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research Square 2021年

作者： Lansky, Jan Mohammadi, Mokhtar Mohammed, Adil Hussein Taher Karim, Sarkhel H. Rashidi, Shima Rahmani, Amir Masoud Hosseinzadeh, Mehdi Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Information Technology Lebanese French University Kurdistan Region Erbil Iraq Department of Communication and computer engineering Cihan university-Erbil Kurdistan Region Erbil Iraq Computer Department College of Science University of Halabja Halabja Iraq Sulaimani Polytechnic University Technical College of Informatics Computer Networks Department Sulaymaniyah Iraq Department of Computer Science University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of computer science Khazar University Baku Azerbaijan Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran Iran

Mobile Edge Computing (MEC) is an interesting technology aimed at providing various processing and storage resources at the edge of the Internet of things (IoT) networks. However, MECs contain limited resources, and they should be managed effectively to improve resource utilization. Workflow scheduling is a process that tries to map the workflow tasks to the most proper set of computing resources regarding some objectives. For this purpose, this paper presents DBOA, a discrete version of the Butterfly Optimization Algorithm (BOA) that applies the Levy flight to improve its convergence speed and prevent the local optima problem. Then, DBOA is applied for DVFS-based data-intensive workflow scheduling and data placement in MEC environments. This scheme also employs the HEFT algorithm's task prioritization method to find the task execution order in the scientific workflows. For evaluating the performance of the proposed scheduling scheme, extensive simulations are conducted on various well-known scientific workflows with different sizes. The obtained experimental results indicate that this method can outperform other algorithms regarding energy consumption, data access overheads, etc. © 2021, CC BY.

关键词： Optimization

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TextAI 3.0 (Multimodal): multimodal sentiment analysis using attention-enabled ensemble-based deep learning in hyperbolic space

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International Journal of Information Technology (Singapore) 2025年 1-16页

作者： Singh, Jaskaran Sanga, Prabhav Khanna, Narendra N. Laird, John R. Singh, Inder M. Kalra, Mannudeep K. Mantella, Laura E. Johri, Amer M. Faa, Gavino Tiwari, Ekta Singh, Rajesh Abbas, Mohamed Saba, Luca Suri, Jasjit S. Stroke Monitoring and Diagnostic Division AtheroPoint™ Roseville 95661 CA United States Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Cardiology Indraprastha APOLLO Hospitals New Delhi 110001 India Department of Neurology and Stroke Program University of Maryland School of Medicine Baltimore United States Department of Radiology Harvard Medical School Boston 02115 MA United States Department of Biomedical and Molecular Sciences Queen’s University Kingston ON Canada Division of Cardiology Department of Medicine Queen’s University Kingston ON Canada Department of Pathology University of Cagliari Cagliari Italy Department of Computer Science Visvesvaraya National Institute of Technology (VNIT) Nagpur 440010 India Department of Research and Innovation Uttaranchal Institute of Technology Uttaranchal University Dehradun 248007 India Central Labs King Khalid University P.O. Box 960 AlQura’a Abha Saudi Arabia Electrical Engineering Department College of Engineering King Khalid University Abha 61421 Saudi Arabia Department of Neurology University of Cagliari Cagliari Italy Department of ECE Idaho State University Pocatello 83209 ID United States Department of CE Graphic Era Deemed to Be University Dehradun 248002 India University Center for Research and Development Chandigarh University Mohali India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Campus Pune India Department of Innovation Global Biomedical Technologies Inc. Roseville 95661 CA United States

The rising prevalence of depression presents a significant global health challenge, underscoring the need for effective early detection through sentiment analysis. Traditional multimodal sentiment analysis methods, which rely on Euclidean space for data representation, often fail to capture the complex hierarchical relationships and non-linear structures inherent in multimodal data. To address this limitation, we propose a novel multimodal architecture that operates in hyperbolic space and incorporates attention-based ensemble learning to enhance feature representation and model performance. We developed and evaluated ten AI models—five utilizing Euclidean space and five leveraging hyperbolic space—on the Spoken Language Understanding Evaluation (SLUE) benchmark dataset. The models were assessed using cross-validation techniques to ensure robustness, and statistical analyses were conducted to validate the findings. Our results indicate that hyperbolic-based multimodal models achieve a 2.32% improvement in accuracy compared to their Euclidean counterparts, while the integration of self-attention mechanisms further contributes an average accuracy gain of 0.96%. Additionally, we observed a 3.76% decline in performance when reducing the training-to-test data ratio from 90:10 to 50:50, highlighting the importance of data distribution in model training. These findings confirm that attention-enhanced multimodal models in hyperbolic space offer superior representations for sentiment analysis. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Attention-enabled Deep learning Euclidean space Hyperbolic space Multimodal Sentiment analysis

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Construction of Multidimensional Stochastic Transformation Blocks

Construction of Multidimensional Stochastic Transformation B...

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2019 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies" IT and QM and IS 2019

作者： Chugunkov, Ilya V. Kliuchnikova, Bogdana V. Yadykin, Igor M. Gulyaev, Vadim A. Chugunkov, Vladimir I. National Research Nuclear University MEPhI Moscow Engineering Physics Institute Department of Computer Systems and Technologies Moscow Russia National Research University MPEI Moscow Power Engineering Institute Department of Energy of High-temperature Heat Engineering Moscow Russia

ISBN: (纸本)9781728125954

The article is devoted to the improvement of stochastic transformation blocks (R-blocks). The principles of R- blocks construction are considered. We review the examples of R- blocks using in the cryptographic algorithms. The structure of two-dimensional stochastic transformation block is proposed, as well as the method for construction of multidimensional R- blocks. The using of improved R-blocks for construction of multidimensional algorithms is proposed. The results of evaluation of statistical properties of the multidimensional stochastic transformation blocks are given. © 2019 IEEE.

关键词： Security of data

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Retraction Note: Multi-stage biomedical feature selection extraction algorithm for cancer detection

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Discover Applied Sciences 2024年第1期7卷 1-1页

作者： Keshta, Ismail Deshpande, Pallavi Sagar Shabaz, Mohammad Soni, Mukesh Bhadla, Mohit kumar Muhammed, Yasser Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Bharati Vidyapeeth (Deemed to Be University) College of Engineering Pune India Arba Minch University Arba Minch Ethiopia Department of CSE University Centre for Research & Development Chandigarh University Mohali India Department of Information Technology Ahmedabad Institute of Technology Ahmedabad India College of Technical Engineering Al-Farahidi University Baghdad Iraq

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Case study on implementation of ANN technique for analysis of erosion of hard coatings in different slurries

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AIP Conference Proceedings 2024年第1期2986卷

作者： Jashanpreet Singh Simranjit Singh Jatinder Pal Singh Satish Kumar Harjot Singh Gill University Center for Research & Development Chandigarh University Mohali 140413 India School of Computer Science Engineering and Technology Bennett University Greater Noida 201310 India School of Mechanical Engineering Lovely Professional University Phagwara 144001 India Department of Mechanical Engineering National Institute of Technology Jamshedpur 831014 India Department of Mechanical Engineering Chandigarh University Mohali 140413 India

ANN can be an alternate to provide the general solution of erosion wear problems. In this case study, the artificial intelligence is utilized to predict the erosion wear in different slurries. The data was collected for the various thermal sprayed coatings namely Stellite, Colmonoy, Ni-20Cr2O3, Ni-20Al2O3, Al-20TiO2, WC-10Co4Cr, WC-10Co4Cr+Y2O3, WC-10Co4Cr+Mo2C, and WC-10Co4Cr+ZrO2. For the above mentioned coatings, three different erodent materials were used such as sand, fly ash, and bottom ash. These coatings materials were coated on SS316L steel using high-velocity oxy-fuel (HVOF) technique. MATLAB 16.0 was used in this study to design a standard multilayer feedforward hierarchical neural network to predict erosion wear. Pearson coefficient (R) values for the testing of the ANN model constructed for sand, fly ash and bottom ash slurries were 0.97534, 0.9638, and 0.95181 respectively. It was found that ANN model designed for sand slurry shows ±4.87% deviation from the experimental data. However, the percentage error discovered between experimental and ANN projected erosion wear was ±4.27 and ±4.98 percent respectively for fly ash and bottom ash slurry, which implies that the experimental data and neural network predictions agreed quite well.

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Broadband Thermal Imaging using Meta-Optics

arXiv

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arXiv 2023年

作者： Huang, Luocheng Han, Zheyi Wirth-Singh, Anna Saragadam, Vishwanath Mukherjee, Saswata Fröch, Johannes E. Tanguy, Quentin A.A. Rollag, Joshua Gibson, Ricky Hendrickson, Joshua R. Hon, Phillip W.C. Kigner, Orrin Coppens, Zachary Böhringer, Karl F. Veeraraghavan, Ashok Majumdar, Arka Department of Electrical and Computer Engineering University of Washington SeattleWA United States Department of Physics University of Washington SeattleWA United States Department of Electrical Engineering Rice University HoustonTX United States KBR Inc. 3725 Pentagon Blvd. Suite 210 BeavercreekOH United States Sensors Directorate Air Force Research Laboratory 2241 Avionics Circle Wright-Patterson AFB OH United States NG Next Northrop Grumman Corporation 1 Space Park Drive Redondo BeachCA United States CFD Research Corporation HuntsvilleAL United States Institute for Nano-Engineered Systems University of Washington SeattleWA United States

Subwavelength diffractive optics known as meta-optics have demonstrated the potential to significantly miniaturize imaging systems. However, despite impressive demonstrations, most meta-optical imaging systems suffer from strong chromatic aberrations, limiting their utilities. Here, we employ inverse-design to create broadband meta-optics operating in the long-wave infrared (LWIR) regime (8-12 µm). Via a deep-learning assisted multi-scale differentiable framework that links meta-atoms to the phase, we maximize the wavelength-averaged volume under the modulation transfer function (MTF) of the meta-optics. Our design framework merges local phase-engineering via meta-atoms and global engineering of the scatterer within a single pipeline. We corroborate our design by fabricating and experimentally characterizing all-silicon LWIR meta-optics. Our engineered meta-optic is complemented by a simple computational backend that dramatically improves the quality of the captured image. We experimentally demonstrate a six-fold improvement of the wavelength-averaged Strehl ratio over the traditional hyperboloid metalens for broadband *** Codes 78A10 Copyright © 2023, The Authors. All rights reserved.

关键词： Infrared imaging

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An Image Stabilization Technique for Long-durational Outdoor Footages Obtained by Visual IoT systems

An Image Stabilization Technique for Long-durational Outdoor...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Yuki Murakami Ken T. Murata Kazutaka Kikuta Michiharu Niimi Tomohiro Kawanabe Takamichi Mizuhara Toshiki Aoki Kazunori Yamamoto Tsutomu Nagatsuma Kazuki Kobayashi Keiichiro Fukuzawa Praphan Pavarangkoon ICT Testbed Research and Development Promotion Center National Institute of Information and Communications Technology (NICT) Tokyo Japan Resilient ICT Reserach Center National Institute of Information and Communications Technology (NICT) Sendai Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology (Kyutech) Fukuoka Japan Clealink Technology Kyoto Japan Faculty of Engineering Shinshu University Nagano Japan Academic Center for Computing and Media Studies (ACCMS) Kyoto University Kyoto Japan Faculty of Information Technology King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9781665427616

Disaster mitigation is a significant issue where modern wireless network systems are expected to play a crucial role. It is believed that Visual IoT is one of the key techniques because of its monitoring abilities of urban and rural areas. The outdoor Visual IoT systems generally transfer a large number of footage every day. To detect information from large-size footage datasets, automatic time subtractions between frames are effective. However, to extract even tiny difference between frames, camera shake causes a serious damage. In this study we first survey long-durational footage transmitted from outdoor cameras installed in a city to examine that the stabilization techniques based on feature keypoints are effective to camera shake. Based on this survey we define a matching index to judge if the stabilization is of use or not for every couple of frames. The index is implemented with help of a camera calibration library in OpenCV using AKAZE feature. We then propose a method to stabilize footage continuously obtained by outdoor Visual IoT systems. Using the matching index, we examine one-day footage to find that stabilization is occasionally not applicable in case when the matching index is relatively larger or smaller than 1 (when matching is complete) or the number of matching pairs using AKAZE keypoints are too few. According to the results we set a threshold value of the matching index. We finally perform this technique to footage recorded on a couple of strongly windy days. The efficiency is numerically and visually confirmed on each footage successfully.

关键词： Visualization Cloud computing Wireless networks Urban areas Cameras Feature extraction Libraries

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Spatially adaptive pyramid feature fusion for scale-aware crowd counting

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Pattern Recognition 2025年 168卷

作者： Shenjian Gong Zhaoliang Yao Wangmeng Zuo Jian Yang PongChi Yuen Shanshan Zhang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Hikvision Research Institute China School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China Department of Computer Science Hong Kong Baptist University Hong Kong

Crowd counting is a challenging task to predict the number of persons in crowded scenes. The major challenge comes from density/scale variation in one single image, which results in a significant performance decrease for most methods. In this work, we aim to explore how to solve the scale variation problem with assistance from the segmentation task. We first simultaneously predict a coarse density map, along with multi-scale segmentation masks, which provide strong priors of background and head regions with different scales; then we propose a Spatially Adaptive Pyramid (SAP) feature fusion block to fully integrate the coarse density map and multi-scale segmentation masks; in SAP, a feature pyramid and corresponding attention pyramid are obtained for multi-scale head regions, and then cross-scale correlations are computed to handle the scale variation problem. These high-dimensional cross-scale correlation features are then used to refine the density map, based on which the final counting numbers are obtained. Experiments show that our proposed SAP block significantly outperforms previous fusion methods, and obtains consistent improvements on various backbones. Compared to previous state-of-the-art methods, our method is better at handling scale variation, surpassing previous methods on the challenging ShanghaiTech A, UCF-QNRF and NWPU datasets.

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Design of a Cryptographically Secure Pseudo Random Number Generator with Grammatical Evolution

Research Square

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research Square 2021年

作者： Ryan, Conor Kshirsagar, Meghana Vaidya, Gauri Cunningham, Andrew Sivaraman, R. Biocomputing and Developmental Systems Group Lero The Science Foundation Ireland Research Centre for Software Computer Science and Information System Department University of Limerick LimerickV94 T9PX Ireland Intel Research and Development Ireland Limited LeixlipW23 CX68 Ireland Department of Electronics Communication Engineering School of Electrical and Electronics Engineering Shanmugha Arts Science Technology and Research Academy Deemed to be University Thanjavur613401 India

This work investigates the potential of evolving an initial seed with Grammatical Evolution (GE), for the construction of cryptographically secure (CS) pseudo-random number generator (PRNG). We harness the flexibility of GE as an entropy source for returning initial seeds. The initial seeds returned by GE demonstrate an average entropy value of 7.920261600000001 which is extremely close to the ideal value of 8. The initial seed combined with our proposed approach, control_flow_incrementor, is used to construct both, GE-PRNG and GE-CSPRNG. The random numbers generated with CSPRNG meet the prescribed National institute of Standards and Technology (NIST) SP800-22 requirements. Monte Carlo simulations established the efficacy of the PRNG. The experimental setup was designed to estimate the value for pi, in which 100,000,000 random numbers were generated by our system and which resulted in returning the value of pi to 3.146564000, with a precision up to six decimal digits. The random numbers by GE-PRNG were compared against those generated by Python’s rand() function for sampling. The sampling results, when measured for accuracy against twenty-nine real world regression datasets, showed that GE-PRNG had less error when compared to Python’s rand() against the ground truths in seventeen of those, while there was no discernible difference in the remaining twelve. © 2021, CC BY.

关键词： Monte Carlo methods

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Nikkei Stock Market Price Index Prediction Using Machine Learning 4

Nikkei Stock Market Price Index Prediction Using Machine Lea...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Harahap, Lukman A. Lipikorn, Rajalida Kitamoto, Asanobu Department of Agricultural Engineering Faculty of Agriculture University of Sumatera Utara Medan Indonesia Machine Intelligence and Multimedia Information Technology Laboratory Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand Research Organization of Information and Systems Department of Informatics National Institute of Informatics Tokyo Japan

Stock market prediction has always been a difficult process, most of the prediction rely solely on the data of the corresponding stock market. Relationship of gold and oil price with stock market performance has been proven significant in some major world stock index. Prediction of stock market price index using machine learning methods is expected to perform well, with the ability of machine learning method to predict using nonlinear inputs. The methods were commonly able to predict relatively well in predicting the values of Japanese Nikkei 225 and Japanese Nikkei 400 indexes. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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