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A knowledge graph is a collection of factual triples (h, r, t) consisting of two entities and one relation. Most knowledge graphs suffer from the incompleteness that there are many missing relations between entities. To predict missing links, each relation is modeled by a binary classifier to predict whether the links between two entities exist or not. Negative sampling is a task to draw negative samples efficiently and effectively from the unobserved triples to train the classifiers. The quality and quantity of the negative samples will highly affect the performance on link prediction.
Naive negative sampling [1] suggests generating negative samples by corrupting one of the entities in the observed triples, e.g. (h’, r, t) or (h, r, t’). Despite the simplicity of naive negative samples, the generated negative samples carry little semantics. For example, given a positive triple (Hulk, movie_genre, Science Fiction), a negative sample (Hulk, movie_genre, New York City) might be generated by naive negative sampling, which will never be a valid triple in the real-world scenario. Instead, we are looking for negative examples, such as (Hulk, movie_genre, Romance), that provide more information to the classifiers. Based on the observation, we only draw the corrupted entities within the set of observed entities that have been linked by the given relation, also known as the ‘range’ for the relation. However, a drawback is that the chance of drawing false negatives is high. Therefore, we further filter the drawn corrupted entities based on the entity-entity co-occurrence. For example, it’s not likely for us to generate a negative sample (Hulk, movie_genre, Adventure) because we know from the dataset that movie genres ‘Science Fiction’ and ‘Adventure’ are highly co-occurred. The first figure shows how the positive [...]

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.

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 [...]

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/

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.