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Articles 2371 - 2400 of 4271
Full-Text Articles in Entire DC Network
Toward Accurate Network Delay Measurement On Android Phones, Weichao Li, Daoyuan Wu, Rocky K. C. Chang, Ricky K. P. Mok
Toward Accurate Network Delay Measurement On Android Phones, Weichao Li, Daoyuan Wu, Rocky K. C. Chang, Ricky K. P. Mok
Research Collection School Of Computing and Information Systems
Measuring and understanding the performance of mobile networks is becoming very important for end users and operators. Despite the availability of many measurement apps, their measurement accuracy has not received sufficient scrutiny. In this paper, we appraise the accuracy of smartphone-based network performance measurement using the Android platform and the network round-trip time (RTT) as the metric. We show that two of the most popular measurement apps-Ookla Speedtest and MobiPerf-have their RTT measurements inflated. We build three test apps that cover three common measurement methods and evaluate them in a testbed. We overcome the main challenge of obtaining a complete …
Hibs-Ksharing: Hierarchical Identity-Based Signature Key Sharing For Automotive, Zhuo Wei, Yanjiang Yang, Yongdong Wu, Jian Weng, Robert H. Deng
Hibs-Ksharing: Hierarchical Identity-Based Signature Key Sharing For Automotive, Zhuo Wei, Yanjiang Yang, Yongdong Wu, Jian Weng, Robert H. Deng
Research Collection School Of Computing and Information Systems
Equipped with various sensors and intelligent systems, modern cars turn into entities with connectivity, autonomy, and safety. Car rental/car sharing is an innovative transportation concept and integral in today's urban living. It enables users to access a fleet of vehicles located throughout cities. Complementing public transportation, the car-sharing service helps people to meet their transportation needs economically and in an environmentally responsible manner. When a customer wants to rent a car from a rental company or an owner wants to share a private car with his/her friends or family members, the customer or the user should gain admission to the …
Modeling Trajectories With Recurrent Neural Networks, Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang
Modeling Trajectories With Recurrent Neural Networks, Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang
Research Collection School Of Computing and Information Systems
Modeling trajectory data is a building block for many smart-mobility initiatives. Existing approaches apply shallow models such as Markov chain and inverse reinforcement learning to model trajectories, which cannot capture the long-term dependencies. On the other hand, deep models such as Recurrent Neura lNetwork (RNN) have demonstrated their strength of modeling variable length sequences. However, directly adopting RNN to model trajectories is not appropriate because of the unique topological constraints faced by trajectories. Motivated by these findings, we design two RNN-based models which can make full advantage of the strength of RNN to capture variable length sequence and meanwhile to …
Can Syntax Help? Improving An Lstm-Based Sentence Compression Model For New Domains, Liangguo Wang, Jing Jiang, Hai Leong Chieu, Chen Hui Ong, Dandan Song, Lejian Liao
Can Syntax Help? Improving An Lstm-Based Sentence Compression Model For New Domains, Liangguo Wang, Jing Jiang, Hai Leong Chieu, Chen Hui Ong, Dandan Song, Lejian Liao
Research Collection School Of Computing and Information Systems
In this paper, we study how to improve thedomain adaptability of a deletion-basedLong Short-Term Memory (LSTM) neuralnetwork model for sentence compression.We hypothesize that syntactic informationhelps in making such modelsmore robust across domains. We proposetwo major changes to the model: usingexplicit syntactic features and introducingsyntactic constraints through Integer LinearProgramming (ILP). Our evaluationshows that the proposed model works betterthan the original model as well as a traditionalnon-neural-network-based modelin a cross-domain setting.
On Return Oriented Programming Threats In Android Runtime, Akshaya Venkateswara Raja, Jehyun Lee, Debin Gao
On Return Oriented Programming Threats In Android Runtime, Akshaya Venkateswara Raja, Jehyun Lee, Debin Gao
Research Collection School Of Computing and Information Systems
Android has taken a large share of operating systems forsmart devices including smartphones, and has been an attractive target to theattackers. The arms race between attackers and defenders typically occurs ontwo front lines — the latest attacking technology and the latest updates to theoperating system (including defense mechanisms deployed). In terms of attackingtechnology, Return-Oriented Programming (ROP) is one of the most sophisticatedattack methods on Android devices. In terms of the operating system updates,Android Runtime (ART) was the latest and biggest change to the Android family.In this paper, we investigate the extent to which Android Runtime (ART) makesReturn-Oriented Programming (ROP) attacks …
Object Detection Meets Knowledge Graphs, Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar
Object Detection Meets Knowledge Graphs, Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar
Research Collection School Of Computing and Information Systems
Object detection in images is a crucial task in computer vision, with important applications ranging from security surveillance to autonomous vehicles. Existing state-of-the-art algorithms, including deep neural networks, only focus on utilizing features within an image itself, largely neglecting the vast amount of background knowledge about the real world. In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm. The framework employs the notion of semantic consistency to quantify and generalize knowledge, which improves object detection through a re-optimization process to achieve …
Managing Sensor Systems For Early Detection Of Mild Cognitive Impairment In Community Elderly: Lessons Learned And Future Work, Boon Thai Ng, Hwee-Pink Tan, Hwee Xian Tan
Managing Sensor Systems For Early Detection Of Mild Cognitive Impairment In Community Elderly: Lessons Learned And Future Work, Boon Thai Ng, Hwee-Pink Tan, Hwee Xian Tan
Research Collection School Of Computing and Information Systems
The aging population is a pertinent issue faced by governments globally. One of the most common and costly health issues associated with the aging population is cognitive decline, leading up to dementia. In this paper, we describe a non-intrusive, continuous and scalable system for early detection of Mild Cognitive Impairment (MCI) in the elderly, which enables early medical interventions to be provided. We focus on the system design and feature extraction of the sensor system, to validate our hypothesis of the use of sensor systems for early detection of MCI. Lessons learned from deploying the sensor system is presented, together …
Flexible Online Task Assignment In Real-Time Spatial Data, Yongxin Tong, Libin Wang, Zimu Zhou, Bolin Ding, Lei Chen, Jieping Ye, Ke Xu
Flexible Online Task Assignment In Real-Time Spatial Data, Yongxin Tong, Libin Wang, Zimu Zhou, Bolin Ding, Lei Chen, Jieping Ye, Ke Xu
Research Collection School Of Computing and Information Systems
The popularity of Online To Offline (O2O) service platforms has spurred the need for online task assignment in real-time spatial data, where streams of spatially distributed tasks and workers are matched in real time such that the total number of assigned pairs is maximized. Existing online task assignment models assume that each worker is either assigned a task immediately or waits for a subsequent task at a fixed location once she/he appears on the platform. Yet in practice a worker may actively move around rather than passively wait in place if no task is assigned. In this paper, we define …
Encoding And Recall Of Spatio-Temporal Episodic Memory In Real Time, Poo-Hee Chang, Ah-Hwee Tan
Encoding And Recall Of Spatio-Temporal Episodic Memory In Real Time, Poo-Hee Chang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Episodic memory enables a cognitive system to improve its performance by reflecting upon past events. In this paper, we propose a computational model called STEM for encoding and recall of episodic events together with the associated contextual information in real time. Based on a class of self-organizing neural networks, STEM is designed to learn memory chunks or cognitive nodes, each encoding a set of co-occurring multi-modal activity patterns across multiple pattern channels. We present algorithms for recall of events based on partial and inexact input patterns. Our empirical results based on a public domain data set show that STEM displays …
Learning To Hallucinate Face Images Via Component Generation And Enhancement, Yibing Song, Jiawei Zhang, Shengfeng He, Linchao Bao, Qingxiong Yang
Learning To Hallucinate Face Images Via Component Generation And Enhancement, Yibing Song, Jiawei Zhang, Shengfeng He, Linchao Bao, Qingxiong Yang
Research Collection School Of Computing and Information Systems
We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methods.
Optimal Security Reductions For Unique Signatures: Bypassing Impossibilities With A Counterexample, Fuchun Fuo, Rongmao Chen, Willy Susilo, Jianchang Lai, Guomin Yang, Yi Mu
Optimal Security Reductions For Unique Signatures: Bypassing Impossibilities With A Counterexample, Fuchun Fuo, Rongmao Chen, Willy Susilo, Jianchang Lai, Guomin Yang, Yi Mu
Research Collection School Of Computing and Information Systems
Optimal security reductions for unique signatures (Coron, Eurocrypt 2002) and their generalization, i.e., efficiently re-randomizable signatures (Hofheinz et al. PKC 2012 & Bader et al. Eurocrypt 2016) have been well studied in the literature. Particularly, it has been shown that under a non-interactive hard assumption, any security reduction (with or without random oracles) for a unique signature scheme or an efficiently re-randomizable signature scheme must loose a factor of at least qsqs in the security model of existential unforgeability against chosen-message attacks (EU-CMA), where qsqs denotes the number of signature queries. Note that the number qsqs can be as large …
Using Cognitive Maps Of Mental Models To Evaluate Learning Challenges: A Case Study, Z. Shen, Keng Siau
Using Cognitive Maps Of Mental Models To Evaluate Learning Challenges: A Case Study, Z. Shen, Keng Siau
Research Collection School Of Computing and Information Systems
Mental models are organized knowledge structures that individuals form to make sense of the world around them. Cognitive maps are the externalized portrayals of mental models in graphical format. Mental models and cognitive maps have been used as an instructional design method, an assessment tool, and a learning strategy in college education. In this paper, we propose a novel use of mental models and cognitive maps as a device to elicit students’ challenges in learning course materials. Our case study in an Information Systems class illustrates how cognitive maps are constructed from students’ mental models, how learning challenges are identified …
Poster: Unobtrusive User Verification Using Piezoelectric Energy Harvesting, Dong Ma, Guohao Lan, Weitao Xu, Mahbub Hassan, Wen Hu
Poster: Unobtrusive User Verification Using Piezoelectric Energy Harvesting, Dong Ma, Guohao Lan, Weitao Xu, Mahbub Hassan, Wen Hu
Research Collection School Of Computing and Information Systems
With the capability to harvest energy from low frequency motions or vibrations, piezoelectric energy harvesting has become a promising solution to achieve self-powered wearable system. Apart from generating energy to power the wearable devices, the output electricity signal of the PEH can also be used as an information source as it reflects the activity or motion patterns of the user. In this paper, we have designed and built an insole-based user authentication system by leveraging the AC voltage generated by the PEH during human walking. Meanwhile, the generated power is also collected and stored, which could be later used as …
Mergeable And Revocable Identity-Based Encryption, Shengmin Xu, Guomin Yang, Yi Mu, Willy Susilo
Mergeable And Revocable Identity-Based Encryption, Shengmin Xu, Guomin Yang, Yi Mu, Willy Susilo
Research Collection School Of Computing and Information Systems
Identity-based encryption (IBE) has been extensively studied and widely used in various applications since Boneh and Franklin proposed the first practical scheme based on pairing. In that seminal work, it has also been pointed out that providing an efficient revocation mechanism for IBE is essential. Hence, revocable identity-based encryption (RIBE) has been proposed in the literature to offer an efficient revocation mechanism. In contrast to revocation, another issue that will also occur in practice is to combine two or multiple IBE systems into one system, e.g., due to the merge of the departments or companies. However, this issue has not …
Mopeye: Opportunistic Monitoring Of Per-App Mobile Network Performance, Daoyuan Wu, Rocky K. C. Chang, Weichao Li, Eric K. T. Cheng, Debin Gao
Mopeye: Opportunistic Monitoring Of Per-App Mobile Network Performance, Daoyuan Wu, Rocky K. C. Chang, Weichao Li, Eric K. T. Cheng, Debin Gao
Research Collection School Of Computing and Information Systems
Crowdsourcing mobile user’s network performance has become an effective way of understanding and improving mobile network performance and user quality-of-experience. However, the current measurement method is still based on the landline measurement paradigm in which a measurement app measures the path to fixed (measurement or web) servers. In this work, we introduce a new paradigm of measuring per-app mobile network performance. We design and implement MopEye, an Android app to measure network round-trip delay for each app whenever there is app traffic. This opportunistic measurement can be conducted automatically without user intervention. Therefore, it can facilitate a large-scale and long-term …
Deshadownet: A Multi-Context Embedding Deep Network For Shadow Removal, Liangqiong Qu, Jiandong Tian, Shengfeng He, Yandong Tang, Rynson W. H. Lau
Deshadownet: A Multi-Context Embedding Deep Network For Shadow Removal, Liangqiong Qu, Jiandong Tian, Shengfeng He, Yandong Tang, Rynson W. H. Lau
Research Collection School Of Computing and Information Systems
Shadow removal is a challenging task as it requires the detection/annotation of shadows as well as semantic understanding of the scene. In this paper, we propose an automatic and end-to-end deep neural network (DeshadowNet) to tackle these problems in a unified manner. DeshadowNet is designed with a multi-context architecture, where the output shadow matte is predicted by embedding information from three different perspectives. The first global network extracts shadow features from a global view. Two levels of features are derived from the global network and transferred to two parallel networks. While one extracts the appearance of the input image, the …
Jfix: Semantics-Based Repair Of Java Programs Via Symbolic Pathfinder, Xuan Bach D. Le, Duc-Hiep Chu, David Lo, Goues Le, Willem Visser
Jfix: Semantics-Based Repair Of Java Programs Via Symbolic Pathfinder, Xuan Bach D. Le, Duc-Hiep Chu, David Lo, Goues Le, Willem Visser
Research Collection School Of Computing and Information Systems
Recently there has been a proliferation of automated program repair (APR) techniques, targeting various programming languages. Such techniques can be generally classified into two families: syntactic- and semantics-based. Semantics-based APR, on which we focus, typically uses symbolic execution to infer semantic constraints and then program synthesis to construct repairs conforming to them. While syntactic-based APR techniques have been shown success- ful on bugs in real-world programs written in both C and Java, semantics-based APR techniques mostly target C programs. This leaves empirical comparisons of the APR families not fully explored, and developers without a Java-based semantics APR technique. We present …
Tlel: A Two-Layer Ensemble Learning Approach For Just-In-Time Defect Prediction, Xinli Yang, David Lo, Xin Xia, Jianling Sun
Tlel: A Two-Layer Ensemble Learning Approach For Just-In-Time Defect Prediction, Xinli Yang, David Lo, Xin Xia, Jianling Sun
Research Collection School Of Computing and Information Systems
Context: Defect prediction is a very meaningful topic, particularly at change-level. Change-level defect prediction, which is also referred as just-in-time defect prediction, could not only ensure software quality in the development process, but also make the developers check and fix the defects in time [1].Objective: Ensemble learning becomes a hot topic in recent years. There have been several studies about applying ensemble learning to defect prediction [2–5]. Traditional ensemble learning approaches only have one layer, i.e., they use ensemble learning once. There are few studies that leverages ensemble learning twice or more. To bridge this research gap, we try to …
Attribute-Based Encryption With Expressive And Authorized Keyword Search, Hui Cui, Robert H. Deng, Joseph K. Liu, Yingjiu Li
Attribute-Based Encryption With Expressive And Authorized Keyword Search, Hui Cui, Robert H. Deng, Joseph K. Liu, Yingjiu Li
Research Collection School Of Computing and Information Systems
To protect data security and privacy in cloud storage systems, a common solution is to outsource data in encrypted forms so that the data will remain secure and private even if storage systems are compromised. The encrypted data, however, must be pliable to search and access control. In this paper, we introduce a notion of attribute-based encryption with expressive and authorized keyword search (ABE-EAKS) to support both expressive keyword search and fine-grained access control over encrypted data in the cloud. In ABE-EAKS, every data user is associated with a set of attributes and is issued a private attribute-key corresponding to …
Cyber Foraging: Fifteen Years Later, Rajesh Krishna Balan, Jason Flinn
Cyber Foraging: Fifteen Years Later, Rajesh Krishna Balan, Jason Flinn
Research Collection School Of Computing and Information Systems
Revisiting Mahadev Satyanarayanan's original vision of cyber foraging and reflecting on the last 15 years of related research, the authors discuss the major accomplishments achieved as well as remaining challenges. They also look to current and future applications that could provide compelling application scenarios for making cyber foraging a widely deployed technology. This article is part of a special issue on pervasive computing revisited.
Cloud-Based Query Evaluation For Energy-Efficient Mobile Sensing, Tianli Mo, Lipyeow Lim, Sougata Sen, Archan Misra, Rajesh Krishna Balan, Youngki Lee
Cloud-Based Query Evaluation For Energy-Efficient Mobile Sensing, Tianli Mo, Lipyeow Lim, Sougata Sen, Archan Misra, Rajesh Krishna Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
In this paper, we reduce the energy overheads of continuous mobile sensing, specifically for the case of context-aware applications that are interested in collective context or events, i.e., events expressed as a set of complex predicates over sensor data from multiple smartphones. We propose a cloud-based query management and optimization framework, called CloQue, that can support thousands of such concurrent queries, executing over a large number of individual smartphones. Our central insight is that the context of different individuals & groups often have significant correlation, and that this correlation can be learned through standard association rule mining on historical data. …
A Domain Based Approach To Social Relation Recognition, Qianru Sun, Bernt Schiele, Mario Fritz
A Domain Based Approach To Social Relation Recognition, Qianru Sun, Bernt Schiele, Mario Fritz
Research Collection School Of Computing and Information Systems
Social relations are the foundation of human daily life. Developing techniques to analyze such relations from visual data bears great potential to build machines that better understand us and are capable of interacting with us at a social level. Previous investigations have remained partial due to the overwhelming diversity and complexity of the topic and consequently have only focused on a handful of social relations. In this paper, we argue that the domain-based theory from social psychology is a great starting point to systematically approach this problem. The theory provides coverage of all aspects of social relations and equally is …
A Decidable Fragment In Separation Logic With Inductive Predicates And Arithmetic, Quang Loc Le, Makoto Tatsuta, Jun Sun, Wei-Ngan Chin
A Decidable Fragment In Separation Logic With Inductive Predicates And Arithmetic, Quang Loc Le, Makoto Tatsuta, Jun Sun, Wei-Ngan Chin
Research Collection School Of Computing and Information Systems
We consider the satisfiability problem for a fragment of separation logic including inductive predicates with shape and arithmetic properties. We show that the fragment is decidable if the arithmetic properties can be represented as semilinear sets. Our decision procedure is based on a novel algorithm to infer a finite representation for each inductive predicate which precisely characterises its satisfiability. Our analysis shows that the proposed algorithm runs in exponential time in the worst case. We have implemented our decision procedure and integrated it into an existing verification system. Our experiment on benchmarks shows that our procedure helps to verify the …
Gms: Grid-Based Motion Statistics For Fast, Ultra-Robust Feature Correspondence, Jiawang Bian, Wen-Yan Lin, Matsushita Yasuyuki, Sai-Kit Yeung, Tan-Dat Nguyen, Ming-Ming Cheng
Gms: Grid-Based Motion Statistics For Fast, Ultra-Robust Feature Correspondence, Jiawang Bian, Wen-Yan Lin, Matsushita Yasuyuki, Sai-Kit Yeung, Tan-Dat Nguyen, Ming-Ming Cheng
Research Collection School Of Computing and Information Systems
Incorporating smoothness constraints into feature matching is known to enable ultra-robust matching. However, such formulations are both complex and slow, making them unsuitable for video applications. This paper proposes GMS (Grid-based Motion Statistics), a simple means of encapsulating motion smoothness as the statistical likelihood of a certain number of matches in a region. GMS enables translation of high match numbers into high match quality. This provides a real-time, ultra-robust correspondence system. Evaluation on videos, with low textures, blurs and wide-baselines show GMS consistently out-performs other real-time matchers and can achieve parity with more sophisticated, much slower techniques.
How Artificial Intelligence Is Impacting Manufacturing Industry, Deepak Srinivasan, Maitreyi Ramesh Swaroop, Balaji Rajaram, Sri Krishan Iyer
How Artificial Intelligence Is Impacting Manufacturing Industry, Deepak Srinivasan, Maitreyi Ramesh Swaroop, Balaji Rajaram, Sri Krishan Iyer
Research Collection School Of Computing and Information Systems
In this survey, we study the impact of Artificial Intelligence (AI) on manufacturing sector. AI methods can be utilized to make new thoughts several ways: by delivering novel mixes of wellknown thoughts; by investigating the capability of theoretical spaces; and by making changes that empower the era of unexplored thoughts. AI will have less trouble in displaying the era of new thoughts than in automating their assessment. We describe the advances that have been made on AI in manufacturing industry. We close with how to overcome the issues in this area.
Elderly Friendliness Evaluation Of Mobile Assistants, Di Wang, Xinjia Yu, Simon Fauvel, Ah-Hwee Tan, Chunyan Miao
Elderly Friendliness Evaluation Of Mobile Assistants, Di Wang, Xinjia Yu, Simon Fauvel, Ah-Hwee Tan, Chunyan Miao
Research Collection School Of Computing and Information Systems
The rapidly increasing elderly population in many developed and developing countries poses great challenges to elderly care systems. To alleviate the problem of a shrinking workforce to deliver elderly care, using mobile intelligent assistants to lessen the caregivers' workload becomes a promising solution. However, the friendliness of such mobile assistants, which is seldom measured in a quantitative manner, may hinder their acceptance by the elderly users. In this paper, we propose a formalized systematic approach named Elderly Friendliness Evaluation Methodology (EFEM) to measure the elderly friendliness of any product, service or system. Furthermore, we apply EFEM to evaluate the elderly …
Demographics Of News Sharing In The U.S. Twittersphere, Julio C.S. Reis, Haewoon Kwak, Jisun An, Johnnatan Messias, Benevenuto Fabrıcio.
Demographics Of News Sharing In The U.S. Twittersphere, Julio C.S. Reis, Haewoon Kwak, Jisun An, Johnnatan Messias, Benevenuto Fabrıcio.
Research Collection School Of Computing and Information Systems
The widespread adoption and dissemination of online news through social media systems have been revolutionizing many segments of our society and ultimately our daily lives. In these systems, users can play a central role as they share content to their friends. Despite that, little is known about news spreaders in social media. In this paper, we provide the first of its kind in-depth characterization of news spreaders in social media. In particular, we investigate their demographics, what kind of content they share, and the audience they reach. Among our main findings, we show that males and white users tend to …
Hierarchical Functional Encryption For Linear Transformations, Shiwei Zhang, Yi Mu, Guomin Yang, Xiaofen Wang
Hierarchical Functional Encryption For Linear Transformations, Shiwei Zhang, Yi Mu, Guomin Yang, Xiaofen Wang
Research Collection School Of Computing and Information Systems
In contrast to the conventional all-or-nothing encryption, functional encryption (FE) allows partial revelation of encrypted information based on the keys associated with different functionalities. Extending FE with key delegation ability, hierarchical functional encryption (HFE) enables a secret key holder to delegate a portion of its decryption ability to others and the delegation can be done hierarchically. All HFE schemes in the literature are for general functionalities and not very practical. In this paper, we focus on the functionality of linear transformations (i.e. matrix product evaluation). We refine the definition of HFE and further extend the delegation to accept multiple keys. …
Privacy-Preserving K-Time Authenticated Secret Handshakes, Yangguang Tian, Shiwei Zhang, Guomin Yang, Yi Mu, Yong Yu
Privacy-Preserving K-Time Authenticated Secret Handshakes, Yangguang Tian, Shiwei Zhang, Guomin Yang, Yi Mu, Yong Yu
Research Collection School Of Computing and Information Systems
Secret handshake allows a group of authorized users to establish a shared secret key and at the same time authenticate each other anonymously. A straightforward approach to design an unlinkable secret handshake protocol is to use either long-term certificate or one-time certificate provided by a trusted authority. However, how to detect the misusing of certificates by an insider adversary is a challenging security issue when using those approaches for unlinkable secret handshake. In this paper, we propose a novel k-time authenticated secret handshake (k-ASH) protocol where each authorized user is only allowed to use the credential for k times. We …
The Making Of A Successful Analytics Master Degree Program: Experiences And Lessons Drawn For A Young And Small Asian University, Michelle L. F. Cheong
The Making Of A Successful Analytics Master Degree Program: Experiences And Lessons Drawn For A Young And Small Asian University, Michelle L. F. Cheong
Research Collection School Of Computing and Information Systems
Singapore Management University's School of Information Systems is a young school within a young and small university in Asia. Being young and small, establishing a successful analytics master degree program required extensive landscape research, assessment of its own strengths and weaknesses, having a committed team, and having a clear vision to meet the ever-changing needs of the industry. The Master of IT in Business (Analytics) program, established since 2011, has grown from an annual intake of 16 to 128 students in six years. This article attempts to describe the design process, challenges faced, decisions made, and the key actions taken, …