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Harnessing LLMs for VQA: A Prompted Benchmark with Animate/Inanimate Keywords

Harnessing LLMs for VQA: A Prompted Benchmark with Animate/I...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Chanwoo Lee Hyunjeong Lee Minsang Kim Hyun Kim Haneol Jang Cheoneum Park Dept. of Software Convergence Seoul Woman's University Seoul South Korea Dept. of Computer Engineering Hanbat National University Daejeon South Korea Dept. of Computer Science and Engineering Korea University Seoul South Korea Superintelligence Creative Research Lab. Electronics and Telecommunications Research Institute Daejeon South Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

In the field of NLP, Large Language Models (LLMs) have recently achieved significant advancements, leading to the development of various benchmarks for their evaluation. Along-side NLP, Vision Language Models (VLMs) have also VLM have also significantly progressed, similar to LLMs. However, benchmarks for VLMs are still relatively underdeveloped compared to those for NLP, and their construction is often costly. In this work, we propose an automatically generated benchmark for evaluating VLMs based on LLMs and conduct a visual question answering task to assess this benchmark. The benchmark includes multiple-choice questions that not only distinguish between animate and inanimate objects but also generate these distinctions automatically, along with entity and object information within images. We evaluate the performance of open VLM using the generated multiple-choice questions, demonstrating the model's capabilities and the significance of the automatically generated benchmark. Finally, we discuss the necessity and future directions for benchmark research in this area.

关键词： Visualization Annotations Large language models Natural languages Benchmark testing Information filters Question answering (information retrieval) Cognition Information and communication technology Convergence

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E-commerce platform based on Machine Learning Recommendation System 6

E-commerce platform based on Machine Learning Recommendation...

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6th International Multi-Topic ICT Conference, IMTIC 2021

作者： Tahir, Muhammad Enam, Rabia Noor Mustafa, Syed Muhammad Nabeel Sir Syed University of Engineering and Technology Dept. of Software Engineering Karachi Pakistan NED University of Engineering and Technology Dept. of Computer Science I.T Karachi Pakistan

ISBN: (纸本)9781665482943

Information overload is one of the potential setbacks to many e-commerce platform users. It is very important to filter the media and the choices that are overwhelming for internet users while making buying decisions using online stores. To solve this problem, recommendation systems are used widely. A recommender system helps users find a product of their own choice by filtering and prioritizing and effectively generating the relevant information to its users. The purpose of a recommender system is to save time and hassle of searching through the World Wide Web, instead it generates specific and relevant content that promotes online transaction and bring satisfaction to the users of e-commerce platforms. The proposed system is an e-commerce platform based on an apparel recommendation system that recommends products on the foundation of the user's preferences. © 2021 IEEE.

关键词： Recommender systems

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Visual object detection using audio data 5

Visual object detection using audio data

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5th International Conference on Computing and Data Science, CONF-CDS 2023

作者： Chaturvedi, Rajnish Kumar Sahu, Dinesh Prasad Tyagi, Manoj Kumar Diwakar, Manoj Singh, Prabhishek Shankar, Achyut Sathishkumar, V.E. Schoolof Computer Science Engineering and Technology Bennett University Greater Noida India Galgotias University India CSE Dept. Graphic Era Deemed to Be University Dehradun India University of Warwick United Kingdom Department of Software Engineering Jeonbuk National University Jeonju-si Jeollabuk-do Korea Republic of

Nowadays Internet of Things (IoT) and Machine Learning (ML) are growing fields. One application of these two fields is object detection, which detects semantic objects using digital images and videos of classes like humans, vehicles, buildings, etc. Visual object detection systems are very effective and accurate due to the appearance information obtained from the cameras. But they face the problem of a limited Field of View. This paper aims to tackle this issue by using audio data to localize the object. A microphone is used to estimate the angular position of the object emitting the sound. Objects currently in the Field of View of a camera are detected and tracked using optical flow, but when they go out of the Field of View, the sound emitted by the object is used by the microphone to calculate the object's angular position. Once the angle is calculated, the camera is rotated in that direction. This thus ensures that the object can be located even if it goes out of frame. Once the object is located through IoT devices, we use ML to identify the person's face. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Prediction of Continuous Targets by Explainable Imbalanced Regression from Omics Data in Childhood Obesity 1

Prediction of Continuous Targets by Explainable Imbalanced R...

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1st Workshop on Explainable Artificial Intelligence for the Medical Domain, EXPLIMED 2024

作者： Arteaga, María Torres-Martos, Álvaro Anguita-Ruiz, Augusto Alcalá-Fdez, Jesús Alcalá, Rafael Gacto, María José Dept. of Computer Science and Artificial Intelligence Univ. of Granada Granada Spain Center of Biomedical Research Univ. of Granada Inst. de investigación Biosanitaria ibs.GRANADA Granada Spain Inst. de Salud Carlos III Madrid Spain Barcelona Spain Univ. of Granada Granada Spain Dept. of Software Engineering Univ. of Granada Granada Spain

Childhood obesity is a persistent challenge for society since it is highly related to insulin resistance and a wide range of other chronic diseases, which impair not only the health of the people, but also the health system itself due to extra costs of treating them. In this context, it is imperative to consider innovative approaches, like eXplainable Artificial Intelligence, to understand the factors underlying childhood obesity. Data imbalance has been thoroughly studied in classification, but scarcely studied in regression since classes do not exist. However, extreme values in continuous domains, which are usually minority are often the most clinically relevant. Facing the imbalance in regression while obtaining explainable models has never been studied. In this application work, we adopt a Machine Learning approach to obtain explainable regression models for imbalanced continuous targets in childhood obesity, as HOMA-IR and Waist Circumference. We consider 79 variables, including targeted metabolomics, targeted proteomics, exposomic data (e.g., the physical activity subdomain), hematological parameters and anthropometry of Spanish children from 3 to 18 years with significant imbalance ratios around 12% in HOMA-IR and Waist Circumference. Even though we mainly focus on extensions of linguistic fuzzy rule-based systems, particularly designed for explainability, we also consider highly accurate complementary approaches as Random Forest that could additionally provide contrast interesting information by post-hoc SHAP. The models so obtained are better considering the relevant minority information (17% improvements in F1). Moreover, they seem to properly explain biologically meaningful relations, as in the case of physical activity data or the one with the follicle stimulating hormone, among others. © 2024 Copyright for this paper by its authors.

关键词： Nutrition

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Exploring Loss Scenarios of STPA with Reinforcement Learning: A Case Study of Platform Screen Door

Exploring Loss Scenarios of STPA with Reinforcement Learning...

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Asia-Pacific Conference on software engineering

作者： Jiyoung Chang Ryeonggu Kwon Gihwon Kwon Dept. of Software Safety and Cyber Security University of Kyonggi Suwon Republic of Korea Dept. of Computer Science University of Kyonggi Suwon Republic of Korea Dept. of AI Computer Engineering University of Kyonggi Suwon Republic of Korea

System-Theoretic Process Analysis (STPA) is performing a potential hazard analysis in the interactions of components within a system. Among STPA's process, deriving loss scenarios by identifying causes of the hazard has become increasingly important due to the complexity of modern systems. In this study, we utilize reinforcement learning to derive state transition paths that lead to hazard as loss scenarios, and extract the frequency of risk frequencies to demonstrate the necessity of safety measures for the respective loss scenarios. It is anticipated that this efficient approach will aid in simulating the system design process and enhancing safety.

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Bi-Directional LSTM Classification of Emotions in EEG Signals

Bi-Directional LSTM Classification of Emotions in EEG Signal...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Ashley Nand Immanuel Johnraja Jebadurai Ocheni Jeremiah Getzi Jeba Leelipushpam Paulraj Jebaveerasingh Jebadurai Dept. of Computer Science and Engineering Karunya Institue of Technology and Science Coimbatore India Dept. of Computer Science and Engineering Government Polytechnique College Tanjore India

ISBN: (纸本)9781665489638

In the field of neuroscience research, the analysis of EEG signals is crucial for comprehending both human behavior and the workings of the brain. The majority of research is being done to find out how EEG signals are usually used to distinguish between various psychological states and emotions. The proposed study has applied the Bi-LSTM approach for emotion categorization, which significantly raises the accuracy of emotion categorization based on the brain waves obtained from EEG. The results show that the system is reliable as the data has been trained by using a variety of classifiers but with the DEAP dataset, the proposed method obtains a higher and quicker accuracy rate with a training period of about 5ms and an average accuracy rate of 94.12%, and is working to classify the emotion such as valence, arousal, and dominance with single electrode channel with the highest accuracy.

关键词： Training Electrodes Neuroscience Databases Psychology Bidirectional control Electroencephalography

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Moving Robots in Unknown Environments Using Potential Field Graphs 2

Moving Robots in Unknown Environments Using Potential Field ...

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2nd International Conference on Advanced Research in Computing, ICARC 2022

作者： Panagoda, Mithila Lokuliyanage, Minuli Senarath, Amadi Nisansala, N.K.V.M. Nethmini Rajapakshe, R.W.A.D.U. Rajapaksha, U.U. Samantha Jayawardena, Chandimal Sri Lanka Institute of Information Technology Dept. of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Deprt. of Computer System and Engineering Malabe Sri Lanka

ISBN: (纸本)9781665407410

The purpose of this research paper is to introduce a new navigation algorithm for Robot Operating System (ROS) based robots which will allow complete autonomous traversal in any given indoor environment. Turtle bot3 burger bot is the sample robot chosen for this project. This will be equipped with a Light Detection and Ranging (LIDAR) scanner with the default settings, ultrasound, Intel Real Sense camera, etc. Environment information gathered via these sensors will be sent into a remote computer for processing since the raspberry pie micro-controller with the turtle bot burger is not enough to carry out a high computation-intensive task. Once the information is received series of tasks will be carried out by the remote personal computer before executing the newly developed navigation algorithm. The data from the LIDAR scanner help to generate a 2-dimensional(2D) potential field graph. Then it will later be used for path planning by navigation algorithm. Furthermore, the Real sense camera is used for object boundary detection to produce more accurate data for the navigation algorithm. Once all the data proceed, data will return to the robot, where the navigation algorithm will start execution in parallel with a recovery behavior algorithm and scan algorithm. The recovery behavior algorithm is responsible for guiding the robot to face away from an obstacle in an event where the predetermined path is obstructed. Furthermore, it is responsible for maintaining accurate data in the cost map. Subsequently, the scanning algorithm will be used to generate maps. © 2022 IEEE.

关键词： Cameras

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Dynamic View Synthesis of Thin Structures with Short-term Movements from Monocular Videos Using Neural Radiance Fields

Dynamic View Synthesis of Thin Structures with Short-term Mo...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Uchitha Rajapaksha Hamid Laga Dean Diepeveen Mohammed Bennamoun Ferdous Sohel School of Information Technology Murdoch University Murdoch WA Australia Dept. of Computer Science and Software Engineering The University of Western Australia Crawley WA Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Learning to generate motions of thin structures such as plant leaves in dynamic view synthesis is challenging. This is because thin structures usually undergo small but fast, non-rigid motions as they interact with air and wind. When given a set of RGB images or videos of a scene with moving thin structures as input, existing methods that map the scene to its corresponding canonical space for rendering novel views fail as the object movements are too subtle compared to the background. Disentangling the objects with thin parts from the background scene is also challenging when the parts show fast and rapid motions. To address these issues, we propose a Neural Radiance Field (NeRF)-based framework that accurately reconstructs thin structures such as leaves and captures their subtle, fast motions. The framework learns the geometry of a scene by mapping the dynamic images to a canonical scene in which the scene remains static. We propose a ray masking network to further decompose the canonical scene into foreground and background, thus enabling the network to focus more on foreground movements. We conducted experiments using a dataset containing thin structures such as leaves and petals, which include image sequences collected by us and one public image sequence. Experiments show superior results compared to existing methods. Video outputs are available at https://***/.

关键词： Geometry Three-dimensional displays Tracking Dynamics Neural radiance field Rendering (computer graphics) Image sequences Image reconstruction Optical flow Videos

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Intrusion Detection System Based on Machine Learning Techniques: A Survey

Intrusion Detection System Based on Machine Learning Techniq...

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Advances in engineering Science and Technology (AEST), International Conference on

作者： Omar I. Sheet Laheeb M. Ibrahim Software Engineering Dept. College of Computer Sciences & Math University of Mosul-

The volume of network traffic data has become so big and complicated as a result of the development in Internet-based services that it is extremely difficult to process using typical data processing techniques. Due to the enormous and complicated nature of network traffic data, fast and effective cybersecurity intrusion detection is a highly difficult task. In order to detect hostile traffic as soon as feasible, a realistic cyber security intrusion detection system should be able to handle a huge volume of network traffic data as quickly as *** paper studies a classification-based intrusion detection algorithms based on Machine Learning for speedy and effective intrusion detection in huge network traffic, including Support Vector Machines (SVM), Random Forests(RF), Decision Trees(DT), Naïve Bayes (NB), Deep Neural Network (DNN), Extreme Gradient Boosting (XG Boost), Nondominated Sorting Genetic Algorithm (NSGA2), as well as Deep Belief Network (DBN) and artificial neural network(AI). It has been applied to ACTUAL TIME The KDD dataset, KDD Cup 99 dataset, NSL-KDD dataset, CICIDS-2017 dataset, CICIDS-2018 dataset, ISCX Dataset, CICAndMal2017 dataset and UNSW-NB15 to compare performance based on rating Intrusion detection systems which are assessed in terms of training time, prediction time, accuracy.

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Identify Initial Nodes for Spreader in A New Diffusion Model Based on Topology (DBT) in Social Networks

Identify Initial Nodes for Spreader in A New Diffusion Model...

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International Conference on Web Research (ICWR)

作者： Ehsan Khosravi Fatemeh Yazdanifar Nikoo Salimi Hamid Ahmadi Beni Dept. of Computer Engineering Sharif University of Technology Tehran Iran Dept. of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran Dept. of the Electrical Electronics Electronics and Telecommunication Engineering and Naval Architecture University of Genoa Genoa Italy Dept. of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran

With the development of science and technology through social networks, the process of diffusion has become a controversial subject. In social networks, the process of diffusion is transmitted from one individual to another. The influence maximization problem, in which at least k nodes are chosen as the initial nodes of the spreader in the smallest amount of time, is one of the most critical aspects of the diffusion process. Furthermore, the initial nodes in the social network should generate the most influence on the other nodes in the network. However, in the algorithms proposed to solve the problem of influence maximization, the real-world factors which affect the influence probability in diffusion models are ignored for example, the influence probability is determined randomly. This problem causes influence maximization algorithms based on diffusion models to face the challenge of time-consuming Monte Carlo simulation. So, the RIM algorithm presented in this article which this method selects the initial nodes of the spreader according on their local reachability. In addition, Due to privacy in social networks, complete information about people such as gender, education, etc. is not available. For this reason, in the RIM algorithm, using the graph structure in social networks, the influence probability is calculated in the new DBT diffusion model. In addition, the RIM algorithm exhibits stable performance in the selection of the spreader's initial nodes in terms of influence spread, and execution time.

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