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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Jaouhar Fattahi Baha Eddine Lakdher Mohamed Mejri Ridha Ghayoula Elyes Manai Marwa Ziadia Department of Computer Science and Software Engineering Laval University Quebec Canada Faculty of Science University of Monastir Monastir Tunisia Faculty of Engineering University of Moncton Canada

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Intentionally mutilated fingerprints pose a significant challenge in forensic identification. Such deliberate actions typically stem from individuals seeking to evade detection or association with past or prospective criminal activities. The detection of damaged fingerprints presents a formidable obstacle for most of current forensic systems, often leading to a pronounced incidence of false negatives. The ramifications of a false negative are profound, as they preclude the establishment of links between suspects and crime scenes, impeding the acquisition of vital evidence and potentially stalling investigative progress. In response to this critical issue, this paper delves into the development of a deep learning based model expressly designed to accurately discern and capture patterns present in damaged fingerprints.

关键词： Deep learning Forensics Transfer learning Process control Fingerprint recognition Robustness Convolutional neural networks Information technology Research and development Optimization

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Efficient and Privacy-Preserving Weighted Nearby-Fit Spatial Keyword Query in Cloud

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IEEE Internet of Things Journal 2025年第12期12卷 20498-20511页

作者： Sun, Lili Lu, Rongxing Zheng, Yandong Zhang, Yonggang Tao, Yi Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China University of New Brunswick Faculty of Computer Science FrederictonNBE3B 5A3 Canada Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Jilin University College of Software Changchun130012 China

In the modern digital landscape, integrating geographic locations and textual descriptions within a geo-textual dataset enhances location-based services (LBS) via spatial keyword queries, as these queries combine spatial and textual information to deliver more precise and personalized results. Additionally, the advent of cloud computing allows data owners to outsource data management and services to the cloud, boosting scalability but introducing efficiency challenges due to complex encryption. Although many schemes have been proposed for spatial keyword queries on encrypted geo-textual data, none supports matching a query keyword set with the keyword sets of multiple objects, a common query type in LBS. Imagine a user seeking to rent a house close to his/her workplace, with easy access to conveniences like supermarkets. By using nearby-fit spatial keyword queries, we can match the desired house with a house-type target object and its nearby amenities, offering more practical and flexible recommendations than traditional spatial keyword queries. Hence, in this paper, we introduce an efficient and privacy-preserving scheme called the Privacy-preserving Weighted Nearby-fit Spatial Keyword (PWNSK) query scheme. First, we design a target-oriented spatial keyword (TOSK) tree for data organization and a TOSK tree based WNSK query algorithm for efficient pruning by simultaneously utilizing locations, keywords, and distances from nearby objects to target objects. For privacy, we develop several protocols, including one for polynomial coefficient re-encoding, based on polynomial coefficient encoding and fully homomorphic encryption. Building on these protocols, we introduce our PWNSK scheme. A thorough security analysis confirms its robustness, while extensive experiments also showcase its effectiveness. © 2014 IEEE.

关键词： Differential privacy

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Analyzing Tools and Techniques for Evaluating Requirements Traceability 25

Analyzing Tools and Techniques for Evaluating Requirements T...

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25th International Multi Topic Conference, INMIC 2023

作者： Hassan, Shoaib Li, Qianmu Yasin, Affan Zubair, Muhammad Nanjing University of Science and Technology School of Computer Science and Engineering Jiangsu Nanjing210094 China Northwestern Polytechnical University School of Software Shaanxi Xian710072 China University of Central Punjab Faculty of Information Technology & Computer Science Lahore Pakistan

ISBN: (纸本)9798350317701

Requirements change throughout the software development lifecycle, from requirement elicitation and analysis to software operation. software requirements can be traced back to their source and shown to depend on one another. According to studies, the tools and techniques for the present traceability methodologies need to be revised to prevent the practical application of traceability. We analyzed evaluation techniques and requirements traceability tools in this article to determine whether evaluation techniques are truly being used and supported by software tools. As a result, we noted no connection between the approaches and tools examined and that some criteria, such as stakeholder communication, difficulty of tools, and requirement stability, needed to be better considered in the evaluated tools. © 2023 IEEE.

关键词： Requirements engineering

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Hyper-Heuristic Strategy for Input-Output-Based Interaction Testing

Hyper-Heuristic Strategy for Input-Output-Based Interaction ...

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Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020

作者： Din, Fakhrud Zamli, Kamal Z. Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang26300 Malaysia Department of Computer Science & IT University of Malakand Lower Dir District KPK Pakistan

ISBN: (纸本)9789813345966

software testing aims at exploring faults within software in order to ensure it meets all necessary specifications. Test case design strategies play key role in software testing. Classical test case design strategies, however, do not sufficiently include support for exploration of faults due to interaction between parameter values. New strategies known as t-way strategies (where t expresses interaction strength) have been developed for finding interaction faults. However, existing t-way strategies for input-output-based relationship (IOR) interaction testing mostly adopt greedy algorithms which often generate poor quality test data. Therefore, this paper presents the design of a new IOR test suite generation strategy called IOR_HH based on the exponential Monte Carlo with counter (EMCQ) hyper-heuristic. EMCQ is a parameter free hyper-heuristic which works as controller of the three implemented low-level meta-heuristic operators, namely crossover, peer learning and global pollination in the proposed IOR_HH strategy. Experimental results demonstrate the impact of the proposed strategy against existing computational strategies for IOR interaction testing. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： software testing

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IPRIns: A tool with the improved precision and recall for insertion detection in the human genome 9

IPRIns: A tool with the improved precision and recall for in...

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9th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2021

作者： Piwluang, Sakkayaphab Wichadakul, Duangdao Software Engineering Program Faculty of Engineering Chulalongkorn University Department of Computer Engineering Bangkok Thailand Faculty of Engineering Chulalongkorn University Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9780738132020

An insertion is a specific type of the structural variations. The identification of insertions in a human genome is essential for the study of diseases or their functional effects. There are many tools available for identifying the insertion type with different methods and strategies. However, most of them could not deliver both good recall and precision, especially for the real datasets sequenced with the paired-end short reads. In this paper, we propose iPRIns, a new computational method for detecting insertions aiming to improve both precision and recall. The proposed method with discovering and filtering processes outperformed all other three tools for 5 out of 10 real datasets, the variations of NA12878, for both precision and recall. iPRIns is released under the open-source GPLv3 license. The source code and documentation are available at https://***/cucpbioinfo/iPRIns. © 2021 IEEE.

关键词： Bioinformatics

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Water Quality Monitoring System for Nile Tilapia Farming Using LoRa Technology

Water Quality Monitoring System for Nile Tilapia Farming Usi...

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International Conference on Information Technology (INCIT)

作者： Nawara Chansiri Ponglert Sangkaphet Buppawan Chaleamwong Chaivichit Kaewklom Supawee Makdee Department of Software Engineering Faculty of Computer Science Ubon Ratchathani Rajabhat University Ubon Thailand Department of Computer Science Faculty of Computer Science Ubon Ratchathani Rajabhat University Ubon Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

Fish farmers operating at Sirindhorn Dam in Ubon Ratchathani have faced significant challenges, including high mortality rates attributable to unstable weather conditions that compromise water quality. To tackle these issues, the Internet of Things has emerged as a tool to enhance aquaculture management. This research seeks to develop a prototype water quality monitoring system using LoRa technology to facilitate data transmission between sensor nodes in a peer-to-peer configuration. The development and testing of the system demonstrate its ability to accurately monitor key water quality parameters, including dissolved oxygen, pH, and water temperature, while allowing for remote data transmission. This research involved a sample group of five program development experts and 15 general users, with various tools employed for system efficiency assessment. A statistical analysis including the mean and standard deviation was performed to evaluate performance. The findings reveal a high overall efficiency assessment, with an average score of 4.43.

关键词： Temperature sensors Temperature measurement Databases LoRa Water quality Peer-to-peer computing Servers Monitoring Farming Testing

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A Hybrid Method for Analyzing the Situation Based on Cumulative Fully Vaccinated and Confirmed Cases of Covid-19 in Malaysia 4

A Hybrid Method for Analyzing the Situation Based on Cumulat...

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4th International Conference on Sustainable Technologies for Industry 4.0, STI 2022

作者： Ahmed, Marzia Sulaiman, Mohd Herwan Mohamad, Ahmad Johari Rahman, Mostafijur Faculty of Electrical & Electronics Engineering Technology Pahang Malaysia Daffodil International University Department of Software Engineering Dhaka Bangladesh Green University of Bangladesh Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9781665490450

SARS-CoV-2 is an infection that affects several organs and has a wide range of symptoms in addition to producing severe acute respiratory syndrome. Millions of individuals were infected when it first started because of how quickly it travelled from its starting location to nearby countries. Anticipating positive Covid-19 incidences is required in order to better understand future risk and take the proper preventative and precautionary measures. As a result, it is critical to create mathematical models that are durable and have as few prediction errors as possible. This study suggests a unique hybrid strategy for examining the status of Covid-19 confirmed patients in conjunction with complete vaccination. First, the selective opposition technique is initially included into the Grey Wolf Optimizer (GWO) in this study to improve the exploration and exploitation capacity for the given challenge. Second, to execute the prediction task with the optimized hyper-parameter values, the Least Squares Support Vector Machines (LSSVM) method is integrated with Selective Opposition based GWO as an objective function. The data source includes daily occurrences of confirmed cases in Malaysia from February 24, 2021 to July 27, 2022. Based on the experimental results, this paper shows that SOGWO-LSSVM outperforms a few other hybrid techniques with ideally adjusted parameters. © 2022 IEEE.

关键词： Support vector machines

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Maize Crop Yield Prediction Using Machine Learning Regression Approach 5th

Maize Crop Yield Prediction Using Machine Learning Regressio...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Bedane, Tarikwa Tesfa Kebede, Kidistie Bizuneh Mohapatra, Sudhir Kumar Das, Tapan Kumar Tripathy, Asis Kumar Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Agricultural Transformation Institute Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

Given maize’s significant role as a staple crop, it becomes imperative to carry out precise crop yield prediction to ensure food security. This research employs machine learning algorithms to analyze historical data related to maize crop yields, with the aim of developing a predictive model. The outcome of this study can serve as a valuable tool for pre-harvest planning and measuring crop production per unit of land area. The dataset utilized in this research spans from 2010 to 2021 and was sourced from Ethiopia’s Central Statistical Agency. Three machine learning regression algorithms—Logistic Regression, XGBoost, and Random Forest—were employed to design the predictive model. After data preparation and preprocessing, a total of 436,358 records were used for model development. The models were further trained with reduced features using principal component analysis (PCA). The experimental results demonstrate that the Random Forest algorithm, trained with a reduced number of features through PCA, yielded superior performance, with a lower error rate and a high R2 score of 0.98. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Security Aspects of Container Orchestration in Kubernetes Environments

Security Aspects of Container Orchestration in Kubernetes En...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Sava Stanišić Milan Vesković Olga Ristić Borislav Đorđević Department of Computer and Software Engineering Faculty of Technical Sciences Čačak Republic of Serbia IMP Computer Systems Mihajlo Pupin Institute Belgrade Republic of Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

Security is one of the key challenges in container orchestration, especially in complex environments. This paper explores the security aspects of implementing containerized applications using Docker within a Kubernetes cluster. The first part of the paper describes Docker, Kubernetes, and various ways of applying them within DevOps methodology. It then presents potential vulnerabilities during the implementation of these technologies, as well as vulnerabilities specific to Docker and Kubernetes. Subsequently, some solutions for securing a Kubernetes environment are described.

关键词： DevOps Scalability Organizations Containers Stability analysis Malware Complexity theory Security Object recognition Resilience

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Chromosome Feature Extraction and Ideogram-Powered Chromosome Categorization

Chromosome Feature Extraction and Ideogram-Powered Chromosom...

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作者： Pysarchuk, Oleksii Mironov, Yurii Faculty of Informatics and Computer Science National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kiev03056 Ukraine Faculty of Cybersecurity Computer and Software Engineering National Aviation University Kiev03058 Ukraine

Chromosomal diseases diagnostics is based on identifying chromosomes and detecting abnormalities in them. It is a sophisticated procedure that is performed manually or partially automated, therefore is prone to human error. Automation of such diagnostics is a complex multistage task that presents many challenges. One of such challenges is chromosome feature extraction and classification. Categorizing chromosomes is necessary for determining a diagnosis. This problem has been previously covered by research papers, but there is no complete solution by now. Moreover, the vast majority of proposals rely on Machine Learning (ML) and Neural Networks (NN). Usage of ML-powered solutions may pose a problem due to difficulty of dataset collection - medical data may be diverse, heterogeneous and hard to collect because of its sensitive nature. Given paper presents a proposal of chromosome feature extraction and categorization without NN. It uses computer Vision (CV) techniques for chromosome feature extraction: medial axis is extracted from a chromosome to identify its direction, and than chromosome bands (colored segments that make up distinct chromosome pattern) are identified along the axis. Having transformed chromosome image into a discrete piece of data, it is passed to a proposed multiple-criteria decision-making algorithm. This algorithm is designed to categorize chromosomes without learning dataset, instead making use of ideogram (schematic reference chromosome) data. The algorithm is focused on processing a chromosome as a set of segments, where each segment can be recognized by a special predicate. A predefined combination of such predicates should allow recognizing chromosome type. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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