Physical Sciences and Mathematics Commons^™

Fusion Of Multimodal Embeddings For Ad-Hoc Video Search, Danny Francis, Phuong Anh Nguyen, Benoit Huet, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

The challenge of Ad-Hoc Video Search (AVS) originates from free-form (i.e., no pre-defined vocabulary) and freestyle (i.e., natural language) query description. Bridging the semantic gap between AVS queries and videos becomes highly difficult as evidenced from the low retrieval accuracy of AVS benchmarking in TRECVID. In this paper, we study a new method to fuse multimodal embeddings which have been derived based on completely disjoint datasets. This method is tested on two datasets for two distinct tasks: on MSR-VTT for unique video retrieval and on V3C1 for multiple videos retrieval.

The Vid3oc And Intvid Datasets For Video Super Resolution And Quality Mapping, S. Kim, G. Li, D. Fuoli, M. Danelljan, Zhiwu Huang, S. Gu, R. Timofte

Research Collection School Of Computing and Information Systems

The current rapid advancements of computational hardware has opened the door for deep networks to be applied for real-time video processing, even on consumer devices. Appealing tasks include video super-resolution, compression artifact removal, and quality enhancement. These problems require high-quality datasets that can be applied for training and benchmarking. In this work, we therefore introduce two video datasets, aimed for a variety of tasks. First, we propose the Vid3oC dataset, containing 82 simultaneous recordings of 3 camera sensors. It is recorded with a multi-camera rig, including a high-quality DSLR camera, a high-end smartphone, and a stereo camera sensor. Second, we …

Evaluation Of Sigfox Lpwan For Sensor-Enabled Homes To Identify At Risk Community Dwelling Seniors, Crys Tan, Hwee-Pink Tan

Research Collection School Of Computing and Information Systems

It is projected that Singapore will become superaged (where 20% of its population will comprise seniors) by 2025. Although various community programs are available to promote active ageing among seniors who are well, provide befriending services for seniors at risk of isolation and care and support for frail and vulnerable seniors, it is not easy to differentiate between `well' seniors and `at risk' seniors. While privacy-preserving z-wave based sensor-enabled homes have been piloted in 100 homes of seniors living alone and have been successful in the timely detection of at-risk seniors, they have limited scalability due to high costs, reliability …

Who, Where, And What To Wear?: Extracting Fashion Knowledge From Social Media, Yunshan Ma, Xun Yang, Lizi Liao, Yixin Cao, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Fashion knowledge helps people to dress properly and addresses not only physiological needs of users, but also the demands of social activities and conventions. It usually involves three mutually related aspects of: occasion, person and clothing. However, there are few works focusing on extracting such knowledge, which will greatly benefit many downstream applications, such as fashion recommendation. In this paper, we propose a novel method to automatically harvest fashion knowledge from social media. We unify three tasks of occasion, person and clothing discovery from multiple modalities of images, texts and metadata. For person detection and analysis, we use the off-the-shelf …

Deep Hashing By Discriminating Hard Examples, Cheng Yan, Guansong Pang, Xiao Bai, Chunhua Shen, Jun Zhou, Edwin Hancock

Research Collection School Of Computing and Information Systems

This paper tackles a rarely explored but critical problem within learning to hash, i.e., to learn hash codes that effectively discriminate hard similar and dissimilar examples, to empower large-scale image retrieval. Hard similar examples refer to image pairs from the same semantic class that demonstrate some shared appearance but have different fine-grained appearance. Hard dissimilar examples are image pairs that come from different semantic classes but exhibit similar appearance. These hard examples generally have a small distance due to the shared appearance. Therefore, effective encoding of the hard examples can well discriminate the relevant images within a small Hamming distance, …

End-To-End Deep Reinforcement Learning For Multi-Agent Collaborative Exploration, Zichen Chen, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Exploring an unknown environment by multiple autonomous robots is a major challenge in robotics domains. As multiple robots are assigned to explore different locations, they may interfere each other making the overall tasks less efficient. In this paper, we present a new model called CNN-based Multi-agent Proximal Policy Optimization (CMAPPO) to multi-agent exploration wherein the agents learn the effective strategy to allocate and explore the environment using a new deep reinforcement learning architecture. The model combines convolutional neural network to process multi-channel visual inputs, curriculum-based learning, and PPO algorithm for motivation based reinforcement learning. Evaluations show that the proposed method …

Multi-Agent Collaborative Exploration Through Graph-Based Deep Reinforcement Learning, Tianze Luo, Budhitama Subagdja, Ah-Hwee Tan, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Autonomous exploration by a single or multiple agents in an unknown environment leads to various applications in automation, such as cleaning, search and rescue, etc. Traditional methods normally take frontier locations and segmented regions of the environment into account to efficiently allocate target locations to different agents to visit. They may employ ad hoc solutions to allocate the task to the agents, but the allocation may not be efficient. In the literature, few studies focused on enhancing the traditional methods by applying machine learning models for agent performance improvement. In this paper, we propose a graph-based deep reinforcement learning approach …

Mixed-Dish Recognition With Contextual Relation Networks, Lixi Deng, Jingjing Chen, Qianru Sun, Xiangnan He, Sheng Tang, Zhaoyan Ming, Yongdong Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Mixed dish is a food category that contains different dishes mixed in one plate, and is popular in Eastern and Southeast Asia. Recognizing individual dishes in a mixed dish image is important for health related applications, e.g. calculating the nutrition values. However, most existing methods that focus on single dish classification are not applicable to mixed-dish recognition. The new challenge in recognizing mixed-dish images are the complex ingredient combination and severe overlap among different dishes. In order to tackle these problems, we propose a novel approach called contextual relation networks (CR-Nets) that encodes the implicit and explicit contextual relations among …

Cognitive And Social Interaction Analysis In Graduate Discussion Forums, Mallika Gokarn Nitin, Swapna Gottipati, Venky Shankararaman

Research Collection School Of Computing and Information Systems

Discussion forums play a key role in building knowledge repositories in an education institute. Asynchronous discussion forums enable part-time graduate professionals to have a better learning experience. This paper reports how a carefully curated discussion forum enhances the cognitive and social interactions among students in a graduate information systems course. In particular, we analyse the cognitive and social interactions and their impact on the student grades. To our surprise, the graduate students with their limited time resources, have higher order cognitive contributions and reasonable amount of social posts. We present the discussion forum design, cognitive and social behaviour analysis, grade …

On Analysing Supply And Demand In Labor Markets: Framework, Model And System, Hendrik Santoso Sugiarto, Ee-Peng Lim, Ngak Leng Sim

Research Collection School Of Computing and Information Systems

The labor market refers to the market between job seekers and employers. As much of job seeking and talent hiring activities are now performed online, a large amount of job posting and application data have been collected and can be re-purposed for labor market analysis. In the labor market, both supply and demand are the key factors in determining an appropriate salary for both job applicants and employers in the market. However, it is challenging to discover the supply and demand for any labor market. In this paper, we propose a novel framework to built a labor market model using …

Smartbfa: A Passive Crowdsourcing System For Point-To-Point Barrier-Free Access, Mohammed Nazir Kamaldin, Susan Kee, Songwei Kong, Chengkai Lee, Huiguang Liang, Alisha Saini, Hwee-Pink Tan, Hwee Xian Tan

Research Collection School Of Computing and Information Systems

At the Bloomberg Live `Sooner Than You Think' forum [1] held in Singapore in 2018, nearly 75% of delegates picked inclusiveness to be the key measure of success for a smart city. An inclusive smart city is a citizen-centered approach that extends the experiences provided by smart city solutions to all citizens, including seniors and persons with disabilities (PwDs).Despite existing regulations on barrier-free accessibility for buildings and public infrastructure, pedestrian infrastructure is generally still inaccessible to PwDs in many parts of the world. In this paper, we present SmartBFA (Smart Mobility and Accessibility for Barrier Free Access) - a publicly-funded …

Why Reinventing The Wheels? An Empirical Study On Library Reuse And Re-Implementation, Bowen Xu, Le An, Ferdian Thung, Foutse Khomh, David Lo

Research Collection School Of Computing and Information Systems

Nowadays, with the rapid growth of open source software (OSS), library reuse becomes more and more popular since a large amount of third- party libraries are available to download and reuse. A deeper understanding on why developers reuse a library (i.e., replacing self-implemented code with an external library) or re-implement a library (i.e., replacing an imported external library with self-implemented code) could help researchers better understand the factors that developers are concerned with when reusing code. This understanding can then be used to improve existing libraries and API recommendation tools for researchers and practitioners by using the developers concerns identified …

Amazon Alexa + Linked Open Data: Theorizing Concerning Relationships Between (Surveillant) Smart-Home Voice Assistants And Linked Open Data, Michelle Nitto

Publications and Research

No abstract provided.

Predicting The Hardness Of Turf Surfaces From A Soil Moisture Sensor Using Iot Technologies, Ann Marie Mckeon

Other

In horseracing, “the going” is a term to describe the racetrack ground conditions. In Ireland presently, a groundskeeper or course clerk walks the racecourse poking it with a blackthorn stick, assesses conditions, and declares the going – it is a subjective measurement.

This thesis will propose using remote low-cost soil moisture sensors to gather high frequency data about the soil water content in the ground and to enable informed decisions to be made. This will remove the subjective element from the ground hardness, and look at the data in an objective way.

The soil moisture sensor will systematically collect high …

Confusion And Information Triggered By Photos In Persona Profiles, Joni Salminen, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Lene Nielsen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We investigate whether additional photos beyond a single headshot makes a persona profile more informative without confusing the end user. We conduct an eye-tracking experiment and qualitative interviews with digital content creators after varying the persona in photos via a single headshot, a headshot and photo of the persona in different contexts, and a headshot with photos of different people with key persona attributes the gender and age. Findings show that contextual photos provide significantly more persona information to end users; however, showing photos of multiple people engenders confusion and lowers informativeness. Also, as anticipated, viewing additional photos requires more …

Lightweight Fine-Grained Search Over Encrypted Data In Fog Computing, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Jian Weng, Hongwei Li, Hui Li

Research Collection School Of Computing and Information Systems

Fog computing, as an extension of cloud computing, outsources the encrypted sensitive data to multiple fog nodes on the edge of Internet of Things (IoT) to decrease latency and network congestion. However, the existing ciphertext retrieval schemes rarely focus on the fog computing environment and most of them still impose high computational and storage overhead on resource-limited end users. In this paper, we first present a Lightweight Fine-Grained ciphertexts Search (LFGS) system in fog computing by extending Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Searchable Encryption (SE) technologies, which can achieve fine-grained access control and keyword search simultaneously. The LFGS can shift …

Applied Deep Learning In Intelligent Transportation Systems And Embedding Exploration, Xiaoyuan Liang

Dissertations

Deep learning techniques have achieved tremendous success in many real applications in recent years and show their great potential in many areas including transportation. Even though transportation becomes increasingly indispensable in people’s daily life, its related problems, such as traffic congestion and energy waste, have not been completely solved, yet some problems have become even more critical. This dissertation focuses on solving the following fundamental problems: (1) passenger demand prediction, (2) transportation mode detection, (3) traffic light control, in the transportation field using deep learning. The dissertation also extends the application of deep learning to an embedding system for visualization …

Cooperation In Maritime Search And Rescue Between Democratic People’S Republic Of Korea, The People’S Republic Of China And The Russian Federation, Kwangmyong Ri

Maritime Safety & Environment Management Dissertations (Dalian)

No abstract provided.

Model Of The State Of Threats To The Access Control System, ‪Durdona Irgasheva

Bulletin of TUIT: Management and Communication Technologies

This article is devoted to the presentation of the threat state model of access control, which allows calculating the probabilities of the impact of threats on the access control system and the probability of opening this system based on taking into account the generalized algorithm for the implementation of external threats, and determines the need to develop additional components of the access control system designed to identify and classify attacks.

Foodai: Food Image Recognition Via Deep Learning For Smart Food Logging, Doyen Sahoo, Hao Wang, Ke Shu, Xiongwei Wu, Hung Le, Palakorn Achananuparp, Ee-Peng Lim, Hoi, Steven C. H.

Research Collection School Of Computing and Information Systems

An important aspect of health monitoring is effective logging of food consumption. This can help management of diet-related diseases like obesity, diabetes, and even cardiovascular diseases. Moreover, food logging can help fitness enthusiasts, and people who wanting to achieve a target weight. However, food-logging is cumbersome, and requires not only taking additional effort to note down the food item consumed regularly, but also sufficient knowledge of the food item consumed (which is difficult due to the availability of a wide variety of cuisines). With increasing reliance on smart devices, we exploit the convenience offered through the use of smart phones …

Social Inclusion In The Digital Era: Rethinking Debates And Narratives In The World Bank Report., Calisto Kondowe, Wallace Chigona

African Conference on Information Systems and Technology

The 2019 (5th) proceedings of ACIST focuses on how African societies are leveraging and can leverage the smart capabilities in digital technologies to address organizational and societal challenges. Technology-enabled solutions offer solutions to many of these challenges. Digital technologies are increasingly becoming integral to and interdependent with the African society.

An Electronic Framework To Shepherd The Pastoral Livestock (Resolve Conflicts In Pastoral Communities), Frezewd Lemma, Anteneh Alemu, Desta Zerihun, Endale Aregu

African Conference on Information Systems and Technology

This paper proposes a tracking framework based on GPS enabled location sensors, the GSM/WCDMA wireless network, and algorithms running in an edge clouds to resolve deadly conflicts that arise in the Africans pastorals community. The paper also proposes an automatic digital identification mechanism that helps resolve conflicts during animals mixup. This algorithmic based solution would totally relief the community from using the traditional identification mechanisms such as hot branding which are known to be cruel to animals. To communicate with the pastorals, the framework takes into consideration the low level literacy of the community as well as their use of …

Sentence-Level Evidence Embedding For Claim Verification With Hierarchical Attention Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Claim verification is generally a task of verifying the veracity of a given claim, which is critical to many downstream applications. It is cumbersome and inefficient for human fact-checkers to find consistent pieces of evidence, from which solid verdict could be inferred against the claim. In this paper, we propose a novel end-to-end hierarchical attention network focusing on learning to represent coherent evidence as well as their semantic relatedness with the claim. Our model consists of three main components: 1) A coherence-based attention layer embeds coherent evidence considering the claim and sentences from relevant articles; 2) An entailment-based attention layer …

Factors Influencing Customers’ Attitude For Using M-Birr Adoption In Ethiopia, Gebremedhin Gebreyohans, Abdurehman Ali

African Conference on Information Systems and Technology

Diffusion of Innovations (DOI) is a theory to explain how and over time new philosophies and then technology diffuse into different contexts. This research tested the attributes of DOI and other variables empirically,usingM-Birr system as the goal of the innovation. The research was conducted among customers of M-Birr service in Addis Ababa, Ethiopia. The data collection instrument was a closed-ended questionnaire administered to 360 respondents of which 211 were returned giving 58.6% return rate. The demographic make-up of the respondents showed that most of them were between the age of 30 to 50 and degree holders. From the factor analysis …

Exploring Delay Dispersal In Us Airport Network, Brandon Sripimonwan, Arun Sathanur

STAR Program Research Presentations

The modeling of delay diffusion in airport networks can potentially help develop strategies to prevent the spread of such delays and disruptions. With this goal, we used the publicly-available historical United States Federal Aviation Administration (FAA) flight data to model the spread of delays in the US airport network. For the major (ASPM-77) airports for January 2017, using a threshold on the volume of flights, we sparsify the network in order to better recognize patterns and cluster structure of the network. We developed a diffusion simulator and greedy optimizer to find the top influential airport nodes that propagate the most …

The Effect Of Conversational Agent Skill On User Behavior During Deception, Ryan M. Schuetzler, G. Mark Grimes, Justin Scott Giboney

Information Systems and Quantitative Analysis Faculty Publications

Conversational agents (CAs) are an integral component of many personal and business interactions. Many recent advancements in CA technology have attempted to make these interactions more natural and human-like. However, it is currently unclear how human-like traits in a CA impact the way users respond to questions from the CA. In some applications where CAs may be used, detecting deception is important. Design elements that make CA interactions more human-like may induce undesired strategic behaviors from human deceivers to mask their deception. To better understand this interaction, this research investigates the effect of conversational skill—that is, the ability of the …

A Pedagogy Of Techno-Social Relationality: Ethics And Digital Multimodality In The Composition Classroom, Kristin M. Ravel

Theses and Dissertations

I bring together the relational ethics of feminist critical theory with approaches of multimodal rhetoric to examine the ethical implications of composing on social media platforms. Most social media platforms are designed to value consumerism, efficiency, quantity of web traffic, and constant synchronous response over concerns of responsible and critical communication. I propose a rhetorical approach of techno-social relationality (TSR) as an intervention against such corporate-minded design. Through this approach, I argue that civil engagement is not limited to people’s social responsibilities but rather is entwined in complex, material-technical contexts. By considering the responsibility of our machines as much as …

Low-Rank Sparse Subspace For Spectral Clustering, Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang

Research Collection School Of Computing and Information Systems

The current two-step clustering methods separately learn the similarity matrix and conduct k means clustering. Moreover, the similarity matrix is learnt from the original data, which usually contain noise. As a consequence, these clustering methods cannot achieve good clustering results. To address these issues, this paper proposes a new graph clustering methods (namely Low-rank Sparse Subspace clustering (LSS)) to simultaneously learn the similarity matrix and conduct the clustering from the low-dimensional feature space of the original data. Specifically, the proposed LSS integrates the learning of similarity matrix of the original feature space, the learning of similarity matrix of the low-dimensional …

Deep Anomaly Detection With Deviation Networks, Guansong Pang, Chunhua Shen, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform indirect optimization of anomaly scores, leading to data-inefficient learning and suboptimal anomaly scoring. Also, they are typically designed as unsupervised learning due to the lack of large-scale labeled anomaly data. As a result, they are difficult to leverage prior knowledge (e.g., a few labeled anomalies) when such information is available as in many real-world anomaly detection …

Adversarial Learning On Heterogeneous Information Networks, Binbin Hu, Yuan Fang, Chuan Shi

Research Collection School Of Computing and Information Systems

Network embedding, which aims to represent network data in alow-dimensional space, has been commonly adopted for analyzingheterogeneous information networks (HIN). Although exiting HINembedding methods have achieved performance improvement tosome extent, they still face a few major weaknesses. Most importantly, they usually adopt negative sampling to randomly selectnodes from the network, and they do not learn the underlying distribution for more robust embedding. Inspired by generative adversarial networks (GAN), we develop a novel framework HeGAN forHIN embedding, which trains both a discriminator and a generatorin a minimax game. Compared to existing HIN embedding methods,our generator would learn the node distribution to …