Jiann-Shiun Yuan

Aminollah Khormali, Jiann-Shiun Yuan

Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset, where training and testing take place on the in-distribution dataset. However, their performance deteriorates significantly when presented with unseen samples. As a result, a reliable deepfake detection system must remain impartial to forgery types, appearance, and quality for guaranteed generalizable detection performance. Despite various attempts to enhance cross-dataset generalization, the problem remains challenging, particularly when testing against common post-processing perturbations, such as video compression or blur. Hence, this study introduces a deepfake detection framework, leveraging a self-supervised pre-training model that delivers exceptional generalization ability, withstanding common corruptions and enabling feature explainability. The framework comprises three key components: a feature extractor based on vision Transformer architecture that is pre-trained via self-supervised contrastive learning methodology, a graph convolution network coupled with a Transformer discriminator, and a graph Transformer relevancy map that provides a better understanding of manipulated regions and further explains the model's decision. To assess the effectiveness of the proposed framework, several challenging experiments are conducted, including in-data distribution performance, cross-dataset, cross-manipulation generalization, and robustness against common post-production perturbations. The results achieved demonstrate the remarkable effectiveness of the proposed deepfake detection framework, surpassing the current state-of-the-art approaches.

Azwad Tamir, Jiann-Shiun Yuan

Recent developments in next generation sequencing technology have led to the creation of extensive, open-source protein databases consisting of hundreds of millions of sequences. To render these sequences applicable in biomedical applications, they must be meticulously annotated by wet lab testing or extracting them from existing literature. Over the last few years, researchers have developed numerous automatic annotation systems, particularly deep learning models based on machine learning and artificial intelligence, to address this issue. In this work, we propose a transformer-based fusion model capable of predicting Gene Ontology (GO) terms from full-scale protein sequences, achieving state-of-the-art accuracy compared to other contemporary machine learning annotation systems. The approach performs particularly well on clustered split datasets, which comprise training and testing samples originating from distinct distributions that are structurally diverse. This demonstrates that the model is able to understand both short and long term dependencies within the enzyme's structure and can precisely identify the motifs associated with the various GO terms. Furthermore, the technique is lightweight and less computationally expensive compared to the benchmark methods, while at the same time not unaffected by sequence length, rendering it appropriate for diverse applications with varying sequence lengths.

Azwad Tamir, Jiann-Shiun Yuan

It is a common practice in modern medicine to prescribe multiple medications simultaneously to treat diseases. However, these medications could have adverse reactions between them, known as Drug-Drug Interactions (DDI), which have the potential to cause significant bodily injury and could even be fatal. Hence, it is essential to identify all the DDI events before prescribing multiple drugs to a patient. Most contemporary research for predicting DDI events relies on either information from Biomedical Knowledge graphs (KG) or drug SMILES, with very few managing to merge data from both to make predictions. While others use heuristic algorithms to extract features from SMILES and KGs, which are then fed into a Deep Learning framework to generate output. In this study, we propose a KG-integrated Transformer architecture to generate an end-to-end fully automated Machine Learning pipeline for predicting DDI events with high accuracy. The algorithm takes full-scale molecular SMILES sequences of a pair of drugs and a biomedical KG as input and predicts the interaction between the two drugs with high precision. The results show superior performance in two different benchmark datasets compared to existing state-of-the-art models especially when the test and training sets contain distinct sets of drug molecules. This demonstrates the strong generalization of the proposed model, indicating its potential for DDI event prediction for newly developed drugs. The model does not depend on heuristic models for generating embeddings and has a minimal number of hyperparameters, making it easy to use while demonstrating outstanding performance in low-data scenarios.

Shayan Taheri, Jiann-Shiun Yuan

Energy efficiency is one of the most important parameters for designing and building a computing system nowadays. Introduction of new transistor and memory technologies to the integrated circuits design have brought hope for low energy very large scale integration (VLSI) circuit design. This excellency is pleasant if the computing system is secure and the energy is not wasted through execution of malicious actions. In fact, it is required to make sure that the utilized transistor and memory devices function correctly and no error occurs in the system operation. In this regard, we propose a built-in-self-test architecture for security checking of the magnetic tunnel junction (MTJ) device under malicious process variations attack. Also, a general identification technique is presented to investigate the behaviour and activities of the employed circuitries within this MTJ testing architecture. The presented identification technique tries to find any abnormal behaviour using the circuit current signal.

Shayan Taheri, Jiann-Shiun Yuan

Security and energy are considered as the most important parameters for designing and building a computing system nowadays. Today's attacks target different layers of the computing system (i.e. software and hardware). Introduction of new transistor technologies to the integrated circuits design is beneficial, especially for low energy requirements. The new devices have unique features and properties that provide security advantages. However, these properties may come to the aid of an adversary to design stronger attacks. Therefore, the advantages as well as the disadvantages of these technologies need to be well studied. This paper demonstrates the area and power efficiency of the tunnel field-effect transistor (TFET) technology along with analyzing its security aspects.