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Rectifying Demonstration Shortcut in In-Context Learning

Rectifying Demonstration Shortcut in In-Context Learning

2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Jang, Joonwon Jang, Sanghwan Kweon, Wonbin Jeon, Minjin Yu, Hwanjo Graduate School of AI POSTECH Korea Republic of Department of Computer Science and Engineering POSTECH Korea Republic of Institute of Artificial Intelligence POSTECH Korea Republic of

ISBN: (纸本)9798891761148

Large language models (LLMs) are able to solve various tasks with only a few demonstrations utilizing their in-context learning (ICL) abilities. However, LLMs often rely on their pre-trained semantic priors of demonstrations rather than on the input-label relationships to proceed with ICL prediction. In this work, we term this phenomenon as the ‘Demonstration Shortcut’. While previous works have primarily focused on improving ICL prediction results for predefined tasks, we aim to rectify the Demonstration Shortcut, thereby enabling the LLM to effectively learn new input-label relationships from demonstrations. To achieve this, we introduce In-Context Calibration, a demonstration-aware calibration method. We evaluate the effectiveness of the proposed method in two settings: (1) the Original ICL Task using the standard label space and (2) the Task Learning setting, where the label space is replaced with semantically unrelated tokens. In both settings, In-Context Calibration demonstrates substantial improvements, with results generalized across three LLM families (OPT, GPT, and Llama2) under various 1 configurations. ©2024 Association for Computational Linguistics.

关键词： Demonstrations

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Technical Analysis of Stock Market Trends using LSTM for Price Prognosis 7

Technical Analysis of Stock Market Trends using LSTM for Pri...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Zade, Nilima Gupta, Kanishk Mutha, Siddhanth Mengshetti, Om Joshi, Gayatri Iyer, R. Kamatchi Lavale Department of Computer Science and Engineering Pune412115 India Universal Ai University Karjat Maharashtra Mumbai India

ISBN: (纸本)9798350342154

This study proposes the utilization of Long Short Term Memory (LSTM) brain networks for specialized analysis of stock exchange patterns and trends to anticipate future stock prices. In this particular study, a comparison is done with three other Machine Learning techniques to show that LSTM is the best implementation. The LSTM model is trained on the previous stock market data, including prices and trading volumes, to learn complex patterns and relationships with stock price trends. The model's accuracy in predicting future stock prices is evaluated, and the results show that LSTM is an effective tool for technical analysis, outperforming traditional time-series forecasting methods. LSTM's ability to capture long term dependencies and unpredictable relationships in the dataset makes it a valuable tool for traders and investors seeking to make informed decisions based on technical analysis of stock market trends. The study demonstrates the potential of LSTM in stock market forecasting, improving investment decision making with accurate price predictions. © 2023 IEEE.

关键词： Long short-term memory

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Deep Learning based Speech Emotion Recognition using Multiple Acoustic Features

Deep Learning based Speech Emotion Recognition using Multipl...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Shwetkranti Taware Anuradha D. Thakare Department of Computer Engg. Pimpri Chinchwad College of Enggineering Nigadi India Department of Computer Science & Engineering(AI&ML) Pimpri Chinchwad College of Engineering Pune India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Automatic speech emotion recognition (ASER) is crucial for multiple robotics applications, social media platforms, human-machine interaction systems, auto-chatbots, etc. However, the ASER performance is poor due to inadequate representation of the emotional speech, poor feature distinctiveness, and less generalization capability. This paper presents ASER based on a Deep Convolutional Neural Network (DCNN) that accepts the multiple acoustic features (MAF) to describe emotional speech. Further, it uses a combination of hybrid particle swarm optimization and Archimedes optimization algorithm (PSO-AOA) for selecting the essential features from MAFs. The outcomes of the MAF-PSO-AOA-DCNN are assessed on the BAUM dataset. It provides an overall accuracy of 95.20%, precision of 0.96, recall of 0.94, and F1-score of 0.95, which is superior to traditional techniques.

关键词： Human computer interaction Deep learning Emotion recognition Social networking (online) Speech recognition Acoustics Particle swarm optimization

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Robust Calibration For Improved Weather Prediction Under Distributional Shift

arXiv

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arXiv 2024年

作者： Gilda, Sankalp Bhandari, Neel Mak, Wendy Panizza, Andrea ML Collective Department of Computer Science and Engineering RV College of Engineering India

In this paper, we present results on improving out-of-domain weather prediction and uncertainty estimation as part of the Shifts Challenge on Robustness and Uncertainty under Real-World Distributional Shift challenge. We find that by leveraging a mixture of experts in conjunction with an advanced data augmentation technique borrowed from the computer vision domain, in conjunction with robust post-hoc calibration of predictive uncertainties, we can potentially achieve more accurate and better-calibrated results with deep neural networks than with boosted tree models for tabular data. We quantify our predictions using several metrics and propose several future lines of inquiry and experimentation to boost performance. © 2024, CC BY-NC-SA.

关键词： Deep neural networks

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An Effective Trusted and Secure based Clustering and Routing using Improved Bat Optimization Algorithm 2

An Effective Trusted and Secure based Clustering and Routing...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Al-Attabi, Kassem Aluvala, Srinivas Kodati, Sarangam Anu, D. Swapna, P. The Islamic University Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Cvr College of Engineering TS Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India Sree Dattha Institute of Engineering and Science Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350317558

Security measures contribute to the overall longevity of the Wireless Sensor Network (WSN) by preventing attacks that could lead to node compromise or depletion of energy resources. WSNs operate in dynamic environments, necessitating adaptable secure clustering algorithms that can effectively respond to frequent changes in network topology. In this work, we introduce the Improved Bat Optimization algorithm (IBOA) for secure clustering and routing in WSN. We subsequently apply the secure-based trust protocol to transmit data on the secured path. The fitness function is obtained by utilizing the well-defined limitations named energy, delay, security, as well as distance. The proposed method achieves better results and attains an energy consumption of 0.1 (J), an average delay of 0.1 sec, Packet Delivery Ratio (PDR) of 97.5%, and throughput of 80% at the number of nodes of 100 respectively when compared to existing methods such as Dempster-Shafer Theory-based Lion Optimization Algorithm clustering (DST-LOA) and Energy Competent Particle Swarm Optimization depending on the Passive Clustering Mechanism (ECPSO-PCM). © 2024 IEEE.

关键词： Cluster Head Improved Bat Optimization Secured Clustering Trusted path Wireless Sensor Network

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Geological Information Extraction from Satellite Imagery Using Deep Learning

Geological Information Extraction from Satellite Imagery Usi...

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International Conference on Computing and Networking Technology (ICCNT)

作者： K. PushpaRani G. Roja R. Anusha C. Dastagiraiah B. Srilatha B. Manjusha Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE(AI & ML) & CSE(DS) GNITS Shaik pet Hyderabad Department of Computer Science and Engineering Anurag University Hyderabad India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Satellite imagery offers extensive information that can be used for a variety of societal applications, from the number of buildings in a metropolis to the land cover types of a specific area. However, extracting such information is a difficult task. In the past, images were scanned manually by domain experts to extract features like buildings, infrastructure, and vegetation, which would take weeks or even months depending on the image size. To address this challenge, we trained our model using U-Net architecture, a deep learning model, because of its advantages in multiscale feature learning and faster processing. Data augmentation is done on the dataset in order to enhance the amount of data and prevent overfitting artificially. The trained model achieved an F1 score of 92% and an impressive precision score of 95%. This automated method can be scaled to large sets of data, reducing the need for human labeling.

关键词： Deep learning Geology Architecture Buildings Vegetation mapping computer architecture Feature extraction Information retrieval Satellite images Data mining

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Diabetes Prediction Using Machine Learning Techniques: A Case Study from Jordan 25

Diabetes Prediction Using Machine Learning Techniques: A Cas...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Batyha, Radwan Samara, Ghassan Al Henawi, Esraa Al-Safarini, Maram Y. Samhan, Ahmad A. Elrashidi, Ali Alluwaici, Mo'Ath Al-Omari, Deana It Zarqa University Department of Data Science Ai Faculty Zarqa Jordan Private University Department of Computer Science Faculty of It Applied Science Amman Jordan Zarqa University Department of Computer Science Faculty of It Zarqa Jordan Zarqa University Department of Software Engineering Faculty of It Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Diabetes is one of the most common diseases in Jordan. It is the main reason of death among Jordanian adult citizens. Worldwide, 48% of all deaths are due to Diabetes occurred before the age of 70 years. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ml) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that BayesNet and NaiveBayes are the best classification models to handle the task of the early prediction of Diabetes in Jordan © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Viewer Engagement: Exploring Generative Long Short Term Memory in Dynamic Ad Customization for Streaming Media 5

Enhancing Viewer Engagement: Exploring Generative Long Short...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Ramagundam, Shashishekhar Karne, Niharika Troy University Software Development & Engineering in Content Discovery & Ai Department of computer Science Computer Science Montgomery Campus AL United States C & Rs Platform services DT Amtrak United States University of south alabama Department of electrical engineering Electrical engineering United States

ISBN: (纸本)9798331504403

In Ad customization, several approaches are employed to improve the relevance level of the message for monitoring the user performance according to the prior search history (e.g., retargeting) or by gathering particular information related to the customer (e.g., micro-targeting). Occasionally, the consumers view the ad developed by the other user, in this situation, the ads are viewed as something that can be related by the user. The quantity of information that is gathered openly or secretly can affect the user choice for the content displayed. However, the users remain as recipients of a strategy, that decides the best ad content. Therefore, an exploring Generative Artificial Intelligence-based Dynamic Ad Customization model for enhancing viewer engagement from streaming media is developed. In this paper, the Generative Long Short-Term Memory (GenLSTM) model is developed for examining customized advertising then includes the customization of specific message elements by individuals, variation in the level of ad customization, and in some cases perceived collaboration with others. Here, the customization classification is defined based on the modification in the marketing mix element that is created by the customer proactively as opposed to the company. In regards to customized advertising, 'ad is something that happens with the viewer'. The customization increases based on the sense of task engagement, strength of the perceived customer relationship with the company, attitude toward the ad, and perceptions of company trust/integrity. Finally, the experimental analysis is conducted based on the relationship with the company, company care, company trust, and task engagement to attain the overall impact on ad customization. © 2024 IEEE.

关键词： Sales

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Deep Learning: A New Way of Looking into Fashion 4

Deep Learning: A New Way of Looking into Fashion

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Thomas, Josh Hanley Roopashree, S. Joy, Jacob Charles Abdullah Kollarath, Haseeb Siby, Arun Dept. CSE-AI and Ml Bengaluru India Rv Institute of Technology and Management Dept. Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474368

Currently, Fashion-Fit using ai focuses on finding the perfect fit for users using GAN's (General Adversarial Net¬works) and various methods proposed in the Artificial Intelligence Industry. However, due to changes in fashion trends and various preferences of the "type of fit"users want for their clothing the prior methodology is not meant for the perfect fit. These days a lot of people wear tighter or even get an oversized fit based on the user's preference. In this paper we use Deep Learning techniques like CNNs and GANs to implement neural body fit by looking at various human body poses from large datasets and concatenating this data to make an accurate "desired"fit based on different human poses. This will set a new benchmark for virtual try-on. In the model being proposed, we are building Convolutional Neural Networks (CNNs) for body segmentation to make it easier to map a human pose. We will then map the human body using CNNs but not necessarily to find the "perfect fit". The CNNs will be used to make measurements that are similar to sizes available within the fashion industry allowing users to see how they would fit within those garments virtually. We will then use 2D renders, using uploaded pictures of the user from 4-5 different angles in order to allow the user to see how he/she would look in the outfit of their choice. These methods can give an accurate prediction helping every fashion e-commerce business. Personalized size and fit recommendations are critical for a fashion e-commerce business. Predicting the optimal fit increases consumer happiness while lowering expenses associated with size-related returns. © 2022 IEEE.

关键词： Convolutional neural networks

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Advanced Sports Performance Analysis using Deep Learning for Posture and Movement Identification

Advanced Sports Performance Analysis using Deep Learning for...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Arvind, S. Hemalatha, K.L. Upendra Roy, B.P. Sandeep, Kasturi Sai Pareek, Piyush Kumar Hyderabad Institute of Technology and Management Department of Cse Hyderabad India Sri Krishna Institute of Technology Department of Information Science and Engineering Bangalore India Channabasaveshwara Institute of Technology Department of Electronics and Communication Engineering Tumkur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350301595

The proposed work explains the method of detecting the area of interest (ROI), extracting pertinent information, distinguishing different postures, and categorising objects, activities, or events by employing recovered characteristics and segmented regions. Furthermore, the paper delves into the challenges of analysing human posture in three dimensions and advises using 3DPoseNet to estimate the 3D coordinates of vertices. Furthermore, the essay investigates the occlusion problem and proposes a general solution. This style involves extensive use of the torso as well as the limbs and head. Finally, the paper emphasises the creation and implementation of deep learning (DL) in motion identification, which has increased detection performance while decreasing complexity. The article emphasises the importance of developing a variety of algorithms and strategies, such as object tracking, event detection, and action identification, as well as combining methods from image processing, machine learning, and computer vision in order to analyse and interpret video data in applications such as surveillance, sports analysis, and medical imaging. Surveillance, sports analysis, and medical imaging are examples of these uses. © 2023 IEEE.

关键词： Extraction

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