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9th international conference on Advanced Cloud and Big Data, CBD 2021

作者： Liu, Yushi Han, Jingjing Zhao, Bin Nanjing Normal University School of Computer and Electronic Information School of Artificial Intelligence Nanjing China Jiangsu Open University Quality Assurance Office Nanjing China

ISBN: (纸本)9781665407458

Semantic trajectory pattern mining is becoming more and more important with the efficient semantic enrichment process methods, which is the process of semantic annotation for spatiotemporal trajectories. Existing works on semantic trajectory pattern mining focus on sequential patterns that occur frequently globally in the semantic trajectory data, ignoring the ones that are locally significant within a region, which is called regional semantic pattern. As a consequence, the existing regional semantic pattern mining algorithm RegMiner is time consuming. Based on this motivation, we propose an improved efficient regional semantic pattern mining algorithm called MRSP, which consists of three parts: candidate semantic category sequence discovery, regional semantic pattern recognition, and parallelization. the innovations of MRSP lie in that we use an improved neighborhood pruning algorithm (NPSP) and an improved neighborhood query algorithm (NSSI) in candidate semantic category sequence discovery mentioned above, and adopt a parallelization algorithm to do calculation based on Hadoop platform. Finally, we evaluate our approach on real dataset in Shanghai. Compared with RegMiner, MRSP has the same effect in mining regional semantic patterns, and the efficiency is greatly improved. © 2022 IEEE.

关键词： pattern recognition

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Leveraging Quantum computing and Optimization to Estimate Financial Crashes in Small and Medium-Sized Enterprises 9

Leveraging Quantum Computing and Optimization to Estimate Fi...

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9th international conference on Computer Science and Engineering, UBMK 2024

作者： Dincer, Ege Coskuner, Berkay Bilaloglu, Ege Koroglu, Bilge Department of Applied Data Science Yapi Kredi Technology Ankara Turkey Department of Applied Data Science Yapi Kredi Technology Istanbul Turkey

ISBN: (纸本)9798350365887

In this study, we focus on estimating financial crashes within a network of small and medium enterprises (SMEs) that are customers of Yapi Kredi Bank. these SMEs have complex financial relationships involving receivables and payables among each other. We developed a quadratically constrained quadratic program as a mathematical optimization model aiming at identifying significant vulnerabilities and potential failure points in this network. By defining key parameters such as crossholding ratios, self-holding fractions, asset ownership proportions, and failure cost functions, we capture the intricate dependencies between these entities. the model is inherently nonlinear due to the quadratic terms and constraints, reflecting the real-world complexities of financial networks. Solving this NP-hard problem using classical methods is challenging;therefore, we leverage quantum computing technology to efficiently explore the solution space and produce decision variables that indicate the likelihood of financial distress for each SME in the network. the difficulty of this problem lies in the potential domino effect, where a single delayed payment can trigger subsequent financial distress among connected entities, leading to a cascading collapse within the financial network. Classical computing solutions for such problems would take an impractical amount of time, estimated at 13.7 billion years. In contrast, using quantum computing, our optimization model solves the problem in approximately 7 seconds. Using financial data from 2019, our model successfully identified 90.04 million TL out of 107 million TL of non-performing loans. Similarly, using data from 2020, it identified 669.92 million TL out of 726 million TL of non-performing loans. Overall, the model accurately predicted 91.2. © 2024 IEEE.

关键词： Cost functions

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Automatic Attendance Monitoring System Using LBPH and HAAR Algorithm 9

Automatic Attendance Monitoring System Using LBPH and HAAR A...

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9th international conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Rathinamala, S. Karthikeyan, A. Abishek, R. Kannan, C.K. KIT-Kalaignar Karunanidhi Institute of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350397376

Every office and organization must have an attendance monitoring system. Every institution has a standard attendance policy that requires the professor or assistant to report the student's name and attendance for each and every session. An automatic attendance monitoring approach will be suggested to replace this human process. Face spotting, an image processing tool, and face identification techniques are used in the research project that is being suggested. this method assists in turning video frames into numbers so that the human profile may be quickly recognized for attendance monitoring. the image captured from the video should be compared with the data saved with the date and time, which will be automatically stored in the memory storage, before a recognized attendance database is formed. the Viola-Jones Face Detection Technique, also known as the HAAR cascade algorithm, and the Local Binary pattern Histogram (LBPH) algorithm were used in the development of the proposed automatic face recognition attendance monitoring system. their combined features are used for attendance monitoring. the human face is first captured using the HAAR cascade technique, and then the LBPH algorithm is utilized to save the distinctive traits of each human face. During the face identification procedure, a predetermined user ID is retrieved from the algorithm data. this will produce the student's attendance data for their presence. the image processing system's data will need to be kept up to date and stored daily. the implementation of an automatic attendance tracking system will benefit from the suggested strategy. Anaconda software is used to implement and analyse the suggested attendance tracking system. © 2023 IEEE.

关键词： Local binary pattern

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An Intelligent Application for Detecting Abnormal Movement patterns and Fall Risk in Elderly People. Preliminary Results 50

An Intelligent Application for Detecting Abnormal Movement P...

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50th Latin American computing conference (CLEI)

作者： Cruz, Diego Robles Toro, Carla Taramasco Univ Valparaiso Escuela Ingn Civil Informat Valparaiso Chile Univ Diego Portales Fac Odontol & Salud Escuela Kinesiol Santiago Chile Univ Andres Bello Fac Ingn Inst Tecnol Innovac Salud & Bienestar Vina Del Mar Chile

ISBN: (纸本)9798331540982;9798331540975

Falls in the elderly population have become a public health problem worldwide, since they represent one of the main causes of disability. Automatic identification of the possible danger of falls would help prevent them before they happen. In this research, an alternative approach is proposed as an automated solution, based on the continuous monitoring of the person for a day, as a "Holter" type recording of movement patterns along with the correlation of the intrinsic and extrinsic factors that predispose to a greater risk of falling. the crossing of all the variables recorded and associated with the risk of falls will allow better preventive decisions to be made against them, reducing their morbidity and, at the same time, the costs and burden of the associated health services. It is proposed to design and implement a smartphone application as a scientific and technological solution that allows solving the problem of estimating the risk of falls in older adults.

关键词： fall risk pattern recognition machine learning

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Two-stage prediction framework for wind power ramps considering probability distribution distance measurement

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ENERGY REPORTS 2023年 9卷 809-818页

作者： He, Hui Lu, Nanyan Cheng, Yongqiang Chen, Bingxu Chen, Bo North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China State Grid Fujian Elect Power Co Fuzhou 350013 Peoples R China China Unicom Big Data Co Ltd Beijing 100011 Peoples R China

the high volatility in wind power and uncertainty of ramp events has brought significant hidden hazards to maintain the stable and safe operation of the power system. In this paper, a two-stage prediction framework is proposed, taking the measurement of the probability density curve and the judgment of ramp-up mode. Aiming at the issue of limited prediction information and the sensitivity of prediction values at extreme points, an uncertainty prediction model for wind power based on a gated recurrent unit quantile regression network is proposed to realize the screening and judgment of ramps based on the prediction results and the divergence measurement of probability density distribution. Faced with notoriously unpredictable ramps and the small-samples poor learning performance, this paper proposes a ramp pattern discrimination model based on a gradient boosting decision tree, which describes the events in a future period from their magnitude and duration. Compared with state-of-the-art ramp forecasting methods, our proposed framework yields highly outstanding performances and realizes the adaptive detection for wind power ramps. (c) 2022 the Authors. Published by Elsevier Ltd. this is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Wind ramp event Interval prediction Gated recurrent unit quantile regression network Probability distribution Ramp pattern recognition Gradient boosting decision tree

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A Metamorphic Relation Identification Method based on GEP 9

A Metamorphic Relation Identification Method based on GEP

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9th international conference on Dependable Systems and their Applications, DSA 2022

作者： Hong, Jie Zhang, Jie Qiu, Qi Ma, Angang Li, Meng Yan, Shiyu Gong, Helin School of Computing University of South China Hunan Hengyang China Eng. and Technol. Res. Ctr. of Software Eval. and Test. for Intellectual Equipment of Hunan Province Hunan Hengyang China Cnnc Key Laboratory on High Trusted Computing Hunan Hengyang China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Metamorphic testing is one of the effective methods to alleviate the test oracle problem. Metamorphic relation is the core of metamorphic testing, and there is no effective automatic identification technology. this paper transforms the metamorphic relation recognition issue into a symbolic expression regression problem. the test input pairs are generated using the preset input pattern, and the output pattern is mined by gene expression programming. After reduction and verification, a valid metamorphic relation is obtained. Experiments show that this method can identify the relation obtained by static analysis and a more significant number. At the same time, compared with existing technologies, it has apparent advantages in effectiveness, reliability, and performance. © 2022 IEEE.

关键词： Static analysis

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A Two-Layered Machine Learning Approach for Anti-Malware Sustainability 9

A Two-Layered Machine Learning Approach for Anti-Malware Sus...

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9th international conference on computing for Sustainable Global Development, INDIACom 2022

作者： Alrammal, Muath Naveed, Munir Sallam, Suzan Tsaramirsis, Georgios Higher Colleges of Technology Faculty of Computer Information Science Abu Dhabi United Arab Emirates Khawarizmi International College General Education Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9789380544441

the need for sophisticated security solutions for mobile devices is an urgent and crucial requirement for the sustainable use of smart phones. the number and scale of sophisticated attacks on smart devices is ever-increasing. there are several limitations and challenges to counter such attacks, for example, the security solutions must be light-weight and sustainable to counter such attacks. Also, the limited capacity of computing resources in mobile devices pose serious challenges for security solutions as they must keep scanning for malware in real-time constraints. the sustainability of such solutions is another issue because of the influx of new malware definitions and patterns. In this work, a security model for android devices is presented using a two layered approach. In the first layer, Monte-Carlo simulations and reinforcement learning techniques work together to extract the malware pattern and the second layer filters the false flags to detect malware. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Reinforcement learning

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A UAV Edge computing Task Offloading Method Based on Reinforcement Learning and the Underwater Lobster Optimization Algorithm 9

A UAV Edge Computing Task Offloading Method Based on Reinfor...

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9th IEEE international conference on Progress in Informatics and computing, PIC 2024

作者： Jiang, Keqin Jiang, Mingyan Yuan, Dongfeng Zhao, Ze School of Information Science and Engineering Shandong University Qingdao China Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350363203

With the rapid development of UAV edge computing, the randomness of task generation and the unpredictability of UAV mobility have made related problems increasingly complex. this not only poses a highly intricate integer optimization problem but also requires swift and effective decision-making based on real-time monitoring. Traditional offline algorithms face numerous challenges in addressing such issues and often struggle to meet the demands of dynamic environments. In response, this paper proposes an innovative heuristic algorithm-the V nderwater Lobster Optimization Algorithm-combined with reinforcement learning techniques to dynamically learn the optimal data transmission path. through this approach, we can flexibly adjust the optimization algorithm's update strategy, effectively achieving dynamic management goals and improving the system's overall performance and response speed. the proposed algorithm successfully addresses the task offloading problem in UAV edge computing. Experimental results show that the algorithm significantly reduces system response time and improves task completion rates, fully demonstrating its potential and advantages in the field of AV edge computing. © 2024 IEEE.

关键词： Mobile edge computing

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Exploring Multimodal Sentiment Analysis Models: A Comprehensive Survey 7

Exploring Multimodal Sentiment Analysis Models: A Comprehens...

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7th international conference on Multimedia Analysis and pattern recognition, MAPR 2024

作者： Dao, Phuong Q. Nguyen-Tat, thien B. Roantree, Mark Ngo, Vuong M. Foreign Trade University HCMC Campus Viet Nam University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Dublin City University Insight Centre for Data Analytics School of Computing Ireland Ho Chi Minh City Open University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350368437

the exponential growth of multimodal content across social media platforms, comprising text, images, audio, and video, has catalyzed substantial interest in artificial intelligence, particularly in multi-modal sentiment analysis (MSA). this study presents a comprehensive survey of 30 research papers published between 2020 and 2024 by eminent publishers such as Elsevier, ACM, IEEE, Springer, and others indexed in Google Scholar. Our analysis primarily focuses on exploring multimodal fusion techniques and features, with specific emphasis on the integration of text and image data. Additionally, the article offers an overview of the evolution, definition, and historical context of MSA. It delves into the current challenges and potential advantages of MSA, investigating recent datasets and sophisticated models. Furthermore, the study provides insights into prospective research directions. Notably, this review offers valuable recommendations for advancing research and developing more robust MSA models, thus serving as a valuable resource for both academic and industry researchers engaged in this burgeoning field. © 2024 IEEE.

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Correction to: Automated facial expression recognition using exemplar hybrid deep feature generation technique (Soft computing, (2023), 27, 13, (8721-8737), 10.1007/s00500-023-08230-9)

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Soft computing 2024年 1-2页

作者： Baygin, Mehmet Tuncer, Ilknur Dogan, Sengul Barua, Prabal Datta Tuncer, Turker Cheong, Kang Hao Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Ardahan University Ardahan Turkey Interior Ministry Elazig Turkey Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland ToowoombaQLD4350 Australia Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Science Mathematics and Technology Cluster Singapore University of Technology and Design Singapore487372 Singapore School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

the author found that the below references were not included in the published article. Kanade T, Cohn JF, Tian Y (2000) Comprehensive database for facial expression analysis. In: Proc-4th IEEE int conf autom face gesture recognition, FG 2000, pp 46–53. Lucey P, Cohn JF, Kanade T et al (2010) the extended Cohn–Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE comput soc conf comput vis pattern recognit-work CVPRW 2010, pp 94–101. Pantic M, Valstar M, Rademaker R, Maat L (2005) Web-based database for facial expression analysis. In: IEEE int conf multimed expo, ICME 2005, pp 317–321. Li S, Deng W, Du J (2017) Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp 2852–2861 Yin L, Wei X, Sun Y et al (2006) A 3D facial expression database for facial behavior research. In: FGR 2006 proc 7th int conf autom face gesture recognit 2006, pp 211–216. Goodfellow IJ, Erhan D, Luc Carrier P et al (2015) Challenges in representation learning: a report on three machine learning contests. Neural Netw 64:59–63. Zhang Z, Luo P, Loy CC, Tang X (2018) From facial expression recognition to interpersonal relation prediction. Int J Comput Vis 126:550–569. Chen L-F, Yen Y-S (2007) Taiwanese facial expression image database. Brain Mapp Lab Inst Brain Sci Natl Yang-Ming Univ Taipei, Taiwan Lyons MJ, Kamachi M, Gyoba J (2020) Coding facial expressions with Gabor wavelets (IVC special issue). Lyons MJ (2021) "Excavating AI" re-excavated: debunking a fallacious account of the JAFFE Dataset. Lundqvist D, Flykt A, Ohman A (1998) the Karolinska directed emotional faces (KDEF). CD ROM from Dep Clin Neurosci Psychol Sect Karolinska Institutet 2–2 Zhao G, Huang X, Taini M et al (2011) Facial expression recognition from near-infrared videos. Image Vis Comput 29:607–619. Goldberger J, Hinton GE, Roweis S, Salakhutdinov R

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