Computer Sciences Commons^™

Leveraging Very-High Spatial Resolution Hyperspectral And Thermal Uav Imageries For Characterizing Diurnal Indicators Of Grapevine Physiology, Matthew Maimaitiyiming, Vasit Sagan, Paheding Sidike, Maitiniyazi Maimaitijiang, Allison J. Miller, Misha Kwasniewski

Michigan Tech Publications, Part 1

Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, …

Rethinking The Weakness Of Stream Ciphers And Its Application To Encrypted Malware Detection, William Stone, Daeyoung Kim, Victor Youdom Kemmoe, Mingon Kang, Junggab Son

Computer Science Faculty Research

One critical vulnerability of stream ciphers is the reuse of an encryption key. Since most stream ciphers consist of only a key scheduling algorithm and an Exclusive OR (XOR) operation, an adversary may break the cipher by XORing two captured ciphertexts generated under the same key. Various cryptanalysis techniques based on this property have been introduced in order to recover plaintexts or encryption keys; in contrast, this research reinterprets the vulnerability as a method of detecting stream ciphers from the ciphertexts it generates. Patterns found in the values (characters) expressed across the bytes of a ciphertext make the ciphertext distinguishable …

3d Face Reconstruction From Single 2d Image Using Distinctive Features, H. M. Rehan Afzal, Suhuai Luo, M. Kamran Afzal, Gopal Chaudhary, Manju Khari, Sathish Kumar

Electrical and Computer Engineering Faculty Publications

3D face reconstruction is considered to be a useful computer vision tool, though it is difficult to build. This paper proposes a 3D face reconstruction method, which is easy to implement and computationally efficient. It takes a single 2D image as input, and gives 3D reconstructed images as output. Our method primarily consists of three main steps: feature extraction, depth calculation, and creation of a 3D image from the processed image using a Basel face model (BFM). First, the features of a single 2D image are extracted using a two-step process. Before distinctive-features extraction, a face must be detected to …

Revisiting The Law Of Confidence In Singapore And A Proposal For A New Tort Of Misuse Of Private Information, Cheng Lim Saw, Zheng Wen Samuel Chan, Wen Min Chai

Research Collection Yong Pung How School Of Law

This article critically examines the recent Court of Appeal decision in I-Admin (Singapore) Pte Ltd v Hong Ying Ting [2020] 1 SLR 1130 and its implications for the law of confidence. The article begins by setting out the decision at first instance, and then on appeal. It argues that the Court of Appeal’s “modified approach” fails to meaningfully engage the plaintiff ’s wrongful gain interest and places the law’s emphasis primarily, if not wholly, on the plaintiff ’s wrongful loss interest. The new framework also appears to have been influenced by English jurisprudence, which has had a long but unhelpful …

Meta-Rcnn: Meta Learning For Few-Shot Object Detection, Xiongwei Wu, Doyen Sahoo, Steven Hoi

Research Collection School Of Computing and Information Systems

Despite significant advances in deep learning based object detection in recent years, training effective detectors in a small data regime remains an open challenge. This is very important since labelling training data for object detection is often very expensive and time-consuming. In this paper, we investigate the problem of few-shot object detection, where a detector has access to only limited amounts of annotated data. Based on the meta-learning principle, we propose a new meta-learning framework for object detection named "Meta-RCNN", which learns the ability to perform few-shot detection via meta-learning. Specifically, Meta-RCNN learns an object detector in an episodic learning …

Co-Design And Evaluation Of An Intelligent Decision Support System For Stroke Rehabilitation Assessment, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Badia

Research Collection School Of Computing and Information Systems

Clinical decision support systems have the potential to improve work flows of experts in practice (e.g. therapist's evidence-based rehabilitation assessment). However, the adoption of these systems is challenging, and the gains of these systems have not fully demonstrated yet. In this paper, we identified the needs of therapists to assess patient's functional abilities (e.g. alternative perspectives with quantitative information on patient's exercise motions). As a result, we co-designed and developed an intelligent decision support system that automatically identifies salient features of assessment using reinforcement learning to assess the quality of motion and generate patient-specific analysis. We evaluated this system with …

Deeprhythm: Exposing Deepfakes With Attentional Visual Heartbeat Rhythms, Hua Qi, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Wei Feng, Yang Liu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

As the GAN-based face image and video generation techniques, widely known as DeepFakes, have become more and more matured and realistic, there comes a pressing and urgent demand for effective DeepFakes detectors. Motivated by the fact that remote visual photoplethysmography (PPG) is made possible by monitoring the minuscule periodic changes of skin color due to blood pumping through the face, we conjecture that normal heartbeat rhythms found in the real face videos will be disrupted or even entirely broken in a DeepFake video, making it a potentially powerful indicator for DeepFake detection. In this work, we propose DeepRhythm, a DeepFake …

Towards Locality-Aware Meta-Learning Of Tail Node Embeddings On Networks, Zemin Liu, Wentao Zhang, Yuan Fang, Xinming Zhang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Network embedding is an active research area due to the prevalence of network-structured data. While the state of the art often learns high-quality embedding vectors for high-degree nodes with abundant structural connectivity, the quality of the embedding vectors for low-degree or tail nodes is often suboptimal due to their limited structural connectivity. While many real-world networks are long-tailed, to date little effort has been devoted to tail node embedding. In this paper, we formulate the goal of learning tail node embeddings as a few-shot regression problem, given the few links on each tail node. In particular, since each node resides …

Sentiment Analysis For Software Engineering: How Far Can Pre-Trained Transformer Models Go?, Ting Zhang, Bowen Xu, Thung Ferdian, Stefanus Agus Haryono, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Extensive research has been conducted on sentiment analysis for software engineering (SA4SE). Researchers have invested much effort in developing customized tools (e.g., SentiStrength-SE, SentiCR) to classify the sentiment polarity for Software Engineering (SE) specific contents (e.g., discussions in Stack Overflow and code review comments). Even so, there is still much room for improvement. Recently, pre-trained Transformer-based models (e.g., BERT, XLNet) have brought considerable breakthroughs in the field of natural language processing (NLP). In this work, we conducted a systematic evaluation of five existing SA4SE tools and variants of four state-of-the-art pre-trained Transformer-based models on six SE datasets. Our work is …

We Mind Your Well-Being: Preventing Depression In Uncertain Social Networks By Sequential Interventions, Aye Phye Phye Aung, Xinrun Wang, Bo An, Xiaoli Li

Research Collection School Of Computing and Information Systems

Mental health has become a major concern according to WHO who estimates that more than 350 million people worldwide are affected by depression. Studies have shown that interventions and social support can reduce stress and depression. However, counselling centers do not have enough resources to provide counselling and social support to all the participants in their interest. This paper helps social support organizations (e.g., university counselling centers) sequentially select the participants for interventions. Unfortunately, previous works do not consider emotion propagation from other neighbours of the influencees and initial uncertainties of mental states and influence. Moreover, they fail to scale …

Cross-Domain Cross-Modal Food Transfer, Bin Zhu, Chong-Wah Ngo, Jingjing Chen

Research Collection School Of Computing and Information Systems

The recent works in cross-modal image-to-recipe retrieval pave a new way to scale up food recognition. By learning the joint space between food images and recipes, food recognition is boiled down as a retrieval problem by evaluating the similarity of embedded features. The major drawback, nevertheless, is the difficulty in applying an already-trained model to recognize different cuisines of dishes unknown to the model. In general, model updating with new training examples, in the form of image-recipe pairs, is required to adapt a model to new cooking styles in a cuisine. Nevertheless, in practice, acquiring sufficient number of image-recipe pairs …

Revocable And Certificateless Public Auditing For Cloud Storage, Yinghui Zhang, Tiantian Zhang, Shengmin Xu, Guowen Xu, Dong Zheng

Research Collection School Of Computing and Information Systems

Plenty of computing and storage resources in the cloud are provided for users with restricted computing and storage resources, which has attracted the attention of many researchers. A generic blockchain-based cloud data auditing scheme is proposed, which is compatible with any blockchains including the bitcoin blockchain. In the data integrity checking scheme, certificateless signature (CLS) can be used to verify the identity of users. Besides, the key exchange is utilized in the key generation, which can eliminate the security channel to achieve system robustness. Considering the real situation, the users who join the cloud storage system may be revoked for …

Reinforcement Learning For Zone Based Multiagent Pathfinding Under Uncertainty, Jiajing Ling, Tarun Gupta, Akshat Kumar

Research Collection School Of Computing and Information Systems

We address the problem of multiple agents finding their paths from respective sources to destination nodes in a graph (also called MAPF). Most existing approaches assume that all agents move at fixed speed, and that a single node accommodates only a single agent. Motivated by the emerging applications of autonomous vehicles such as drone traffic management, we present zone-based path finding (or ZBPF) where agents move among zones, and agents' movements require uncertain travel time. Furthermore, each zone can accommodate multiple agents (as per its capacity). We also develop a simulator for ZBPF which provides a clean interface from the …

Efficient Ciphertext-Policy Attribute-Based Encryption With Blackbox Traceability, Shengmin Xu, Jiaming Yuan, Guowen Xu, Yingjiu Li, Ximeng Liu, Yinghui Zhang, Zuobin Yang

Research Collection School Of Computing and Information Systems

Traitor tracing scheme is a paradigm to classify the users who illegal use of their decryption keys in cryptosystems. In the ciphertext-policy attribute-based cryptosystem, the decryption key usually contains the users’ attributes, while the real identities are hidden. The decryption key with hidden identities enables malicious users to intentionally leak decryption keys or embed the decryption keys in the decryption device to gain illegal profits with a little risk of being discovered. To mitigate this problem, the concept of blackbox traceability in the ciphertext-policy attribute-based scheme was proposed to identify the malicious user via observing the I/O streams of the …

Using Student Perceptions To Design Smart Class Participation Tools: A Technology Framework, Swapna Gottipati, Shankararaman, Venky, Mark Wei Jie Ng

Research Collection School Of Computing and Information Systems

Our research full paper studies the perceptions of students and proposes technology design framework for smart class participation tools. Participation in classroom discussions has been observed to improve student comprehension and performance. In our contemporary tertiary educational context, student participation is characterised in mainly two forms; inclass discussions and online forums. Both forms of participation generate voluminous amounts of knowledge. However, the present difficulty in capturing and analysing these forms of participation leads to loss of knowledge and insights, which otherwise could be very useful. This study aims to analyse ways to improve data capture as well as data analysis …

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 …

Knowledge Enhanced Neural Fashion Trend Forecasting, Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Fashion trend forecasting is a crucial task for both academia and industry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real fashion trends. Towards insightful fashion trend forecasting, this work focuses on investigating fine-grained fashion element trends for specific user groups. We first contribute a large-scale fashion trend dataset (FIT) collected from Instagram with extracted time series fashion element records and user information. Furthermore, to effectively model the time series data of fashion elements with rather complex patterns, we propose …

Catch You If You Deceive Me: Verifiable And Privacy-Aware Truth Discovery In Crowdsensing Systems, Guowen Xu, Hongwei Li, Shengmin Xu, Hao Ren, Yonghui Zhang, Jianfei Sun, Robert H. Deng

Research Collection School Of Computing and Information Systems

Truth Discovery (TD) is to infer truthful information by estimating the reliability of users in crowdsensing systems. To protect data privacy, many Privacy-Preserving Truth Discovery (PPTD) approaches have been proposed. However, all existing PPTD solutions do not consider a fundamental issue of trust. That is, if the data aggregator (e.g., the cloud server) is not trustworthy, how can an entity be convinced that the data aggregator has correctly performed the PPTD? A "lazy"cloud server may partially follow the deployed protocols to save its computing and communication resources, or worse, maliciously forge the results for some shady deals. In this paper, …

Co2vec: Embeddings Of Co-Ordered Networks Based On Mutual Reinforcement, Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Philips Kokoh Prasetyo

Research Collection School Of Computing and Information Systems

We study the problem of representation learning for multiple types of entities in a co-ordered network where order relations exist among entities of the same type, and association relations exist across entities of different types. The key challenge in learning co-ordered network embedding is to preserve order relations among entities of the same type while leveraging on the general consistency in order relations between different entity types. In this paper, we propose an embedding model, CO2Vec, that addresses this challenge using mutually reinforced order dependencies. Specifically, CO2Vec explores in-direct order dependencies as supplementary evidence to enhance order representation learning across …

European Floating Strike Lookback Options: Alpha Prediction And Generation Using Unsupervised Learning, Tristan Lim, Aldy Gunawan, Chin Sin Ong

Research Collection School Of Computing and Information Systems

This research utilized the intrinsic quality of European floating strike lookback call options, alongside selected return and volatility parameters, in a K-means clustering environment, to recommend an alpha generative trading strategy. The result is an elegant easy-to-use alpha strategy based on the option mechanisms which identifies investment assets with high degree of significance. In an upward trending market, the research had identified European floating strike lookback call option as an evaluative criterion and investable asset, which would both allow investors to predict and profit from alpha opportunities. The findings will be useful for (i) buy-side investors seeking alpha generation and/or …

Deep Reinforcement Learning Approach To Solve Dynamic Vehicle Routing Problem With Stochastic Customers, Waldy Joe, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In real-world urban logistics operations, changes to the routes and tasks occur in response to dynamic events. To ensure customers’ demands are met, planners need to make these changes quickly (sometimes instantaneously). This paper proposes the formulation of a dynamic vehicle routing problem with time windows and both known and stochastic customers as a route-based Markov Decision Process. We propose a solution approach that combines Deep Reinforcement Learning (specifically neural networks-based TemporalDifference learning with experience replay) to approximate the value function and a routing heuristic based on Simulated Annealing, called DRLSA. Our approach enables optimized re-routing decision to be generated …

Providing Predictable Performance During Network Contingencies, Phani Krishna Penumarthi

Theses and Dissertations

In IP backbone networks, packets may get dropped due to: i) lack of viable next hops when a link/router fails, ii) forwarding loops during network convergence, and iii) buffer overflows in case of congestion. Similarly, packets may be lost in wireless networks due to variations in signal strength between a pair of mobile nodes. This dissertation explores the possibility of providing a predictable performance during such network contingencies in wired backbone networks and robotic wireless networks.

First, we study the feasibility of developing a combination of local reroute and global update mechanisms that can achieve loop-free convergence, while performing disruption-free …

Camera Placement Meeting Restrictions Of Computer Vision, Sara Aghajanzadeh, Roopasree Naidu, Shuo-Han Chen, Caleb Tung, Abhinav Goel, Yung-Hsiang Lu, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

In the blooming era of smart edge devices, surveillance cam- eras have been deployed in many locations. Surveillance cam- eras are most useful when they are spaced out to maximize coverage of an area. However, deciding where to place cam- eras is an NP-hard problem and researchers have proposed heuristic solutions. Existing work does not consider a signifi- cant restriction of computer vision: in order to track a moving object, the object must occupy enough pixels. The number of pixels depends on many factors (how far away is the object? What is the camera resolution? What is the focal length?). …

Modular Neural Networks For Low-Power Image Classification On Embedded Devices, Abhinav Goel, Sara Aghajanzadeh, Caleb Tung, Shuo-Han Chen, George K. Thiruvathukal, Yung-Hisang Lu

Computer Science: Faculty Publications and Other Works

Embedded devices are generally small, battery-powered computers with limited hardware resources. It is difficult to run deep neural networks (DNNs) on these devices, because DNNs perform millions of operations and consume significant amounts of energy. Prior research has shown that a considerable number of a DNN’s memory accesses and computation are redundant when performing tasks like image classification. To reduce this redundancy and thereby reduce the energy consumption of DNNs, we introduce the Modular Neural Network Tree architecture. Instead of using one large DNN for the classifier, this architecture uses multiple smaller DNNs (called modules) to progressively classify images …

The Utility Of Multiple Structure Torsion Angle Alignment In Protein Active Site Description (Asd), Devaun L. Mcfarland

Theses and Dissertations

Proteins are responsible for various functions throughout organisms, or within the systems, they operate. Active-sites or functional/ binding sites are regions responsible for activity in a protein; they serve as a catalyst for reactions, attach or bind to other molecules (ligands), and maintain function. With the profusion of protein sequence and structure data, it's increasingly relevant to develop automated methods of identifying and investigating active-sites for proteins. Active-sites identification will have a direct impact: in better understanding molecular basis for diseases, assisting in drug design, the study of targeting mutants, and for functional annotation of unknown proteins. The proper knowledge …

The Future Of Work Now: Ai-Driven Transaction Surveillance At Dbs Bank, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon. Steve Miller of Singapore Management University and I co-authored the story.

Message From The General Co-Chairs And The Program Co-Chairs, Christoph Treude, Hongyu Zhang, Kelly Blincoe, Zhenchang Xing

Research Collection School Of Computing and Information Systems

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Cleanpage: Fast And Clean Document And Whiteboard Capture, Jane Courtney

Articles

The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for the high-quality digitization of content from pages and whiteboards to sharable online material. However, capturing this information is not always easy nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop, and …

Chapter 3: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana

Open Educational Resources

No abstract provided.

Chapter 2: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana

Open Educational Resources

No abstract provided.