Computer Sciences Commons^™

Evaluating Performance Of Openmp Tasks In A Seismic Stencil Application, Eric Raut, Jie Meng, Mauricio Araya-Polo, Barbara Chapman

Department of Applied Mathematics & Statistics Faculty Publications

Simulations based on stencil computations (widely used in geosciences) have been dominated by the MPI+OpenMP programming model paradigm. Little effort has been devoted to experimenting with task-based parallelism in this context. We address this by introducing OpenMP task parallelism into the kernel of an industrial seismic modeling code, Minimod. We observe that even for these highly regular stencil computations, taskified kernels are competitive with traditional OpenMP-augmented loops, and in some experiments tasks even outperform loop parallelism.

This promising result sets the stage for more complex computational patterns. Simulations involve more than just the stencil calculation: a collection of kernels is …

Resveratrol And Tumor Microenvironment: Mechanistic Basis And Therapeutic Targets, Wamidh H. Talib, Ahmad Riyad Alsayed, Faten Farhan, Lina T. Al Kury

All Works

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Resveratrol (3,40,5 trihydroxystilbene) is a naturally occurring non-flavonoid polyphenol. It has various pharmacological effects including antioxidant, anti-diabetic, anti-inflammatory and anti-cancer. Many studies have given special attention to different aspects of resveratrol anti-cancer properties and proved its high efficiency in targeting multiple cancer hallmarks. Tumor microenvironment has a critical role in cancer development and progression. Tumor cells coordinate with a cast of normal cells to aid the malignant behavior of cancer. Many cancer supporting players were detected in tumor microenvironment. These players include blood and lymphatic vessels, infiltrating immune cells, stromal fibroblasts …

Rethinking Mistake In The Age Of Algorithms: Quoine Pte Ltd V B2c2 Ltd, Vincent Ooi, Kian Peng Soh

Research Collection Yong Pung How School Of Law

Good traders remove emotion from the decision-making process. Automated trading algorithms have enabled this, allowing one to trade round the clock, and without the constant need to monitor one’s investments. But software has gremlins. Given the vast amounts of money involved in such trades, it was only a matter of time before disputes involving automated trading software came before the courts. The decision in Quoine v B2C2 (“Quoine”) represents the first time an apex court in the Commonwealth has ruled on the applicability of contractual principles to situations involving automated trading software.

Coronavirus: Pandemics, Artificial Intelligence And Personal Data: How To Manage Pandemics Using Ai And What That Means For Personal Data Protection, Warren B. Chik

Research Collection Yong Pung How School Of Law

This chapter discusses the hearing of essential and urgent court matters in the Singapore courts during the COVID-19 pandemic. On 27 march 2020, the Singapore judiciary notified courst users that remote hearings were to be implemented for certain types of hearings by means of video and telephone conferencing facilities. Court users were also provided with indicative lists of matters which might be considered essential and urgent.

Pine: Enabling Privacy-Preserving Deep Packet Inspection On Tls With Rule-Hiding And Fast Connection Establishment, Jianting Ning, Xinyi Huang, Geong Sen Poh, Shengmin Xu, Jia-Chng Loh, Jain Weng, Robert H. Deng

Research Collection School Of Computing and Information Systems

Transport Layer Security Inspection (TLSI) enables enterprises to decrypt, inspect and then re-encrypt users’ traffic before it is routed to the destination. This breaks the end-to-end security guarantee of the TLS specification and implementation. It also raises privacy concerns since users’ traffic is now known by the enterprises, and third-party middlebox providers providing the inspection services may additionally learn the inspection or attack rules, policies of the enterprises. Two recent works, BlindBox (SIGCOMM 2015) and PrivDPI (CCS 2019) propose privacy-preserving approaches that inspect encrypted traffic directly to address the privacy concern of users’ traffic. However, BlindBox incurs high preprocessing overhead …

London Heathrow Airport Uses Real-Time Analytics For Improving Operations, Xiaojia Guo, Yael Grushka-Cockayne, Bert De Reyck

Research Collection Lee Kong Chian School Of Business

Improving airport collaborative decision making is at the heart of airport operations centers (APOCs) recently established in several major European airports. In this paper, we describe a project commissioned by Eurocontrol, the organization in charge of the safety and seamless flow of European air traffic. The project’s goal was to examine the opportunities offered by the colocation and real-time data sharing in the APOC at London’s Heathrow airport, arguably the most advanced of its type in Europe. We developed and implemented a pilot study of a real-time data-sharing and collaborative decision-making process, selected to improve the efficiency of Heathrow’s operations. …

Is The Transit Industry Prepared For The Cyber Revolution? Policy Recommendations To Enhance Surface Transit Cyber Preparedness, Scott Belcher, Terri Belcher, Eric Greenwald, Brandon Thomas

Mineta Transportation Institute

The intent of this study is to assess the readiness, resourcing, and structure of public transit agencies to identify, protect from, detect, respond to, and recover from cybersecurity vulnerabilities and threats. Given the multitude of connected devices already in use by the transit industry and the vast amount of data generated (with more coming online soon), the transit industry is vulnerable to malicious cyber-attack and other cybersecurity-related threats. This study reviews the state of best cybersecurity practices in public surface transit; outlines U.S. public surface transit operators’ cybersecurity operations; assesses U.S. policy on cybersecurity in public surface transportation; and provides …

Hierarchical Multimodal Attention For End-To-End Audio-Visual Scene-Aware Dialogue Response Generation, Hung Le, Doyen Sahoo, Nancy F. Chen, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

This work is extended from our participation in the Dialogue System Technology Challenge (DSTC7), where we participated in the Audio Visual Scene-aware Dialogue System (AVSD) track. The AVSD track evaluates how dialogue systems understand video scenes and responds to users about the video visual and audio content. We propose a hierarchical attention approach on user queries, video caption, audio and visual features that contribute to improved evaluation results. We also apply a nonlinear feature fusion approach to combine the visual and audio features for better knowledge representation. Our proposed model shows superior performance in terms of both objective evaluation and …

Querying Recurrent Convoys Over Trajectory Data, Munkh-Erdene Yadamjav, Zhifeng Bao, Baihua Zheng, Farhana M. Choudhury, Hanan Samet

Research Collection School Of Computing and Information Systems

Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. Existing studies on mining co-moving objects do not consider an important correlation between co-moving objects, which is the reoccurrence of the movement pattern. In this study, we define a problem of finding recurrent pattern of co-moving objects from streaming trajectories and propose an efficient solution that enables us to discover recent co-moving object patterns repeated within a given time period. Experimental results on …

Privacy-Preserving Outsourced Calculation Toolkit In The Cloud, Ximeng Liu, Robert H. Deng, Kim-Kwang Raymond Choo, Yang Yang, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

In this paper, we propose a privacy-preserving outsourced calculation toolkit, Pockit, designed to allow data owners to securely outsource their data to the cloud for storage. The outsourced encrypted data can be processed by the cloud server to achieve commonly-used plaintext arithmetic operations without involving additional servers. Specifically, we design both signed and unsigned integer circuits using a fully homomorphic encryption (FHE) scheme, construct a new packing technique (hereafter referred to as integer packing), and extend the secure circuits to its packed version. This achieves significant improvements in performance compared with the original secure signed/unsigned integer circuit. The secure integer …

Urban Scale Trade Area Characterization For Commercial Districts With Cellular Footprints, Yi Zhao, Zimu Zhou, Xu Wang, Tongtong Liu, Zheng Yang

Research Collection School Of Computing and Information Systems

Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantify where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies, because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this article, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. We show that compared to traditional cellular data …

Persona Perception Scale: Development And Exploratory Validation Of An Instrument For Evaluating Individuals' Perceptions Of Personas, Joni Salminen, Joao M. Santos, Haewoon Kwak, Jisun An, Soon-Gyo Jung

Research Collection School Of Computing and Information Systems

Although used in many domains, the evaluation of personas is difficult due to the lack of validated measurement instruments. To tackle this challenge, we propose the Persona Perception Scale (PPS), a survey instrument for evaluating how individuals perceive personas. We develop the scale by reviewing relevant literature from social psychology, persona studies, and Human-Computer Interaction to find relevant constructs and items for measuring persona perceptions. Following initial pilot testing, we conduct an exploratory validation of the scale with 412 respondents and find that the constructs and items of the scale perform satisfactorily for deployment. The research has implications for both …

A Genetic Algorithm To Minimise Number Of Vehicles In An Electric Vehicle Routing Problem, Kiian Leong Bertran Queck, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Electric Vehicles (EVs) and charging infrastructure are starting to become commonplace in major cities around the world. For logistics providers to adopt an EV fleet, there are many factors up for consideration, such as route planning for EVs with limited travel range as well as long-term planning of fleet size. In this paper, we present a genetic algorithm to perform route planning that minimises the number of vehicles required. Specifically, we discuss the challenges on the violations of constraints in the EV routing problem (EVRP) arising from applying genetic algorithm operators. To overcome the challenges, techniques specific to addressing the …

Smart Contract Repair, Xiao Liang Yu, Omar Al-Bataineh, David Lo, Abhik Roychoudhury

Research Collection School Of Computing and Information Systems

Smart contracts are automated or self-enforcing contracts that can be used to exchange assets without having to place trust in third parties. Many commercial transactions use smart contracts due to their potential benefits in terms of secure peer-to-peer transactions independent of external parties. Experience shows that many commonly used smart contracts are vulnerable to serious malicious attacks, which may enable attackers to steal valuable assets of involving parties. There is, therefore, a need to apply analysis and automated repair techniques to detect and repair bugs in smart contracts before being deployed. In this work, we present the first general-purpose automated …

An Empirical Study Of The Dependency Networks Of Deep Learning Libraries, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia

Research Collection School Of Computing and Information Systems

Deep Learning techniques have been prevalent in various domains, and more and more open source projects in GitHub rely on deep learning libraries to implement their algorithms. To that end, they should always keep pace with the latest versions of deep learning libraries to make the best use of deep learning libraries. Aptly managing the versions of deep learning libraries can help projects avoid crashes or security issues caused by deep learning libraries. Unfortunately, very few studies have been done on the dependency networks of deep learning libraries. In this paper, we take the first step to perform an exploratory …

Time-Warped Sparse Non-Negative Factorization For Functional Data Analysis, Chen Zhang, Steven C. H. Hoi, Fugee Tsung

Research Collection School Of Computing and Information Systems

This article proposes a novel time-warped sparse non-negative factorization method for functional data analysis. The proposed method on the one hand guarantees the extracted basis functions and their coefficients to be positive and interpretable, and on the other hand is able to handle weakly correlated functions with different features. Furthermore, the method incorporates time warping into factorization and hence allows the extracted basis functions of different samples to have temporal deformations. An efficient framework of estimation algorithms is proposed based on a greedy variable selection approach. Numerical studies together with case studies on real-world data demonstrate the efficacy and applicability …

Group Instance: Flexible Co-Location Resistant Virtual Machine Placement In Iaas Clouds, Vu Duc Long, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

This paper proposes and analyzes a new virtual machine (VM) placement technique called Group Instance to deal with co-location attacks in public Infrastructure-as-a-Service (IaaS) clouds. Specifically, Group Instance organizes cloud users into groups with pre-determined sizes set by the cloud provider. Our empirical results obtained via experiments with real-world data sets containing million of VM requests have demonstrated the effectiveness of the new technique. In particular, the advantages of Group Instance are three-fold: 1) it is simple and highly configurable to suit the financial and security needs of cloud providers, 2) it produces better or at least similar performance compared …

Towards Generating Thread-Safe Classes Automatically, Haichi Wang, Zan Wang, Jun Sun, Shuang Lin, Ayesha Sadiq, Yuan Fang Li

Research Collection School Of Computing and Information Systems

The existing concurrency model for Java (or C) requires programmers to design and implement thread-safe classes by explicitly acquiring locks and releasing locks. Such a model is error-prone and is the reason for many concurrency bugs. While there are alternative models like transactional memory, manually writing locks remains prevalent in practice. In this work, we propose AutoLock, which aims to solve the problem by fully automatically generating thread-safe classes. Given a class which is assumed to be correct with sequential clients, AutoLock automatically generates a thread-safe class which is linearizable, and does it in a way without requiring a specification …

Towards Interpreting Recurrent Neural Networks Through Probabilistic Abstraction, Guoliang Dong, Jingyi Wang, Jun Sun, Yang Zhang, Xinyu Wang, Ting Dai, Jin Song Dong, Xingen Wang

Research Collection School Of Computing and Information Systems

Neural networks are becoming a popular tool for solving many realworld problems such as object recognition and machine translation, thanks to its exceptional performance as an end-to-end solution. However, neural networks are complex black-box models, which hinders humans from interpreting and consequently trusting them in making critical decisions. Towards interpreting neural networks, several approaches have been proposed to extract simple deterministic models from neural networks. The results are not encouraging (e.g., low accuracy and limited scalability), fundamentally due to the limited expressiveness of such simple models.In this work, we propose an approach to extract probabilistic automata for interpreting an important …

Detectif: Unified Detection And Correction Of Iot Faults In Smart Homes, Madhumita Maliick, Archan Misra, Niloy Ganguly, Youngki Lee

Research Collection School Of Computing and Information Systems

This paper tackles the problem of detecting a comprehensive set of sensor faults that can occur in IoT-instrumented smart homes customized to infer Activities of Daily Living (ADL) from the activation of sensor sets. Specifically, sensors can suffer faults that (a) span durations that vary between several seconds to hours, (b) can result in both missing or false-alarm sensor-events. Previous fault detection approaches are geared primarily to identify missing faults (absence of sensor readings) of a permanent (very long-lived) nature, or sporadic false-alarm events. We propose DetectIF, a fault-detection framework that detects faults of varying time duration, and identifies both …

Research Directions For Sharing Economy Issues, Robert J. Kauffman, Maurizio Naldi

Research Collection School Of Computing and Information Systems

The sharing economy proposes a new approach to designing and delivering products and services, that aims at avoiding waste, improving efficiency, and favoring bottom-up change. In this research commentary, we survey the current state of things and propose some directions for research. We first describe the industries, products, and services currently representing the sharing paradigm, the technology platforms enabling it, the business models driving it, and the regulatory issues. We envisage that promising areas of research should include: (1) devising more efficient algorithms; (2) considering ecological and prosocial objective functions; (3) dealing with regulatory issues; (4) expanding the span of …

Deepstyle: User Style Embedding For Authorship Attribution Of Short Texts, Zhiqiang Hu, Roy Ka-Wei Lee, Lei Wang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Authorship attribution (AA), which is the task of finding the owner of a given text, is an important and widely studied research topic with many applications. Recent works have shown that deep learning methods could achieve significant accuracy improvement for the AA task. Nevertheless, most of these proposed methods represent user posts using a single type of features (e.g., word bi-grams) and adopt a text classification approach to address the task. Furthermore, these methods offer very limited explainability of the AA results. In this paper, we address these limitations by proposing DeepStyle, a novel embedding-based framework that learns the representations …

Vehicle Routing Problem With Reverse Cross-Docking: An Adaptive Large Neighborhood Search Algorithm, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu

Research Collection School Of Computing and Information Systems

Cross-docking is a logistics strategy that aims at less transportation costs and fast customer deliveries. Incorporating an efficient vehicle routing could increase the benefits of the cross-docking. In this paper, the vehicle routing problem with reverse cross-docking (VRP-RCD) is studied. Reverse logistics has attracted more attention due to its ability to gain more profit and maintain the competitiveness of a company. VRP-RCD includes a four-level supply chain network: suppliers, cross-dock, customers, and outlets, with the objective of minimizing vehicle operational and transportation costs. A two-phase heuristic that employs an adaptive large neighborhood search (ALNS) with various destroy and repair operators …

A Lattice-Based Key-Insulated And Privacy-Preserving Signature Scheme With Publicly Derived Public Key, Wenling Liu, Zhen Liu, Khoa Nguyen, Guomin Yang, Yu Yu

Research Collection School Of Computing and Information Systems

As a widely used privacy-preserving technique for cryptocurrencies, Stealth Address constitutes a key component of Ring Confidential Transaction (RingCT) protocol and it was adopted by Monero, one of the most popular privacy-centric cryptocurrencies. Recently, Liu et al. [EuroS&P 2019] pointed out a flaw in the current widely used stealth address algorithm that once a derived secret key is compromised, the damage will spread to the corresponding master secret key, and all the derived secret keys thereof. To address this issue, Liu et al. introduced Key-Insulated and Privacy-Preserving Signature Scheme with Publicly Derived Public Key (PDPKS scheme), which captures the functionality, …

Weakly Paired Multi-Domain Image Translation, M.Y. Zhang, Zhiwu Huang, D.P. Paudel, J. Thoma, Gool L. Van

Research Collection School Of Computing and Information Systems

In this paper, we aim at studying the new problem of weakly paired multi-domain image translation. To this end, we collect a dataset that contains weakly paired images from multiple domains. Two images are considered to be weakly paired if they are captured from nearby locations and share an overlapping field of view. These images are possibly captured by two asynchronous cameras—often resulting in images from separate domains, e.g. summer and winter. Major motivations for using weakly paired images are: (i) performance improvement towards that of paired data; (ii) cheap labels and abundant data availability. For the first time in …

A Performance-Sensitive Malware Detection System Using Deep Learning On Mobile Devices, Ruitao Feng, Sen Chen, Xiaofei Xie, Guozhu Meng, Shang-Wei Lin, Yang Liu

Research Collection School Of Computing and Information Systems

Currently, Android malware detection is mostly performed on server side against the increasing number of malware. Powerful computing resource provides more exhaustive protection for app markets than maintaining detection by a single user. However, apart from the applications (apps) provided by the official market (i.e., Google Play Store), apps from unofficial markets and third-party resources are always causing serious security threats to end-users. Meanwhile, it is a time-consuming task if the app is downloaded first and then uploaded to the server side for detection, because the network transmission has a lot of overhead. In addition, the uploading process also suffers …

Human-Like Summaries From Heterogeneous And Time-Windowed Software Development Artefacts, Mahfouth Alghamdi, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Automatic text summarisation has drawn considerable interest in the area of software engineering. It is challenging to summarise the activities related to a software project, (1) because of the volume and heterogeneity of involved software artefacts, and (2) because it is unclear what information a developer seeks in such a multi-document summary. We present the first framework for summarising multi-document software artefacts containing heterogeneous data within a given time frame. To produce human-like summaries, we employ a range of iterative heuristics to minimise the cosine-similarity between texts and high-dimensional feature vectors. A first study shows that users find the automatically …

Simulation Of Physical Field Of 300ka Prebaked Aluminum Reduction Cell, Shuiping Zeng, Shasha Wang, Rongjuan Wang

Journal of System Simulation

Abstract: The SY300 Prebake cell of some factory as a basic simplified model, the solidworks software was used to build a three-dimensional physical model of aluminum reduction cell. In the use of some assumptions, reasonable ideal field segmentation and under the premise of sharing model information and results, the physical model of electric field, magnetic field, flow field and its calculation module of coupling field were established respectively by using the the finite element software Comsol and Matlab Multiphysics software. On Comsol platform, electric, magnetic field of aluminum electrolytic cell were solved when replacing the anode, and current density …

Low Complexity Method Based On Fc-Ce-Slm For Papr Reduction In Ofdm System, Ce Ji, Lijuan Wang

Journal of System Simulation

Abstract: Selected mapping (SLM) algorithm uses Cross Entropy (CE) algorithm to get the most optimal sign sequence so that SLM can make the effect of PAPR Reduction achieve optimum. However, it needs too many times iterative computations, so it will increase the complexity of the algorithm. In order to solve this problem, a fast convergence factor in the CE-SLM algorithm was introduced to make the sampling probability converge to 0 or 1 quickly in the system, which reduced the iterative times of getting the optimal sign sequence. Computer simulation results show that the approved algorithm reduces the iterative times of …

Research On Parallel Simulation Of Hypersonic Vehicles, Xuegong Sun, Chunye Gong

Journal of System Simulation

Abstract: The system simulation of hypersonic vehicles was a multi-physics coupling process. It needed many iterations with large scale data to finish the simulation. To fix up the computational challenges, the distributed simulation was developed. But distributed simulation had no obvious effect on the computational efficiency. In order to enhance the computational efficiency, the grid parallel method and model parallel algorithm were studied based on the high performance cluster system. The parallel simulation method for fluid dynamics, data exchange method for partition boundary and load balance technology for parallel computing were developed and applied to the system simulation of hypersonic …