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Research Collection School Of Computing and Information Systems

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Articles 5431 - 5460 of 8479

Full-Text Articles in Computer Sciences

Online Learning To Rank For Content-Based Image Retrieval, Ji Wan, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Xingyu Gao, Dayong Wang, Yongdong. Zhang, Jintao Li Jul 2015

Online Learning To Rank For Content-Based Image Retrieval, Ji Wan, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Xingyu Gao, Dayong Wang, Yongdong. Zhang, Jintao Li

Research Collection School Of Computing and Information Systems

A major challenge in Content-Based Image Retrieval (CBIR) is to bridge the semantic gap between low-level image contents and high-level semantic concepts. Although researchers have investigated a variety of retrieval techniques using different types of features and distance functions, no single best retrieval solution can fully tackle this challenge. In a real-world CBIR task, it is often highly desired to combine multiple types of different feature representations and diverse distance measures in order to close the semantic gap. In this paper, we investigate a new framework of learning to rank for CBIR, which aims to seek the optimal combination of …

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Cross-Promotion In Social Media: Choosing The Right Allies, Tingting Song, Qian Tang Jul 2015

Cross-Promotion In Social Media: Choosing The Right Allies, Tingting Song, Qian Tang

Research Collection School Of Computing and Information Systems

This paper investigates the strategic use of cross-promotion for content producers in social media. In particular, we study how a producer chooses other producers to cross-promote so as to maximize the expected benefits of them cross-promoting him/her in return. Theories on homophily effect and social influence suggest that cross-promoted producers are more likely to cross-promote the initiator in return when they are in the similar categories or share more common friends and when the initiator has higher status. However, the cross-promotion from producers of different categories and social groups (i.e., share fewer common friends) tend to benefit the initiator more. …

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Solar: Scalable Online Learning Algorithms For Ranking, Jialei Wang, Ji Wan, Yongdong Zhang, Steven C. H. Hoi Jul 2015

Solar: Scalable Online Learning Algorithms For Ranking, Jialei Wang, Ji Wan, Yongdong Zhang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Traditional learning to rank methods learn ranking models from training data in a batch and offline learning mode, which suffers from some critical limitations, e.g., poor scalability as the model has to be retrained from scratch whenever new training data arrives. This is clearly nonscalable for many real applications in practice where training data often arrives sequentially and frequently. To overcome the limitations, this paper presents SOLAR- a new framework of Scalable Online Learning Algorithms for Ranking, to tackle the challenge of scalable learning to rank. Specifically, we propose two novel SOLAR algorithms and analyze their IR measure bounds theoretically. …

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A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features, Jianfei Yu, Jing Jiang Jul 2015

A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

We present a simple yet effective unsupervised domain adaptation method that can be generally applied for different NLP tasks. Our method uses unlabeled target domain instances to induce a set of instance similarity features. These features are then combined with the original features to represent labeled source domain instances. Using three NLP tasks, we show that our method consistently out-performs a few baselines, including SCL, an existing general unsupervised domain adaptation method widely used in NLP. More importantly, our method is very easy to implement and incurs much less computational cost than SCL.

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An Empirical Study Of Classifier Combination On Cross-Project Defect Prediction, Yun Zhang, David Lo, Xin Xia, Jianling Sun Jul 2015

An Empirical Study Of Classifier Combination On Cross-Project Defect Prediction, Yun Zhang, David Lo, Xin Xia, Jianling Sun

Research Collection School Of Computing and Information Systems

To help developers better allocate testing and debugging efforts, many software defect prediction techniques have been proposed in the literature. These techniques can be used to predict classes that are more likely to be buggy based on past history of buggy classes. These techniques work well as long as a sufficient amount of data is available to train a prediction model. However, there is rarely enough training data for new software projects. To deal with this problem, cross-project defect prediction, which transfers a prediction model trained using data from one project to another, has been proposed and is regarded as …

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Probabilistic Inference Based Message-Passing For Resource Constrained Dcops, Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham Jul 2015

Probabilistic Inference Based Message-Passing For Resource Constrained Dcops, Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Distributed constraint optimization (DCOP) is an important framework for coordinated multiagent decision making. We address a practically useful variant of DCOP, called resource-constrained DCOP (RC-DCOP), which takes into account agents’ consumption of shared limited resources. We present a promising new class of algorithm for RC-DCOPs by translating the underlying co- ordination problem to probabilistic inference. Using inference techniques such as expectation- maximization and convex optimization machinery, we develop a novel convergent message-passing algorithm for RC-DCOPs. Experiments on standard benchmarks show that our approach provides better quality than previous best DCOP algorithms and has much lower failure rate. Comparisons against an …

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Improving Patient Flow With Data-Driven Patient Prioritization Method In The Emergency Department, Kar Way Tan, Sean Shao Wei Lam Jul 2015

Improving Patient Flow With Data-Driven Patient Prioritization Method In The Emergency Department, Kar Way Tan, Sean Shao Wei Lam

Research Collection School Of Computing and Information Systems

We aim to improve the length-of-stay (LOS) of patients in the Emergency Department (ED) ambulatory care area. We propose the use of real-time computerized physician order entry data and ED patient flow management system to estimate the consultation time of patients re-entering the queue to consult a doctor again after receiving treatment or results of tests. The estimation allows decision-makers to apply dynamic prioritization strategies that help the ED to identify patients who can complete their ED treatment process quickly, freeing up resources in the ED and lowering overall LOS.

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Landmark Classification With Hierarchical Multi-Modal Exemplar Feature, Lei Zhu, Jialie Shen, Hai Jin, Liang Xie, Ran Zheng Jul 2015

Landmark Classification With Hierarchical Multi-Modal Exemplar Feature, Lei Zhu, Jialie Shen, Hai Jin, Liang Xie, Ran Zheng

Research Collection School Of Computing and Information Systems

Landmark image classification attracts increasing research attention due to its great importance in real applications, ranging from travel guide recommendation to 3-D modelling and visualization of geolocation. While large amount of efforts have been invested, it still remains unsolved by academia and industry. One of the key reasons is the large intra-class variance rooted from the diverse visual appearance of landmark images. Distinguished from most existing methods based on scalable image search, we approach the problem from a new perspective and model landmark classification as multi-modal categorization, which enjoys advantages of low storage overhead and high classification efficiency. Toward this …

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A New Public Remote Integrity Checking Scheme With User Privacy, Yiteng Feng, Yi Mu, Guomin Yang, Joseph Liu Jul 2015

A New Public Remote Integrity Checking Scheme With User Privacy, Yiteng Feng, Yi Mu, Guomin Yang, Joseph Liu

Research Collection School Of Computing and Information Systems

With a cloud storage, users can store their data files on a remote cloud server with a high quality on-demand cloud service and are able to share their data with other users. Since cloud servers are not usually regarded as fully trusted and the cloud data can be shared amongst users, the integrity checking of the remote files has become an important issue. A number of remote data integrity checking protocols have been proposed in the literature to allow public auditing of cloud data by a third party auditor (TPA). However, user privacy is not taken into account in most …

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A New General Framework For Secure Public Key Encryption With Keyword Search, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo, Xiaofen Wang Jul 2015

A New General Framework For Secure Public Key Encryption With Keyword Search, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo, Xiaofen Wang

Research Collection School Of Computing and Information Systems

Public Key Encryption with Keyword Search (PEKS), introduced by Boneh et al. in Eurocrypt’04, allows users to search encrypted documents on an untrusted server without revealing any information. This notion is very useful in many applications and has attracted a lot of attention by the cryptographic research community. However, one limitation of all the existing PEKS schemes is that they cannot resist the Keyword Guessing Attack (KGA) launched by a malicious server. In this paper, we propose a new PEKS framework named Dual-Server Public Key Encryption with Keyword Search (DS-PEKS). This new framework can withstand all the attacks, including the …

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A Comparative Study Between Motivated Learning And Reinforcement Learning, James T. Graham, Janusz A. Starzyk, Zhen Ni, Haibo He, T.-H. Teng, Ah-Hwee Tan Jul 2015

A Comparative Study Between Motivated Learning And Reinforcement Learning, James T. Graham, Janusz A. Starzyk, Zhen Ni, Haibo He, T.-H. Teng, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper analyzes advanced reinforcement learning techniques and compares some of them to motivated learning. Motivated learning is briefly discussed indicating its relation to reinforcement learning. A black box scenario for comparative analysis of learning efficiency in autonomous agents is developed and described. This is used to analyze selected algorithms. Reported results demonstrate that in the selected category of problems, motivated learning outperformed all reinforcement learning algorithms we compared with.

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An Adaptive Computational Model For Personalized Persuasion, Yilin Kang, Ah-Hwee Tan, Chunyan Miao Jul 2015

An Adaptive Computational Model For Personalized Persuasion, Yilin Kang, Ah-Hwee Tan, Chunyan Miao

Research Collection School Of Computing and Information Systems

While a variety of persuasion agents have been created and applied in different domains such as marketing, military training and health industry, there is a lack of a model which can provide a unified framework for different persuasion strategies. Specifically, persuasion is not adaptable to the individuals’ personal states in different situations. Grounded in the Elaboration Likelihood Model (ELM), this paper presents a computational model called Model for Adaptive Persuasion (MAP) for virtual agents. MAP is a semi-connected network model which enables an agent to adapt its persuasion strategies through feedback. We have implemented and evaluated a MAP-based virtual nurse …

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Using Tweets To Help Sentence Compression For News Highlights Generation, Zhongyu Wei, Yang Liu, Chen Li, Wei Gao Jul 2015

Using Tweets To Help Sentence Compression For News Highlights Generation, Zhongyu Wei, Yang Liu, Chen Li, Wei Gao

Research Collection School Of Computing and Information Systems

We explore using relevant tweets of a given news article to help sentence compression for generating compressive news highlights. We extend an unsupervised dependency-tree based sentence compression approach by incorporating tweet information to weight the tree edge in terms of informativeness and syntactic importance. The experimental results on a public corpus that contains both news articles and relevant tweets show that our proposed tweets guided sentence compression method can improve the summarization performance significantly compared to the baseline generic sentence compression method.

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Personalized Sentiment Classification Based On Latent Individuality Of Microblog Users, Kaisong Song, Shi Feng, Wei Gao, Daling Wang, Ge Yu, Kam-Fai Wong Jul 2015

Personalized Sentiment Classification Based On Latent Individuality Of Microblog Users, Kaisong Song, Shi Feng, Wei Gao, Daling Wang, Ge Yu, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Sentiment expression in microblog posts often reflects user’s specific individuality due to different language habit, personal character, opinion bias and so on. Existing sentiment classification algorithms largely ignore such latent personal distinctions among different microblog users. Meanwhile, sentiment data of microblogs are sparse for individual users, making it infeasible to learn effective personalized classifier. In this paper, we propose a novel, extensible personalized sentiment classification method based on a variant of latent factor model to capture personal sentiment variations by mapping users and posts into a low-dimensional factor space. We alleviate the sparsity of personal texts by decomposing the posts …

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An Automatic Approach To Detect Unusual Events In Software Repositories, Larissa Leite, Christoph Treude, Fernando Figueira Filho Jul 2015

An Automatic Approach To Detect Unusual Events In Software Repositories, Larissa Leite, Christoph Treude, Fernando Figueira Filho

Research Collection School Of Computing and Information Systems

This work presents an automatic approach to detect unusual events in software repositories. The approach collects data from source code repositories and analyzes new commits based on historical data in order to detect unusual events that are displayed to developers and managers in an awareness tool.

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Structured Learning From Heterogeneous Behavior For Social Identity Linkage, Siyuan Liu, Shuhui Wang, Feida Zhu Jul 2015

Structured Learning From Heterogeneous Behavior For Social Identity Linkage, Siyuan Liu, Shuhui Wang, Feida Zhu

Research Collection School Of Computing and Information Systems

Social identity linkage across different social media platforms is of critical importance to business intelligence by gaining from social data a deeper understanding and more accurate profiling of users. In this paper, we propose a solution framework, HYDRA, which consists of three key steps: (I) we model heterogeneous behavior by long-term topical distribution analysis and multi-resolution temporal behavior matching against high noise and information missing, and the behavior similarity are described by multi-dimensional similarity vector for each user pair; (II) we build structure consistency models to maximize the structure and behavior consistency on users' core social structure across different platforms, …

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Towards City-Scale Mobile Crowdsourcing: Task Recommendations Under Trajectory Uncertainties, Chen Cen, Shih-Fen Cheng, Hoong Chuin Lau, Archan Misra Jul 2015

Towards City-Scale Mobile Crowdsourcing: Task Recommendations Under Trajectory Uncertainties, Chen Cen, Shih-Fen Cheng, Hoong Chuin Lau, Archan Misra

Research Collection School Of Computing and Information Systems

In this work, we investigate the problem of large-scale mobile crowdsourcing, where workers are financially motivated to perform location-based tasks physically. Unlike current industry practice that relies on workers to manually pick tasks to perform, we automatically make task recommendation based on workers’ historical trajectories and desired time budgets. The challenge of predicting workers’ trajectories is that it is faced with uncertainties, as a worker does not take same routes every day. In this work, we depart from deterministic modeling and study the stochastic task recommendation problem where each worker is associated with several predicted routine routes with probabilities. We …

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Production Cost Heterogeneity In A Circular-City Model, Mei Lin, Ruhai Wu Jul 2015

Production Cost Heterogeneity In A Circular-City Model, Mei Lin, Ruhai Wu

Research Collection School Of Computing and Information Systems

We derive the closed-form solution characterizing the equilibrium in a circular-city model with competing firms of heterogeneous production costs. Tractability issues in this setting are well known and have not been resolved in prior work. In this paper, the equilibrium solution illustrates effects of production costs on firms’ strategic decisions, their aggregate profit, and consumer surplus.

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S-Looper: Automatic Summarization For Multipath String Loops, Xiaofei Xie, Yang Liu, Wei Le, Xiaohong Li, Hongxu Chen Jul 2015

S-Looper: Automatic Summarization For Multipath String Loops, Xiaofei Xie, Yang Liu, Wei Le, Xiaohong Li, Hongxu Chen

Research Collection School Of Computing and Information Systems

Loops are important yet most challenging program constructs to analyze for various program analysis tasks. Existing loop analysis techniques mainly handle well loops that contain only integer variables with a single path in the loop body. The key challenge in summarizing a multiple-path loop is that a loop traversal can yield a large number of possibilities due to the different execution orders of these paths located in the loop; when a loop contains a conditional branch related to string content, we potentially need to track every character in the string for loop summarization, which is expensive. In this paper, we …

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Qcri: Answer Selection For Community Question Answering - Experiment For Arabic And English, Massimo Nicosia, Simone Filice, Alberto Barron-Cedeno, Iman Saleh, Hamdy Mubarak, Wei Gao, Preslav Nakov, Giovanni Da San Martino, Alessandro Moschitti, Kareem Darwish, Lluis Marquz Marquz, Shafiq Joty, Walid Magdy Magdy Jun 2015

Qcri: Answer Selection For Community Question Answering - Experiment For Arabic And English, Massimo Nicosia, Simone Filice, Alberto Barron-Cedeno, Iman Saleh, Hamdy Mubarak, Wei Gao, Preslav Nakov, Giovanni Da San Martino, Alessandro Moschitti, Kareem Darwish, Lluis Marquz Marquz, Shafiq Joty, Walid Magdy Magdy

Research Collection School Of Computing and Information Systems

This paper describes QCRI’s participation in SemEval-2015 Task 3 “Answer Selection in Community Question Answering”, which targeted real-life Web forums, and was offered in both Arabic and English. We apply a supervised machine learning approach considering a manifold of features including among others word n-grams, text similarity, sentiment analysis, the presence of specific words, and the context of a comment. Our approach was the best performing one in the Arabic subtask and the third best in the two English subtasks

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Unsupervised Celebrity Face Naming In Web Videos, Lei Pang, Chong-Wah Ngo Jun 2015

Unsupervised Celebrity Face Naming In Web Videos, Lei Pang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This paper investigates the problem of celebrity face naming in unconstrained videos with user-provided metadata. Instead of relying on accurate face labels for supervised learning, a rich set of relationships automatically derived from video content and knowledge from image domain and social cues is leveraged for unsupervised face labeling. The relationships refer to the appearances of faces under different spatio-temporal contexts and their visual similarities. The knowledge includes Web images weakly tagged with celebrity names and the celebrity social networks. The relationships and knowledge are elegantly encoded using conditional random field (CRF) for label inference. Two versions of face annotation …

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Discriminant Analysis On Riemannian Manifold Of Gaussian Distributions For Face Recognition With Image Sets, Wen. Wang, Ruiping. Wang, Zhiwu Huang, Shiguang. Shan, Xilin. Chen Jun 2015

Discriminant Analysis On Riemannian Manifold Of Gaussian Distributions For Face Recognition With Image Sets, Wen. Wang, Ruiping. Wang, Zhiwu Huang, Shiguang. Shan, Xilin. Chen

Research Collection School Of Computing and Information Systems

This paper presents a method named Discriminant Analysis on Riemannian manifold of Gaussian distributions (DARG) to solve the problem of face recognition with image sets. Our goal is to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent image set as Gaussian Mixture Model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. In the light of information geometry, the Gaussians lie on a specific Riemannian manifold. To encode such Riemannian geometry properly, we investigate several distances between Gaussians and …

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Face Video Retrieval With Image Query Via Hashing Across Euclidean Space And Riemannian Manifold, Y. Li, R. Wang, Zhiwu Huang, S. Shan, X. Chen Jun 2015

Face Video Retrieval With Image Query Via Hashing Across Euclidean Space And Riemannian Manifold, Y. Li, R. Wang, Zhiwu Huang, S. Shan, X. Chen

Research Collection School Of Computing and Information Systems

Retrieving videos of a specific person given his/her face image as query becomes more and more appealing for applications like smart movie fast-forwards and suspect searching. It also forms an interesting but challenging computer vision task, as the visual data to match, i.e., still image and video clip are usually represented quite differently. Typically, face image is represented as point (i.e., vector) in Euclidean space, while video clip is seemingly modeled as a point (e.g., covariance matrix) on some particular Riemannian manifold in the light of its recent promising success. It thus incurs a new hashing-based retrieval problem of matching …

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Projection Metric Learning On Grassmann Manifold With Application To Video Based Face Recognition, Zhiwu Huang, R. Wang, S. Shan, X. Chen Jun 2015

Projection Metric Learning On Grassmann Manifold With Application To Video Based Face Recognition, Zhiwu Huang, R. Wang, S. Shan, X. Chen

Research Collection School Of Computing and Information Systems

In video based face recognition, great success has been made by representing videos as linear subspaces, which typically lie in a special type of non-Euclidean space known as Grassmann manifold. To leverage the kernel-based methods developed for Euclidean space, several recent methods have been proposed to embed the Grassmann manifold into a high dimensional Hilbert space by exploiting the well established Project Metric, which can approximate the Riemannian geometry of Grassmann manifold. Nevertheless, they inevitably introduce the drawbacks from traditional kernel-based methods such as implicit map and high computational cost to the Grassmann manifold. To overcome such limitations, we propose …

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Semi-Supervised Domain Adaptation With Subspace Learning For Visual Recognition, Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, Tao Mei Jun 2015

Semi-Supervised Domain Adaptation With Subspace Learning For Visual Recognition, Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, Tao Mei

Research Collection School Of Computing and Information Systems

In many real-world applications, we are often facing the problem of cross domain learning, i.e., to borrow the labeled data or transfer the already learnt knowledge from a source domain to a target domain. However, simply applying existing source data or knowledge may even hurt the performance, especially when the data distribution in the source and target domain is quite different, or there are very few labeled data available in the target domain. This paper proposes a novel domain adaptation framework, named Semi-supervised Domain Adaptation with Subspace Learning (SDASL), which jointly explores invariant lowdimensional structures across domains to correct data …

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Improving Automatic Name-Face Association Using Celebrity Images On The Web, Zhineng Chen, Bailan Feng, Chong-Wah Ngo, Caiyan Jia, Xiangsheng Huang Jun 2015

Improving Automatic Name-Face Association Using Celebrity Images On The Web, Zhineng Chen, Bailan Feng, Chong-Wah Ngo, Caiyan Jia, Xiangsheng Huang

Research Collection School Of Computing and Information Systems

This paper investigates the task of automatically associating faces appearing in images (or videos) with their names. Our novelty lies in the use of celebrity Web images to facilitate the task. Specifically, we first propose a method named Image Matching (IM), which uses the faces in images returned from name queries over an image search engine as the gallery set of the names, and a probe face is classified as one of the names, or none of them, according to their matching scores and compatibility characterized by a proposed Assigning-Thresholding (AT) pipeline. Noting IM could provide guidance for association for …

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Multimodal Learning With Deep Boltzmann Machine For Emotion Prediction In User Generated Videos, Lei Pang, Chong-Wah Ngo Jun 2015

Multimodal Learning With Deep Boltzmann Machine For Emotion Prediction In User Generated Videos, Lei Pang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Detecting emotions from user-generated videos, such as“anger” and “sadness”, has attracted widespread interest recently. The problem is challenging as effectively representing video data with multi-view information (e.g., audio, video or text) is not trivial. In contrast to the existing works that extract features from each modality (view) separately followed by early or late fusion, we propose to learn a joint density model over the space of multi-modal inputs (including visual, auditory and textual modalities) with Deep Boltzmann Machine (DBM). The model is trained directly on the user-generated Web videos without any labeling effort. More importantly, the deep architecture enlightens the …

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Adaptive Resource Provisioning Mechanism In Vees For Improving Performance Of Hla-Based Simulations, Zengxiang Li, Wentong Cai, Stephen John Turner, Xiaorong Li, Nguyen Binh Duong Ta Jun 2015

Adaptive Resource Provisioning Mechanism In Vees For Improving Performance Of Hla-Based Simulations, Zengxiang Li, Wentong Cai, Stephen John Turner, Xiaorong Li, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

Parallel and distributed simulations (or High-Level Architecture (HLA)-based simulations) employing optimistic synchronization allow federates to advance simulation time freely at the risk of overoptimistic executions and execution rollbacks. As a result, the simulation performance may degrade significantly due to the simulation workload imbalance among federates. In this article, we investigate the execution of parallel and distributed simulations on Cloud and data centers with Virtual Execution Environments (VEEs). In order to speed up simulation execution, an Adaptive Resource Provisioning Mechanism in Virtual Execution Environments (ArmVee) is proposed. It is composed of a performance monitor and a resource manager. The former measures …

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Verifying Parameterized Timed Security Protocols, Li Li, Jun Sun, Yang Liu, Jin Song Dong Jun 2015

Verifying Parameterized Timed Security Protocols, Li Li, Jun Sun, Yang Liu, Jin Song Dong

Research Collection School Of Computing and Information Systems

Quantitative timing is often explicitly used in systems for better security, e.g., the credentials for automatic website logon often has limited lifetime. Verifying timing relevant security protocols in these systems is very challenging as timing adds another dimension of complexity compared with the untimed protocol verification. In our previous work, we proposed an approach to check the correctness of the timed authentication in security protocols with fixed timing constraints. However, a more difficult question persists, i.e., given a particular protocol design, whether the protocol has security flaws in its design or it can be configured secure with proper parameter values? …

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Retail Precinct Management: A Case Of Commercial Decentralization In Singapore, Robert De Souza, Hoong Chuin Lau, Mark Goh, Lindawati, Wee-Siong Ng, Puay-Siew Tan Jun 2015

Retail Precinct Management: A Case Of Commercial Decentralization In Singapore, Robert De Souza, Hoong Chuin Lau, Mark Goh, Lindawati, Wee-Siong Ng, Puay-Siew Tan

Research Collection School Of Computing and Information Systems

The synchronized last mile logistics concept seeks to address, through coordinated collaboration, several challenges that hinder reliability, cost efficiency, effective resource planning, scheduling and utilization; and increasingly, sustainability objectives. Subsequently, the meeting of service level and contractual commitments are competitively impacted with any loss of efficiency. These challenges, against a backdrop of Singapore, can essentially be addressed in selected industry sectors through a better understanding of logistics structures; innovative supply chain designs and coordination of services, operations and processes coupled with concerted policies and supply chain strategies.

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