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    MCL Research on Type Prediction for Knowledge Graph Learning

MCL Research on Type Prediction for Knowledge Graph Learning

Entity type is a very important piece of information in Knowledge Graphs. Researchers have leveraged entity type information to get better results in many Knowledge Graph related tasks such as link prediction. Besides, entity type is also important for Information Extraction tasks including entity linking and relation extraction. However, Knowledge Graph entity type information is often incomplete and noisy. Therefore, there is a need to develop effective algorithms for predicting missing types for entities.

Knowledge Graph (KG) Embeddings in complex vector space have demonstrated superior performance in relation prediction and triple classification. Representing entities and relations in complex space has several advantages than traditional models such as better expressive power, and better capabilities of modeling one-to-many and asymmetric relations. We leverage these characteristics of complex KG Embeddings and formulate the type prediction problem as a complex space regression problem. Experimental results confirm our hypothesis that the expressiveness of embedding models correlates with the performance on type prediction. Our newly proposed method achieves state-of-the-art results in type prediction for many benchmarking datasets.

[1] Sun, Zhiqing, et al. “Rotate: Knowledge graph embedding by relational rotation in complex space.” arXiv preprint arXiv:1902.10197 (2019).

[2] Zhao, Yu, et al. “Connecting embeddings for knowledge graph entity typing.” arXiv preprint arXiv:2007.10873 (2020).

— by Xiou Ge

By |October 11th, 2021|News|Comments Off on MCL Research on Type Prediction for Knowledge Graph Learning|

Introduction to MCL New Visitor – Rafael Luiz Testa

In Fall 2021, we have a new MCL member, Rafael Luiz Testa, joining our big family. Here is a short interview with Rafael with our great welcome.

1. Could you briefly introduce yourself and your research interests?

My name is Rafael Luiz Testa. I received Bachelor’s (2014) and Master’s (2018) degrees in Information Systems from the University of São Paulo, Brazil. I am currently pursuing my PhD degree at the University of São Paulo with a nine-month stay at MCL/USC. I have been working on computer graphics since 2012. In 2013, I joined a project to help people with psychiatric disorders to recognize emotions in facial expressions. My main research interests are image/video analysis and synthesis, as well as facial expression.

2. What is your impression about MCL and USC?

The USC and MCL are both internationally recognized for the quality of their work. I am so happy that I found here at MCL a place where everyone has plenty of opportunities to achieve their best. The MCL Director, Dr. C.-C. Jay Kuo, instigates innovation and collaboration between students. Furthermore, the MCL has such a friendly and kind environment.

3. What is your future expectation and plan in MCL?

I believe my stay at MCL will give me an opportunity to see my research from an entirely novel perspective. I hope I can enjoy to the fullest such an innovative environment. In the future, I would like to be a professor and pursue an academic research career. Thus, I am confident that the work developed at MCL will significantly impact my future projects and help me achieve my career goals.

By |October 3rd, 2021|News|Comments Off on Introduction to MCL New Visitor – Rafael Luiz Testa|

MCL Research on Geographic Fake Images Detection

Misinformation on the Internet and social media, ranging from fake news to fake multimedia such as images and videos, is a significant threat to our society. Effective misinformation detection has become a research focus, driven by commercial and government funding. With the fast-growing deep learning techniques, real-looking fake images can be easily generated using generative adversarial networks (GANs). The problem of fake satellite images detection was recently introduced. Fake satellite images could be generated with the intention of hiding important infrastructure and/or creating fake buildings to deceive others. Although it may be feasible to check whether these images are real or fake using another satellite, the cost is high. Furthermore, the general public and media do not have the proper resource to verify the authenticity of fake satellite images. Consequently, fake satellite images pose serious challenges to our society, as recognized by government organizations concerned about the political and military implications of such technology. Handcrafted features were used for fake satellite image detection, and its best detection performance measured by the F-1 score is 87%. 

A new method, called PSL-DefakeHop, is proposed to detect fake satellite images based on the parallel subspace learning (PSL) framework in this work. The DefakeHop method was developed previously for the detection of Deepfake generated faces under the successive subspace learning (SSL) framework. PSL is proposed to extract features from responses of multiple single-stage filter banks (or called PixelHops), which operate in parallel, and it improves SSL that extracts features from multi-stage cascaded filter banks. PSL has two advantages. First, PSL preserves discriminant features often lie in high-frequency channels, which are however ignored by SSL. Second, decisions from multiple filter banks can be ensembled to further improve detection accuracy. To [...]

By |September 20th, 2021|News|Comments Off on MCL Research on Geographic Fake Images Detection|

Congratulations to Yeji Shen for Passing His Defense

Congratulations to Yeji Shen for passing his defense on Sep 7, 2021. His Ph.D. thesis is entitled “Labeling Cost Reduction Techniques for Deep Learning: Methodologies and Applications”. Here we invite Yeji to share a brief introduction of his thesis and some words he would like to say at the end of the Ph.D. study journey.

1) Abstract of Thesis

Deep learning has contributed to a significant performance boost of many computer vision tasks. Still, the success of most existing deep learning techniques relies on a large number of labeled data. While data labeling is costly, a natural question arises: is it possible to achieve better performance with the same budget of data labeling? We provide two directions to address the problem: more efficient utilization of the budget or supplementing unlabeled data with no labeling cost. Specifically, in this dissertation, we study three problems related to the topic of reducing the labeling cost: 1) active learning that aims at identifying most informative unlabeled samples for labeling; 2) weakly supervised 3D human pose estimation that utilizes a special type of unlabeled data, action-frozen people videos, to help improve the performance with few manual annotations; and 3) self-supervised representation learning on a large-scale dataset of images with text and user-input tags at no additional labeling cost.

In the first part of this talk, we will introduce our representation work which mainly focuses on the utilization of textual information in images. Text information inside images could provide valuable cues for image understanding. We propose a simple but effective representation learning framework, called the Self-Supervised Representation learning of Images with Texts (SSRIT). SSRIT exploits optical character recognition (OCR) signals in a self-supervision manner. SSRIT constructs a representation that is trained to predict whether [...]

By |September 12th, 2021|News|Comments Off on Congratulations to Yeji Shen for Passing His Defense|

MCL Research on Domain Specific Word Embedding

Word embeddings, also known as distributed word representations, learn real-valued vectors that encode words’ meaning. They have been widely used in many Natural Language Processing (NLP) tasks, such as text classification, part-of-speech tagging, parsing, and machine translation. Text classification is a task where the input texts have to be classified into different categories based on their content. Word embedding methods have been tailored to text classification for performance improvement.

In this research, two task-specific dependency-based word embedding methods are proposed for Text classification. In contrast with universal word embedding methods that work for generic tasks, we design task-specific word embedding methods to offer better performance in a specific task. Our methods follow the PPMI matrix factorization framework and derive word contexts from the dependency parse tree. As compared linear contexts, dependency-based contexts can find long-range contexts and exclude less informative contexts. One example is shown in Fig. 2, where the target word is ‘found’. Guided by the dependency par-sing tree, its closely related words (e.g. ‘he’, ‘dog’) can be easily identified. In contrast, less related words (e.g. ‘skinny’, ‘fragile’) are gathered by linear contexts.

Firstly, to construct robust and informative contexts, we use dependency relation which represents the word’s syntactic function to locate the keywords in the sentence and treat the keywords and the neighbor words in the dependency parse tree as contexts.

To further increase the text classification performance, we make our word embedding learns from word-context as well as word-class co-occurrence statistics. We combine the word-context and word-class mutual information into a single matrix for factorization.

It is shown by experimental results they outperform several state-of-the-art word embedding methods.

 

Image credits:

Image showing a simple example algorithm framework for text classification is from https://laptrinhx.com/nlp-multiclass-text-classification-machine-learning-model-using-count-vector-bow-tf-idf-2622024659/

By |September 7th, 2021|News|Comments Off on MCL Research on Domain Specific Word Embedding|

Welcome MCL New Member – Armin Bazarjani

In Fall 2021, we have a new MCL member, Armin Bazarjani, joining our big family. Here is a short interview with Armin with our great welcome.

1. Could you briefly introduce yourself and your research interests?

I received both my BS and MS degrees in electrical engineering from USC. I am now working as a researcher in MCL as I also prepare to apply for PhD programs for the Fall 2022 cycle. My overarching interests are in machine learning and statistical pattern recognition, and I hope to one day be able to meaningfully apply these interests to the fields of computational sustainability and computational neuroscience. Other than my academic interests, I really enjoy hiking/backpacking, cooking, and generally being outdoors and away from the computer whenever I can manage the time.

2. Could you briefly introduce yourself and your research interests?

It should come as no surprise that I am a big fan of the culture at USC as I have received two degrees here now! From my experience, everybody at USC has been kind and supportive in all areas of scholastic pursuit. Additionally, many of the students here are more well-rounded as they don’t purely focus their identity on academic achievement only. I have only been with MCL for a few weeks now so it is difficult to say anything with relative assurity, especially because I started here during the COVID pandemic and haven’t been able to enjoy my lab members’ company in person. I will say, with the few interactions I have had, the lab seems very well driven and organized. I also really like the advising style of Professor Kuo as he seems to strike a good balance between how hands on he is and how [...]

By |August 29th, 2021|News|Comments Off on Welcome MCL New Member – Armin Bazarjani|

Welcome MCL New Member – Qingyang Zhou

In Fall 2021, we have a new MCL member, Qingyang Zhou, joining our big family. Here is a short interview with Qingyang with our great welcome.

1. Could you briefly introduce yourself and your research interests?

Hello, I am Qingyang Zhou, a new member of MCL in 21fall. I received my Bachelor’s and Master’s degrees both in Shanghai, China. My research interest is multimedia signal processing, especially video coding and point cloud compression. I am passionate about these research areas and I have been working on different video coding standards for over 3 years before coming to USC. I am very happy to share with you my experience in this field.

2. Could you briefly introduce yourself and your research interests?

MCL is filled with productive and professional researchers. The lab offers us many different research directions from multimedia signal processing to computer vision. Professor Kuo is creative, hardworking, and full of insightful thoughts. I believe I would receive excellent academic training and become a good researcher at MCL. USC is also an excellent place with both a beautiful campus and strong academic background. I am very happy that I could continue my research at USC.

3. What is your future expectation and plan in MCL?

At MCL, I will continue to do research on multimedia-related areas. I have been doing this for years and there are still many questions waiting for me to solve. For the next few months, my attention will be mainly focused on Point Cloud Compression. For the next several years, I would like to get my academic skills improved and make good preparations before entering the industry or academia.

By |August 22nd, 2021|News|Comments Off on Welcome MCL New Member – Qingyang Zhou|

MCL Research on Image Denoising

As a fundamental Computer vision problem, image denoising aims at reducing noise images to improve resolutions.  As a sub-topic of image restoration, image denoising not only has wide applications in practical problems, but also can be important pre-processing procedures for other CV or NLP problems.

Traditionally, algorithms by patch-wise denoising, like Non-local Mean and BM3D, usually assume the noise are Gaussian noise try to reduce noise by the randomness of noise and signal preservation across similar patches. After CNN architecture introduced to CV field, similar to other image restoration problems like super-resolution, deblurring, and dehazing, denoising problem also developed out CNN-based methods, with two main streams: one focus more on pixel-wise restoration and the other cares more about overall pleasure. Besides, combining different image restoration problems together, that building a more general model which can work on multiple image restoration problems gradually obtained more attention.

With better performance achieved in denoising problem, more and more algorithms suffer from the model size and reference speed. We would like to introduce SSL principle to tackle denoising problem with comparable performance while with higher efficiency in the future.

Image credit: Dabov, Kostadin, et al. “Image denoising by sparse 3-D transform-domain collaborative filtering.” IEEE Transactions on image processing 16.8 (2007): 2080-2095.

By |June 21st, 2021|News|Comments Off on MCL Research on Image Denoising|

Welcome MCL New Member – Xinyu Wang

In Summer 2021, we have a new MCL member, Xinyu Wang, joining our big family. Here is a short interview with Xinyu with our great welcome.

1. Could you briefly introduce yourself and your research interests?

My name is Xinyu Wang, I am a Master student in Electrical Engineering, and it’s my second year at USC. I am new here as a summer intern. My research interests mainly include machine learning and robotics, and I will work on image forensics related topics this summer at MCL.

2. Could you briefly introduce yourself and your research interests?

MCL has a group of motivated and intelligent people, who are full of passion about their research. And I am impressed by the open and friendly atmosphere here. People are encouraged to show their ideas and help each other, and everyone is friendly and supportive.

3. What is your future expectation and plan in MCL?

This summer, I am working with Yao on image forensics topics using the green learning method, under the supervision of Professor Kuo. I believe this will be a great opportunity for me to further explore machine learning and working on this interesting topic. I also hope to make new friends here and make lasting connections with MCL members.

By |June 13th, 2021|News|Comments Off on Welcome MCL New Member – Xinyu Wang|

Welcome MCL New Member – Peida Han

In Summer 2021, we have a new MCL member, Peida Han, joining our big family. Here is a short interview with Peida with our great welcome.

1. Could you briefly introduce yourself and your research interests?

My name is Peida Han, and I am a first year master student in Computer Science (artificial intelligence) at USC. I received my Bachelor’s degree in Computer Science and Engineering from the Ohio State University in 2016. I previously worked on some machine learning based projects such as an autonomous aerial system on drones in my undergrad. With strong interest in image processing for practical real life applications, I am currently working on the Breast Cancer Image segmentation project in MCL.

2. What is your impression about MCL and USC?

My impression about MCL is that the lab members are friendly and motivating. I feel everyone is approachable and the whole group like to help each other out. In addition, everyone is dedicated to their work and I am inspired to work hard and learn from them. USC provides valuable resources from the perspectives of both academia and industry. My impression of USC is that students have access to resources easily and professors have high standards of course quality, and there are many other valuable resources. I am glad to be a part of the Trojan family.

3. What is your future expectation and plan in MCL?

My expectation in MCL is to explore my potentials in pure research, especially in the image processing field. And I am glad I can be involved in the research of image segmentation and hope that could be helpful to society. I learnt great a lot from Prof. Kuo and I hope I can contribute my own efforts in MCL. It [...]

By |June 6th, 2021|News|Comments Off on Welcome MCL New Member – Peida Han|