn Seow, Lai Soon Lee
Vehicle Routing: Review Of Benchmark Datasets, Aldy Gunawan, Graham Kendall, Barry Mccollum, Hsin-Vonn Seow, Lai Soon Lee
Research Collection School Of Computing and Information Systems
The Vehicle Routing Problem (VRP) was formally presented to the scientific literature in 1959 by Dantzig and Ramser (DOI:10.1287/mnsc.6.1.80). Sixty years on, the problem is still heavily researched, with hundreds of papers having been published addressing this problem and the many variants that now exist. Many datasets have been proposed to enable researchers to compare their algorithms using the same problem instances where either the best known solution is known or, in some cases, the optimal solution is known. In this survey paper, we provide a list of Vehicle Routing Problem datasets, categorized to enable researchers to have easy access …
Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Many real world systems involve interaction among large number of agents to achieve a common goal, for example, air traffic control. Several model-free RL algorithms have been proposed for such settings. A key limitation is that the empirical reward signal in model-free case is not very effective in addressing the multiagent credit assignment problem, which determines an agent's contribution to the team's success. This results in lower solution quality and high sample complexity. To address this, we contribute (a) an approach to learn a differentiable reward model for both continuous and discrete action setting by exploiting the collective nature of …
Context-Aware Outstanding Fact Mining From Knowledge Graphs, Yueji Yang, Yuchen Li, Panagiotis Karras, Anthony Tung
Context-Aware Outstanding Fact Mining From Knowledge Graphs, Yueji Yang, Yuchen Li, Panagiotis Karras, Anthony Tung
Research Collection School Of Computing and Information Systems
An Outstanding Fact (OF) is an attribute that makes a target entity stand out from its peers. The mining of OFs has important applications, especially in Computational Journalism, such as news promotion, fact-checking, and news story finding. However, existing approaches to OF mining: (i) disregard the context in which the target entity appears, hence may report facts irrelevant to that context; and (ii) require relational data, which are often unavailable or incomplete in many application domains. In this paper, we introduce the novel problem of mining Contextaware Outstanding Facts (COFs) for a target entity under a given context specified by …
Automated Taxi Queue Management At High-Demand Venues, Mengyu Ji, Shih-Fen Cheng
Automated Taxi Queue Management At High-Demand Venues, Mengyu Ji, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
In this paper, we seek to identify an effective management policy that could reduce supply-demand gaps at taxi queues serving high-density locations where demand surges frequently happen. Unlike current industry practice, which relies on broadcasting to attract taxis to come and serve the queue, we propose more proactive and adaptive approaches to handle demand surges. Our design objective is to reduce the cumulative supply-demand gaps as much as we could by sending notifications to individual taxis. To address this problem, we first propose a highly effective passenger demand prediction system that is based on the real-time flight arrival information. By …
Estimating Homophily In Social Networks Using Dyadic Predictions, George Berry, Antonio Sirianni, Ingmar Weber, Jisun An, Michael Macy
Estimating Homophily In Social Networks Using Dyadic Predictions, George Berry, Antonio Sirianni, Ingmar Weber, Jisun An, Michael Macy
Research Collection School Of Computing and Information Systems
Predictions of node categories are commonly used to estimate homophily and other relational properties in networks. However, little is known about the validity of using predictions for this task. We show that estimating homophily in a network is a problem of predicting categories of dyads (edges) in the graph. Homophily estimates are unbiased when predictions of dyad categories are unbiased. Node-level prediction models, such as the use of names to classify ethnicity or gender, do not generally produce unbiased predictions of dyad categories and therefore produce biased homophily estimates. Bias comes from three sources: sampling bias, correlation between model errors …
Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Many real world systems involve interaction among large number of agents to achieve a common goal, for example, air traffic control. Several model-free RL algorithms have been proposed for such settings. A key limitation is that the empirical reward signal in model-free case is not very effective in addressing the multiagent credit assignment problem, which determines an agent's contribution to the team's success. This results in lower solution quality and high sample complexity. To address this, we contribute (a) an approach to learn a differentiable reward model for both continuous and discrete action setting by exploiting the collective nature of …
A Lagrangian Column Generation Approach For The Probabilistic Crowdsourced Logistics Planning, Chung-Kyun Han, Shih-Fen Cheng
A Lagrangian Column Generation Approach For The Probabilistic Crowdsourced Logistics Planning, Chung-Kyun Han, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
In recent years we have increasingly seen the movement for the retail industry to move their operations online. Along the process, it has created brand new patterns for the fulfillment service, and the logistics service providers serving these retailers have no choice but to adapt. The most challenging issues faced by all logistics service providers are the highly fluctuating demands and the shortening response times. All these challenges imply that maintaining a fixed fleet will either be too costly or insufficient. One potential solution is to tap into the crowdsourced workforce. However, existing industry practices of relying on human planners …
Thunderrw: An In-Memory Graph Random Walk Engine, Shixuan Sun, Yuhang Chen, Shengliang Lu, Bingsheng He, Yuchen Li
Thunderrw: An In-Memory Graph Random Walk Engine, Shixuan Sun, Yuhang Chen, Shengliang Lu, Bingsheng He, Yuchen Li
Research Collection School Of Computing and Information Systems
As random walk is a powerful tool in many graph processing, mining and learning applications, this paper proposes an efficient inmemory random walk engine named ThunderRW. Compared with existing parallel systems on improving the performance of a single graph operation, ThunderRW supports massive parallel random walks. The core design of ThunderRW is motivated by our profiling results: common RW algorithms have as high as 73.1% CPU pipeline slots stalled due to irregular memory access, which suffers significantly more memory stalls than the conventional graph workloads such as BFS and SSSP. To improve the memory efficiency, we first design a generic …
An Adaptive Large Neighborhood Search For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiawan, Aldy Gunawan
An Adaptive Large Neighborhood Search For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiawan, Aldy Gunawan
Research Collection School Of Computing and Information Systems
This study addresses a variant of the Electric Vehicle Routing Problem with Mixed Fleet, named as the Green Mixed Fleet Vehicle Routing Problem with Realistic Energy Consumption and Partial Recharges. This problem contains three important characteristics — realistic energy consumption, partial recharging policy, and carbon emissions. An adaptive Large Neighborhood Search heuristic is developed for the problem. Experimental results show that the proposed ALNS finds optimal solutions for most small-scale benchmark instances in a significantly faster computational time compared to the performance of CPLEX solver. Moreover, it obtains high quality solutions for all medium- and large-scale instances under a reasonable …
The Multi-Vehicle Cycle Inventory Routing Problem: Formulation And A Metaheuristic Approach, Vincent F. Yu, Audrey Tedja Widjaja, Aldy Gunawan, Pieter Vansteenwegen
The Multi-Vehicle Cycle Inventory Routing Problem: Formulation And A Metaheuristic Approach, Vincent F. Yu, Audrey Tedja Widjaja, Aldy Gunawan, Pieter Vansteenwegen
Research Collection School Of Computing and Information Systems
This paper presents a new variant of the Multi-Vehicle Cyclic Inventory Routing Problem (MV-CIRP) which aims to determine a subset of customers to be visited, the appropriate number of vehicles used, and the corresponding cycle time and route sequence, such that the total cost (e.g. transportation, inventory, and rewards) is minimized. The MV-CIRP is formulated as a mixed-integer nonlinear programming model. We propose a Simulated Annealing (SA) based algorithm to solve the problem. SA is first tested on the available benchmark Single-Vehicle CIRP (SV-CIRP) instances and compared to the state-of-the-art algorithms. SA is then tested on the benchmark MV-CIRP instances …
Vibransee: Enabling Simultaneous Visible Light Communication And Sensing, Ila Nitin Gokarn, Archan Misra
Vibransee: Enabling Simultaneous Visible Light Communication And Sensing, Ila Nitin Gokarn, Archan Misra
Research Collection School Of Computing and Information Systems
Driven by the ubiquitous proliferation of low-cost LED luminaires, visible light communication (VLC) has been established as a high-speed communications technology based on the high-frequency modulation of an optical source. In parallel, Visible Light Sensing (VLS) has recently demonstrated how vision-based at-a-distance sensing of mechanical vibrations (e.g., of factory equipment) can be performed using high frequency optical strobing. However, to date, exemplars of VLC and VLS have been explored in isolation, without consideration of their mutual dependencies. In this work, we explore whether and how high-throughput VLC and high-coverage VLS can be simultaneously supported. We first demonstrate the existence of …
Step-Wise Deep Learning Models For Solving Routing Problems, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Step-Wise Deep Learning Models For Solving Routing Problems, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
Routing problems are very important in intelligent transportation systems. Recently, a number of deep learning-based methods are proposed to automatically learn construction heuristics for solving routing problems. However, these methods do not completely follow Bellman's Principle of Optimality since the visited nodes during construction are still included in the following subtasks, resulting in suboptimal policies. In this article, we propose a novel step-wise scheme which explicitly removes the visited nodes in each node selection step. We apply this scheme to two representative deep models for routing problems, pointer network and transformer attention model (TAM), and significantly improve the performance of …
On M-Impact Regions And Standing Top-K Influence Problems, Bo Tang, Kyriakos Mouratidis, Mingji Han
On M-Impact Regions And Standing Top-K Influence Problems, Bo Tang, Kyriakos Mouratidis, Mingji Han
Research Collection School Of Computing and Information Systems
In this paper, we study the ��-impact region problem (mIR). In a context where users look for available products with top-�� queries, mIR identifies the part of the product space that attracts the most user attention. Specifically, mIR determines the kind of attribute values that lead a (new or existing) product to the top-�� result for at least a fraction of the user population. mIR has several applications, ranging from effective marketing to product improvement. Importantly, it also leads to (exact and efficient) solutions for standing top-�� impact problems, which were previously solved heuristically only, or whose current solutions face …
When Program Analysis Meets Bytecode Search: Targeted And Efficient Inter-Procedural Analysis Of Modern Android Apps In Backdroid, Daoyuan Wu, Debin Gao, Robert H. Deng, Rocky Chang
When Program Analysis Meets Bytecode Search: Targeted And Efficient Inter-Procedural Analysis Of Modern Android Apps In Backdroid, Daoyuan Wu, Debin Gao, Robert H. Deng, Rocky Chang
Research Collection School Of Computing and Information Systems
Widely-used Android static program analysis tools,e.g., Amandroid and FlowDroid, perform the whole-app interprocedural analysis that is comprehensive but fundamentallydifficult to handle modern (large) apps. The average app size hasincreased three to four times over five years. In this paper, weexplore a new paradigm of targeted inter-procedural analysis thatcan skip irrelevant code and focus only on the flows of securitysensitive sink APIs. To this end, we propose a technique calledon-the-fly bytecode search, which searches the disassembled appbytecode text just in time when a caller needs to be located. In thisway, it guides targeted (and backward) inter-procedural analysisstep by step until reaching …
Set Team Orienteering Problem With Time Windows, Aldy Gunawan, Vincent F. Yu, Andros Nicas Sutanto, Panca Jodiawan
Set Team Orienteering Problem With Time Windows, Aldy Gunawan, Vincent F. Yu, Andros Nicas Sutanto, Panca Jodiawan
Research Collection School Of Computing and Information Systems
This research introduces an extension of the Orienteering Problem (OP), known as Set Team Orienteering Problem with Time Windows (STOPTW), in which customers are first grouped into clusters. Each cluster is associated with a profit that will be collected if at least one customer within the cluster is visited. The objective is to find the best route that maximizes the total collected profit without violating time windows and time budget constraints. We propose an adaptive large neighborhood search algorithm to solve newly introduced benchmark instances. The preliminary results show the capability of the proposed algorithm to obtain good solutions within …
First Train Timetabling And Bus Service Bridging In Intermodal Bus-And-Train Transit Networks, Liujiang Kang, Hao Li, Huijun Sun, Jianjun Wu, Zhiguang Cao, Nsabimana Buhigiro
First Train Timetabling And Bus Service Bridging In Intermodal Bus-And-Train Transit Networks, Liujiang Kang, Hao Li, Huijun Sun, Jianjun Wu, Zhiguang Cao, Nsabimana Buhigiro
Research Collection School Of Computing and Information Systems
Subway system is the main mode of transportation for city dwellers and is a quite signif-icant backbone to a city's operations. One of the challenges of subway network operation is the scheduling of the first trains each morning and its impact on transfers. To deal with this challenge, some cities (e.g. Beijing) use bus 'bridging' services, temporarily substitut -ing segments of the subway network. The present paper optimally identifies when to start each train and bus bridging service in an intermodal transit network. Starting from a mixed integer nonlinear programming model for the first train timetabling problem, we linearize and …
Coordinating Multi-Party Vehicle Routing With Location Congestion Via Iterative Best Response, Waldy Joe, Hoong Chuin Lau
Coordinating Multi-Party Vehicle Routing With Location Congestion Via Iterative Best Response, Waldy Joe, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This work is motivated by a real-world problem of coordinating B2B pickup-delivery operations to shopping malls involving multiple non-collaborative Logistics Service Providers (LSPs) in a congested city where space is scarce. This problem can be categorized as a Vehicle Routing Problem with Pickup and Delivery, Time Windows and Location Congestion with multiple LSPs (or ML-VRPLC in short), and we propose a scalable, decentralized, coordinated planning approach via iterative best response. We formulate the problem as a strategic game where each LSP is a self-interested agent but is willing to participate in a coordinated planning as long as there are sufficient …
Minimum Coresets For Maxima Representation Of Multidimensional Data, Yanhao Wang, Michael Mathioudakis, Yuchen Li, Kian-Lee Tan
Minimum Coresets For Maxima Representation Of Multidimensional Data, Yanhao Wang, Michael Mathioudakis, Yuchen Li, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
Coresets are succinct summaries of large datasets such that, for a given problem, the solution obtained from a coreset is provably competitive with the solution obtained from the full dataset. As such, coreset-based data summarization techniques have been successfully applied to various problems, e.g., geometric optimization, clustering, and approximate query processing, for scaling them up to massive data. In this paper, we study coresets for the maxima representation of multidimensional data: Given a set �� of points in R �� , where �� is a small constant, and an error parameter �� ∈ (0, 1), a subset �� ⊆ �� …
Solving The Winner Determination Problem For Online B2b Transportation Matching Platforms, Hoong Chuin Lau, Baoxiang Li
Solving The Winner Determination Problem For Online B2b Transportation Matching Platforms, Hoong Chuin Lau, Baoxiang Li
Research Collection School Of Computing and Information Systems
We consider the problem of matching multiple shippers and transporters participating in an online B2B last-mile logistics platform in an emerging market. Each shipper places a bid that is made up of multiple jobs, where each job comprises key information like the weight, volume, pickup and delivery locations, and time windows. Each transporter specifies its vehicle capacity, available time periods, and a cost structure. We formulate the mathematical model and provide a Branch-and-Cut approach to solve small-scale problem instances exactly and larger scale instances heuristically using an Adaptive Large Neighbourhood Search approach. To increase the win percentage of both shippers …
Hierarchical Reinforcement Learning: A Comprehensive Survey, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek
Hierarchical Reinforcement Learning: A Comprehensive Survey, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek
Research Collection School Of Computing and Information Systems
Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future …
Ultrapin: Inferring Pin Entries Via Ultrasound, Liu, Ximing, Robert H. Deng, Robert H. Deng
Ultrapin: Inferring Pin Entries Via Ultrasound, Liu, Ximing, Robert H. Deng, Robert H. Deng
Research Collection School Of Computing and Information Systems
While PIN-based user authentication systems such as ATM have long been considered to be secure enough, they are facing new attacks, named UltraPIN, which can be launched from commodity smartphones. As a target user enters a PIN on a PIN-based user authentication system, an attacker may use UltraPIN to infer the PIN from a short distance (50 cm to 100 cm). In this process, UltraPIN leverages smartphone speakers to issue human-inaudible ultrasound signals and uses smartphone microphones to keep recording acoustic signals. It applies a series of signal processing techniques to extract high-quality feature vectors from low-energy and high-noise signals …
Approximate Difference Rewards For Scalable Multigent Reinforcement Learning, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Approximate Difference Rewards For Scalable Multigent Reinforcement Learning, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We address the problem ofmultiagent credit assignment in a large scale multiagent system. Difference rewards (DRs) are an effective tool to tackle this problem, but their exact computation is known to be challenging even for small number of agents. We propose a scalable method to compute difference rewards based on aggregate information in a multiagent system with large number of agents by exploiting the symmetry present in several practical applications. Empirical evaluation on two multiagent domains - air-traffic control and cooperative navigation, shows better solution quality than previous approaches.
A Matheuristic Algorithm For The Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu
A Matheuristic Algorithm For The Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu
Research Collection School Of Computing and Information Systems
This paper studies the integration of the vehicle routing problem with cross-docking (VRPCD). The aim is to find a set of routes to deliver products from a set of suppliers to a set of customers through a cross-dock facility, such that the operational and transportation costs are minimized, without violating the vehicle capacity and time horizon constraints. A two-phase matheuristic based on column generation is proposed. The first phase focuses on generating a set of feasible candidate routes in both pickup and delivery processes by implementing an adaptive large neighborhood search algorithm. A set of destroy and repair operators are …