Canada

We present an imagification approach for multivariate time-series data tailored to constrained NN-based forecasting model training environments. Our imagification process consists of two key steps: Re-stacking and time embedding. In the Re-stacking stage, time-series data are arranged based on high correlation, forming the first image channel using a sliding window technique. The time embedding stage adds two additional image channels by incorporating real-time information. We evaluate our method by comparing it with three benchmark imagification techniques using a simple CNN-based model. Additionally, we conduct a comparison with LSTM, a conventional time-series forecasting model. Experimental results demonstrate that our proposed approach achieves three times faster model training termination while maintaining forecasting accuracy.

AAAI 2024

Multivariate Time-Series Imagification with Time Embedding in Constrained Environments (Student Abstract) | VIDEO

convolutional neural netwroks

imagification

multivariate time-series data

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

technical paper

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Anomaly detection is a critical task across various domains. Fundamentally, anomaly detection models offer methods to identify unusual patterns that do not align with expected behaviors. Notably, in the medical field, detecting anomalies in medical imagery or biometrics can facilitate early diagnosis of diseases. Consequently, we propose the Skip-GANomaly++ model, an enhanced and more efficient version of the conventional anomaly detection models. The proposed model's performance was evaluated through comparative experiments. Experimental results demonstrated superior performance across most classes compared to the previous models.

Skip-GANomaly++: Skip Connections and Residual Blocks for Anomaly Detection (Student Abstract)

Solar power generation has recently been in the spotlight as global warming continues to worsen. However, two significant problems may hinder solar power generation, considering that solar panels are installed outside. The first is soiling, which accumulates on solar panels, and the second is a decrease in sunlight owing to bad weather.
In this paper, we will demonstrate that the solar power generation forecasting can increase when considering soiling and sunlight information. We first introduce a dataset containing images of clean and soiled solar panels, sky images, and weather information. For accurate solar power generation forecasting, we propose a new multimodal model that aggregates various features related to weather, soiling, and sunlight. The experimental results demonstrated the high accuracy of our proposed multimodal model.

Solar Power Generation Forecasting via Multimodal Feature Fusion (Student Abstract) | VIDEO

The population characteristics of the datasets related to the same task may vary significantly and merging them may harm performance. In this paper, we propose a novel method of domain adaptation called "cross-adaptation". It allows for implicit adaptation to the target domain without the need for any labeled examples across this domain. We test our approach on 9 datasets for SARS-CoV-2 detection from complete blood count from different hospitals around the world. Results show that our solution is universal with respect to various classification algorithms and allows for up to a 10pp increase in F1 score on average.

Target-Free Domain Adaptation through Cross-Adaptation (Student Abstract) | VIDEO

Current practice of mobility scooter user authentication using physical keys and traditional password-based one-time security mechanisms cannot meet the needs of many mobility scooter riders, especially senior citizens having issues in recalling memory. Now seamless authentication approaches are needed to provide ongoing protection for mobility scooters against takeovers and unauthorized access. Existing continuous authentication techniques do not work well in a mobility scooter setting due to issues such as user comfort, deployment cost and enrollment time, among others. In that direction, our contributions in this research effort are two-fold: (i) we propose a novel system that incorporates advances in few-shot learning, hierarchical processing, and contextual embedding to establish continuous authentication for mobility scooter riders using only posture data. This security system, trained on data collected from real mobility scooter riders, demonstrates quick enrollment and easy deployability, while successfully serving as an unobtrusive first layer of security. (ii) we provide to the research community the largest publicly available repository of mobility scooter riders' body key-points data to enable further research in this direction.

Rider Posture-Based Continuous Authentication with Few-Shot Learning for Mobility Scooters (Student Abstract) | VIDEO

Facial Expression Recognition (FER) is an extensively explored research problem in the domain of computer vision and artificial intelligence. FER, a supervised learning problem, requires significant training data representative of multiple socio-cultural demographic attributes. However, most of the FER dataset consists of images annotated by humans, which propagates individual and demographic biases. This work attempts to mitigate this bias using representation learning based on latent spaces, thereby increasing a deep learning model's fairness and overall accuracy.

FAIR-FER: A Latent Alignment Approach for Mitigating Bias in Facial Expression Recognition (Student Abstract)

Chronic Obstructive Pulmonary Disorder (COPD) is a prevalent respiratory disease that significantly impacts the quality of life of affected individuals. This paper presents COPD-FlowNet, a novel deep-learning framework that leverages a custom Generative Adversarial Network (GAN) to generate synthetic Computational Fluid Dynamics (CFD) velocity flow field images specific to the trachea of COPD patients. These synthetic images serve as a valuable resource for data augmentation and model training. Additionally, COPD-FlowNet incorporates a custom Convolutional Neural Network (CNN) architecture to predict the location of the obstruction site.

COPD-FlowNet: Elevating Non-invasive COPD Diagnosis with CFD Simulations (Student Abstract)

Influence Maximization is the task of selecting optimal nodes maximising the influence spread in social networks. This study proposes a Discretized Quantum-based Salp Swarm Algorithm (DQSSA) for optimizing influence diffusion in social networks. By discretizing meta-heuristic algorithms and infusing them with quantum-inspired enhancements, we address issues like premature convergence and low efficacy. The proposed method, guided by quantum principles, offers a promising solution for Influence Maximisation. Experiments on four real-world datasets reveal DQSSA's superior performance as compared to established cutting-edge algorithms.

DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)

With the help of Vision transformers (ViTs), medical image segmentation was able to achieve outstanding performance. In particular, they overcome the limitation of convolutional neural networks (CNNs) which rely on local receptive fields. ViTs use self-attention mechanisms to consider relationships between all image pixels or patches simultaneously. However, they require large datasets for training and did not perform well on capturing low-level features. To that end, we propose DDViT, a novel ViT model that unites a CNN to alleviate data-hunger for medical image segmentation with two multi-scale feature representations. Significantly, our approach incorporates a ViT with a plug-in domain adapter (DA) with Double-Level Fusion (DLF) technique, complemented by a mutual knowledge distillation paradigm, facilitating the seamless exchange of knowledge between a universal network and specialized domain-specific network branches. The DLF framework plays a pivotal role in our encoder-decoder architecture, combining the innovation of the TransFuse module with a robust CNN-based encoder. Extensive experimentation across diverse medical image segmentation datasets underscores the remarkable efficacy of DDViT when compared to alternative approaches based on CNNs and Transformer-based models.

DDViT: Double-Level Fusion Domain Adapter Vision Transformer (Student Abstract)

Reverse engineering involves analyzing the design, architecture, and functionality of systems, and is crucial for legacy systems. Legacy systems are outdated software systems that are still in use and often lack proper documentation, which makes their maintenance and evolution challenging. To address this, we introduce SC2Req, utilizing the Generative Pre-trained Transformer (GPT) for automated code analysis and requirement generation. This approach aims to convert source code into understandable requirements and bridge the gap between those two. Through experiments on diverse software projects, SC2Req shows the potential to enhance the accuracy and efficiency of the translation process. This approach not only facilitates faster software development and easier maintenance of legacy systems but also lays a strong foundation for future research, promoting better understanding and communication in software development.

Bridging the Gap between Source Code and Requirements Using GPT (Student Abstract)

Implicit field representations offer an effective way of generating 3D object shapes. They leverage an implicit decoder (IM-NET) trained to take a 3D point coordinate concatenated with a shape encoding and to output a value indicating whether the point is outside the shape. This approach enables the efficient rendering of visually plausible objects but also has some significant limitations, resulting in a cumbersome training procedure and empty spaces within the rendered mesh. In this paper, we introduce a new HyperCube architecture based on interval arithmetic that enables direct processing of 3D voxels, trained using a hypernetwork paradigm to enforce model convergence. The code is available at https://github.com/mproszewska/hypercube.

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Skip-GANomaly++: Skip Connections and Residual Blocks for Anomaly Detection (Student Abstract)

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