Макаров Илья Андреевич

EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas

2024 · CHAPTER · en

Pose Networks Unveiled: Bridging the Gap for Monocular Depth Perception

2024 · CHAPTER · en

Depth estimation is essential in Augmented Reality applications, enabling realistic object placement, scene understanding, spatial mapping, interaction, and environment awareness. This paper proposes a method to enhance depth model performance without increasing inference costs by improving the pose network in a selfsupervised learning setup. In particular, we enrich spatial information in the pose network by incorporating features from different scales and normalized coordinates. It is experimentally shown on the KITTI dataset that our approach achieves a 2-7% improvement in the abs rel metric when compared to baseline techniques.

LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering

2024 · ARTICLE · en

We present LLM-KT, a flexible framework designed to enhance collaborative filtering (CF) models by seamlessly integrating LLM (Large Language Model)-generated features. Unlike existing methods that rely on passing LLM-generated features as direct inputs, our framework injects these features into an intermediate layer of any CF model, allowing the model to reconstruct and leverage the embeddings internally. This modelagnostic approach works with a wide range of CF models without requiring architectural changes, making it adaptable to various recommendation scenarios. Our framework is built for easy integration and modification, providing researchers and developers with a powerful tool for extending CF model capabilities through efficient knowledge transfer. We demonstrate its effectiveness through experiments on the MovieLens and Amazon datasets, where it consistently improves baseline CF models. Experimental studies showed that LLM-KT is competitive with the state-of-the-art methods in context-aware settings but can be applied to a broader range of CF models than current approaches. Index Terms—Large Language Model (LLM), recommender systems, knowledge transfer, RecBole framework

Graph Neural Networks With Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data

2024 в печати · ARTICLE · en

Timely detection and accurate diagnosis of faults in technological processes can significantly reduce production costs in manufacturing. Modern industrial equipment, equipped with numerous sensors, generates vast amounts of data, providing opportunities for advanced fault detection and diagnosis. While convolutional and recurrent neural networks have achieved state-of-the-art performance, they often overlook the correlations and hidden relationships among sensor signals. To address this, we propose a graph neural network (GNN) architecture that constructs graphs of sensor relationships from data. We evaluated five methods for training different types of adjacency matrices allowing to set certain restrictions on the structure of the graph. The resulting graph structures were analyzed and potential for their use in transfer learning was evaluated. Additionally, we developed an architecture that uses multiple adjacency matrices, which reduces the number of trainable parameters while maintaining high prediction quality. Our models demonstrated state-of-the-art performance on the Tennessee Eastman Process dataset, showcasing their potential for fault diagnosis on multivariate sensor data.

AADMIP: Adversarial Attacks and Defenses Modeling in Industrial Processes

2024 · CHAPTER · en

The development of the smart manufacturing trend includes the integration of Artificial Intelligence technologies into industrial processes. One example of such implementation is deep learning models that diagnose the current state of a technological process. Recent studies have demonstrated that small data perturbations, named adversarial attacks, can significantly affect the correct predictions of such models. This fact is critical in industrial systems, where AI-based decisions can be made to manage physical equipment. In this work, we present a system which can help to evaluate the robustness of technological process diagnosis models to adversarial attacks, as well as consider protection options. We briefly review the system's modules and also consider some useful applications. Our demo video is available at: http://tinyurl.com/3by9zcj5

Time Series Generation with GANs for Momentum Effect Simulation on Moscow Stock Exchange

2024 в печати · CHAPTER · en

The ability to accurately simulate financial markets is crucial, as it allows researchers and practitioners to rigorously test and refine trading strategies without the high risks associ-ated with real-world experimentation. By leveraging Generative Adversarial Networks (GANs), this research aims to enhance the robustness and effectiveness of trading strategies by providing a controlled environment to assess potential outcomes and strategy resilience under varied market conditions. In this work, we propose the application of GAN s for simu-1ating multidimensional time series in the context of developing and testing trading strategies. We conduct an experimental study to jointly simulate the log-returns of several stocks on Moscow Exchange for Momentum effect evaluation. Compared to traditional methods such as bootstrapping, GANs can better model and interpolate the non-parametric complex nature of the data, providing an increased diverse sample size. This methodology could be beneficial for investors seeking new opportunities to test and tune hyperparameters of other trading strategies.

Ti-DC-GNN: Incorporating Time-Interval Dual Graphs for Recommender Systems

2023 · CHAPTER · en

Recommender systems are essential for personalized content delivery and have become increasingly popular recently. However, traditional recommender systems are limited in their ability to capture complex relationships between users and items. Dynamic graph neural networks (DGNNs) have recently emerged as a promising solution for improving recommender systems by incorporating temporal and sequential information in dynamic graphs. In this paper, we propose a novel method, "Ti-DC-GNN" (Time-Interval Dual Causal Graph Neural Networks), based on an intermediate representation of graph evolution as a sequence of time-interval graphs. The main parts of the method are the novel forms of interval graphs: graph of causality and graph of consequence that explicitly preserve inter-relationships between edges (user-items interactions). The local and global message passing are developed based on edge memory to identify short-term and long-term dependencies. Experiments on several well-known datasets show that our method consistently outperforms modern temporal GNNs with node memory alone in dynamic edge prediction tasks.

Fast Search of Face Recognition Model for a Mobile Device Based on Neural Architecture Comparator

2023 · ARTICLE · en

This paper addresses the face recognition task for offline mobile applications. Using AutoML techniques, a novel technological framework is proposed to develop a fast neural network-based facial feature extractor for a concrete device. First, the Once-for-All SuperNet is trained on a large facial dataset. Each device is characterized by its lookup table, which contains the running times of inference in each layer of the SuperNet. An evolutionary search is then used to select the most accurate subnetwork within a limit on the maximum expected latency. It is proposed to train a neural architecture comparator using Gradient Boosted Trees to choose the better subnetwork in this search. Experimental face verification and recognition results demonstrate the robustness of the novel method to various facial region positions. The best model achieves an identification accuracy of 98.7% for the LFW dataset in less than 5 ms on the Qualcomm Snapdragon 865 GPU.

Dealing With Sparse Rewards Using Graph Neural Networks

2023 · ARTICLE · en

Deep reinforcement learning in partially observable environments is a difficult task in itself and can be further complicated by a sparse reward signal. Most tasks involving navigation in three-dimensional environments provide the agent with minimal information. Typically, the agent receives a visual observation input from the environment and is rewarded once at the end of the episode. A good reward function could substantially improve the convergence of reinforcement learning algorithms for such tasks. The classic approach to increasing the density of the reward signal is to augment it with supplementary rewards. This technique is called reward shaping. In this study, we propose two modifications of one of the recent reward shaping methods based on graph convolutional networks: the first involving advanced aggregation functions, and the second utilizing the attention mechanism. We empirically validate the effectiveness of our solutions for the task of navigation in a 3D environment with sparse rewards. For the solution featuring the attention mechanism, we can also show that the learned attention is concentrated on edges corresponding to important transitions in the 3D environment.

SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes

2023 · ARTICLE · en

Modern industrial facilities generate large volumes of raw sensor data during the production process. This data is used to monitor and control the processes and can be analyzed to detect and predict process abnormalities. Typically, the data has to be annotated by experts in order to be used in predictive modeling. However, manual annotation of large amounts of data can be difficult in industrial settings. In this paper, we propose SensorSCAN, a novel method for unsupervised fault detection and diagnosis, designed for industrial chemical process monitoring. We demonstrate our model's performance on two publicly available datasets of the Tennessee Eastman Process with various faults. The results show that our method significantly outperforms existing approaches (+0.2-0.3 TPR for a fixed FPR) and effectively detects most of the process faults without expert annotation. Moreover, we show that the model fine-tuned on a small fraction of labeled data nearly reaches the performance of a SOTA model trained on the full dataset. We also demonstrate that our method is suitable for real-world applications where the number of faults is not known in advance. The code is available at https://github.com/AIRI-Institute/sensorscan.