Reviewerly/exHarmony huggingface dataset:
Learning to Find Local Correspondences

Author(s): Dai, Ji | Advisor(s): Nguyen, Truong Q | Abstract: Finding local feature correspondences is recognized as one of the fundamental tasks in computer vision. It serves as an unalienable component for various applications in both 2D and 3D regimes. Such applications include but are not limited to structure from motion, image registration, image retrieval, 3D scene registration, SLAM, etc. In this dissertation, we study approaches to improve the overall performance of establishing local correspondences in various contexts. These contexts can be defined and categorized along multiple dimensions, for example, sparse v.s. dense feature, 2D v.s. 3D feature. To be more specific, the contexts we address in this thesis are sparse 2D local feature, dense 2D local feature, and sparse 3D local feature.There has been abundant research on the above topics and we recently see a ubiquitous trend of learning-based approaches taking over the handcrafted ones. While these learned methods showcase exceptional matching performance and additional robustness against both geometric and photometric distortions, we notice some merits of the good old handcrafted methods are partially comprised or not inherited at all. We investigate the impact by incorporating such merits into each context. For sparse 2D local feature, we first propose an explicit scheme that trains a network to estimate orientation for each feature. Encouraged by the primary results, we further propose a novel pipeline that accurately estimates the relative affine transformation between feature pairs during the matching process and restore the geometric distortion for the corresponding pairs. For dense 2D local feature, we propose a pyramid structured network and a novel concept called motion cue to compute optical flow for video data. For sparse 3D local feature, we propose an occlusion-aware voxelization method with a multiple-resolution convolution network to deal with the occlusion challenge.For the last two chapters, we include two works on related applications: view synthesis and stereo panoramic imaging. Our view synthesis algorithm addresses the challenging occlusion filling problem with a hierarchical clustering approach. Stereo panorama is a popular content in virtual reality. We present a systematic solution that captures the surrounding environment with camera array and providing users an immersive viewing experience with the freedom of selecting the preferred perspective and baseline.
OntoForInfoScience e Onto4ALLEditor: metodologia e editor de ontologias como facilitadores na construção de ontologias por especialistas do domínio e cientistas da informação

Currently, ontologies are frequently used in sciences such as Linguistics, Information Science and Computer Science, especially in the areas of Conceptual Modeling and Knowledge Representation. One of the great challenges in ontologies is their costly and complex construction process, which can lead to errors in knowledge representation and information retrieval. The use of ontology methodologies and editors still represents a challenge to domain experts and information professionals, whose formation in the area does not privilege requirements for such activities. In this scenario, this paper presents a building methodology - OntoForInfoScience - and an ontology editor software - Onto4ALLEditor - whose main purpose is to provide a theoretical basis and functionalities of the tool in order to facilitate the development of ontologies by different types of users, mainly domain experts and information scientists. The research also includes an evaluation of ontology editors, currently available, and a brief discussion on the use and possibilities of OntoForInfoScience and Onto4ALLEditor in this context. As expected results of the proposed approach, it is intended to contribute to the popularization of the use of ontologies in Information Science and related areas.
KNOW MYSELF – A PSYCHOMETRIC TEST SYSTEM

In general, psychometric test is getting popular all around the world. Most of the big companies are using psychometric test to help in short listing the candidates who apply to work in their company. Know Myself is a system that provides various types of psychometrictests. This report describes the problems on existing psychometric test systems in the market. Reviews are done on the psychometric test systems in the market. Comparison between existing psychometric test systems and Know Myself is discussed and documented. Finally, a webapplication system named Know Myself is developed to provide various types of psychometric tests. The conclusion and future work are discussed and documented in the report.
Lightweight Semantic Segmentation Convolutional Neural Network Based on SKNet

Semantic segmentation plays a very important role in computer vision. It can be used in many real-world applications, such as virtual reality and augmented reality, robotics and autopilot technology. The existing models have large amount of network parameters and high complexity, and can not fully extract the context information of the image. In order to solve the problem of high complexity and poor real-time performance of DenseNet, the backbone network of DenseAspp model, this paper proposes an image semantic segmentation method based on Shufflenetv2. The lightweight convolutional neural network Shufflenet-v2 is used to replace DenseNet as the backbone network of the segmentation model to extract features, which effectively reduces the amount of parameters and calculation of the model and improves the real-time performance of the segmentation algorithm. SkNet, a selective convolution kernel mechanism, enables each neuron to adaptively select the size of receptive field according to the multi-scale information of input features, thus improving the segmentation accuracy.
A Bag of Tricks for Dialogue Summarization

Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization. In this work, we explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to multiple speakers, negation understanding, reasoning about the situation, and informal language understanding. Using a pretrained sequence-to-sequence language model, we explore speaker name substitution, negation scope highlighting, multi-task learning with relevant tasks, and pretraining on in-domain data. Our experiments show that our proposed techniques indeed improve summarization performance, outperforming strong baselines.
An Efficacious E-voting Mechanism using Blockchain to Preserve Data Integrity in Fog Nodes

Uma Maheswari, K. Mary Saira Bhanu, S. Nickolas, S.Fog computing is one of the fast-evolving technologies that play the central part in the Internet of Things (IoT) where data is generated in one end and stored after processing in another end. Before the introduction of fog, all the IoT applications depend on the cloud for storage, retrieval and processing of data. In order to manage a large amount of IoT data that focuses on reducing latency and bandwidth, the concept of fog computing is introduced. The idea is to reduce network traffic and provide instant response. The data stored in the fog storage may be altered or tampered by malicious users, malware, human error or power failure. Ensuring the integrity of fog data is essential for the correct functioning of the entire system. In this paper, an architecture is proposed to ensure the integrity of data stored on fog nodes using blockchain technology and an electronic voting (e-voting) system is implemented using the proposed architecture.
Towards End-To-End Learning-Based Algorithms in Motion Planning

Author(s): Miao, Yinglong | Advisor(s): Yip, Michael C.; Christensen, Henrik I. | Abstract: Motion planning is one of the most critical tasks in robotics, as it is one of the few critical functions for robot autonomy. This component requires fast computations and generalization to different environments for problems such as collision avoidance. Deep learning, as a new fast-growing field, offers great advances in computational speed and generalization. It has shown success in computer vision and reinforcement learning, which is closely related to motion planning. In this thesis, we will investigate the combination of learning and motion planning methods. Specifically, we separately consider individual components of motion planning tasks. By combining the proposed learning-based methods for each component, we can obtain an integrated end-to-end learning-based motion planning algorithm. We show experimental results for each component. In general, our learning-based methods showed high computational speed with generalization in several motion planning tasks.
What is SemEval evaluating?: A Systematic Analysis of Evaluation Campaigns in NLP

SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems. This paper provides a systematic quantitative analysis of SemEval aiming to evidence the patterns of the contributions behind SemEval. By understanding the distribution of task types, metrics, architectures, participation and citations over time we aim to answer the question on what is being evaluated by SemEval.
Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings.

Natural language inference (NLI) requires models to learn and apply commonsense knowledge. These reasoning abilities are particularly important for explainable NLI systems that generate a natural language explanation in addition to their label prediction. The integration of external knowledge has been shown to improve NLI systems, here we investigate whether it can also improve their explanation capabilities. For this, we investigate different sources of external knowledge and evaluate the performance of our models on in-domain data as well as on special transfer datasets that are designed to assess fine-grained reasoning capabilities. We find that different sources of knowledge have a different effect on reasoning abilities, for example, implicit knowledge stored in language models can hinder reasoning on numbers and negations. Finally, we conduct the largest and most fine-grained explainable NLI crowdsourcing study to date. It reveals that even large differences in automatic performance scores do neither reflect in human ratings of label, explanation, commonsense nor grammar correctness.
DocEnhance: Data Stewardship

Presentation at the online workshop FAIR data competences in doctoral education programmes, 26. - 27.05.21, arranged by FAIRsFAIR. https://www.eua.eu/events/180-fair-data-competences-in-doctoral-education-programmes.html.
Neural Program Generation Modulo Static Analysis

State-of-the-art neural models of source code tend to be evaluated on the generation of individual expressions and lines of code, and commonly fail on long-horizon tasks such as the generation of entire method bodies. We propose to address this deficiency using weak supervision from a static program analyzer. Our neurosymbolic method allows a deep generative model to symbolically compute, using calls to a static-analysis tool, long-distance semantic relationships in the code that it has already generated. During training, the model observes these relationships and learns to generate programs conditioned on them. We apply our approach to the problem of generating entire Java methods given the remainder of the class that contains the method. Our experiments show that the approach substantially outperforms state-of-the-art transformers and a model that explicitly tries to learn program semantics on this task, both in terms of producing programs free of basic semantic errors and in terms of syntactically matching the ground truth.
LibGuides: Engineering: Library Materials

This subject guide covers resources available at NUS Libraries for Engineering. Libraries' training materials can be found in this tab
Dialogue State Tracking with a Language Model using Schema-Driven Prompting

Task-oriented conversational systems often use dialogue state tracking to represent the user’s intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, often using task-specific architectures with special-purpose classifiers. Recently, good results have been obtained using more general architectures based on pretrained language models. Here, we introduce a new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots. We further improve performance by augmenting the prompting with schema descriptions, a naturally occurring source of in-domain knowledge. Our purely generative system achieves state-of-the-art performance on MultiWOZ 2.2 and achieves competitive performance on two other benchmarks: MultiWOZ 2.1 and M2M. The data and code will be available at https://github.com/chiahsuan156/DST-as-Prompting.
Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lens

Much of the world’s population experiences some form of disability during their lifetime. Caution must be exercised while designing natural language processing (NLP) systems to prevent systems from inadvertently perpetuating ableist bias against people with disabilities, i.e., prejudice that favors those with typical abilities. We report on various analyses based on word predictions of a large-scale BERT language model. Statistically significant results demonstrate that people with disabilities can be disadvantaged. Findings also explore overlapping forms of discrimination related to interconnected gender and race identities.
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game

Simulated DAG models may exhibit properties that, perhaps inadvertently, render their structure identifiable and unexpectedly affect structure learning algorithms. Here, we show that marginal variance tends to increase along the causal order for generically sampled additive noise models. We introduce varsortability as a measure of the agreement between the order of increasing marginal variance and the causal order. For commonly sampled graphs and model parameters, we show that the remarkable performance of some continuous structure learning algorithms can be explained by high varsortability and matched by a simple baseline method. Yet, this performance may not transfer to real-world data where varsortability may be moderate or dependent on the choice of measurement scales. On standardized data, the same algorithms fail to identify the ground-truth DAG or its Markov equivalence class. While standardization removes the pattern in marginal variance, we show that data generating processes that incur high varsortability also leave a distinct covariance pattern that may be exploited even after standardization. Our findings challenge the significance of generic benchmarks with independently drawn parameters. The code is available at https://github.com/Scriddie/Varsortability.
Impossible Differential Cryptanalysis of LiCi Block Cipher

LiCi algorithm is a newly lightweight block cipher. Due to its new design idea adopted by Patil et al, it has the advantages of compact design, low energy consumption and less chip area, thus is is especially suitable for resource-constrained environments. Currently, its security receives extensively attention, and Patil et al. claimed that the 16-round reduced LiCi can sufficiently resist both differential attack and linear attack. In this paper, a new 10-round impossible differential distinguisher is constructed based on the differential characteristics of the S-box and the meet-in-the-middle technique. Moreover, on the basis of this distinguisher, a 16-round impossible differential attack on LiCi is proposed by respectively extending 3-round forward and backward via the key scheduling scheme. This attack requires a time complexity of about 283.08 16-round encryptions, a data complexity of about 259.76 chosen plaintexts, and a memory complexity of 276.76 data blocks, which illustrates that the 16-round LiCi cipher can not resist impossible differential attack.
Stance Detection in German News Articles

The widespread use of the Internet and the rapid dissemination of information poses the challenge of identifying the veracity of its content. Stance detection, which is the task of predicting the position of a text in regard to a specific target (e.g. claim or debate question), has been used to determine the veracity of information in tasks such as rumor classification and fake news detection. While most of the work and available datasets for stance detection address short texts snippets extracted from textual dialogues, social media platforms, or news headlines with a strong focus on the English language, there is a lack of resources targeting long texts in other languages. Our contribution in this paper is twofold. First, we present a German dataset of debate questions and news articles that is manually annotated for stance and emotion detection. Second, we leverage the dataset to tackle the supervised task of classifying the stance of a news article with regards to a debate question and provide baseline models as a reference for future work on stance detection in German news articles.
Reform and Innovation of Practical English Talents Training Mode in Colleges and Universities

Under the background of economic globalization, the demand for high-quality and practical English professionals is increasing.With the background of economic development and the open and inclusive policy, enterprises are encouraged to actively carry out international trade cooperation and increase the volume of international trade business.On this basis, it is necessary to absorb high-quality English professionals to lay a foundation for enterprises to carry out international business cooperation and follow-up development.However, there are some limitations in the way of cultivating talents.Therefore, it is necessary to truly implement the practical talent training program in light of the actual situation.
Research on the Contour Capture Algorithm of the Key Action of Dance with Computer-Aided

In order to solve the problem that the current key dance motion contour capture algorithm cannot effectively capture the concave part of the key dance motion contour, which leads to large contour capture error and long response time, a computer-aided key dance motion contour capture algorithm is proposed. The action sequence without background is obtained from the dance video, and the action sequence is optimized to reduce the interference of contour capture. Algorithm tracks the posture change of the object in the dance action sequence, determines the contour capture area, and completes the contour capture of the dance key action. Experimental results show that the proposed method can effectively improve the capture accuracy and shorten the response time.
USING EDUCATIONAL GAME APPLICATIONS TO READ WITHOUT SPEAKING IN PAUD PERMATA HATI KISARAN

This study was conducted as an effort to determine the use of educational game applications in learning to read without spelling at PAUD Permata Hati Kisaran. This research method uses the SDLC Waterfall method. Waterfall or Classic Life Cycle is a method that is widely used in Software Engineering, this method takes a systematic and sequential approach from the level of system requirements and then goes to the analysis, design, implementation, and system testing stages. It is called Waterfall because the stages that are passed must wait for the completion of the previous stage and run sequentially. The educational game application presents subject matter for recognizing letters and reading sentences that are equipped with display text, images and letters that can be seen clearly and are equipped with the sounds of letters and sentences. The results of the analysis show that the use of educational game applications is able to train learning to read without spelling in words Permata Hati Range Early Childhood Education. The use of educational game applications has benefits in the learning process at Permata Hati Kisaran PAUD. The use of educational game applications makes it easier to teach reading without spelling at Permata Hati Kisaran PAUD.
TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts

Recent models in developing summarization systems consist of millions of parameters and the model performance is highly dependent on the abundance of training data. While most existing summarization corpora contain data in the order of thousands to one million, generation of large-scale summarization datasets in order of couple of millions is yet to be explored. Practically, more data is better at generalizing the training patterns to unseen data. In this paper, we introduce TLDR9+ –a large-scale summarization dataset– containing over 9 million training instances extracted from Reddit discussion forum ([HTTP]). This dataset is specifically gathered to perform extreme summarization (i.e., generating one-sentence summary in high compression and abstraction) and is more than twice larger than the previously proposed dataset. We go one step further and with the help of human annotations, we distill a more fine-grained dataset by sampling High-Quality instances from TLDR9+ and call it TLDRHQ dataset. We further pinpoint different state-of-the-art summarization models on our proposed datasets.
Incipient Anomaly Detection with Ensemble Learning

Author(s): Jin, Baihong | Advisor(s): Sangiovanni-Vincentelli, Alberto | Abstract: Anomaly detection techniques are important in system health monitoring applications (e.g., fault detection and disease diagnosis). By recognizing suspicious patterns in data, anomaly detection models can tell whether system has degraded from the normal operating condition into a faulty or diseased state. To avoid unnecessary losses, it is desirable to have a way to identify incipient anomalies, i.e. to detect potential problems in their early stages of development. In buildings, early detection of incipient faults can help reduce maintenance and repair costs, save energy, and enhance occupant comfort. In healthcare, if incipient diseases can be discovered early, effective treatments can be applied and can prevent diseases from progressing into more severe stages. However, it is difficult to accurately identify incipient anomalies while at the same time not incurring into too many false alarms. Incipient anomalies present milder deviations compared to severe ones, and are difficult to detect and diagnose due to their close resemblance to normal operating conditions. Anomaly detection approaches based on supervised Machine Learning (ML) rely on high-quality labeled data to build accurate classifiers. However, the lack of incipient anomaly examples in the training data can pose severe risks to anomaly detection methods that are built upon ML techniques, because these anomalies can be easily mistaken as normal operating conditions.Ensemble learning is widely applied in ML to improve model performance and to mitigate decision risks. In ensemble approaches, predictions from a diverse set of learners are combined to obtain a joint decision with lower bias and variance. Recently, various methods have been explored in literature for estimating prediction uncertainties using ensemble learning. To address this challenge of incipient anomalies, I propose in this dissertation to utilize the uncertainty information available from ensemble learning to identify potential misclassified incipient anomalies. We will show that ensemble learning methods can give improved performance on incipient anomalies and identify common pitfalls in these models through extensive experiments on two real-world applications—detection of chiller faults and diagnosing diabetic retinopathy diseases. A theoretical analysis that compares the two popular strategies for extracting uncertainty information will also be given. We will also discuss how to design more effective ensemble models for detecting incipient anomalies.