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An application on forecasting for stock market prices: hybrid of some metaheuristic algorithms with multivariate adaptive regression splines

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International Journal of Intelligent Computing and Cybernetics 2023年第4期16卷 847-866页

作者： Dilek Sabancı Serhat Kılıçarslan Kemal Adem Department of Mathematics Tokat Gaziosmanpas¸a UniversityTokatTurkiye Department of Software Engineering Bandırma Onyedi Eylul UniversityBalıkesirTurkiye Department of Computer Engineering Sivas University of Science and TechnologySivasTurkiye

Purpose:Borsa Istanbul 100 Index,known as BIST100,is the main indicator to measure the performance of the 100 highest stocks publicly traded in Borsa Istanbul concerning market and trading *** 100 index prediction is a popular research domain for its complex data structure caused by stock price,commodity,interest rate and exchange rate *** study proposed hybrid models using both Genetic,Particle Swarm Optimization,Harmony Search and Greedy algorithms from metaheuristic algorithms approach for dimension reduction,and MARS for ***/methodology/approach:This paper aims to model in the simplest way through metaheuristic algorithms hybridized with the MARS model the effects of stock,commodity,interest and exchange rate variables on BIST 100 during the Covid-19 pandemic period(in the process of closing)between January 2020 and June ***:The most suitable hybrid model was chosen as PSO&MARS by calculating the RMSE,MSE,GCV,MAE,MAD,MAPE and R2 measurements of training,test and overall dataset to check every model’s *** results demonstrated that the proposed PSO&MARS hybrid modeling procedure gave results both as good as the MARS model and a simpler and non-complex model ***/value:Using metaheuristic algorithms as a supporting tool for variable selection can help to identify important independent variables and contribute to the establishment of more non-complex ***,test and overall dataset to check every model’s efficiency.

关键词： Dimension reduction Metaheuristic algorithms MARS Feature selection BIST 100

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YFLM: An Improved Levenberg-Marquardt Algorithm for Global Bundle Adjustment 41st

YFLM: An Improved Levenberg-Marquardt Algorithm for Global ...

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41st computer Graphics International Conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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Optimization of Interactive Videos Empowered the Experience of Learning Management System

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computer Systems science & engineering 2023年第7期46卷 1021-1038页

作者： Muhammad Akram Muhammad Waseem Iqbal M.Usman Ashraf Erssa Arif Khalid Alsubhi Hani Moaiteq Aljahdali Department of Computer Science Superior University Lahore54000Pakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia

The Learning management system(LMS)is now being used for uploading educational content in both distance and blended *** platform has two types of users:the educators who upload the content,and the students who have to access the *** students,usually rely on text notes or books and video tutorials while their exams are conducted with formal *** assessments and examination criteria are ineffective with restricted learning space which makes the student tend only to read the educational contents and videos instead of interactive *** aim is to design an interactive LMS and examination video-based interface to cater the issues of educators and *** is designed according to Human-computer interaction(HCI)principles to make the interactive User interface(UI)through User experience(UX).The interactive lectures in the form of annotated videos increase user engagement and improve the self-study context of users involved in *** interface design defines how the design will interact with users and how the interface exchanges *** findings show that interactive videos for LMS allow the users to have a more personalized learning experience by engaging in the educational *** result shows a highly personalized learning experience due to the interactive video and quiz within the video.

关键词： User interface user experience learning management system linear nonlinear video interactive video visual design

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Research on key technologies of edge cache in virtual data space across WAN

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Frontiers of computer science 2023年第1期17卷 15-34页

作者： Jiantong HUO Yaowen XU Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China College of Software Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China College of Computer Science and Technology Zhejiang UniversityHangZhou 310013China

The authors of this paper have previously proposed the global virtual data space system (GVDS) to aggregate the scattered and autonomous storage resources in China’s national supercomputer grid (National Supercomputing Center in Guangzhou, National Supercomputing Center in Jinan, National Supercomputing Center in Changsha, Shanghai Supercomputing Center, and computer Network Information Center in Chinese Academy of sciences) into a storage system that spans the wide area network (WAN), which realizes the unified management of global storage resources in China. At present, the GVDS has been successfully deployed in the China National Grid environment. However, when accessing and sharing remote data in the WAN, the GVDS will cause redundant transmission of data and waste a lot of network bandwidth resources. In this paper, we propose an edge cache system as a supplementary system of the GVDS to improve the performance of upper-level applications accessing and sharing remote data. Specifically, we first designs the architecture of the edge cache system, and then study the key technologies of this architecture: the edge cache index mechanism based on double-layer hashing, the edge cache replacement strategy based on the GDSF algorithm, the request routing based on consistent hashing method, and the cluster member maintenance method based on the SWIM protocol. The experimental results show that the edge cache system has successfully implemented the relevant operation functions (read, write, deletion, modification, etc.) and is compatible with the POSIX interface in terms of function. Further, it can greatly reduce the amount of data transmission and increase the data access bandwidth when the accessed file is located at the edge cache system in terms of performance, i.e., its performance is close to the performance of the network file system in the local area network (LAN).

关键词： virtual data space system wide area network edge cache redundant data transmission

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No-reference Image Contrast Quality Assessment based on the Degree of Uniformity in Probability Distribution

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IEIE Transactions on Smart Processing and Computing 2022年第2期11卷 85-91页

作者： Kim, Wonvin Yim, Changhoon Department of Computer Software Korean Bible University Seoul Korea Republic of Department of Computer Science and Engineering Konkuk University Seoul Korea Republic of

We propose a novel contrast-based no-reference image quality assessment (NR-IQA) method. The proposed method quantifies the degree of uniformity in the probability distribution of the pixel values of an image to measure contrast quality without the need for a reference image. To quantify the degree of symmetric uniformity in the distribution of pixel values for image contrast assessment, we divide the intensity levels into left and right ranges recursively at each depth as a binary tree. This symmetric hierarchical recursive calculation of the degree of uniformity for contrast measurement provides a good metric for NR-IQA. Experimental results show that the proposed method is more consistent with the levels of contrast decrement and is superior in assessing contrast-enhanced and dehazed images compared with state-of-the-art and classic NR-IQA methods. The proposed method is more suited to real-time applications due to its lower computational complexity compared with previous methods. © 2022 The Institute of Electronics and Information Engineers.

关键词： Probability distributions

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Enhancing Pakistan Rice Plant Disease Detection: A Highly Effective Pre-Trained CNN Model 25

Enhancing Pakistan Rice Plant Disease Detection: A Highly Ef...

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25th International Multi Topic Conference, INMIC 2023

作者： Arshed, Muhammad Asad Akram, Zeeshan Bajwa, Haroon Zahid Azeem, Muhammad Huzaifa Dawood, Muhammad Ahmad Riaz, Faisal Department of Software Engineering Lahore Pakistan Department of Computer Science Lahore Pakistan Department of Infs Lahore Pakistan

ISBN: (纸本)9798350317701

Rice, a crucial global food source, faces numerous challenges impacting its quality and quantity. Pakistan is a significant rice producer and needs more agricultural technological advancements. Conventional disease detection methods for rice plants are time-consuming and ineffective. However, recent progress in agricultural technology, particularly machine learning & deep learning, holds great promise for improved disease identification. In this study, different datasets were considered for preparing a dataset that is specifically related to Pakistan's environmental rice diseases. This study uses a prepared dataset to train the proposed fine-tuned ResNet101V2 model and for the robustness of the proposed model comparison with ResNet101, ResNet50V2, and DenseNet121 also performed in this study. Among the models evaluated, ResNet101V2 exhibited superior performance with a test accuracy of 81%, demonstrating high accuracy in identifying six distinct classes, including five rice leaf diseases (Leaf Blight, Leaf Smut, Hispa, Leaf Blast, and Brown Spot) and one healthy category specific to Pakistan environments. This study represents a significant advancement toward developing more efficient disease detection and management systems in rice farming, ultimately leading to reduced financial losses and improved agricultural practices. © 2023 IEEE.

关键词： Agricultural technology

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ESL127: Emirate Sign Language Dataset(s) and e-Dictionary (Version 2.0) Utilizing Deep Learning Recognition Models 15

ESL127: Emirate Sign Language Dataset(s) and e-Dictionary (V...

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15th International Conference on Innovations in Information Technology, IIT 2023

作者： Ahmed, Ahmed Abdelhadi Gochoo, Munkhjargal Taghizadeh, Mohamad Otgonbold, Munkh-Erdene Batnasan, Ganzorig Department of Computer Science and Software Engineering Al Ain United Arab Emirates Iran University of Science and Technology Department of Electrical Engineering Iran

ISBN: (纸本)9798350382396

As stated by the United Arab Emirates's (UAE) Community Development Authority (CDA), there are around 3,065 individuals with hearing disabilities in the country. These individuals often struggle to communicate with broader society and rely on scarce sign language (SL) interpreters. Moreover, Arabic's dialects diversity compounds the issue by causing dialects in the Arabic Sign Language (ArSL). Hence, the call for a standardized reference for ArSL in the region is a priority. To address these challenges, we've developed an Emirate Sign Language (ESL) electronic dictionary (e-dictionary) with a dataset of 127 signs and 50 sentences, recorded by hearing-impaired individuals in the UAE with various degrees of deafness. Supervised by certified interpreters and validated by ESL's department head at CDA in Dubai, the recordings were made using Azure Kinect DK, resulting in 708 recordings. The dataset is then processed to 10fps. The e-dictionary offers features such as webcam-based sign recognition using YOLOv8 technology, voice-based signing via Arabic Automatic Speech Recognition, text-based signing, and words spelling in ArSL. © 2023 IEEE.

关键词： Character recognition

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Efficient and Lossless Integrity-preserving Training Scheme for High-dimensional Logistic Regression over Vertical Data

Efficient and Lossless Integrity-preserving Training Scheme ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xu, Wei Zhu, Hui Liu, Ruikang Zheng, Yandong Wang, Fengwei Feng, Dengguo Xidian University School of Cyber Engineering Xi'an710126 China Institute of Software State Key Laboratory of Computer Science Beijing100190 China

ISBN: (纸本)9798350351255

Logistic regression is a widely used and efficient machine learning algorithm for data analysis. However, data is often distributed among different participants, and building high-quality models often involves data interaction, which might easily lead to sensitive information leakage. While some schemes have been proposed to address the data security issue through privacy-preserving technologies such as multi-party computation, homomorphic encryption, and trusted execution environment (TEE), they still suffer from some limitations in terms of security, efficiency, and model integrity and are not suitable for the secure outsourcing training. Therefore, in this paper, we propose an efficient and lossless integrity-preserving training for high-dimensional logistic regression over vertically partitioned data. First, we utilize a block strategy to encrypt the data in batches and outsource them to a cloud with TEE to achieve the non-interactive training. Considering TEE's limited memory, we employ random sampling technology to achieve mini-batch training within TEE, which ensures the data security. We find that the cloud might launch the active attacks to drop the model accuracy, such as lazy attack and replacement attack. To prevent the attacks, we introduce an integrity protection mechanism to verify data integrity and reduce verification frequency using a mixed-probability checking method, including fixed and random probability checking. Detailed analysis has confirmed that our scheme is secure and integrity-preserving. Extensive experiments have demonstrated that our scheme can achieve lossless, non-interactive, and efficient model training. © 2024 IEEE.

关键词： Information leakage

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Automated object recognition in high-resolution optical remote sensing imagery

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National science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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Understanding the Impact of Unexpected Cobot Movements on Human Stress Levels: A Time Series Classification Task

Understanding the Impact of Unexpected Cobot Movements on Hu...

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2024 Workshop on Embracing Human-Aware AI in Industry 5.0, HAII5.0 2024

作者： Shilon, Lior Shevtsov, Vladyslav Schillreff, Nadia Rother, Anne Mark, Shlomo Spiliopoulou, Myra Department of Software Engineering Sami Shamoon College of Engineering Ashdod Israel Faculty of Computer Science Otto-von-Guericke-University Magdeburg Germany

This study investigates the impact of unexpected stimuli on participants’ stress levels during human-robot interactions (HRI). We designed an experiment, where a cobot performs writing tasks, as well as some unexpected actions, intended to surprise the human observer. During this experiment, we are continuously monitoring physiological responses through Electrodermal Activity (EDA) and Electrocardiogram (ECG) sensors. Our goal was to capture the relationship between surprising actions by the cobot and indicators of change in the human stress levels. We employed time series classification techniques, namely ROCKET, Shapelet Transform and WEASEL, along with Support Vector Machine (SVM) analysis on temporal segments. This approach enabled us to develop a binary classification model on stress levels. Our research contributes to the design of AI systems that can detect changes in the stress levels of humans who interact with cobots that perform unexpected actions. Thus, we work towards the improvement of safety protocols in HRI. © 2024 Copyright for this paper by its authors.

关键词： Collaborative robots

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