Canada

It is necessary to explore an effective point cloud completion mechanism that is of great significance for real-world tasks such as autonomous driving, robotics applications, and multi-target tracking. In this paper, we propose a point cloud completion method using a self-supervised transformer model based on the contextual constraints of scene flow. Our method uses the multi-frame point cloud context relationship as a guide to generate a series of token proposals, this priori condition ensures the stability of the point cloud completion. The experimental results show that the method proposed in this paper achieves high accuracy and good stability.

AAAI 2024

Scene Flow Prior Based Point Cloud Completion with Masked Transformer (Student Abstract)

3d computer vision motion & tracking scene analysis & understanding

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

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.<br><br><br><br>

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. 

Hashing-based recommendation (HR) methods, whose core idea is mapping users and items into hamming space, are common practice to improve item retrieval efficiency. However, existing HR fails to align optimization objective (i.e., Bayesian Personalized Ranking) and evaluation metric (i.e., Recall), leading to suboptimal performance. In this paper, we propose a smooth recall loss (termed as SRLoss), which targets Recall as the optimization objective. Due to the existence of discrete constraints, the optimization problem is NP-hard. To this end, we propose an approximation-adjustable gradient estimator to solve our problem. Experimental Results demonstrate the effectiveness of our proposed method.

Optimizing Recall in Deep Graph Hashing Framework for Item Retrieval (Student Abstract)

DNA methylation is an epigenetic mechanism for regulating gene expression, and it plays an important role in many biological processes. While methylation sites can be identified using laboratory techniques, much work is being done on developing computational approaches using machine learning. Here, we present a deep-learning algorithm for determining the 5-methylcytosine status of a DNA sequence. We propose an ensemble framework that treats the self-attention score as an explicit feature that is added to the encoder layer generated by fine-tuned language models. We evaluate the performance of the model under different data distribution scenarios.

Leverage the Explainability of Transformer Models to Improve the DNA 5-Methylcytosine Identification (Student Abstract)

With the abundance of learning resources available on massive open online courses (MOOCs) platforms, the issue of interactive data sparsity has emerged as a significant challenge.This paper introduces MRMLREC, an efficient MOOC video recommendation which consists of two main stages: multi-relational representation and multi-level recommendation, aiming to solve the problem of data sparsity. In the multi-relational representation stage, MRMLREC adopts a tripartite approach, constructing relational graphs based on temporal sequences, courses-videos relation, and knowledge concepts-video relation. These graphs are processed by a Graph Convolution Network (GCN) and two variant Graph Attention Networks (GAT) to derive representations. A variant of the Long Short-Term Memory Network (LSTM) then integrates these multi-dimensional data to enhance the overall representation. The multi-level recommendation stage introduces three prediction tasks at varying levels—courses, knowledge concepts, and videos—to mitigate data sparsity and improve the interpretability of video recommendations. Beam search (BS) is employed to identify top-β items at each level, refining the subsequent level's search space and enhancing recommendation efficiency. Additionally, an optional layer offers both personalization and diversification modes, ensuring variety in recommended videos and maintaining learner engagement. Comprehensive experiments demonstrate the effectiveness of MRMLREC on two real-world instances from Xuetang X.

MRMLREC: A Two-Stage Approach for Addressing Data Sparsity in MOOC Video Recommendation (Student Abstract)

Efficient exploration for an agent is challenging in reinforcement learning (RL). In this paper, a novel actor-critic framework namely virtual action actor-critic (VAAC), is proposed to address the challenge of efficient exploration in RL. This work is inspired by humans' ability to imagine the potential outcomes of their actions without actually taking them. In order to emulate this ability, VAAC introduces a new actor called virtual actor (VA), alongside the conventional actor-critic framework. Unlike the conventional actor, the VA takes the virtual action to anticipate the next state without interacting with the environment. With the virtual policy following a Gaussian distribution, the VA is trained to maximize the anticipated novelty of the subsequent state resulting from a virtual action. If any next state resulting from available actions does not exhibit high anticipated novelty, training the VA leads to an increase in the virtual policy entropy. Hence, high virtual policy entropy represents that there is no room for exploration. The proposed VAAC aims to maximize a modified Q function, which combines cumulative rewards and the negative sum of virtual policy entropy. Experimental results show that the VAAC improves the exploration performance compared to existing algorithms.

Virtual Action Actor-Critic Framework for Exploration (Student Abstract)

Data distillation is a technique of reducing a large dataset into a smaller dataset. The smaller dataset can then be used to train a model which can perform comparably to a model trained on the full dataset. Past works have examined this approach for image datasets, focusing on neural networks as target models. However, tabular datasets pose new challenges not seen in images. A sample in tabular dataset is a one dimensional vector unlike the two (or three) dimensional pixel grid of images, and Non-NN models such as XGBoost can often outperform neural network (NN) based models. Our contribution in this work is two-fold: 1) We show in our work that data distillation methods from images do not translate directly to tabular data; 2) We propose a new distillation method that consistently outperforms the baseline for multiple different models, including non-NN models such as XGBoost.

Effective Data Distillation for Tabular Datasets (Student Abstract)

Interpreting deep neural networks through examining neurons offers distinct advantages when it comes to exploring the inner workings of Deep Neural Networks. Previous research has indicated that specific neurons within deep vision networks possess semantic meaning and play pivotal roles in model performance. Nonetheless, the current methods for generating neuron semantics heavily rely on human intervention, which hampers their scalability and applicability. To address this limitation, this paper proposes a novel post-hoc framework for generating semantic explanations of neurons with large foundation models, without requiring human intervention or prior knowledge. Experiments are conducted with both qualitative and quantitative analysis to verify the effectiveness of our proposed approach.

Automated Natural Language Explanation of Deep Visual Neurons with Large Models (Student Abstract) | VIDEO

Simultaneous Coalition Structure Generation and Assignment (SCSGA) is an important research problem in multi-agent systems. Given n agents and m tasks, the aim of SCSGA is to form m disjoint coalitions of n agents such that between the coalitions and tasks there is a one-to-one mapping, which ensures each coalition is capable of accomplishing the assigned task. SCSGA with Multi-dimensional Features (SCSGA-MF) extends the problem by introducing a d-dimensional vector for each agent and task. We propose a heuristic algorithm called Multiple Distance Metric (MDM) approach to solve SCSGA-MF. Experimental results confirm that MDM produces near optimal solutions, while being feasible for large-scale inputs within a reasonable time frame.

Coalition Formation for Task Allocation Using Multiple Distance Metrics (Student Abstract)

Dairy owners invest heavily to keep their animals healthy. There is good reason to hope that technologies such as computer vision and artificial intelligence (AI) could reduce costs, yet obstacles arise when adapting these advanced tools to farming environments. In this work, we applied AI tools to dairy cow teat localization and teat shape classification, obtaining a model that achieves a mean average precision of 0.783. This digital twin-driven approach is intended as a first step towards automating and accelerating the detection and treatment of hyperkeratosis, mastitis, and other medical conditions that significantly burden the dairy industry.

Digital Twin-Driven Teat Localization and Shape Identification for Dairy Cow (Student Abstract)

In the era of large language models like Chatgpt, maintaining academic integrity in programming education has become challenging due to potential misuse. There's a pressing need for reliable detectors to identify Chatgpt-generated code. While previous studies have tackled model-generated text detection, identifying such code remains uncharted territory. In this paper, we introduce a novel method to discern Chatgpt-generated code. We employ targeted masking perturbation, emphasizing code sections with high perplexity. Fine-tuned CodeBERT is utilized to replace these masked sections, generating subtly perturbed samples. Our scoring system amalgamates overall perplexity, variations in code line perplexity, and burstiness. In this scoring scheme, a higher rank for the original code suggests it's more likely to be chatgpt-generated. The underlying principle is that code generated by models typically exhibits consistent, low perplexity and reduced burstiness, with its ranking remaining relatively stable even after subtle modifications. In contrast, human-written code, when perturbed, is more likely to produce samples that the model prefers. Our approach significantly outperforms current detectors, especially against OpenAI's text-davinci-003 model, with the average AUC rising from 0.56 (GPTZero baseline) to 0.87.

ChatGPT-Generated Code Assignment Detection Using Perplexity of Large Language Models (Student Abstract)

The problem of finding the minimum three-dimensional Kochen–Specker (KS) vector system, an important problem in quantum foundations, has remained open for over 55 years. We present a new method to address this problem based on a combination of a Boolean satisfiability (SAT) solver and a computer algebra system (CAS). Our approach improved the lower bound on the size of a KS system from 22 to 24. More importantly, we provide the first computer-verifiable proof certificate of a lower bound to the KS problem with a proof size of 41.6 TiB for order 23. The efficiency is due to the powerful combination of SAT solvers and CAS-based orderly generation.

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