A Deep learning based food recognition system for lifelog images

In this paper, we propose a deep learning based system for food recognition from personal life archive im- ages. The system first identifies the eating moments based on multi-modal information, then tries to focus and enhance the food images available in these moments, and finally, exploits GoogleNet as the core of the learning process to recognise the food category of the images. Preliminary results, experimenting on the food recognition module of the proposed system, show that the proposed system achieves 95.97% classification accuracy on the food images taken from the personal life archive from several lifeloggers, which potentially can be extended and applied in broader scenarios and for different types of food categories

Hybrid Transformer Network for Deepfake Detection

Deepfake media is becoming widespread nowadays because of the easily available tools and mobile apps which can generate realistic looking deepfake videos/images without requiring any technical knowledge. With further advances in this field of technology in the near future, the quantity and quality of deepfake media is also expected to flourish, while making deepfake media a likely new practical tool to spread mis/disinformation. Because of these concerns, the deepfake media detection tools are becoming a necessity. In this study, we propose a novel hybrid transformer network utilizing early feature fusion strategy for deepfake video detection. Our model employs two different CNN networks, i.e., (1) XceptionNet and (2) EfficientNet-B4 as feature extractors. We train both feature extractors along with the transformer in an end-to-end manner on FaceForensics++, DFDC benchmarks. Our model, while having relatively straightforward architecture, achieves comparable results to other more advanced state-of-the-art approaches when evaluated on FaceForensics++ and DFDC benchmarks. Besides this, we also propose novel face cut-out augmentations, as well as random cut-out augmentations. We show that the proposed augmentations improve the detection performance of our model and reduce overfitting. In addition to that, we show that our model is capable of learning from considerably small amount of data.publishedVersio

VEGAS: a variable length-based genetic algorithm for ensemble selection in deep ensemble learning.

In this study, we introduce an ensemble selection method for deep ensemble systems called VEGAS. The deep ensemble models include multiple layers of the ensemble of classifiers (EoC). At each layer, we train the EoC and generates training data for the next layer by concatenating the predictions for training observations and the original training data. The predictions of the classifiers in the last layer are combined by a combining method to obtain the final collaborated prediction. We further improve the prediction accuracy of a deep ensemble model by searching for its optimal configuration, i.e., the optimal set of classifiers in each layer. The optimal configuration is obtained using the Variable-Length Genetic Algorithm (VLGA) to maximize the prediction accuracy of the deep ensemble model on the validation set. We developed three operators of VLGA: roulette wheel selection for breeding, a chunk-based crossover based on the number of classifiers to generate new offsprings, and multiple random points-based mutation on each offspring. The experiments on 20 datasets show that VEGAS outperforms selected benchmark algorithms, including two well-known ensemble methods (Random Forest and XgBoost) and three deep learning methods (Multiple Layer Perceptron, gcForest, and MULES)

Detecting Out-of-Context Image-Caption Pairs in News: A Counter-Intuitive Method

The growth of misinformation and re-contextualized media in social media and news leads to an increasing need for fact-checking methods. Concurrently, the advancement in generative models makes cheapfakes and deepfakes both easier to make and harder to detect. In this paper, we present a novel approach using generative image models to our advantage for detecting Out-of-Context (OOC) use of images-caption pairs in news. We present two new datasets with a total of $6800$ images generated using two different generative models including (1) DALL-E 2, and (2) Stable-Diffusion. We are confident that the method proposed in this paper can further research on generative models in the field of cheapfake detection, and that the resulting datasets can be used to train and evaluate new models aimed at detecting cheapfakes. We run a preliminary qualitative and quantitative analysis to evaluate the performance of each image generation model for this task, and evaluate a handful of methods for computing image similarity.Comment: ACM International Conference on Content-Based Multimedia Indexing (CBMI '23

Impact of the usage of Vietnamese consonant-vowel (CV) structure on the intelligibility of Vietnamese speakers of English

This paper reports on findings from an investigation into the potential impact of the mother tongue of 50 Vietnamese adult EFL learners on their English intelligibility, with a particular focus on CV (consonant - vowel) syllable structure. The data from this quantitative study indicate that participants applied the Vietnamese CV syllable structure (open syllables CV.V) to the pronunciation of English CVC syllable structure (closed syllables CVC.V particularly in polysyllabic words and words with CVL (consonant – vowel – lateral) structure, potentially affecting speech intelligibility. These outcomes contribute to research on EFL speakers’ intelligibility

Phenolic glucosides from the leaves of Desmodium gangeticum (L.) DC

From the water-soluble extract of the leaves of Desmodium gangeticum, three phenolic glucosides were isolated. By means of spectroscopic methods they were identified as methyl salicylate β-D-glucopyranoside (1), leonuriside A (2) and syringaresinol-4'-O-β-D-glucopyranoside (3). These compounds were isolated for the first time from the genus Desmodium. Compound 1 significantly inhibited a-glucosidase in comparison with diabetic drug acarbose

A usage analytics model for analysing user behaviour in IBM academic cloud

Usage in the software domain refers to the knowledge about how end- users use the application and how the application responds to the user’s actions. Usage can be revealed by monitoring the user’s interaction with the application. However, in the cloud environment, it is non-trivial to understand the interactions of the users by using only the monitoring solutions. For example, user’s behaviour, user’s usage pattern, which features of a cloud application are critical for a user, to name a few, cannot be extracted using only the existing monitoring tools. Understanding these information require additional analysis, which can be done by using usage analytics. For this purpose, in this paper, we propose a novel process model design for incorporating Usage Analytics in a cloud environment. We evaluate this proposed process model in the context of academic applications and services in the cloud, with the focus on IBM Academic Cloud

DCU at the NTCIR-13 Lifelog-2 Task

In this work, we outline the submissions of Dublin City University (DCU) team, the organisers, to the NTCIR-13 Lifelog-2 Task. We submitted runs to the Lifelog Semantics Access (LSAT) and the Lifelog Insight (LIT) sub-tasks