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Articles 1561 - 1590 of 7454

Full-Text Articles in Physical Sciences and Mathematics

Design Of A Two-Echelon Freight Distribution System In Last-Mile Logistics Considering Covering Locations And Occasional Drivers, Vincent F. Yu, Panca Jodiawan, Ming-Lu Hou, Aldy Gunawan Oct 2021

Design Of A Two-Echelon Freight Distribution System In Last-Mile Logistics Considering Covering Locations And Occasional Drivers, Vincent F. Yu, Panca Jodiawan, Ming-Lu Hou, Aldy Gunawan

Research Collection School Of Computing and Information Systems

This research addresses a new variant of the vehicle routing problem, called the two-echelon vehicle routing problem with time windows, covering options, and occasional drivers (2E-VRPTW-CO-OD). In this problem, two types of fleets are available to serve customers, city freighters and occasional drivers (ODs), while two delivery options are available to customers, home delivery and alternative delivery. For customers choosing the alternative delivery, their demands are delivered to one of the available covering locations for them to pick up. The objective of 2E-VRPTW-CO-OD is to minimize the total cost consisting of routing costs, connection costs, and compensations paid to ODs …

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Towards Source-Aligned Variational Models For Cross-Domain Recommendation, Aghiles Salah, Thanh-Binh Tran, Hady W. Lauw Oct 2021

Towards Source-Aligned Variational Models For Cross-Domain Recommendation, Aghiles Salah, Thanh-Binh Tran, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Data sparsity is a long-standing challenge in recommender systems. Among existing approaches to alleviate this problem, cross-domain recommendation consists in leveraging knowledge from a source domain or category (e.g., Movies) to improve item recommendation in a target domain (e.g., Books). In this work, we advocate a probabilistic approach to cross-domain recommendation and rely on variational autoencoders (VAEs) as our latent variable models. More precisely, we assume that we have access to a VAE trained on the source domain that we seek to leverage to improve preference modeling in the target domain. To this end, we propose a model which learns …

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Countering Attacker Data Manipulation In Security Games, Andrew R. Butler, Thanh H. Nguyen, Arunesh Sinha Oct 2021

Countering Attacker Data Manipulation In Security Games, Andrew R. Butler, Thanh H. Nguyen, Arunesh Sinha

Research Collection School Of Computing and Information Systems

. Defending against attackers with unknown behavior is an important area of research in security games. A well-established approach is to utilize historical attack data to create a behavioral model of the attacker. However, this presents a vulnerability: a clever attacker may change its own behavior during learning, leading to an inaccurate model and ineffective defender strategies. In this paper, we investigate how a wary defender can defend against such deceptive attacker. We provide four main contributions. First, we develop a new technique to estimate attacker true behavior despite data manipulation by the clever adversary. Second, we extend this technique …

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Links Do Matter: Understanding The Drivers Of Developer Interactions In Software Ecosystems, Subhajit Datta, Amrita Bhattacharjee, Subhashis Majumder Oct 2021

Links Do Matter: Understanding The Drivers Of Developer Interactions In Software Ecosystems, Subhajit Datta, Amrita Bhattacharjee, Subhashis Majumder

Research Collection School Of Computing and Information Systems

Studies of collaborating individuals engaged in collective enterprises usually focus on the individuals, rather than the links supporting their interaction. Accordingly, large scale software development ecosystems have also been examined primarily in terms of developer engagement. We posit that communication links between developers play a central role in the sustenance and effectiveness of such ecosystems. In this paper, we investigate whether and how developer attributes relate to the importance of the communication channels between them. We present a technique using 2nd order Markov models to extract features of interest of the links and apply the technique on data from a …

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Aixfood'21: 3rd Workshop On Aixfood, Ricardo Guerrero, Michael Spranger, Shuqiang Jiang, Chong-Wah Ngo Oct 2021

Aixfood'21: 3rd Workshop On Aixfood, Ricardo Guerrero, Michael Spranger, Shuqiang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Food and cooking analysis present exciting research and application challenges for modern AI systems, particularly in the context of multimodal data such as images or video. A meal that appears in a food image is a product of a complex progression of cooking stages, often described in the accompanying textual recipe form. In the cooking process, individual ingredients change their physical properties, become combined with other food components, all to produce a final, yet highly variable, appearance of the meal. Recognizing food items or meals on a plate from images or videos, their physical properties such as the amount, nutritional …

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Mining Informal & Short Student Self-Reflections For Detecting Challenging Topics: A Learning Outcomes Insight Dashboard, De Lin Ong, Gottipati Swapna, Siaw Ling Lo, Venky Shankararaman Oct 2021

Mining Informal & Short Student Self-Reflections For Detecting Challenging Topics: A Learning Outcomes Insight Dashboard, De Lin Ong, Gottipati Swapna, Siaw Ling Lo, Venky Shankararaman

Research Collection School Of Computing and Information Systems

Having students write short self-reflections at the end of each weekly session enables them to reflect on what they have learnt in the session and topics they find challenging. Analysing these self-reflections provides instructors with insights on how to address the missing conceptions and misconceptions of the students and appropriately plan and deliver the next session. Currently, manual methods adopted to analyse these student reflections are time consuming and tedious. This paper proposes a solution model that uses content mining and NLP techniques to automate the analysis of short self-reflections. We evaluate the solution model by studying its implementation in …

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From Continuity To Editability: Inverting Gans With Consecutive Images, Yangyang Xu, Yong Du, Wenpeng Xiao, Xuemiao Xu, Shengfeng He Oct 2021

From Continuity To Editability: Inverting Gans With Consecutive Images, Yangyang Xu, Yong Du, Wenpeng Xiao, Xuemiao Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

Existing GAN inversion methods are stuck in a paradox that the inverted codes can either achieve high-fidelity reconstruction, or retain the editing capability. Having only one of them clearly cannot realize real image editing. In this paper, we resolve this paradox by introducing consecutive images (e.g., video frames or the same person with different poses) into the inversion process. The rationale behind our solution is that the continuity of consecutive images leads to inherent editable directions. This inborn property is used for two unique purposes: 1) regularizing the joint inversion process, such that each of the inverted codes is semantically …

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Differentiated Learning For Multi-Modal Domain Adaptation, Jianming Lv, Kaijie Liu, Shengfeng He Oct 2021

Differentiated Learning For Multi-Modal Domain Adaptation, Jianming Lv, Kaijie Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

Directly deploying a trained multi-modal classifier to a new environment usually leads to poor performance due to the well-known domain shift problem. Existing multi-modal domain adaptation methods treated each modality equally and optimize the sub-models of different modalities synchronously. However, as observed in this paper, the degrees of domain shift in different modalities are usually diverse. We propose a novel Differentiated Learning framework to make use of the diversity between multiple modalities for more effective domain adaptation. Specifically, we model the classifiers of different modalities as a group of teacher/student sub-models, and a novel Prototype based Reliability Measurement is presented …

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From Contexts To Locality: Ultra-High Resolution Image Segmentation Via Locality-Aware Contextual Correlation, Qi Li, Weixiang Yang, Wenxi Liu, Yuanlong Yu, Shengfeng He Oct 2021

From Contexts To Locality: Ultra-High Resolution Image Segmentation Via Locality-Aware Contextual Correlation, Qi Li, Weixiang Yang, Wenxi Liu, Yuanlong Yu, Shengfeng He

Research Collection School Of Computing and Information Systems

Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications. In this paper, we innovate the widely used high-resolution image segmentation pipeline, in which an ultrahigh resolution image is partitioned into regular patches for local segmentation and then the local results are merged into a high-resolution semantic mask. In particular, we introduce a novel locality-aware contextual correlation based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations. Additionally, we present a contextual semantics refinement …

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Analyzing Devops Teaching Strategies: An Initial Study, Samuel Ferino, Marcelo Fernandes, Anny K. Fernandes, Uirá Kulesza, Eduardo Aranha, Christoph Treude Oct 2021

Analyzing Devops Teaching Strategies: An Initial Study, Samuel Ferino, Marcelo Fernandes, Anny K. Fernandes, Uirá Kulesza, Eduardo Aranha, Christoph Treude

Research Collection School Of Computing and Information Systems

DevOps refers to a set of practices that integrate software development and operations with the primary aim to enable the continuous delivery of high-quality software. DevOps has also promoted several challenges to software engineering teaching. In this paper, we present a preliminary study that analyzes existing teaching strategies reported in the literature. Our findings indicate a set of approaches highlighting the use of environments to support teaching. Our work also investigates how these environments can contribute to address existing challenges and recommendations of DevOps teaching.

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Contrasting Third-Party Package Management User Experience, Syful Islam, Raula Kula, Christoph Treude, Takashi Ishio, Kenichi Matsumoto Oct 2021

Contrasting Third-Party Package Management User Experience, Syful Islam, Raula Kula, Christoph Treude, Takashi Ishio, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

The management of third-party package dependencies is crucial to most technology stacks, with package managers acting as brokers to ensure that a verified package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of package ecosystems with their own management features. While recent studies have shown that developers struggle to migrate their dependencies, the common assumption is that package ecosystems are used without any issue. In this study, we explore 13 package ecosystems to understand whether their features correlate with the experience of their users. By studying experience through the questions that …

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Eargate: Gait-Based User Identification With In-Ear Microphones, Andrea Ferlini, Dong Ma, Cecilia Mascolo Oct 2021

Eargate: Gait-Based User Identification With In-Ear Microphones, Andrea Ferlini, Dong Ma, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

Human gait is a widely used biometric trait for user identification and recognition. Given the wide-spreading, steady diffusion of earworn wearables (Earables) as the new frontier of wearable devices, we investigate the feasibility of earable-based gait identification. Specifically, we look at gait-based identification from the sounds induced by walking and propagated through the musculoskeletal system in the body. Our system, EarGate, leverages an in-ear facing microphone which exploits the earable’s occlusion effect to reliably detect the user’s gait from inside the ear canal, without impairing the general usage of earphones. With data collected from 31 subjects, we show that EarGate …

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An Exploratory Study Of Social Support Systems To Help Older Adults In Managing Mobile Safety, Tamir Mendel, Debin Gao, David Lo, Eran Toch Oct 2021

An Exploratory Study Of Social Support Systems To Help Older Adults In Managing Mobile Safety, Tamir Mendel, Debin Gao, David Lo, Eran Toch

Research Collection School Of Computing and Information Systems

Older adults face increased safety challenges, such as targeted online fraud and phishing, contributing to the growing technological divide between them and younger adults. Social support from family and friends is often the primary way older adults receive help, but it may also lead to reliance on others. We have conducted an exploratory study to investigate older adults' attitudes and experiences related to mobile social support technologies for mobile safety. We interviewed 18 older adults about their existing support and used the think-aloud method to gather data about a prototype for providing social support during mobile safety challenges. Our findings …

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Uncovering Patterns In Reviewers' Feedback To Scene Description Authors, Rosiana Natalie, Jolene Kar Inn Loh, Huei Suen Tan, Joshua Shi-Hao Tseng, Hernisa Kacorri, Kotaro Hara Oct 2021

Uncovering Patterns In Reviewers' Feedback To Scene Description Authors, Rosiana Natalie, Jolene Kar Inn Loh, Huei Suen Tan, Joshua Shi-Hao Tseng, Hernisa Kacorri, Kotaro Hara

Research Collection School Of Computing and Information Systems

Audio descriptions (ADs) can increase access to videos for blind people. Researchers have explored different mechanisms for generating ADs, with some of the most recent studies involving paid novices; to improve the quality of their ADs, novices receive feedback from reviewers. However, reviewer feedback is not instantaneous. To explore the potential for real-time feedback through automation, in this paper, we analyze 1,120 comments that 40 sighted novices received from a sighted or a blind reviewer. We find that feedback patterns tend to fall under four themes: (i) Quality; commenting on different AD quality variables, (ii) Speech Act; the utterance or …

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Sylpeniot: Symmetric Lightweight Predicate Encryption For Data Privacy Applications In Iot Environments, Tran Viet Xuan Phuong, Willy Susilo, Guomin Yang, Jongkil Kim, Yangwai Chow, Dongxi Liu Oct 2021

Sylpeniot: Symmetric Lightweight Predicate Encryption For Data Privacy Applications In Iot Environments, Tran Viet Xuan Phuong, Willy Susilo, Guomin Yang, Jongkil Kim, Yangwai Chow, Dongxi Liu

Research Collection School Of Computing and Information Systems

Privacy preserving mechanisms are essential for protecting data in IoT environments. This is particularly challenging as IoT environments often contain heterogeneous resource-constrained devices. One method for protecting privacy is to encrypt data with a pattern or metadata. To prevent information leakage, an evaluation using the pattern must be performed before the data can be retrieved. However, the computational costs associated with typical privacy preserving mechanisms can be costly. This makes such methods ill-suited for resource-constrained devices, as the high energy consumption will quickly drain the battery. This work solves this challenging problem by proposing SyLPEnIoT – Symmetric Lightweight Predicate Encryption …

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Weakly-Supervised Video Anomaly Detection With Contrastive Learning Of Long And Short-Range Temporal Features, Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro Oct 2021

Weakly-Supervised Video Anomaly Detection With Contrastive Learning Of Long And Short-Range Temporal Features, Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing abnormal events, with each video represented as a bag of video snippets. Although current methods show effective detection performance, their recognition of the positive instances, i.e., rare abnormal snippets in the abnormal videos, is largely biased by the dominant negative instances, especially when the abnormal events are subtle anomalies that exhibit only small differences compared with normal events. This issue is exacerbated in many methods that ignore important video temporal dependencies. To address this issue, …

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Constrained Contrastive Distribution Learning For Unsupervised Anomaly Detection And Localisation In Medical Images, Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W. Verjans, Rajvinder Singh Oct 2021

Constrained Contrastive Distribution Learning For Unsupervised Anomaly Detection And Localisation In Medical Images, Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W. Verjans, Rajvinder Singh

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy) samples that do not conform to the expected normal patterns. UAD has two main advantages over its fully supervised counterpart. Firstly, it is able to directly leverage large datasets available from health screening programs that contain mostly normal image samples, avoiding the costly manual labelling of abnormal samples and the subsequent issues involved in training with extremely class-imbalanced data. Further, UAD approaches can potentially detect and localise any type of lesions that deviate from the normal patterns. One significant challenge faced …

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Learning To Adversarially Blur Visual Object Tracking, Qing Guo, Ziyi Cheng, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Yang Liu, Jianjun Zhao Oct 2021

Learning To Adversarially Blur Visual Object Tracking, Qing Guo, Ziyi Cheng, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Yang Liu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Motion blur caused by the moving of the object or camera during the exposure can be a key challenge for visual object tracking, affecting tracking accuracy significantly. In this work, we explore the robustness of visual object trackers against motion blur from a new angle, i.e., adversarial blur attack (ABA). Our main objective is to online transfer input frames to their natural motion-blurred counterparts while misleading the state-of-the-art trackers during the tracking process. To this end, we first design the motion blur synthesizing method for visual tracking based on the generation principle of motion blur, considering the motion information and …

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Missing Data Imputation For Solar Yield Prediction Using Temporal Multi-Modal Variational Auto-Encoder, Meng Shen, Huaizheng Zhang, Yixin Cao, Fan Yang, Yonggang Wen Oct 2021

Missing Data Imputation For Solar Yield Prediction Using Temporal Multi-Modal Variational Auto-Encoder, Meng Shen, Huaizheng Zhang, Yixin Cao, Fan Yang, Yonggang Wen

Research Collection School Of Computing and Information Systems

The accurate and robust prediction of short-term solar power generation is significant for the management of modern smart grids, where solar power has become a major energy source due to its green and economical nature. However, the solar yield prediction can be difficult to conduct in the real world where hardware and network issues can make the sensors unreachable. Such data missing problem is so prevalent that it degrades the performance of deployed prediction models and even fails the model execution. In this paper, we propose a novel temporal multi-modal variational auto-encoder (TMMVAE) model, to enhance the robustness of short-term …

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Causal Attention For Unbiased Visual Recognition, Tan Wang, Chang Zhou, Qianru Sun, Hanwang Zhang Oct 2021

Causal Attention For Unbiased Visual Recognition, Tan Wang, Chang Zhou, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Attention module does not always help deep models learn causal features that are robust in any confounding context, e.g., a foreground object feature is invariant to different backgrounds. This is because the confounders trick the attention to capture spurious correlations that benefit the prediction when the training and testing data are IID (identical & independent distribution); while harm the prediction when the data are OOD (out-of-distribution). The sole fundamental solution to learn causal attention is by causal intervention, which requires additional annotations of the confounders, e.g., a “dog” model is learned within “grass+dog” and “road+dog” respectively, so the “grass” and …

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Disentangling Hate In Online Memes, Ka Wei, Roy Lee, Rui Cao, Ziqing Fan, Jing Jiang, Wen Haw Chong Oct 2021

Disentangling Hate In Online Memes, Ka Wei, Roy Lee, Rui Cao, Ziqing Fan, Jing Jiang, Wen Haw Chong

Research Collection School Of Computing and Information Systems

Hateful and offensive content detection has been extensively explored in a single modality such as text. However, such toxic information could also be communicated via multimodal content such as online memes. Therefore, detecting multimodal hateful content has recently garnered much attention in academic and industry research communities. This paper aims to contribute to this emerging research topic by proposing DisMultiHate, which is a novel framework that performed the classification of multimodal hateful content. Specifically, DisMultiHate is designed to disentangle target entities in multimodal memes to improve the hateful content classification and explainability. We conduct extensive experiments on two publicly available …

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Measuring Data Collection Diligence For Community Healthcare, Galawala Ramesha Samurdhi Karunasena, M. S. Ambiya, Arunesh Sinha, R. Nagar, S. Dalal, Abdullah. H., D. Thakkar, D. Narayanan, M. Tambe Oct 2021

Measuring Data Collection Diligence For Community Healthcare, Galawala Ramesha Samurdhi Karunasena, M. S. Ambiya, Arunesh Sinha, R. Nagar, S. Dalal, Abdullah. H., D. Thakkar, D. Narayanan, M. Tambe

Research Collection School Of Computing and Information Systems

Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of highquality public health data is a significant challenge in developing countries primarily due to non-diligent data collection by community health workers (CHWs). Our use of the word non-diligence here is to emphasize that poor data collection is often not a deliberate action by CHW but arises due to a myriad of factors, sometime beyond the control of the CHW. In this work, we define and test a data collection diligence score. This challenging unlabeled data problem is handled by building upon domain expert’s guidance …

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Multi-Modal Recommender Systems: Hands-On Exploration, Quoc Tuan Truong, Aghiles Salah, Hady Wirawan Lauw Oct 2021

Multi-Modal Recommender Systems: Hands-On Exploration, Quoc Tuan Truong, Aghiles Salah, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Recommender systems typically learn from user-item preference data such as ratings and clicks. This information is sparse in nature, i.e., observed user-item preferences often represent less than 5% of possible interactions. One promising direction to alleviate data sparsity is to leverage auxiliary information that may encode additional clues on how users consume items. Examples of such data (referred to as modalities) are social networks, item’s descriptive text, product images. The objective of this tutorial is to offer a comprehensive review of recent advances to represent, transform and incorporate the different modalities into recommendation models. Moreover, through practical hands-on sessions, we …

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On The Usability (In)Security Of In-App Browsing Interfaces In Mobile Apps, Zicheng Zhang, Daoyuan Wu, Lixiang Li, Debin Gao Oct 2021

On The Usability (In)Security Of In-App Browsing Interfaces In Mobile Apps, Zicheng Zhang, Daoyuan Wu, Lixiang Li, Debin Gao

Research Collection School Of Computing and Information Systems

Due to the frequent encountering of web URLs in various application scenarios (e.g., chatting and email reading), many mobile apps build their in-app browsing interfaces (IABIs) to provide a seamless user experience. Although this achieves user-friendliness by avoiding the constant switching between the subject app and the system built-in browser apps, we find that IABIs, if not well designed or customized, could result in usability security risks. In this paper, we conduct the first empirical study on the usability (in)security of in-app browsing interfaces in both Android and iOS apps. Specifically, we collect a dataset of 25 high-profile mobile apps …

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Integrated Discourse Analysis & Learning Skills Framework For Class Conversations, Devyn Wei Hung Tan, Gottipati Swapna, Kyong Jin Shim, Shankararaman, Venky Oct 2021

Integrated Discourse Analysis & Learning Skills Framework For Class Conversations, Devyn Wei Hung Tan, Gottipati Swapna, Kyong Jin Shim, Shankararaman, Venky

Research Collection School Of Computing and Information Systems

Constructive interactions through discussion forums allow students to open their horizons and thought processes to acquire more knowledge and develop skills. Thus, discussion forums play an important role in supporting learning. Additionally, the discussion forum provides the content for creating a knowledge repository. It contains discussion threads related to key course topics that are debated by the students. One approach to understanding the student learning experience is through the analysis of the discussion threads. This research proposes the application of discourse analysis and collaborative learning frameworks to discussion forums to gain further insights into the student’s learning in a classroom. …

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Latent Class Analysis For Identifying Subclasses Of Depression Using Jmp Pro 16, Karishma Yadav, Fei Fei Sue-Ann Seet, Tin Seong Kam, Tin Seong Kam Oct 2021

Latent Class Analysis For Identifying Subclasses Of Depression Using Jmp Pro 16, Karishma Yadav, Fei Fei Sue-Ann Seet, Tin Seong Kam, Tin Seong Kam

Research Collection School Of Computing and Information Systems

According to WHO, “Depression is a leading cause of disability worldwide and is a major contributor to the overall global burden of disease”. A major stumbling block in the care of depressed patients remains the accurate diagnosis of the severity of depression. Patient Health Questionnaire (PHQ-9), a 9-question instrument is widely used for diagnosing and determining the severity of depression. However, the popularly used 5-Category of depression severity based on the sum of responses to the 9 questions was overly subjective. In view of this limitation, our paper aims to demonstrate how Latent Class Analysis of JMP Pro can be …

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Transporting Causal Mechanisms For Unsupervised Domain Adaptation, Zhongqi Yue, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang Oct 2021

Transporting Causal Mechanisms For Unsupervised Domain Adaptation, Zhongqi Yue, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Existing Unsupervised Domain Adaptation (UDA) literature adopts the covariate shift and conditional shift assumptions, which essentially encourage models to learn common features across domains. However, due to the lack of supervision in the target domain, they suffer from the semantic loss: the feature will inevitably lose nondiscriminative semantics in source domain, which is however discriminative in target domain. We use a causal view—transportability theory [41]—to identify that such loss is in fact a confounding effect, which can only be removed by causal intervention. However, the theoretical solution provided by transportability is far from practical for UDA, because it requires the …

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Prediction Of Synthetic Lethal Interactions In Human Cancers Using Multi-View Graph Auto-Encoder, Zhifeng Hao, Di Wu, Yuan Fang, Min Wu, Ruichu Cai, Xiaoli Li Oct 2021

Prediction Of Synthetic Lethal Interactions In Human Cancers Using Multi-View Graph Auto-Encoder, Zhifeng Hao, Di Wu, Yuan Fang, Min Wu, Ruichu Cai, Xiaoli Li

Research Collection School Of Computing and Information Systems

Synthetic lethality (SL) is a very important concept for the development of targeted anticancer drugs. However, experimental methods for SL detection often suffer from various issues like high cost and low consistency across cell lines. Hence, computational methods for predicting novel SLs have recently emerged as complements for wet-lab experiments. In addition, SL data can be represented as a graph where nodes are genes and edges are the SL interactions. It is thus motivated to design advanced graph-based machine learning algorithms for SL prediction. In this paper, we propose a novel SL prediction method using Multi-view Graph Auto-Encoder (SLMGAE). We …

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Cloud, Edge And Fog Computing: Trends And Case Studies, Eng Lieh Ouh, Stanislaw Jarzabek, Geok Shan Lim, Masayoshi Ogawa Oct 2021

Cloud, Edge And Fog Computing: Trends And Case Studies, Eng Lieh Ouh, Stanislaw Jarzabek, Geok Shan Lim, Masayoshi Ogawa

Research Collection School Of Computing and Information Systems

As it is done today, an informal – solely based on experts’ intuition – evaluation of profitability of adopting cloud services is undependable and not scalable as there are many conflicting factors and constraints such evaluation should account for. The revenue from service tenants and the cost of implementing the service architecture are the leading service factors that drive profitability. Cloud service architectures also need to handle a growing number of tenants with increasingly diverse requirements which must be weighed against the capabilities and costs of various service architectures, particularly single- versus multi-tenanted models. We believe a conceptual model enumerating …

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Assessing Generalizability Of Codebert, Xin Zhou, Donggyun Han, David Lo Oct 2021

Assessing Generalizability Of Codebert, Xin Zhou, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Pre-trained models like BERT have achieved strong improvements on many natural language processing (NLP) tasks, showing their great generalizability. The success of pre-trained models in NLP inspires pre-trained models for programming language. Recently, CodeBERT, a model for both natural language (NL) and programming language (PL), pre-trained on code search dataset, is proposed. Although promising, CodeBERT has not been evaluated beyond its pre-trained dataset for NL-PL tasks. Also, it has only been shown effective on two tasks that are close in nature to its pre-trained data. This raises two questions: Can CodeBERT generalize beyond its pre-trained data? Can it generalize to …

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