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Evaluating Large Language Models: Challenges, Limitations, and Future Directions

Evaluating Large Language Models: Challenges, Limitations, a...

引用

international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Li-Hua Li Agus Cahyo Nugroho Department of Information Management College of Informatics Chaoyang University of Technology Taichung Taiwan

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Large Language Models (LLMs) have transformed Natural Language Processing (NLP) by providing previously unheard-of capabilities in text production, translation, and comprehension. Large Language Models have advanced rapidly. Nevertheless, there are several obstacles and restrictions associated with the implementation and assessment of these models. This study offers a thorough analysis of the situation of LLMs today, with an emphasis on the methods for assessing their effectiveness and significance. Key issues like prejudice, moral dilemmas, scalability, and transparency are noted, and the shortcomings of the current frameworks for evaluation are also covered. Our goal in examining these problems is to identify the areas that require improvement. In addition, we suggest possible direction for future investigation, stressing the importance of inclusive datasets, improved interpretability, and stronger assessment criteria. In addition to guiding the creation of more morally and effectively constructed AI systems, our mission is to promote a greater understanding of LLMs.

关键词： measurement Ethics Translation Large language models Scalability Training data Production Natural language processing Safety Testing

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Design and Analysis of Kinematic Model for a 12-DOF Bipedal Robot

Design and Analysis of Kinematic Model for a 12-DOF Bipedal ...

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Artificial Intelligence and Emerging Technology (Global AI Summit), international conference on

作者： Navneet Tiwari Rabesh Kumar Singh Manish Raj Mechatronics Centre for Advanced Studies AKTU Lucknow UP India Manufacturing Technology and Automation Centre for Advanced Studies AKTU Lucknow UP India School of Computer Science Engineering Bennett University Greater Noida UP India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Designing and controlling bipedal robots with intricate kinematic structures, particularly those with numerous degrees of freedom, presents considerable challenges. This research provides an in-depth examination of kinematic modelling for a 12-DoF bipedal robot, emphasizing the accurate implementation of forward and inverse kinematic methods. Utilizing DenavitHartenberg parameters, the research systematically explores the kinematic relationships between the robot's joints and links, facilitating accurate calculations of the robot's pose and movement. A significant aspect of this research is developing a forward kinematic model that predicts end-effector positions alongside an inverse kinematic approach to compute the necessary joint angles for achieving specific positions. The insights derived from this study contribute theoretically and practically to understanding kinematic modeling in high-DoF bipedal robots. By emphasizing the necessity for precise modeling, the findings aim to enhance robotic control and performance. This study sets the stage for future developments in the creation and deployment of advanced bipedal robotic systems.

关键词： Legged locomotion Accuracy Service robots Computational modeling Prototypes Kinematics Solids Mathematical models Artificial intelligence Testing

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Analysis of Drone Security

Analysis of Drone Security

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Computing in Natural Sciences, Biomedicine and Engineering (COMCONF), international conference on

作者： Martin Stefanov Milen Petrov Adelina Aleksieva-Petrova Department of Software Technologies Sofia University “St. Kl. Ohridski” Sofia Bulgaria Department of Computer Systems Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350353358

ISBN: (纸本)9798350353365

The growing adoption of unmanned aerial vehicles (UAVs) heightens the risk of cyber threats compromising their security. Implementing robust protection measures and tools is essential to prevent misuse and safeguard personal data and public safety. This paper investigates network security by evaluating the resilience of drones against common cyber attacks. A threat model is constructed to identify a set of attack vectors, which are then tested for potential exploitation. The findings are documented and analyzed, with conclusions drawn for each penetration test conducted.

关键词： Threat modeling Network security Biomedical measurement Autonomous aerial vehicles Vectors Protection Software tools Standards Drones Testing

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Effective Retrieval Augmentation for Knowledge-Based Vision Question Answering

Effective Retrieval Augmentation for Knowledge-Based Vision ...

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international conference on Behavior, Economic and Social Computing (BESC)

作者： Jiaqi Deng School of Computer Science University of Technology Sydney Sydney Australia

ISBN: (数字)9798331531904

ISBN: (纸本)9798331531911

Knowledge-based Vision Question Answering (KBVQA) systems aim to answer natural language questions grounded in image contents by retrieving and integrating relevant knowledge from external knowledge bases to generate the final answers. These systems have diverse application scenarios ranging from general cross-modal understanding to specialized domains like healthcare or robotics. While significant progress has been made, several key obstacles still persist: (1) multimodal pretrained models remain underinvestigated in KB-VQA, and existing methods can incur high computational costs; (2) Current KB-VQA systems tend to extract noisy information from external knowledge sources, which might impair the answer quality; (3) Cross-modal interactions between images and textual questions are still insufficient at the current state, potentially limiting the system's ability to comprehend essential contents. This doctoral consortium paper intends to provide a comprehensive review of existing efforts in KB-VQA systems and offer insights into potential future research directions in this domain. We highlight the importance of improving multimodal pretraining techniques, mitigating noise in knowledge integration, and enhancing interaction modeling between images and texts to improve the performance and the robustness of KB-VQA systems. Proposed research methodology will also be outlined in this paper.

关键词： Social computing Reviews Computational modeling Knowledge based systems Noise Medical services Question answering (information retrieval) Robustness Noise measurement Robots

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ARIMA Model-Based Prediction of pH for Hydroponic Vertical Farm

ARIMA Model-Based Prediction of pH for Hydroponic Vertical F...

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Pioneering Developments in computer Science & Digital Technologies (IC2SDT), international conference on

作者： Gaganjot Kaur Jaspinder Kaur Prashant Upadhyaya Department of Mechatronics Engineering Chandigarh University Punjab India Department of Electronics Engineering National Institute of Technology Delhi India Department of Electronics Engineering Chandigarh University Punjab India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Hydroponics is a way of growing plants without soil, relies heavily on exact management of nutrient solutions, including maintaining optimal pH levels for plant growth. In this study, we explore the application of Autoregressive Integrated Moving Average (ARIMA) modeling to predict pH levels in hydroponic systems. The research leverages historical pH data collected from hydroponic setups to develop ARIMA models capable of forecasting pH trends. The study outlines the process of data preprocessing, including stationarity checks and differencing, followed by model selection and validation. The efficacy of the ARIMA models is evaluated based on performance metric root mean squared error (RMSE).

关键词： measurement Analytical models Data preprocessing Hydroponics Predictive models Soil Nutrients Market research Data models Forecasting

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Implementation and Performance measurement of the SUHDSA (Secure, Useful, and High-performance Data-Stream Anonymization)+ Algorithm

Implementation and Performance Measurement of the SUHDSA (Se...

引用

international conference on computer Communications and Networks (ICCCN)

作者： Jiyeon Lee Soonseok Kim Dept. AI Information Security Halla University Wonju Korea

ISBN: (数字)9798350384611

ISBN: (纸本)9798350348439

This paper focuses on tables in microdata format, where each tuple contains specific individual information collected in real-time and stored at the tuple level. The collected information within each tuple, being personal, necessitates privacy protection. Therefore, we need to anonymize this information, which is the main theme of this paper. In this context, we have proposed the SUHDSA algorithm, an improvement over Sopaoglu and Abul's UBDSA algorithm, and have further enhanced its security with the proposed SUHDSA+ algorithm. This paper aims to validate the results of our previously proposed SUHDSA+ algorithm through empirical experiments. The experiments show that while the existing SUHDSA+ takes between 3.94 to 7.98 seconds longer in execution time compared to the SUHDSA algorithm, it performs about 45.6% faster than the original UBDSA. In terms of information loss, there was an increase of up to 18% compared to the original SUHDSA; however, when the k-value increased from 3 to 10, the loss rate was up to 19% less, indicating a significant impact of the increased k-value.

关键词： Privacy Codes Data integrity Computational modeling Approximation algorithms Real-time systems Loss measurement

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Dataset Infant Anonymization with Pose and Emotion Retention

Dataset Infant Anonymization with Pose and Emotion Retention

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international conference on Automatic Face and Gesture Recognition

作者： Mason Lary Matthew Klawonn Daniel Messinger Ifeoma Nwogu Dept. of Computer Science and Engineering University at Buffalo NY Information Directorate U.S. Air Force Research Lab Dept. of Psychology University of Miami Coral Gables FL USA

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

We demonstrate a procedure for the anonymization of infant subjects in videos such that salient behavioral information is retained. This method also creates a new identity that is consistent temporally across video frames. We present an overview of this anonymization process, which involves moving through the latent space of a generative model with an infant specific latent space traversal technique. We apply the technique on videos of infants, a historically difficult source of data, and make comparisons to other state-of-the-art anonymization systems. Metrics demonstrate an improved ability to retain emotional content of videos during the anonymization process, even during extreme emotions or poses, while maintaining a consistent identity throughout.

关键词： measurement Data privacy computer vision Face recognition Computational modeling Gesture recognition Data collection

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Strategic abilities of asynchronous agents: Semantic side effects 20

Strategic abilities of asynchronous agents: Semantic side ef...

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20th international conference on Autonomous Agents and Multiagent systems, AAMAS 2021

作者： Jamroga, Wojciech Penczek, Wojciech Sidoruk, Teofil Institute of Computer Science PAS Int. Centre for Security Reliability and Trust Univ. of Luxembourg Luxembourg Institute of Computer Science Polish Academy of Sciences Warsaw Poland Institute of Computer Science PAS Faculty of Math. and Inf. Science Warsaw University of Technology Poland

ISBN: (纸本)9781713832621

Recently, we have proposed a framework for verification of agents' abilities in asynchronous multi-agent systems, together with an algorithm for automated reduction of models [14, 16]. The semantics was built on the modeling tradition of distributed systems. As we show here, this can sometimes lead to counterintuitive interpretation of formulas when reasoning about the outcome of strategies. © 2021 international Foundation for Autonomous Agents and Multiagent systems (***). All rights reserved.

关键词： Semantics

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Deep Belief Networks and LSTM for Precise Weather Forecasting

Deep Belief Networks and LSTM for Precise Weather Forecastin...

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international conference on ent Computing and Control systems (ICICCS)

作者： J. Rama Devi Pavitra Pullela Upputholla Nandini Nagur Baji Shaik Pondugula Ajithesh Reddy Computer Science Dept PVPSIT Vijayawada A.P India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Heavy rainfall often leads to various natural hazards, making accurate rainfall forecasting crucial for implementing effective preventive measures. This study proposes a novel three-phase forecasting model comprising Pre-processing, Feature Extraction, and Forecasting to improve prediction accuracy. The model leverages advanced deep learning algorithms, specifically Deep-Belief Networks (DBN) and Long Short-Term Memory (LSTM), to analyze historical rainfall data and detect complex patterns. The primary objective of this work is to enhance forecasting precision by identifying intricate relationships within meteorological data, thereby providing valuable insights for disaster preparedness and risk mitigation. The proposed model’s performance is systematically evaluated against existing approaches using multiple metrics, demonstrating its superior predictive capabilities. By enabling early interventions, this method contributes to proactive disaster management, reducing the impact of extreme weather events.

关键词： measurement Rain Accuracy Computational modeling Weather forecasting Predictive models Feature extraction Data models Forecasting Long short term memory

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Advancing Virtual Reality Interaction: A Ring-Shaped Controller and Pose Tracking

Advancing Virtual Reality Interaction: A Ring-Shaped Control...

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IEEE international conference on Robotics and Automation (ICRA)

作者： Zhuqing Zhang Dongxuan Li Jiayao Ma Yijia He Pan Ji Rong Xiong Hongdong Li Yue Wang State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control Zhejiang University Zhejiang China Tencent Holdings Tencent XR Vision Labs Shenzhen China College of Engineering and Computer Science Australian National University Canberra

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Ensuring robust tracking of controllers’ movement is critical for human-robot interaction in virtual reality (VR) scenarios. This paper proposes a robust tracking algorithm based on a novel wearable ring-shaped controller equipped with an inertial measurement unit (IMU) and a light-emitting diode (LED). This novel controller design allows users to free up their hands for more immersive experiences. To track the controller’s motion accurately and robustly, we resort to various forms of visual measurements, including 6 DoF and 5 DoF pose measurements from hand gesture detection, as well as 3 DoF position measurement and 2 DoF image measurement derived from the LED. We theoretically analyze the performances of these observation models and propose an optimal observation model combination scheme. Moreover, the necessity and rationale of online estimating system gravity are illustrated. The effectiveness of our tracking method is validated through extensive experiments.

关键词： Solid modeling Analytical models Visualization measurement units Tracking Virtual reality Inertial navigation

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