Systems Architecture Commons^™

Modeling Multiple Tasks In Recommendation Systems, Dinh Hieu Do

Dissertations and Theses Collection (Open Access)

Traditional research in recommendation systems has largely centered on the static offline supervised learning setting. In this paradigm, all available user-item interaction data is collected and partitioned into fixed training, validation, and test sets. Models are developed and evaluated in this controlled environment, where the underlying data distribution is assumed to remain unchanged. This approach offers clear advantages: it simplifies experimentation, enables reproducible benchmarking, and allows for straightforward comparisons between algorithms.

However, this static offline setting does not reflect the realities faced by modern recommendation systems. In real-world applications, data is dynamic and ever-evolving, where new users and items are …

Why Are Fairness Concerns So Important? Lessons From A Last-Mile Transportation System, Yiwei Chen, Hai Wang

Research Collection School Of Computing and Information Systems

The Last-Mile Problem refers to the provision of travel service for passengers from the nearest public transportation node to the final destination. The Last-Mile Transportation System (LMTS), which has recently emerged, provides on-demand last-mile transportation service for passengers. We consider an LMTS that consists of two types of passengers, regular-type passengers and special-type passengers (e.g., seniors, disabled people). The valuation of the last-mile service for special-type passengers is statistically higher than the one for regular-type passengers. Passengers incur disutility from waiting for the last-mile service. In this paper, we explore two fairness constraints on special-type passengers: (1) the fare for …

Incorporating Intrinsic Structures Into Entity Matching And Representation Learning, Ween Jiann Lee

Dissertations and Theses Collection (Open Access)

The proliferation of internet-connected devices and online services has generated vast amounts of user-generated content in various formats, such as text, visual, and spatial information. Despite the potential of advanced deep learning techniques, challenges such as fragmentation, lack of cohesive structure, and the inability to capture intrinsic data structures persist, affecting data amalgamation and quality. Our research addresses these challenges by enhancing entity matching and representation learning across graph, semi-ordered, and spatial data. These advancements have significant implications for applications in transportation, recommendation systems, and urban planning.

In entity matching, we introduce Robust BiPoly-Matching and Semi-Ordered Bidirectional Poly-Matching. Matching records …

Microkarta: Visualising Microservice Architectures, Oscar Manglaras, Alex Farkas, Peter Fule, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Conceptualising and debugging a microservice architecture can be a challenge for developers due to the complex topology of inter-service communication, which may only apparent when viewing the architecture as a whole. In this paper, we present MicroKarta, a dashboard containing three types of network diagram that visualise complex microservice architectures, and that are designed to address problems faced by developers of these architectures. Initial feedback from industry developers has been positive. This dashboard can be used by developers to explore and debug microservice architectures, and can be used to compare the effectiveness of different types of network visualisation for assisting …

Superminds At Work: The Promise Of Human-Ai Collaboration, Thomas W. Malone

Asian Management Insights

Massachusetts Institute of Technology (MIT) Center for Collective Intelligence Director Professor Thomas W. Malone’s scholarship offers deep insights into the promise afforded by the synergies between human intelligence and technology. According to Professor Malone, the boundaries between human intellect and technological prowess are becoming increasingly blurred, but this may not be a bad thing for humankind. In Asian Management Insights’ inaugural Pulse Point interview, we get to learn more about the concept of ‘collective intelligence’, which explores how a partnership between humans and Artificial Intelligence (AI) can be catalysed to make ground-breaking advancements in addressing the wicked problems of our …

Transfer-Once-For-All: Ai Model Optimization For Edge, Achintya Kundu, Laura Wynter, Rhui Dih Lee, Luis Angel Bathen

Research Collection School Of Computing and Information Systems

Weight-sharing neural architecture search aims to optimize a configurable neural network model (supernet) for a variety of deployment scenarios across many devices with different resource constraints. Existing approaches use evolutionary search to extract models of different sizes from a supernet trained on a very large data set, and then fine-tune the extracted models on the typically small, real-world data set of interest. The computational cost of training thus grows linearly with the number of different model deployment scenarios. Hence, we propose Transfer-Once-For-All (TOFA) for supernet-style training on small data sets with constant computational training cost over any number of edge …

Fortifying The Seams Of Software Systems, Hong Jin Kang

Dissertations and Theses Collection (Open Access)

A seam in software is a place where two components within a software system meet. There are more seams in software now than ever before as modern software systems rely extensively on third-party software components, e.g., libraries. Due to the increasing complexity of software systems, understanding and improving the reliability of these components and their use is crucial. While the use of software components eases the development process, it also introduces challenges due to the interaction between the components.

This dissertation tackles problems associated with software reliability when using third-party software components. Developers write programs that interact with libraries through …

Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan Bose, Arunesh Sinha, Tien Mai

Research Collection School Of Computing and Information Systems

Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of fractionals, non-convex optimization which is used for decision making in prominent areas such as facility location and security games. In contrast to previous work, we find it more tractable to optimize the equivalent variance regularized form of DRO rather than the minimax form. We transform the variance regularized form to a mixed-integer second order cone program (MISOCP), which, while guaranteeing near global optimality, does not scale enough …

A Recommendation System Approach To Tune A Qubo Solver, Siong Thye Goh, Jianyuan Bo, Matthieu Parizy, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

There are two major challenges to solving constrained optimization problems using a QuadraticUnconstrained Binary Optimization or QUBO solver (QS). First, we need to tune both the underlyingproblem parameters and the algorithm parameters. Second, the solution returned from a QSmight not be feasible. While it is common to use automated tuners such as SMAC and Hyperopt totune the algorithm parameters, the initial search ranges input for the auto tuner affect the performanceof the QS. In this paper, we propose a framework that resembles the Algorithm Selection(AS) framework to tune algorithm parameters for an annealing-based QS. To cope with constraints,we focus on …

Neural Network Based Scene Text Recognition [Us Patent Us 2022/0237403 A1], Pan Zhou, Peng Tang, Ran Xu, Steven Chu Hong Hoi

Research Collection School Of Computing and Information Systems

A system uses a neural network based model to perform scene text recognition. The system achieves high accuracy of prediction of text from scenes based on a neural network architecture that uses double attention mechanism. The neural network based model includes a convolutional neural network component that outputs a set of visual features and an attention extractor neural network component that determines attention scores based on the visual features. The visual features and the attention scores are combined to generate mixed features that are provided as input to a character recognizer component that determines a second attention score and recognizes …

Neural Architecture Search Of Spd Manifold Networks, R.S. Sukthanker, Zhiwu Huang, S. Kumar, E. G. Endsjo, Y. Wu, Gool L. Van

Research Collection School Of Computing and Information Systems

In this paper, we propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks, aiming to automate the design of SPD neural architectures. To address this problem, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design. Further, we model our new NAS problem with a one-shot training process of a single supernet. Based on the supernet modeling, we exploit a differentiable NAS algorithm on our relaxed continuous search space for SPD neural architecture search. Statistical evaluation of our method on drone, action, and emotion recognition …

Neural Architecture Search As Sparse Supernet, Y. Wu, A. Liu, Zhiwu Huang, S. Zhang, Gool L. Van

Research Collection School Of Computing and Information Systems

This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the NAS problem as a sparse supernet using a new continuous architecture representation with a mixture of sparsity constraints. The sparse supernet enables us to automatically achieve sparsely-mixed paths upon a compact set of nodes. To optimize the proposed sparse supernet, we exploit a hierarchical accelerated proximal gradient algorithm within a bi-level optimization framework. Extensive experiments on Convolutional Neural Network and Recurrent Neural Network search demonstrate that the proposed method is capable of searching for …

Theory-Inspired Path-Regularized Differential Network Architecture Search, Pan Zhou, Caiming Xiong, Richard Socher, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Despite its high search efficiency, differential architecture search (DARTS) often selects network architectures with dominated skip connections which lead to performance degradation. However, theoretical understandings on this issue remain absent, hindering the development of more advanced methods in a principled way. In this work, we solve this problem by theoretically analyzing the effects of various types of operations, e.g. convolution, skip connection and zero operation, to the network optimization. We prove that the architectures with more skip connections can converge faster than the other candidates, and thus are selected by DARTS. This result, for the first time, theoretically and explicitly …

Tpr: Text-Aware Preference Ranking For Recommender Systems, Yu-Neng Chuang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yuan Fang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Textual data is common and informative auxiliary information for recommender systems. Most prior art utilizes text for rating prediction, but rare work connects it to top-N recommendation. Moreover, although advanced recommendation models capable of incorporating auxiliary information have been developed, none of these are specifically designed to model textual information, yielding a limited usage scenario for typical user-to-item recommendation. In this work, we present a framework of text-aware preference ranking (TPR) for top-N recommendation, in which we comprehensively model the joint association of user-item interaction and relations between items and associated text. Using the TPR framework, we construct a joint …

Off-Policy Reinforcement Learning For Efficient And Effective Gan Architecture Search, Tian Yuan, Wang Qin, Zhiwu Huang, Wen Li, Dengxin Dai, Minghao Yang, Jun Wang, Olga Fink

Research Collection School Of Computing and Information Systems

In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search. The key idea is to formulate the GAN architecture search problem as a Markov decision process (MDP) for smoother architecture sampling, which enables a more effective RL-based search algorithm by targeting the potential global optimal architecture. To improve efficiency, we exploit an off-policy GAN architecture search algorithm that makes efficient use of the samples generated by previous policies. Evaluation on two standard benchmark datasets (i.e., CIFAR-10 and STL-10) demonstrates that the proposed method is …

Scalable Multi-Agent Reinforcement Learning For Aggregation Systems, Tanvi Verma

Dissertations and Theses Collection (Open Access)

Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc. for matching restaurants to customers. In these systems, a centralized entity (e.g., Uber) aggregates supply and assigns them to demand so as to optimize a central metric such as profit, number of requests, delay etc. However, individuals (e.g., drivers, delivery boys) in the system are self interested and they try to maximize their own long term profit. The central entity has the full view of the system and …

Online Spatio - Temporal Demand Supply Matching, Meghna Lowalekar

Dissertations and Theses Collection (Open Access)

The rapid growth of cities in developing world coupled with the increase in rural to urban migration have led to cities being identified as the key actor for any nation's economy. Shared mobility has become an integral part of life of people in cities as it improves efficiency and enhances transportation accessibility. As a result, the mismatch between the demand and supply of shared mobility resources has a direct impact on people's life. Thus, the goal of my dissertation is to develop solution strategies for these real-time (online) spatio-temporal demand supply matching problems for shared mobility resources which can enhance …

Automated Synthesis Of Local Time Requirement For Service Composition, Étienne André, Tian Huat Tan, Manman Chen, Shuang Liu, Jun Sun, Yang Liu, Jin Song Dong

Research Collection School Of Computing and Information Systems

Service composition aims at achieving a business goal by composing existing service-based applications or components. The response time of a service is crucial, especially in time-critical business environments, which is often stated as a clause in service-level agreements between service providers and service users. To meet the guaranteed response time requirement of a composite service, it is important to select a feasible set of component services such that their response time will collectively satisfy the response time requirement of the composite service. In this work, we use the BPEL modeling language that aims at specifying Web services. We extend it …

Digitalization In Practice: The Fifth Discipline Advantage, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

Purpose The purpose of this paper is to provide advice to organizations on how to become successful in the digital age. The paper revisits Peter Senge's (1990) notion of the learning organization and discusses the relevance of systems thinking and the other four disciplines, namely, personal mastery, mental models, shared vision and team learning in the context of the current digitalization megatrend. Design/methodology/approach This paper is based on content analysis of essays from international organizations, strategy experts and management scholars, and insights gained from the author's consulting experience. A comparative case study from the health and social sector is also …

Supporting Software Architecture Maintenance By Providing Task-Specific Recommendations, Matthias Galster, Christoph Treude, Kelly Blincoe

Research Collection School Of Computing and Information Systems

During software maintenance, developers have different information needs (e.g., to understand what type of maintenance activity to perform, the impact of a maintenance activity and its effort). However, information to support developers may be distributed across various sources. Furthermore, information captured in formal architecture documentation may be outdated. In this paper, we put forward a late breaking idea and outline a solution to improve the productivity of developers by providing task-specific recommendations based on concrete information needs that arise during software maintenance.

Trust Architecture And Reputation Evaluation For Internet Of Things, Juan Chen, Zhihong Tian, Xiang Cui, Lihua Yin, Xianzhi Wang

Research Collection School Of Computing and Information Systems

Internet of Things (IoT) represents a fundamental infrastructure and set of techniques that support innovative services in various application domains. Trust management plays an important role in enabling the reliable data collection and mining, context-awareness, and enhanced user security in the IoT. The main tasks of trust management include trust architecture design and reputation evaluation. However, existing trust architectures and reputation evaluation solutions cannot be directly applied to the IoT, due to the large number of physical entities, the limited computation ability of physical entities, and the highly dynamic nature of the network. In comparison, it generally requires a general …

Sensitive Behavior Analysis Of Android Applications On Unrooted Devices In The Wild, Xiaoxiao Tang

Dissertations and Theses Collection (Open Access)

Dynamic analysis is widely used in malware detection, taint analysis, vulnerability detection, and other areas for enhancing the security of Android. Compared to static analysis, dynamic analysis is immune to common code obfuscation techniques and dynamic code loading. Existing dynamic analysis techniques rely on in-lab running environment (e.g., modified systems, rooted devices, or emulators) and require automatic input generators to execute the target app. However, these techniques could be bypassed by anti-analysis techniques that allow apps to hide sensitive behavior when an in-lab environment is detected through predefined heuristics (e.g., IMEI number of the device is invalid). Meanwhile, current input …

Automatic, Highly Accurate App Permission Recommendation, Zhongxin Liu, Xin Xia, David Lo, John Grundy

Research Collection School Of Computing and Information Systems

To ensure security and privacy, Android employs a permission mechanism which requires developers to explicitly declare the permissions needed by their applications (apps). Users must grant those permissions before they install apps or during runtime. This mechanism protects users’ private data, but also imposes additional requirements on developers. For permission declaration, developers need knowledge about what permissions are necessary to implement various features of their apps, which is difficult to acquire due to the incompleteness of Android documentation. To address this problem, we present a novel permission recommendation system named PerRec for Android apps. PerRec leverages mining-based techniques and data …

Driving And Effective Data-Ready Culture: How Companies Can Take On A Datadriven Approach To 11 Business, Johnson Poh

MITB Thought Leadership Series

TECHNOLOGY has turned the tables in favour of consumers, enabling them to find goods and services faster and access more choices. Companies now compete more intensely to capture consumers’ mindshare and scour for ways to keep their products relevant. But every coin has two sides. While technology has empowered consumers with choice, it has also offered companies a plethora of data to understand consumers better. This puts the odds in favour of companies that can leverage on data to gain consumer insights and meet their business objectives.

Empathetic Computing For Inclusive Application Design, Kenny Choo Tsu Wei

Dissertations and Theses Collection (Open Access)

The explosive growth of the ecosystem of personal and ambient computing de- vices coupled with the proliferation of high-speed connectivity has enabled ex- tremely powerful and varied mobile computing applications that are used every- where. While such applications have tremendous potential to improve the lives of impaired users, most mobile applications have impoverished designs to be inclusive– lacking support for users with specific disabilities. Mobile app designers today haveinadequate support to design existing classes of apps to support users with specific disabilities, and more so, lack the support to design apps that specifically target these users. One way to resolve …

Exploring Experiential Learning Model And Risk Management Process For An Undergraduate Software Architecture Course, Eng Lieh Ouh, Yunghans Irawan

Research Collection School Of Computing and Information Systems

This paper shares our insights on exploring theexperiential learning model and risk management process todesign an undergraduate software architecture course. The keychallenge for undergraduate students to appreciate softwarearchitecture design is usually their limited experience in thesoftware industry. In software architecture, the high-level designprinciples are heuristics lacking the absoluteness of firstprinciples which for inexperienced undergraduate students, thisis a frustrating divergence from what they used to value. From aneducator's perspective, teaching software architecture requirescontending with the problem of how to express this level ofabstraction practically and also make the learning realistic. Inthis paper, we propose a model adapting the concepts ofexperiential learning …

Teaching Adult Learners On Software Architecture Design Skills, Eng Lieh Ouh, Yunghans Irawan

Research Collection School Of Computing and Information Systems

Software architectures present high-level views ofsystems, enabling developers to abstract away the unnecessarydetails and focus on the overall big picture. Designing a softwarearchitecture is an essential skill in software engineering and adultlearners are seeking this skill to further progress in their career.With the technology revolution and advancements in this rapidlychanging world, the proportion of adult learners attendingcourses for continuing education are increasing. Their learningobjectives are no longer to obtain good grades but the practicalskills to enable them to perform better in their work and advancein their career. Teaching software architecture to upskill theseadult learners requires contending with the problem of …

Proactive And Reactive Resource/Task Allocation For Agent Teams In Uncertain Environments, Pritee Agrawal

Dissertations and Theses Collection (Open Access)

Synergistic interactions between task/resource allocation and multi-agent coordinated planning/assignment exist in many problem domains such as trans- portation and logistics, disaster rescue, security patrolling, sensor networks, power distribution networks, etc. These domains often feature dynamic environments where allocations of tasks/resources may have complex dependencies and agents may leave the team due to unforeseen conditions (e.g., emergency, accident or violation, damage to agent, reconfiguration of environment).

Fimce: A Fully Isolated Micro-Computing Environment For Multicore Systems, Siqi Zhao, Xuhua Ding

Research Collection School Of Computing and Information Systems

Virtualization-based memory isolation has been widely used as a security primitive in various security systems to counter kernel-level attacks. In this article, our in-depth analysis on this primitive shows that its security is significantly undermined in the multicore setting when other hardware resources for computing are not enclosed within the isolation boundary. We thus propose to construct a fully isolated micro-computing environment (FIMCE) as a new primitive. By virtue of its architectural niche, FIMCE not only offers stronger security assurance than its predecessor, but also features a flexible and composable environment with support for peripheral device isolation, thus greatly expanding …

Neural Correlates Of States Of User Experience In Gaming Using Eeg And Predictive Analytics, Chandana Mallapragada, Fiona Fui-Hoon Nah, Keng Siau, Langtao Chen, Tejaswini Yelamanchili

Research Collection School Of Computing and Information Systems

In this research, we will analyze EEG signals to obtain neural correlate classifications of user experience by applying predictive analytics. Boredom, flow, and anxiety are three states experienced by users interacting with a computer-based system. A within-subjects experiment was used to collect EEG data for these three states and a baseline. We will apply predictive analytics including linear regression, support vector machine, and neural networks to analyze and classify the EEG data for these three states of user experience.