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Articles 1 - 30 of 43
Full-Text Articles in Engineering
Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu
Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu
Research Collection School Of Computing and Information Systems
Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient …
Fastklee: Faster Symbolic Execution Via Reducing Redundant Bound Checking Of Type-Safe Pointers, Haoxin Tu, Lingxiao Jiang, Xuhua Ding, He Jiang
Fastklee: Faster Symbolic Execution Via Reducing Redundant Bound Checking Of Type-Safe Pointers, Haoxin Tu, Lingxiao Jiang, Xuhua Ding, He Jiang
Research Collection School Of Computing and Information Systems
Symbolic execution (SE) has been widely adopted for automatic program analysis and software testing. Many SE engines (e.g., KLEE or Angr) need to interpret certain Intermediate Representations (IR) of code during execution, which may be slow and costly. Although a plurality of studies proposed to accelerate SE, few of them consider optimizing the internal interpretation operations. In this paper, we propose FastKLEE, a faster SE engine that aims to speed up execution via reducing redundant bound checking of type-safe pointers during IR code interpretation. Specifically, in FastKLEE, a type inference system is first leveraged to classify pointer types (i.e., safe …
Efficient Navigation For Constrained Shortest Path With Adaptive Expansion Control, Wenwen Xia, Yuchen Li, Wentian Guo, Shenghong Li
Efficient Navigation For Constrained Shortest Path With Adaptive Expansion Control, Wenwen Xia, Yuchen Li, Wentian Guo, Shenghong Li
Research Collection School Of Computing and Information Systems
In many route planning applications, finding constrained shortest paths (CSP) is an important and fundamental problem. CSP aims to find the shortest path between two nodes on a graph while satisfying a path constraint. Solving CSPs requires a large search space and is prohibitively slow on large graphs, even with the state-of-the-art parallel solution on GPUs. The reason lies in the lack of effective navigational information and pruning strategies in the search procedure. In this paper, we propose SPEC, a Shortest Path Enhanced approach for solving the exact CSP problem. Our design rationales of SPEC rely on the observation that …
A Quality Metric For K-Means Clustering Based On Centroid Locations, Manoj Thulasidas
A Quality Metric For K-Means Clustering Based On Centroid Locations, Manoj Thulasidas
Research Collection School Of Computing and Information Systems
K-Means clustering algorithm does not offer a clear methodology to determine the appropriate number of clusters; it does not have a built-in mechanism for K selection. In this paper, we present a new metric for clustering quality and describe its use for K selection. The proposed metric, based on the locations of the centroids, as well as the desired properties of the clusters, is developed in two stages. In the initial stage, we take into account the full covariance matrix of the clustering variables, thereby making it mathematically similar to a reduced chi2. We then extend it to account for …
Remgen: Remanufacturing A Random Program Generator For Compiler Testing, Haoxin Tu, He Jiang, Xiaochen Li, Zhide Zhou, Lingxiao Jiang, Lingxiao Jiang
Remgen: Remanufacturing A Random Program Generator For Compiler Testing, Haoxin Tu, He Jiang, Xiaochen Li, Zhide Zhou, Lingxiao Jiang, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Program generators play a critical role in generating bug-revealing test programs for compiler testing. However, existing program generators have been tamed nowadays (i.e., compilers have been hardened against test programs generated by them), thus calling for new solutions to improve their capability in generating bug-revealing test programs. In this study, we propose a framework named Remgen, aiming to Remanufacture a random program Generator for this purpose. RemgEnaddresses the challenges of the synthesis of diverse code snippets at a low cost and the selection of the bug-revealing code snippets for constructing new test programs. More specifically, RemgEnfirst designs a grammar-aided synthesis …
Reflecting On Experiences For Response Generation, Chenchen Ye, Lizi Liao, Suyu Liu, Tat-Seng Chua
Reflecting On Experiences For Response Generation, Chenchen Ye, Lizi Liao, Suyu Liu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Multimodal dialogue systems attract much attention recently, but they are far from skills like: 1) automatically generate context- specific responses instead of safe but general responses; 2) naturally coordinate between the different information modalities (e.g. text and image) in responses; 3) intuitively explain the reasons for generated responses and improve a specific response without re-training the whole model. To approach these goals, we propose a different angle for the task - Reflecting Experiences for Response Generation (RERG). This is supported by the fact that generating a response from scratch can be hard, but much easier if we can access other …
Softskip: Empowering Multi-Modal Dynamic Pruning For Single-Stage Referring Comprehension, Dulanga Weerakoon, Vigneshwaran Subbaraju, Tuan Tran, Archan Misra
Softskip: Empowering Multi-Modal Dynamic Pruning For Single-Stage Referring Comprehension, Dulanga Weerakoon, Vigneshwaran Subbaraju, Tuan Tran, Archan Misra
Research Collection School Of Computing and Information Systems
Supporting real-time referring expression comprehension (REC) on pervasive devices is an important capability for human-AI collaborative tasks. Model pruning techniques, applied to DNN models, can enable real-time execution even on resource-constrained devices. However, existing pruning strategies are designed principally for uni-modal applications, and suffer a significant loss of accuracy when applied to REC tasks that require fusion of textual and visual inputs. We thus present a multi-modal pruning model, LGMDP, which uses language as a pivot to dynamically and judiciously select the relevant computational blocks that need to be executed. LGMDP also introduces a new SoftSkip mechanism, whereby 'skipped' visual …
An Attribute-Aware Attentive Gcn Model For Attribute Missing In Recommendation, Fan Liu, Zhiyong Cheng, Lei Zhu, Chenghao Liu, Liqiang Nie
An Attribute-Aware Attentive Gcn Model For Attribute Missing In Recommendation, Fan Liu, Zhiyong Cheng, Lei Zhu, Chenghao Liu, Liqiang Nie
Research Collection School Of Computing and Information Systems
As important side information, attributes have been widely exploited in the existing recommender system for better performance. However, in the real-world scenarios, it is common that some attributes of items/users are missing (e.g., some movies miss the genre data). Prior studies usually use a default value (i.e., "other") to represent the missing attribute, resulting in sub-optimal performance. To address this problem, in this paper, we present an attribute-aware attentive graph convolution network (A(2)-GCN). In particular, we first construct a graph, where users, items, and attributes are three types of nodes and their associations are edges. Thereafter, we leverage the graph …
A Carbon-Aware Planning Framework For Production Scheduling In Mining, Nurual Asyikeen Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi
A Carbon-Aware Planning Framework For Production Scheduling In Mining, Nurual Asyikeen Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi
Research Collection School Of Computing and Information Systems
Managing the flow of excavated materials from a mine pit and the subsequent processing steps is the logistical challenge in mining. Mine planning needs to consider various geometric and resource constraints while maximizing the net present value (NPV) of profits over a long horizon. This mine planning problem has been modelled and solved as a precedence constrained production scheduling problem (PCPSP) using heuristics, due to its NP-hardness. However, the recent push for sustainable and carbon-aware mining practices calls for new planning approaches. In this paper, we propose an efficient temporally decomposed greedy Lagrangian relaxation (TDGLR) approach to maximize profits while …
Two-Phase Matheuristic For The Vehicle Routing Problem With Reverse Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu
Two-Phase Matheuristic For The Vehicle Routing Problem With Reverse Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu
Research Collection School Of Computing and Information Systems
Cross-dockingis a useful concept used by many companies to control the product flow. It enables the transshipment process of products from suppliers to customers. This research thus extends the benefit of cross-docking with reverse logistics, since return process management has become an important field in various businesses. The vehicle routing problem in a distribution network is considered to be an integrated model, namely the vehicle routing problem with reverse cross-docking (VRP-RCD). This study develops a mathematical model to minimize the costs of moving products in a four-level supply chain network that involves suppliers, cross-dock, customers, and outlets. A matheuristic based …
Choice-Based Crowdshipping: A Dynamic Task Display Problem, Alp Arslan, Firat Kilci, Shih-Fen Cheng, Archan Misra
Choice-Based Crowdshipping: A Dynamic Task Display Problem, Alp Arslan, Firat Kilci, Shih-Fen Cheng, Archan Misra
Research Collection School Of Computing and Information Systems
This paper studies the integration of the crowd workforce into a generic last-mile delivery setting in which a set of known delivery requests should be fulfilled at a minimum cost. In this setting, the crowd drivers are able to choose to perform a parcel delivery among the available and displayed requests. We specifically investigate the question: what tasks should be displayed to an individual driver, so as to minimize the overall delivery expenses? In contrast to past approaches, where drivers are either (a) given the choice of a single task chosen so as to optimize the platform’s profit, or (b) …
Fed-Ltd: Towards Cross-Platform Ride Hailing Via Federated Learning To Dispatch, Yansheng Wang, Yongxin Tong, Zimu Zhou, Ziyao Ren, Yi Xu, Guobin Wu, Weifeng Lv
Fed-Ltd: Towards Cross-Platform Ride Hailing Via Federated Learning To Dispatch, Yansheng Wang, Yongxin Tong, Zimu Zhou, Ziyao Ren, Yi Xu, Guobin Wu, Weifeng Lv
Research Collection School Of Computing and Information Systems
Learning based order dispatching has witnessed tremendous success in ride hailing. However, the success halts within individual ride hailing platforms because sharing raw order dispatching data across platforms may leak user privacy and business secrets. Such data isolation not only impairs user experience but also decreases the potential revenues of the platforms. In this paper, we advocate federated order dispatching for cross-platform ride hailing, where multiple platforms collaboratively make dispatching decisions without sharing their local data. Realizing this concept calls for new federated learning strategies that tackle the unique challenges on effectiveness, privacy and efficiency in the context of order …
Integrating Forward And Reverse Logistics In Vehicle Routing Problem With Cross-Docking, Vincent F. Yu, Pham T. Anh, Aldy Gunawan
Integrating Forward And Reverse Logistics In Vehicle Routing Problem With Cross-Docking, Vincent F. Yu, Pham T. Anh, Aldy Gunawan
Research Collection School Of Computing and Information Systems
A closed-loop supply chain is one of the vital parts for maintaining the success of enterprises, where forward and reverse logistics are integrated to eliminate wastes (e.g., transportation costs). However, previous studies related to the Vehicle Routing Problem have almost overlooked this integration. This research therefore introduces a variant of the Vehicle Routing Problem with cross-docking (VRPCD) by simultaneously considering three additional factors: (1) various types of vehicles in terms of their capacities and unit travel costs; (2) multiple cross-docks; and (3) the integration of forward and reverse logistics. In particular, the flows of the network consist of distributing goods …
Submodularity And Local Search Approaches For Maximum Capture Problems Under Generalized Extreme Value Models, Tien Thanh Dam, Thuy Anh Ta, Tien Mai
Submodularity And Local Search Approaches For Maximum Capture Problems Under Generalized Extreme Value Models, Tien Thanh Dam, Thuy Anh Ta, Tien Mai
Research Collection School Of Computing and Information Systems
We study the maximum capture problem in facility location under random utility models, i.e., the problem of seeking to locate new facilities in a competitive market such that the captured user demand is maximized, assuming that each customer chooses among all available facilities according to a random utility maximization model. We employ the generalized extreme value (GEV) family of discrete choice models and show that the objective function in this context is monotonic and submodular. This finding implies that a simple greedy heuristic can always guarantee a (1−1/e) approximation solution. We further develop a new algorithm combining a greedy heuristic, …
Joint Chance-Constrained Staffing Optimization In Multi-Skill Call Centers, Tien Thanh Dam, Thuy Anh Ta, Tien Mai
Joint Chance-Constrained Staffing Optimization In Multi-Skill Call Centers, Tien Thanh Dam, Thuy Anh Ta, Tien Mai
Research Collection School Of Computing and Information Systems
This paper concerns the staffing optimization problem in multi-skill call centers. The objective is to find a minimal cost staffing solution while meeting a target level for the quality of service (QoS) to customers. We consider a staffing problem in which joint chance constraints are imposed on the QoS of the day. Our joint chance-constrained formulation is more rational capturing the correlation between different call types, as compared to separate chance-constrained versions considered in previous studies. We show that, in general, the probability functions in the joint-chance constraints display S-shaped curves, and the optimal solutions should belong to the concave …
Interpreting Trajectories From Multiple Views: A Hierarchical Self-Attention Network For Estimating The Time Of Arrival, Zebin Chen, Xiaolin Xiao, Yue-Jiao Gong, Jun Fang, Nan Ma, Hua Chai, Zhiguang Cao
Interpreting Trajectories From Multiple Views: A Hierarchical Self-Attention Network For Estimating The Time Of Arrival, Zebin Chen, Xiaolin Xiao, Yue-Jiao Gong, Jun Fang, Nan Ma, Hua Chai, Zhiguang Cao
Research Collection School Of Computing and Information Systems
Estimating the time of arrival is a crucial task in intelligent transportation systems. Although considerable efforts have been made to solve this problem, most of them decompose a trajectory into several segments and then compute the travel time by integrating the attributes from all segments. The segment view, though being able to depict the local traffic conditions straightforwardly, is insufficient to embody the intrinsic structure of trajectories on the road network. To overcome the limitation, this study proposes multi-view trajectory representation that comprehensively interprets a trajectory from the segment-, link-, and intersection-views. To fulfill the purpose, we design a hierarchical …
Time Dependent Orienteering Problem With Time Windows And Service Time Dependent Profits, M. Khodadadian, A. Divsalar, C. Verbeeck, Aldy Gunawan, P. Vansteenwegen
Time Dependent Orienteering Problem With Time Windows And Service Time Dependent Profits, M. Khodadadian, A. Divsalar, C. Verbeeck, Aldy Gunawan, P. Vansteenwegen
Research Collection School Of Computing and Information Systems
This paper addresses the time dependent orienteering problem with time windows and service time dependent profits (TDOPTW-STP). In the TDOPTW-STP, each vertex is assigned a minimum and a maximum service time and the profit collected at each vertex increases linearly with the service time. The goal is to maximize the total collected profit by determining a subset of vertices to be visited and assigning appropriate service time to each vertex, considering a given time budget and time windows. Moreover, travel times are dependent of the departure times. To solve this problem, a mixed integer linear model is formulated and a …
Lightweight Privacy-Preserving Spatial Keyword Query Over Encrypted Cloud Data, Yutao Yang, Yinbin Miao, Kim-Kwang Raymond Choo, Robert H. Deng
Lightweight Privacy-Preserving Spatial Keyword Query Over Encrypted Cloud Data, Yutao Yang, Yinbin Miao, Kim-Kwang Raymond Choo, Robert H. Deng
Research Collection School Of Computing and Information Systems
With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatio-textual data is outsourced to the cloud server to reduce the local high storage and computing burdens, but at the same time causes security issues such as data privacy leakage. Thus, extensive privacy-preserving spatial keyword query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) for encryption, but ASPE has proven to be insecure. And the existing spatial range query schemes require users to provide more information about the query range and generate a large amount of …
Multi-Objective Evolutionary Algorithm Based On Rbf Network For Solving The Stochastic Vehicle Routing Problem, Yunyun Niu, Jie Shao, Jianhua Xiao, Wen Song, Zhiguang Cao
Multi-Objective Evolutionary Algorithm Based On Rbf Network For Solving The Stochastic Vehicle Routing Problem, Yunyun Niu, Jie Shao, Jianhua Xiao, Wen Song, Zhiguang Cao
Research Collection School Of Computing and Information Systems
Solving the multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is challenging due to its non-deterministic property and conflicting objectives. Most multi -objective evolutionary algorithm dealing with this problem update current population without any guidance from previous searching experience. In this paper, a multi -objective evolutionary algorithm based on artificial neural networks is proposed to tackle the MO-VRPSD. Particularly, during the evolutionary process, a radial basis function net-work (RBFN) is exploited to learn the potential knowledge of individuals, generate hypoth-esis and instantiate hypothesis. The RBFN evaluates individuals with different scores and generates new individuals with higher quality while taking into …
Multi-Agent Reinforcement Learning For Traffic Signal Control Through Universal Communication Method, Qize Jiang, Minhao Qin, Shengmin Shi, Weiwei Sun Sun, Baihua Zheng
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 …
Harnessing Confidence For Report Aggregation In Crowdsourcing Environments, Hadeel Alhosaini, Xianzhi Wang, Lina Yao, Zhong Yang, Farookh Hussain, Ee-Peng Lim
Harnessing Confidence For Report Aggregation In Crowdsourcing Environments, Hadeel Alhosaini, Xianzhi Wang, Lina Yao, Zhong Yang, Farookh Hussain, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Crowdsourcing is an effective means of accomplishing human intelligence tasks by leveraging the collective wisdom of crowds. Given reports of various accuracy degrees from workers, it is important to make wise use of these reports to derive accurate task results. Intuitively, a task result derived from a sufficient number of reports bears lower uncertainty, and higher uncertainty otherwise. Existing report aggregation research, however, has largely neglected the above uncertainty issue. In this regard, we propose a novel report aggregation framework that defines and incorporates a new confidence measure to quantify the uncertainty associated with tasks and workers, thereby enhancing result …
Consensus Formation On Heterogeneous Networks, Edoardo Fadda, Junda He, Claudia J. Tessone, Paolo Barucca
Consensus Formation On Heterogeneous Networks, Edoardo Fadda, Junda He, Claudia J. Tessone, Paolo Barucca
Research Collection School Of Computing and Information Systems
Reaching consensus-a macroscopic state where the system constituents display the same microscopic state-is a necessity in multiple complex socio-technical and techno-economic systems: their correct functioning ultimately depends on it. In many distributed systems-of which blockchain-based applications are a paradigmatic example-the process of consensus formation is crucial not only for the emergence of a leading majority but for the very functioning of the system. We build a minimalistic network model of consensus formation on blockchain systems for quantifying how central nodes-with respect to their average distance to others-can leverage on their position to obtain competitive advantage in the consensus process. We …
Who Is Missing? Characterizing The Participation Of Different Demographic Groups In A Korean Nationwide Daily Conversation Corpus, Haewoon Kwak, Jisun An, Kunwoo Park
Who Is Missing? Characterizing The Participation Of Different Demographic Groups In A Korean Nationwide Daily Conversation Corpus, Haewoon Kwak, Jisun An, Kunwoo Park
Research Collection School Of Computing and Information Systems
A conversation corpus is essential to build interactive AI applications. However, the demographic information of the participants in such corpora is largely underexplored mainly due to the lack of individual data in many corpora. In this work, we analyze a Korean nationwide daily conversation corpus constructed by the National Institute of Korean Language (NIKL) to characterize the participation of different demographic (age and sex) groups in the corpus.
Officers: Operational Framework For Intelligent Crime-And-Emergency Response Scheduling, Jonathan David Chase, Siong Thye Goh, Tran Phong, Hoong Chuin Lau
Officers: Operational Framework For Intelligent Crime-And-Emergency Response Scheduling, Jonathan David Chase, Siong Thye Goh, Tran Phong, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In the quest to achieve better response times in dense urban environments, law enforcement agencies are seeking AI-driven planning systems to inform their patrol strategies. In this paper, we present a framework, OFFICERS, for deployment planning that learns from historical data to generate deployment schedules on a daily basis. We accurately predict incidents using ST-ResNet, a deep learning technique that captures wide-ranging spatio-temporal dependencies, and solve a large-scale optimization problem to schedule deployment, significantly improving its scalability through a simulated annealing solver. Methodologically, our approach outperforms our previous works where prediction was done using Generative Adversarial Networks, and optimization was …
Taxi Travel Time Based Geographically Weighted Regression Model (Gwr) For Modeling Public Housing Prices In Singapore, Yi’An Wang, Fangyi Cai, Shih-Fen Cheng, Bo Wu, Kai Cao
Taxi Travel Time Based Geographically Weighted Regression Model (Gwr) For Modeling Public Housing Prices In Singapore, Yi’An Wang, Fangyi Cai, Shih-Fen Cheng, Bo Wu, Kai Cao
Research Collection School Of Computing and Information Systems
In this research, a taxi travel time based Geographically Weighted Regression model (GWR) is proposed and utilized to model the public housing price in the case study of Singapore. In addition, a comparison between the proposed taxi data driven GWR and other models, such as ordinary least squares model (OLS), GWR model based on Euclidean distance and GWR model based on public transport travel time, have also been carried out. Results indicates that taxi travel time based GWR model has better fitting performance than the OLS model, and slightly better than the Euclidean distance-based GWR model, however, it is not …
Competition And Third-Party Platform-Integration In Ride-Sourcing Markets, Yaqian Zhou, Hai Yang, Jintao Ke, Hai Wang, Xinwei Li
Competition And Third-Party Platform-Integration In Ride-Sourcing Markets, Yaqian Zhou, Hai Yang, Jintao Ke, Hai Wang, Xinwei Li
Research Collection School Of Computing and Information Systems
Recently, some third-party integrators attempt to integrate the ride services offered by multiple independent ride-sourcing platforms. Accordingly, passengers can request ride through the integrators and receive ride service from any one of the ride-sourcing platforms. This novel business model, termed as third-party platform-integration in this work, has potentials to alleviate market fragmentation cost resulting from demand splitting among multiple platforms. Although most existing studies focus on operation strategies for one single monopolist platform, much less is known about the competition and platform-integration and their implications on operation strategy and system efficiency. In this work, we propose mathematical models to describe …
An Empirical Study Of Memorization In Nlp, Xiaosen Zheng, Jing Jiang
An Empirical Study Of Memorization In Nlp, Xiaosen Zheng, Jing Jiang
Research Collection School Of Computing and Information Systems
A recent study by Feldman (2020) proposed a long-tail theory to explain the memorization behavior of deep learning models. However, memorization has not been empirically verified in the context of NLP, a gap addressed by this work. In this paper, we use three different NLP tasks to check if the long-tail theory holds. Our experiments demonstrate that top-ranked memorized training instances are likely atypical, and removing the top-memorized training instances leads to a more serious drop in test accuracy compared with removing training instances randomly. Furthermore, we develop an attribution method to better understand why a training instance is memorized. …
Message-Locked Searchable Encryption: A New Versatile Tool For Secure Cloud Storage, Xueqiao Liu, Guomin Yang, Willy Susilo, Joseph Tonien, Rongmao Chen, Xixiang Lv
Message-Locked Searchable Encryption: A New Versatile Tool For Secure Cloud Storage, Xueqiao Liu, Guomin Yang, Willy Susilo, Joseph Tonien, Rongmao Chen, Xixiang Lv
Research Collection School Of Computing and Information Systems
Message-Locked Encryption (MLE) is a useful tool to enable deduplication over encrypted data in cloud storage. It can significantly improve the cloud service quality by eliminating redundancy to save storage resources, and hence user cost, and also providing defense against different types of attacks, such as duplicate faking attack and brute-force attack. A typical MLE scheme only focuses on deduplication. On the other hand, supporting search operations on stored content is another essential requirement for cloud storage. In this article, we present a message-locked searchable encryption (MLSE) scheme in a dual-server setting, which achieves simultaneously the desirable features of supporting …
Hierarchical Value Decomposition For Effective On-Demand Ride Pooling, Hao Jiang, Pradeep Varakantham
Hierarchical Value Decomposition For Effective On-Demand Ride Pooling, Hao Jiang, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
On-demand ride-pooling (e.g., UberPool, GrabShare) services focus on serving multiple different customer requests using each vehicle, i.e., an empty or partially filled vehicle can be assigned requests from different passengers with different origins and destinations. On the other hand, in Taxi on Demand (ToD) services (e.g., UberX), one vehicle is assigned to only one request at a time. On-demand ride pooling is not only beneficial to customers (lower cost), drivers (higher revenue per trip) and aggregation companies (higher revenue), but is also of crucial importance to the environment as it reduces the number of vehicles required on the roads. Since …
Benchmarking Library Recognition In Tweets, Ting Zhang, Divya Prabha Chandrasekaran, Ferdian Thung, David Lo
Benchmarking Library Recognition In Tweets, Ting Zhang, Divya Prabha Chandrasekaran, Ferdian Thung, David Lo
Research Collection School Of Computing and Information Systems
Software developers often use social media (such as Twitter) to shareprogramming knowledge such as new tools, sample code snippets,and tips on programming. One of the topics they talk about is thesoftware library. The tweets may contain useful information abouta library. A good understanding of this information, e.g., on thedeveloper’s views regarding a library can be beneficial to weigh thepros and cons of using the library as well as the general sentimentstowards the library. However, it is not trivial to recognize whethera word actually refers to a library or other meanings. For example,a tweet mentioning the word “pandas" may refer to …