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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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Parameter-Efficient Federated Cooperative Learning for 3-D Object Detection in Autonomous Driving

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IEEE Internet of Things Journal 2025年第12期12卷 20314-20325页

作者： Chi, Fangyuan Wang, Yixiao Nasiopoulos, Panos Leung, Victor C. M. The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Toronto AI Lab LG Electronics TorontoONM5V 1M3 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

In the rapidly evolving field of autonomous driving, accurately detecting and understanding dynamic environments remains a challenge. Federated learning (FL) offers a promising approach by integrating decentralized models from multiple connected autonomous vehicles (CAVs) to enhance the performance of deep-learning (DL)-based object detection methods. However, traditional FL faces hurdles, such as extensive data synchronization requirements, limited data variance, and high communication costs. This article introduces a federated cooperative learning framework that addresses these challenges by combining data from both CAVs and roadside units. The framework combines local cooperative perception with global FL through a parameter-efficient FL adapter and a lazy communication strategy, improving DL-based object detection capabilities across diverse driving scenarios while significantly reducing bandwidth requirements. We also present a novel multiagent-multitown dataset Vehicle-to-Everything-Fed, specifically developed to validate the effectiveness of our approach under various conditions. Notably, our framework retains 97.51% of the detection accuracy achieved by full-model FL, while utilizing only 1.4% of the bandwidth typically required, demonstrating substantial improvements over conventional FL strategies. This study underscores the potential of our tailored approach to substantially enhance autonomous vehicle technologies with minimal resource utilization. © 2014 IEEE.

关键词： Bandwidth

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Fraud Detection in Credit Card through the Machine Learning Algorithms

Fraud Detection in Credit Card through the Machine Learning ...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Sandhya Kummarikuta Ganesh Karthik Muppagowni Keerthi Patam Mounika Pennera Upendra Patimeedi Poreddy Chandra Harshith Reddy Dept. AI&ML School of Computing Mohan Babu University Tirupati India Department of CSE GITAM School of Technology GITAM University-Bengaluru Campus Bengaluru India Dept. Computer Science and Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The threat of credit card fraud is high and so there should be efficient ways to combat it. The research is being employed to focus on how to enhance credit card fraud detection, machine learning methodologies can be employed. The major benefit of this structured design model is that it observes many transaction variables and find out any potential threat transaction since its trained to do so. To start the project, a large dataset of both authentic and fraudulent credit card transactions will be collected and preprocessed. For the machine to learn how we want it to extract correct location, time, date and users' behavior and an important amount of transaction has been made. This improves our dataset by adding relevant information. This research is based on identifying fake activities it will discuss how to XGBoost, CatBoost and Logistic Regression used for this purpose.

关键词： Logistic regression Machine learning algorithms Machine learning Credit cards Fraud

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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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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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Building ai-Driven Frameworks for Real-Time Threat Detection and Mitigation in IoT Networks 7

Building AI-Driven Frameworks for Real-Time Threat Detection...

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7th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2025

作者： Joshi, Manjushri Tiwari, Ankita Dhabliya, Dharmesh Lavate, Santosh H. Ajani, Samir N. Gandhi, Yatin ECE(AI&ML) Department of Polytechnic & Skill Development Dr.Vishwanath Karad MIT World Peace University Pune India Department of Engineering Mathematics College of Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India Department of Information Technology Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Electronics and Telecommunication Engineering AISSMS College of Engineering Pune India School of Computer Science and Engineering Ramdeobaba University (RBU) Nagpur India Competent Softwares Maharashtra Nagpur India

ISBN: (纸本)9798331515683

The fast growth of Internet of Things (IoT) networks has made security risks more complicated and numerous. To find and stop these threats in real time, we need new and creative solutions. This article talks about an ai-powered system that can make IoT networks safer by finding and stopping threats as they happen. The system uses both machine learning (ml) and deep learning (DL) to look at real-time data from IoT devices and find strange patterns that could be signs of an attack. This is done by using advanced algorithms, like decision trees, neural networks, and reinforcement learning, to learn new things all the time and adjust to new dangers. To improve the efficiency of identification and cut down on false positives, a mixed method is used that combines controlled and unstructured learning. The system also has an automatic mitigating procedure that separates infected devices and stops them from doing more damage when risks are found. Compared to standard security methods, our results show that the proposed structure makes danger identification and prevention much more effective in IoT networks. The system is very good at finding different kinds of threats, like DDoS, malware, and illegal access, and it does this while using very little computing power. This means it can be used in IoT settings with limited resources. This study provides a flexible and scalable way to protect the IoT environment, which is becoming more and more linked. © 2025 IEEE.

关键词： ai-driven framework Anomaly detection Deep learning IoT network security Machine learning Real-time threat detection

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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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Boli: A dataset for understanding stuttering experience and analyzing stuttered speech

Boli: A dataset for understanding stuttering experience and ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Batra, Ashita Narang, Mannas Sharma, Neeraj Kumar Das, Pradip K. Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati781039 India Mehta Family School of Data Science and AI Indian Institute of Technology Guwahati Guwahati781039 India

ISBN: (纸本)9798350368741

There is a growing need for diverse, high-quality stuttered speech data, particularly in the context of Indian languages. This paper introduces Project Boli, a multi-lingual stuttered speech dataset designed to advance scientific understanding and technology development for individuals who stutter, particularly in India. The dataset constitutes (a) anonymized metadata (gender, age, country, mother tongue) and responses to a questionnaire about how stuttering affects their daily lives, (b) captures both read speech (using the Rainbow Passage) and spontaneous speech (through image description tasks) for each participant and (c) includes detailed annotations of five stutter types: blocks, prolongations, interjections, sound repetitions and word repetitions. We present a comprehensive analysis of the dataset, including the data collection procedure, experience summarization of people who stutter, severity assessment of stuttering events and technical validation of the collected data. The dataset is released as an open access to further speech technology development. © 2025 IEEE.

关键词： Indian stuttered speech dataset Intelligibility assessment read speech spontaneous speech Stuttering event detection

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