Poetry For Seers Or The Peruvian Visual Poetic Tradition In Front Of New Media,
2020
Pontificia Universidad Catolica del Peru
Poetry For Seers Or The Peruvian Visual Poetic Tradition In Front Of New Media, Michael Hurtado, Pamela Medina, Enrique García, Michael Prado
Electronic Literature Organization Conference 2020
Since the first decades of the twentieth century, Peruvian poetic tradition has been characterized by experimental uses of language. Among these possibilities, some records tensioned this medium from the link with the plastic arts, as in the case of the poetry of José María Eguren, while others opted for the playing with the spatiality and visuality of the blank sheet, such as in the case of the work of Carlos Oquendo de Amat. However, it is not until the appearance of the poetry of César Vallejo, specifically with a poems like Trilce in 1922, that these breakages force us to …
Helion’S Snapshot Module,
2020
Christopher Newport University
Helion’S Snapshot Module, Nii-Kwartei Quartey
Cybersecurity Undergraduate Research Showcase
During my undergraduate research, I spent my time working with a home automation program known as Helion, specifically, its Snapshot module. I was tasked with learning new material and completing part of the webpage that were unfinished. I also had to get a little creative when working on a design that users could find appealing. There were times I found working on Helion difficult but overall, working with Helion’s Snapshot Module is something that will help me improve with my undergraduate studies.
Deep Learning Of Facial Embeddings And Facial Landmark Points For The Detection Of Academic Emotions,
2020
Singapore Management University
Deep Learning Of Facial Embeddings And Facial Landmark Points For The Detection Of Academic Emotions, Hua Leong Fwa
Research Collection School Of Computing and Information Systems
Automatic emotion recognition is an actively researched area as emotion plays a pivotal role in effective human communications. Equipping a computer to understand and respond to human emotions has potential applications in many fields including education, medicine, transport and hospitality. In a classroom or online learning context, the basic emotions do not occur frequently and do not influence the learning process itself. The academic emotions such as engagement, frustration, confusion and boredom are the ones which are pivotal to sustaining the motivation of learners. In this study, we evaluated the use of deep learning on FaceNet embeddings and facial landmark …
Expertise Style Transfer: A New Task Towards Better Communication Between Experts And Laymen,
2020
Singapore Management University
Expertise Style Transfer: A New Task Towards Better Communication Between Experts And Laymen, Yixin Cao, Ruihao Shui, Liangming Pan, Min-Yen Kan, Zhiyuan Lu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style …
Improving Event Detection Via Open-Domain Event Trigger Knowledge,
2020
Singapore Management University
Improving Event Detection Via Open-Domain Event Trigger Knowledge, Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, Jun Xie
Research Collection School Of Computing and Information Systems
Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the small scale of training data, previous methods perform poorly on unseen/sparsely labeled trigger words and are prone to overfitting densely labeled trigger words. To address the issue, we propose a novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations. Experiments on benchmark ACE2005 show that our model outperforms nine strong baselines, is especially effective for unseen/sparsely labeled trigger words. The source code is released on https://github.com/shuaiwa16/ekd.git.
Tree-Augmented Cross-Modal Encoding For Complex-Query Video Retrieval,
2020
Singapore Management University
Tree-Augmented Cross-Modal Encoding For Complex-Query Video Retrieval, Xun Yang, Jianfeng Dong, Yixin Cao, Xun Wang, Meng Wang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
The rapid growth of user-generated videos on the Internet has intensified the need for text-based video retrieval systems. Traditional methods mainly favor the concept-based paradigm on retrieval with simple queries, which are usually ineffective for complex queries that carry far more complex semantics. Recently, embedding-based paradigm has emerged as a popular approach. It aims to map the queries and videos into a shared embedding space where semantically-similar texts and videos are much closer to each other. Despite its simplicity, it forgoes the exploitation of the syntactic structure of text queries, making it suboptimal to model the complex queries. To facilitate …
The Meaning Of Red And Green In User Interfaces For The Color Deficient,
2020
Portland State University
The Meaning Of Red And Green In User Interfaces For The Color Deficient, Bassel Hamieh
University Honors Theses
Around 108 million web users are color blind which is a problem when the way we communicate over the web or interfaces is through the use of color. Red and green are two colors that are especially heavily used in interface design because of their strong symbolic associations; red being a sign to warn or stop and green being the opposite. This has a large effect on red-green color blind people who are not able to perceive either of those colors correctly. Many solutions exist that aim to help through color differentiation but none take into account color symbolism. With …
An Improved Bone Age Assessment Using Advanced Image Processing And Deep Learning Approach,
2020
Universiti Malaya
An Improved Bone Age Assessment Using Advanced Image Processing And Deep Learning Approach, Kim Meng Liang
Student Works (2020-2029)
Pediatricians often apply bone age assessment to measure the skeletal maturity of children and to predict the future height. These discrepancies are good indicators for diagnosing growth disorders. Normally, left hand skeletal is employed in this assessment. The low quality of ossification sites of carpals deteriorates the pediatrician’s visibility in inspecting the pertinent radiographic manifestations. This in turn affects the bone age assessment. Therefore, we have to enhance the quality before assessing them. Histogram equalization is one of the contrast enhancement techniques that suit this type of enhancement. Existing histogram equalizations, however, are confronting with problems in preserving the brightness …
A Process Model For Designing Performance Dashboard Using Visualization Techniques,
2020
Universiti Malaya
A Process Model For Designing Performance Dashboard Using Visualization Techniques, Bahar Muhammad Nasim
Student Works (2020-2029)
Data visualization is the presentation of data in a pictorial or graphical format. It enables decision-makers to see data analysis presented visually, so they can observe difficult concepts or identify new patterns. With interactive visualization, we can take the concept a step further by using technology to drill down into charts and graphs for more detail, interactively changing what data see and how it is processed. With the help of data visualization, it is expected to promote creative data exploration. A performance dashboard is one of the most common use cases for data visualization, and it enables decision-makers such as …
Unsupervised Monocular Depth Estimation With Multi-Scale Structural Similarity Powered Loss Function,
2020
Universiti Malaya
Unsupervised Monocular Depth Estimation With Multi-Scale Structural Similarity Powered Loss Function, Kohan Ali
Student Works (2020-2029)
Depth Estimation refers to a set of techniques and algorithms that aim to obtain a representation of spatial information of a scene. Nowadays specific hardware such as sensors, radars and multiple-view-recording cameras are being used in order to acquire depth data of a scene. Modern approaches use deep learning to address this task by trying to learn depth information in a supervised manner. However, this approach requires a large amount ground-truth data for a particular scene so that a model can be trained successfully. Also preparing ground-truth data for a range of environments is a challenging and expensive task to …
A Review On Eye-Tracking Metrics For Sleepiness,
2020
Singapore Management University
A Review On Eye-Tracking Metrics For Sleepiness, Debasis Roy, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Sleepiness that can arise from sleep deprivation can increase human errors in task performance and create workplace hazards and accidents. Hence, it is critical to detect sleepiness to minimize hazards and human errors. This paper provides a review of the literature on eye tracking metrics that can be used to detect sleepiness. These metrics include blink duration, blink frequency, saccade latency, saccade peak velocity, saccade accuracy, smooth pursuit velocity gain, fixation rate, pupil size, and latency to pupil constriction.
Design And Test Of An Autonomy Monitoring Service To Detect Divergent Behaviors On Unmanned Aerial Systems,
2020
Air Force Institute of Technology
Design And Test Of An Autonomy Monitoring Service To Detect Divergent Behaviors On Unmanned Aerial Systems, Loay Y. Almannaei
Theses and Dissertations
Operation of Unmanned Aerial Vehicles (UAV) support many critical missions in the United State Air Force (USAF). Monitoring abnormal behavior is one of many responsibilities of the operator during a mission. Some behaviors are hard to be detect by an operator, especially when flying one or more autonomous vehicles; as such, detections require a high level of attention and focus to flight parameters. In this research, a monitoring system and its algorithm are designed and tested for a target fixed-wing UAV. The Autonomy Monitoring Service (AMS) compares the real vehicle or simulated Vehicle with a similar simulated vehicle using Software …
Hyperbolic Visual Embedding Learning For Zero-Shot Recognition,
2020
Singapore Management University
Hyperbolic Visual Embedding Learning For Zero-Shot Recognition, Shaoteng Liu, Jingjing Chen, Liangming Pan, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
This paper proposes a Hyperbolic Visual Embedding Learning Network for zero-shot recognition. The network learns image embeddings in hyperbolic space, which is capable of preserving the hierarchical structure of semantic classes in low dimensions. Comparing with existing zeroshot learning approaches, the network is more robust because the embedding feature in hyperbolic space better represents class hierarchy and thereby avoid misleading resulted from unrelated siblings. Our network outperforms exiting baselines under hierarchical evaluation with an extremely challenging setting, i.e., learning only from 1,000 categories to recognize 20,841 unseen categories. While under flat evaluation, it has competitive performance as state-of-the-art methods but …
Transferring And Regularizing Prediction For Semantic Segmentation,
2020
Singapore Management University
Transferring And Regularizing Prediction For Semantic Segmentation, Yiheng Zhang, Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Dong Liu, Tao Mei
Research Collection School Of Computing and Information Systems
Semantic segmentation often requires a large set of images with pixel-level annotations. In the view of extremely expensive expert labeling, recent research has shown that the models trained on photo-realistic synthetic data (e.g., computer games) with computer-generated annotations can be adapted to real images. Despite this progress, without constraining the prediction on real images, the models will easily overfit on synthetic data due to severe domain mismatch. In this paper, we novelly exploit the intrinsic properties of semantic segmentation to alleviate such problem for model transfer. Specifically, we present a Regularizer of Prediction Transfer (RPT) that imposes the intrinsic properties …
Cookgan: Causality Based Text-To-Image Synthesis,
2020
Singapore Management University
Cookgan: Causality Based Text-To-Image Synthesis, Bin Zhu, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper addresses the problem of text-to-image synthesis from a new perspective, i.e., the cause-and-effect chain in image generation. Causality is a common phenomenon in cooking. The dish appearance changes depending on the cooking actions and ingredients. The challenge of synthesis is that a generated image should depict the visual result of action-on-object. This paper presents a new network architecture, CookGAN, that mimics visual effect in causality chain, preserves fine-grained details and progressively upsamples image. Particularly, a cooking simulator sub-network is proposed to incrementally make changes to food images based on the interaction between ingredients and cooking methods over a …
Exploring Category-Agnostic Clusters For Open-Set Domain Adaptation,
2020
Singapore Management University
Exploring Category-Agnostic Clusters For Open-Set Domain Adaptation, Yingwei Pan, Ting Yao, Yehao Li, Chong-Wah Ngo, Tao Mei
Research Collection School Of Computing and Information Systems
Unsupervised domain adaptation has received significant attention in recent years. Most of existing works tackle the closed-set scenario, assuming that the source and target domains share the exactly same categories. In practice, nevertheless, a target domain often contains samples of classes unseen in source domain (i.e., unknown class). The extension of domain adaptation from closedset to such open-set situation is not trivial since the target samples in unknown class are not expected to align with the source. In this paper, we address this problem by augmenting the state-of-the-art domain adaptation technique, Self-Ensembling, with category-agnostic clusters in target domain. Specifically, we …
Self-Trained Deep Ordinal Regression For End-To-End Video Anomaly Detection,
2020
Singapore Management University
Self-Trained Deep Ordinal Regression For End-To-End Video Anomaly Detection, Guansong Pang, Cheng Yan, Chunhua Shen, Anton Van Den Hengel, Xiao Bai
Research Collection School Of Computing and Information Systems
Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. However, they are challenged by the relative scarcity of instances of depression in the data. In this work we introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks, namely one-class metric learning and anomaly ranking. The auxiliary tasks introduce an inductive bias that improves the classification model’s generalizability on small depression …
Ntire 2020 Challenge On Video Quality Mapping: Methods And Results,
2020
Singapore Management University
Ntire 2020 Challenge On Video Quality Mapping: Methods And Results, D. Fuoli, Zhiwu Huang, M. Danelljan, R. Timofte, H. Wang, L. Jin, D. Su, J. Liu, J. Lee, M. Kudelski, L. Bala, D. Hryboy, M. Mozejko, M. Li, S. Li, B. Pang, C. Lu, Li C., He D., Li F.
Research Collection School Of Computing and Information Systems
This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain. The challenge includes both a supervised track (track 1) and a weakly-supervised track (track 2) for two benchmark datasets. In particular, track 1 offers a new Internet video benchmark, requiring algorithms to learn the map from more compressed videos to less compressed videos in a supervised training manner. In track 2, algorithms are required to learn the quality mapping from one device to another when their quality varies substantially and weaklyaligned video pairs …
Visual Commonsense R-Cnn,
2020
Singapore Management University
Visual Commonsense R-Cnn, Tan Wang, Jianqiang Huang, Hanwang Zhang, Qianru Sun
Research Collection School Of Computing and Information Systems
We present a novel unsupervised feature representation learning method, Visual Commonsense Region-based Convolutional Neural Network (VC R-CNN), to serve as an improved visual region encoder for high-level tasks such as captioning and VQA. Given a set of detected object regions in an image (e.g., using Faster R-CNN), like any other unsupervised feature learning methods (e.g., word2vec), the proxy training objective of VC R-CNN is to predict the contextual objects of a region. However, they are fundamentally different: the prediction of VC R-CNN is by using causal intervention: P(Y|do(X)), while others are by using the conventional likelihood: P(Y|X). This is also …
Mnemonics Training: Multi-Class Incremental Learning Without Forgetting,
2020
Tianjin University
Mnemonics Training: Multi-Class Incremental Learning Without Forgetting, Yaoyao Liu, Yuting Su, An-An Liu, Bernt Schiele, Qianru Sun
Research Collection School Of Computing and Information Systems
Multi-Class Incremental Learning (MCIL) aims to learn new concepts by incrementally updating a model trained on previous concepts. However, there is an inherent trade-off to effectively learning new concepts without catastrophic forgetting of previous ones. To alleviate this issue, it has been proposed to keep around a few examples of the previous concepts but the effectiveness of this approach heavily depends on the representativeness of these examples. This paper proposes a novel and automatic framework we call mnemonics, where we parameterize exemplars and make them optimizable in an end-to-end manner. We train the framework through bilevel optimizations, i.e., model-level and …
