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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Lakshmi Patibandla, R.S.M. Manoj, Madupalli Patnaik, Vantharam Sai Sushmitha Jagadeesh, Alapati Sasidhar, Bathina Department of CSE Koneru Lakshmaiah Education Foundation AP Guntur India Department of IT and CA School of Computing and Informatics VFSTR Deemed to Be University AP Guntur India

ISBN: (纸本)9789819773558

Some individuals are unable to use computers due to medical conditions. The idea of eye controls is particularly advantageous for the advancement of natural input as well as, and this is key, for the underprivileged and the disabled. Also, they are able to operate the computer autonomously by incorporating a controlling system. It benefits those with disabilities more. Those who can use computers without a keyboard are needed. This one is especially helpful for individuals who can move the cursor with their eyes. In this study, a camera is used to document eye movement. First, find the centre of the pupil of the eye. The pointer will then travel differently depending on the multiple variations in pupil position. All of these programmes share the fact that keyboard and mouse input is the primary technique used while using a personal computer. Although this wouldn't be a problem for someone in good health, it can be an impassable barrier for those with a restricted range of motion in their limbs. In these situations, it would be better to employ input techniques that rely on the brain region's stronger capabilities, such as eye movements. A system that uses a low-cost technique to operate a mouse pointer on a computing device was created to allow such alternative input methods. The eye tracker uses photos captured by a modified webcam to follow the motions of the user's eyes. The computer screen is then graphed using these eye movements to place the mouse pointer appropriately. Automatically altering the location of the eyes while moving the mouse. A webcam is used to photograph eye movement. The mouse cursor can be moved by moving the face up, down, left, and right, and mouse actions may be controlled by speaking and blinking the eyes. Several algorithms, including the Haar Cascade algorithm, Template Matching, and Hough transformation, are utilised to carry out these tasks. Our solution is primarily designed to enable successful computer communication for persons wi

关键词： Hough transforms

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Kiss up, Kick down: Exploring Behavioral Changes in Multi-modal Large Language Models with Assigned Visual Personas

Kiss up, Kick down: Exploring Behavioral Changes in Multi-mo...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Sun, Seungjong Lee, Eungu Baek, Seo Yeon Hwang, Seunghyun Lee, Wonbyung Nan, Dongyan Jansen, Bernard J. Kim, Jang Hyun Department of Human-Artificial Intelligence Interaction Sungkyunkwan University Korea Republic of College of Computing and Informatics Sungkyunkwan University Korea Republic of Qatar Computing Research Institute Hamad Bin Khalifa University Qatar

ISBN: (纸本)9798891761643

This study is the first to explore whether multi-modal large language models (LLMs) can align their behaviors with visual personas, addressing a significant gap in the literature that predominantly focuses on text-based personas. We developed a novel dataset of 5K fictional avatar images for assignment as visual personas to LLMs, and analyzed their negotiation behaviors based on the visual traits depicted in these images, with a particular focus on aggressiveness. The results indicate that LLMs assess the aggressiveness of images in a manner similar to humans and output more aggressive negotiation behaviors when prompted with an aggressive visual persona. Interestingly, the LLM exhibited more aggressive negotiation behaviors when the opponent's image appeared less aggressive than their own, and less aggressive behaviors when the opponent's image appeared more aggressive. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Personality Traits Prediction from Text via Machine Learning

Personality Traits Prediction from Text via Machine Learning

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2022 IEEE World Conference on Applied Intelligence and computing, AIC 2022

作者： Bruno, Alessandro Singh, Gurmeet Bournemouth University Department of Computing and Informatics Poole United Kingdom

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

Social media platforms have been expanding their user bases. For example, LinkedIn counts 917 million monthly visitors, while Twitter has 3.62 billion monthly visitors. YouTube has 22.77 billion monthly visitors, and Instagram has 2.86 billion monthly visitors. Reports confirm data size increase of the social media networks above by 20-30% every day. With the spread of COVID-19, the same platforms have been broadly used by the worldwide collectiveness to socialize and stay amongst people. Analyzing text from Social Networking sites helps recognize individuals' personality traits automatically. A person's personality refers to their unique characteristics that shape their habits, behaviour, attitude, and cognitive tendencies. In this work, several machine learning techniques are surveyed to estimate personality traits from input text using the Myers-Briggs Type Indicator (MBTI) model. Experiments are run over a freely accessible dataset from Kaggle. In addition, techniques such as tokenization, word stemming, stop word elimination, and feature selection, utilizing TF-IDF, are used to analyze personality traits further. © 2022 IEEE.

关键词： Machine learning

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Data Temperature Informed Streaming for Optimising Large-Scale Multi-Tiered Storage

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Big Data Mining and Analytics 2024年第2期7卷 371-398页

作者： Dominic Davies-Tagg Ashiq Anjum Ali Zahir Lu Liu Muhammad Usman Yaseen Nick Antonopoulos Department of Computing University of DerbyDerbyDE221GBUK Department of Informatics University of LeicesterLeicesterLE17RHUK Department of Computer Science COMSATS University IslamabadIslamabad 45550Pakistan Edinburgh Napier University EdinburghEH114BNUK

Data temperature is a response to the ever-growing amount of *** data have to be stored,but they have been observed that only a small portion of the data are accessed more frequently at any one *** leads to the concept of hot and cold *** data can be migrated away from high-performance nodes to free up performance for higher priority *** studies classify hot and cold data primarily on the basis of data age and usage *** present this as a limitation in the current implementation of data *** is due to the fact that age automatically assumes that all new data have priority and that usage is purely *** propose new variables and conditions that influence smarter decision-making on what are hot or cold data and allow greater user control over data location and their *** identify new metadata variables and user-defined variables to extend the current data temperature *** further establish rules and conditions for limiting unnecessary movement of the data,which helps to prevent wasted input output(I/O)*** also propose a hybrid algorithm that combines existing variables and new variables and conditions into a single data *** proposed system provides higher accuracy,increases performance,and gives greater user control for optimal positioning of data within multi-tiered storage solutions.

关键词： data temperature hot and cold data multi-tiered storage metadata variable multi-temperature system

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Enhancing Transaction Monitoring Controls to Detect Money Laundering Using Machine Learning 18

Enhancing Transaction Monitoring Controls to Detect Money La...

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18th IEEE International Conference on e-Business Engineering, ICEBE 2022

作者： Oztas, Berkan Cetinkaya, Deniz Adedoyin, Festus Budka, Marcin Bournemouth University Department of Computing and Informatics Bournemouth United Kingdom

ISBN: (纸本)9781665492447

Money laundering has become a great economic problem with huge consequences on society and financial institutions in the last decade. Current anti-money laundering (AML) procedures within the industry are either inefficient due to criminals' increasingly sophisticated approaches or technological advancements. This paper provides an extended abstract to identify and analyze the machine learning methods to detect money laundering through transaction monitoring in the literature. Moreover, the paper identifies research gaps and based on the observed limitations, suggests future research directions and areas in need of improvements. © 2022 IEEE.

关键词： Machine learning

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Selecting Machine Learning Models for Student Performance Prediction Aligned with Pedagogical Objectives

Selecting Machine Learning Models for Student Performance Pr...

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Khan, Ijaz Zabil, Mohammed Hazli Mohamed Ahmad, Abdul Rahim Jabeur, Nafaa College of Computing and Informatics Univsiti Tenaga Nasional Department of Computing Kajang Malaysia Buraimi University College Information Technology Dept. Al-Buraimi Oman Deparment of Computing and Informatics' Kajang Malaysia German University of Technology Computer Science Dept. Muscat Oman

ISBN: (纸本)9798350302684

Machine learning classifiers emerge as productive tools to develop prediction models which forecast the final outcome of the students, in a course, and provide an opportunity to the instructor to take appropriate measures. A single prediction model may not be enough to achieve all the objectives of an instructor. The selection of appropriate prediction models is yet a challenging task. This paper proposes a novel framework that applies a set of machine learning classifiers over the training dataset of a course. Along with the training dataset, the instructor clarifies the prime of objective of the binary model whether it must focus over the identification of fail, pass, or both students. The framework recommends a convenient model to achieve the specific objectives of the instructor. This research concludes that models inclined to reduce misclassification of minority class are suitable if the primary objective of the institution is the correct identification of students who are struggling to achieve the minimum course requirements. Further, a model specialized solely in amplifying the correct classification of majority class instance is appropriate if the aim is to focus on the correct identification of excellent students. The empirical analysis in this research leads towards the fact that accuracy alone is not an adequate metric to assess the performance of a model and that systematic selection of evaluation metrics is required to develop constructive models. © 2023 IEEE.

关键词： Students

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Development of Kansei-Based Visualization Pattern for E-Learning Website 4th

Development of Kansei-Based Visualization Pattern for E-Lear...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Aning, Alberto Mohd Naim, Nur Faraha Baharum, Aslina Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Kota Kinabalu88450 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia

ISBN: (纸本)9789819729760

Through their websites, a lot of businesses and people directly offer their services. The way in which these services are offered may affect users emotionally. A website that elicits pleasant emotions in its users can make them feel happy, engaged, trustworthy, or satisfied. Well-known for its ability to analyze customer emotional demands and define the connection between emotions and product design aspects, Kansei engineering (KE) is a user-centered technology. The development of e-learning software, e-learning authoring tools, learning management systems, and other related technologies is currently a major investment made by universities, training centers, and government bodies. Even though there are many advantages for educators using e-learning technology, getting students to use these services can be difficult, especially for the e-learning websites of Malaysia’s higher education institutions (HEIs). Few standards exist for user interface (UI) design that encourages emotional connection on the e-learning platforms of Malaysian HEIs. Students were disengaging from e-learning platforms in substantial numbers because of this issue. Hence, this paper aims to explore users’ needs and emotions while using an HEI website and produce a new guideline for HEIs e-learning website. Using the card sorting technique, a visualization pattern has been presented as a design guideline for an e-learning graphical user interface (GUI) website for HEIs in Malaysia. Positive outcomes have been obtained by implementing the suggested guideline on the GUI of the prototype website. The purpose of this guideline is to help HEIs as well as website developers and designers create e-learning websites that will keep students interested in e-learning over time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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GlucoFriend: Glycaemic Variability Prediction for Diabetes Patients 2

GlucoFriend: Glycaemic Variability Prediction for Diabetes P...

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

作者： Guruge, Shehan Fernando, Pumudu Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9781665407410

Glycaemic Variability is one of the main complication diabetes patients' face frequently. Even though smart equipment has been invented for blood glucose levels management, majority of the diabetes patients rely on Glucometer and other manual methods to measure and record their blood glucose levels in Sri Lanka. This research focuses on providing a solution for people in need, for predicting their blood glucose levels to benefit them in managing their Glycaemic levels. This study further focuses on the prediction ability of a sparser dataset (only 3 recordings per day) and proposes a methodology for predicting the blood glucose values for new patients. Entire study is divided into four broader categories as, personalized prediction with carb, personalized prediction without carb, non-personalized prediction with carb, and non-personalized prediction without carb. Out of the above four modules, personalized prediction with carb obtained the maximum prediction accuracy of mean rate of 85.3% for 8 patients. non-personalized prediction module obtained the maximum Glycaemic prediction accuracy mean rate of 79%. © 2022 IEEE.

关键词： Forecasting

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Efficient Power Control for UAV Based on Trajectory and Game Theory

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Computers, Materials & Continua 2023年第3期74卷 5589-5606页

作者： Fadhil Mukhlif Ashraf Osman Ibrahim Norafida Ithnin Roobaea Alroobaea Majed Alsafyani Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

Due to the fact that network space is becoming more limited,the implementation of ultra-dense networks(UDNs)has the potential to enhance not only network coverage but also network *** Aerial Vehicle(UAV)communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes.A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this *** the IoT system,the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless *** quality of service(QoS)offered by the cognitive node was interpreted as a price-based utility function,which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’net utility *** energyefficient non-cooperative game theory power allocation with pricing strategy abbreviated as(EE-NGPAP)is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things(IoT)wireless *** has also been demonstrated,theoretically and by the use of simulations,that the Nash equilibrium does exist and that it is one of a *** proposed energy harvesting approach was shown,through simulations,to significantly reduce the typical amount of power thatwas *** is taken into consideration to agree with the objective of 5G *** order to converge to Nash Equilibrium(NE),the method that is advised only needs roughly 4 iterations,which makes it easier to utilise in the real world,where things aren’t always the same.

关键词： UAV spiral&sigmoid trajectory drones IoT game theory energy efficiency 6G

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Towards the Selection of Distance Metrics for k-NN Classifier in Students' Performance Prediction Modeling

Towards the Selection of Distance Metrics for k-NN Classifie...

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Khan, Ijaz Mohamed Zabil, Mohammed Hazli Ahmad, Abdul Rahim Jabeur, Nafaa College of Computing and Informatics' Univsiti Tenaga Nasional Department of Computing Kajang Malaysia Buraimi University College Information Technology Dept. Al-Buraimi Oman Deparmtne of Computing and Informatics' Kajang Malaysia German University of Technology Computer Science Dept. Muscat Oman

ISBN: (纸本)9798350302684

This paper investigates the impact of changing distance metrics on the performance of the k-NN classifier. The study investigates the variation in models performance with changing distance metric and value of k in the context of students' performance prediction models. The research utilizes datasets specifically designed for students' performance prediction modeling. Starting with a I-NN model, the experiments increment the value of k by 2 until the size of the dataset is reached. The experiments are repeated with different distance metrics derived from Minkowski derivation, including Euclidean, Manhattan, and Chebyshev. The findings indicate that there is no unanimously dominant distance metric for every dataset. However, the Euclidean and Manhattan distance metrics emerge effective, while Chebyshev exhibits lower performance. The research concludes Euclidean and Manhattan distance metrics as appropriate metrics for students' performance prediction modeling using the k-NN classifier. © 2023 IEEE.

关键词： Forecasting

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