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Latent Logic Tree Extraction for Event Sequence Explanation from LLMs 41

Latent Logic Tree Extraction for Event Sequence Explanation ...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Song, Zitao Yang, Chao Wang, Chaojie An, Bo Li, Shuang School of Computer Science and Engineering Nanyang Technological University Singapore School of Data Science The Chinese University of Hong Kong Shenzhen China Skywork AI Singapore

Modern high-stakes systems, such as healthcare or robotics, often generate vast streaming event sequences. Our goal is to design an efficient, plug- and-play tool to elicit logic tree-based explanations from Large Language Models (LLMs) to provide customized insights into each observed event sequence. Built on the temporal point process model for events, our method employs the likelihood function as a score to evaluate generated logic trees. We propose an amortized Expectation-Maximization (EM) learning framework and treat the logic tree as latent variables. In the E-step, we evaluate the posterior distribution over the latent logic trees using an LLM prior and the likelihood of the observed event sequences. LLM provides a high-quality prior for the latent logic trees, however, since the posterior is built over a discrete combinatorial space, we cannot get the closed-form solution. We propose to generate logic tree samples from the posterior using a learnable GFlowNet, which is a diversity-seeking generator for structured discrete variables. The M-step employs the generated logic rules to approximate marginalization over the posterior, facilitating the learning of model parameters and refining the tunable LLM prior parameters. In the online setting, our locally built, lightweight model will iteratively extract the most relevant rules from LLMs for each sequence using only a few iterations. Empirical demonstrations showcase the promising performance and adaptability of our framework. Copyright 2024 by the author(s)

关键词： Trees (mathematics)

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Hidden Emotion Detection using Speech, Text, Facial Expressions and Neural Networks 5

Hidden Emotion Detection using Speech, Text, Facial Expressi...

引用

5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Reddy, Ravula Tarun Palaniswamy, Suja Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350379884

Understanding and recognition of human emotions are very crucial in various fields. This paper proposes a new approach to show the different feelings that are hidden using multi-modalities like video, audio, and textual data. A custom dataset AED-FST has been used in this work which has nearly 317 samples each consisting of some audio clip, some visual media, and a question-and-answer pair. From all these entirely diverse sources, the goal is to create a model that could possibly detect emotional cues. It found hidden emotions with a 67% accuracy, representing how well it could pick up on these weak cues from all different modalities. This method contributes immensely to emotion detection in extremely wide fields, including psychological diagnoses and human-computer interaction, to mention just a few. © 2024 IEEE.

关键词： Human computer interaction

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Exploring Deep Learning architectures for Crop and Weed Identification 3

Exploring Deep Learning architectures for Crop and Weed Iden...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Akaash Dravid, J. Karthik, R.K. Sreekumar, K. School of Computing Amrita Vishwavidyapeetham Department of Computer Science and IT Kochi India

ISBN: (纸本)9798350372120

In agriculture, weeds always prove to be a major threat. It is tedious to do weeding at a later stage when the crops and the weeds are significantly grown. Incorporating technology in agriculture has revolutionized both industries in many ways. In the AI approach towards agriculture, neural networks are employed for identifying and classifying crops and weeds. However, what features they learn, how they learn the difference between crops and weeds is what we need to investigate. Many vegetable crop seedlings and weeds look similar, so a significantly large amount of data is necessary for the model to classify them. In this paper we are using CNN (Convolutional Neural Networks) and Efficientnet architectures to classify the crops and weeds. We further analyze the computational efficiency and model complexity of the architectures, providing insights into their practical applicability in real-world agricultural settings. This helps in better understanding of deep learning techniques and selecting features to perform this task. We have obtained an accuracy of 91% for CNN architecture and 99% for both B3, B4 of EfficientNet Architecture. © 2024 IEEE.

关键词： Crops

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Deep Reinforcement Learning in Stock Trading: Evaluating DDPG and DQN Strategies 6

Deep Reinforcement Learning in Stock Trading: Evaluating DDP...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Kodurupaka, Nithin Basavadeepthi, H.M. Pecheti, Shiva Teja Amudha, J. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350306613

This study presents a comparative analysis of the Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithms in the context of stock trading, focusing on historical stock price data of S&P 500 stock index. The primary objective is to evaluate the effectiveness of these RL algorithms in making trading decisions and achieving financial performance relative to a passive buy-And-hold strategy. The assessment revolves around key metrics, with a special emphasis on the buy, hold, and sell indications as essential signals for trading actions. Results indicate that DQN outperforms DDPG, showcasing a smaller total loss during the evaluation period. This suggests that DQN may offer superior capabilities in limiting financial losses and making effective trading decisions compared to DDPG, providing valuable insights into algorithm selection for dynamic trading environments. It is crucial to recognize the significance of buy, hold, and sell indications as key elements in assessing the performance of RL algorithms in stock trading scenarios. © 2024 IEEE.

关键词： Algorithmic Trading Buy-And-Hold Strategy DDPG Trading Agent Deep Neural Networks Deep Reinforcement Learning DQN Portfolio Performance Total Return

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RBLA: Rank-Based-LoRA-Aggregation for Fine-Tuning Heterogeneous Models in FLaaS 31st

RBLA: Rank-Based-LoRA-Aggregation for Fine-Tuning Heterogen...

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31st International Conference on Web Services, ICWS 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Shuaijun Tavallaie, Omid Nazemi, Niousha Zomaya, Albert Y. School of Computer Science The University of Sydney Camperdown Australia Department of Engineering Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9783031770715

Federated Learning (FL) is a promising privacy-aware distributed learning framework that can be deployed on various devices, such as mobile phones, desktops, and devices equipped with CPUs or GPUs. In the context of server-based Federated Learning as a Service (FLaaS), FL enables a central server to coordinate the training process across multiple devices without direct access to local data, thereby enhancing privacy and data security. Low-Rank Adaptation (LoRA) is a method that efficiently fine-tunes models by focusing on a low-dimensional subspace of the model’s parameters. This approach significantly reduces computational and memory costs compared to fine-tuning all parameters from scratch. When integrated with FL, particularly in a FLaaS environment, LoRA allows for flexible and efficient deployment across diverse hardware with varying computational capabilities by adjusting the local model’s rank. However, in LoRA-enabled FL, different clients may train models with varying ranks, which poses challenges for model aggregation on the server. Current methods for aggregating models of different ranks involve padding weights to a uniform shape, which can degrade the global model’s performance. To address this issue, we propose Rank-Based LoRA Aggregation (RBLA), a novel model aggregation method designed for heterogeneous LoRA structures. RBLA preserves key features across models with different ranks. This paper analyzes the issues with current padding methods used to reshape models for aggregation in a FLaaS environment. Then, we introduce RBLA, a rank-based aggregation method that maintains both low-rank and high-rank features. Finally, we demonstrate the effectiveness of RBLA through comparative experiments with state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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An optimal hybrid cascade regional convolutional network for cyberattack detection

An optimal hybrid cascade regional convolutional network for...

引用

作者： Alqahtani, Ali Khan, Surbhi Bhatia Department of Networks and Communications Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Data Science School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Cyber-physical systems (CPS) and the Internet of Things (IoT) technologies link urban systems through networks and improve the delivery of quality services to residents. To enhance municipality services, information and communication technologies (ICTs) are integrated with urban systems. However, the large number of sensors in a smart city generates a significant amount of delicate data, like medical records, credit card numerics, and location coordinates, which are transported across a network to data centers for analysis and processing. This makes smart cities vulnerable to cyberattacks because of the resource constraints of their technology infrastructure. Applications for smart cities pose many security challenges, such as zero-day attacks resulting from exploiting weaknesses in various protocols. Therefore, this paper proposes an optimal hybrid transit search-cascade regional convolutional neural network (hybrid TS-Cascade R-CNN) to detect cyberattacks. The proposed model combines the hybrid transit-search approach with the cascade regional convolutional neural network to create an optimal solution for cyberattack detection. The cascade regional convolutional network uses a hybrid transit search algorithm to enhance the effectiveness of cyberattack detection. By integrating these two approaches, the system can leverage both global traffic patterns and local indicators to improve the accuracy of attack detection. During the training process, the proposed model recognizes and classifies malicious input even in the presence of sophisticated attacks. Finally, the experimental analysis is carried out for various attacks based on different metrics. The accuracy rate attained by the proposed approach is 99.2%, which is acceptable according to standards. © 2023 John Wiley & Sons Ltd.

关键词： Zero-day attack

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Enhancing Academic Integrity in Online Assessments: Introducing an Effective Online Exam Proctoring Model using YOLO 2

Enhancing Academic Integrity in Online Assessments: Introduc...

引用

2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Singh, Tripty Nair, Rekha R. Babu, Tina Duraisamy, Prakash Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Science and Engineering University of Wisconsin Green BayWI United States

The issue of cheating during exams has become a significant concern, gaining widespread attention from the public and media. This issue has been made worse by the transition of educational institutions to an online format, making it simpler to engage in academic dishonesty. To overcome this problem, developed an innovative solution known as the Online Exam Proctoring Model using You Only Look Once (YOLO) Model. With the use of cutting-edge computer vision algorithms, the proposed model makes use of data from cameras and microphones to automatically spot and prevent cheating during online exams. Face orientation, face spoofing detection, mouth tracking, audio analysis, mobile device recognition, and person counting are among the primary elements built into the model. Object detection algorithms, in conjunction with pre-trained YOLO weights, empower the model to detect mobile devices and count the number of individuals present in the exam environment. The automatic termination of exams upon detection of mobile devices, multiple persons, or no persons for 10 seconds acts as a strong deterrent against cheating. The suggested methodology created a reliable and effective tool for online examinations, guaranteeing the academic integrity and enabling impartial assessment of the skills and knowledge of learners. By implementing this solution, educational institutions can foster a secure online exam environment, promoting a culture of honesty and trust between students and educators. As navigate the evolving environment of education, the model stands as a reliable ally in upholding the credibility and authenticity of online learning experiences. © 2024 Elsevier B.V.. All rights reserved.

关键词： Object detection

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Empowering data structure visualization through modification in Python 15

Empowering data structure visualization through modification...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Puvvadi, Harsha Vardhan Angala, Hari Prasad Swaroop, Bandi Vishnu Reddy, Busa Thanuj Sathwik Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

This article introduces a novel approach to data structure visualization through the development of a new programming language, utilizing Python's Lex-YACC library for lexical analysis and parsing, and the Turtle module for graphics in the backend. The proposed language enables users to visualize various data structures such as arrays, stacks, and queues, along with their operations, facilitating a deeper understanding of these fundamental concepts in computer science. By combining the power of a custom scripting language with Python's versatile libraries, this research aims to empower users to interactively explore and comprehend complex data structures through intuitive visualizations. © 2024 IEEE.

关键词： Python

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LDA Topic Modeling for Bioinformatics Terms in arXiv Documents 9th

LDA Topic Modeling for Bioinformatics Terms in arXiv Documen...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Karnyoto, Andrea Stevens Henry, Matthew Martianus Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

A wide variety of disciplines contribute to bioinformatics research, including computer science, biology, chemistry, mathematics, and physics. This study determines the number of research articles published on arXiv classified as bioinformatics topics and the most frequently used bioinformatics terms using topic modeling, Latent Dirichlet Allocation (LDA). An algorithm based on LDA is used to discover topics hidden within large collections of documents through the use of statistical analysis. Our research examined 226453 articles on arXiv between January 2023 and January 2024. As a result, there are more than 10521 articles categorized into bioinformatics topics. Most commonly, 6352 documents are in the "Mathematical Physics" category. The second most popular category is "computer science," with 2950 documents. Accordingly, the terms 'RNA,' 'sequence,' 'tree,' and 'homology' are the three most commonly used terms in bioinformatics. The study of RNA plays a vital role in molecular biology;thus, the study of RNA is prevalent in bioinformatics. Sequential data refer to the order in which nucleotides or amino acids can be found in a DNA molecule or a protein. © 2024 The Authors.

关键词： arXiv bioinformatics Latent Dirichlet Allocation

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An Exploratory Computational Approach for Elucidating a Nonstructural Protein Interaction in Dengue Virus 2 9th

An Exploratory Computational Approach for Elucidating a Nons...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University 11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings. © 2024 The Authors.

关键词： Molecular docking

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