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15th Annual ACM Symposium on Cloud Computing, SoCC 2024

作者： Tripathi, Shiv Bhushan Mishra, Debadatta Department of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798400712869

Application containers are widely used in contemporary cloud computing environments. Migration of containers across hosts provides cost-effective cloud management by enabling improved server consolidation, load balancing and enhanced fault tolerance. One of the primary objectives of container migration is to reduce the service downtime of applications hosted in containers. The service downtime depends on performing the migration activities efficiently, specifically from the time the container is stopped on the source host till it is restored and fully functional at the destination host. In this paper, we show that, the state-of-the-art pre-copy migration strategy for containers using checkpoint and restore techniques (e.g., CRIU) inflates the downtime due to its inherent limitations in the restoration procedures, particularly for containers with large memory working set size. We propose PCLive to address this bottleneck using a pipelined restore mechanism. Compared to the baseline CRIU pre-copy migration, PCLive results in up to ∼38.8x reduction in restoration time which leads to a reduction of service downtime by up to ∼2.7x for migration of a container hosting the Redis key-value store over an one Gbps network. We also present comprehensive comparative analysis of the resource cost for the proposed solution along with additional optimizations to demonstrate that PCLive can reduce the application downtime in a resource efficient manner leveraging its flexible and efficient design choices. © 2024 Owner/Author.

关键词： Restoration

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Network Intrusion Detection in Simulated Military Environment Using Machine Learning and Blockchain

Network Intrusion Detection in Simulated Military Environmen...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Vijayalakshmi, S. Yellamilli, Rohan Shobikaa, R. Subasree, V. Vidya, R. Keerthanaporkodi, S. PSG College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350378177

Network Intrusion Detection Systems are critical in bolstering an organization's security infrastructure. The goal of our paper is to design and build a specialized intrusion detection system for simulated military scenarios. The solution classifies network accesses using machine learning techniques and seamlessly integrates the Ethereum blockchain for secure anomalous access record-keeping and auditing. The machine learning component classifies the records as normal or anomalous using the ensemble learning technique, which is the random forest classifier. Feature selection techniques like Variance Threshold, Spearman's correlation coefficient, Pearson's correlation coefficient, Mutual Information and Chi square tests were used to improve the accuracy of the model and reduce dimensionality. The final accuracy of the model was brought to 96.69%. It then sends the records to the blockchain environment for storage. To ensure data integrity and transparency, anomalous access events will be time-stamped, hashed using IPFS, and published to the blockchain. By providing a hash value and a unique id as input, the user will be able to add new anomalous entries. Furthermore, the user will be able to examine the hashed anomalous data for each csv file using the proper id. © 2024 IEEE.

关键词： Network intrusion

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Machine Learning Observation on the Prediction of Diabetes Mellitus Disease 1

Machine Learning Observation on the Prediction of Diabetes M...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Kumar, Pabbathi Dinesh Priya, Sakala Lakshmi Kumar, Kukatlapalli Pradeep Rego, Anil Melwyn Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

Diabetes disease has become as one of the common syndromes in many of the age groups. Diabetes can result in high blood sugar levels, a heart attack, or heart disease. This is one of the fastest developing illnesses, and it requires regular care. After seeing the doctor and being diagnosed, the patient is typically compelled to obtain their reports. Because this procedure is time-consuming and costly, we have the option of using ML approaches to solve this problem. Our research aims to foster a framework prepared to do all the more precisely foreseeing a patient's diabetes risk level. To develop models, classification methods such as Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Random Forest Classifier are employed. The results indicate that the techniques are quite accurate. The result showed that the prediction with the Logistic Regression model acquired the highest accuracy. © 2023 IEEE.

关键词： Forecasting

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The Development of Machine Learning innovation technology for Data Mining In Smart Healthcare 2

The Development of Machine Learning innovation technology fo...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Manikandan, V. Sharma, Sonal Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The field of Smart Healthcare has undergone a revolution due to the development of machine learning technologies. Artificial intelligence (AI) has a subfield called machine learning that allows computers to learn from data and develop over time without explicit programming. Healthcare workers can now discover trends, diagnose diseases, and make decisions based on more precise and timely data thanks to machine learning's capacity to comprehend and analyze enormous amounts of data. Finding patterns and links in massive datasets is known as data mining. Data mining is used in Smart Healthcare to locate patterns in patient data that can be utilized to comprehend a patient's health status better, diagnose specific disorders, and forecast the most effective course of therapy. Data mining can create novel cures and treatments customized to a person's genetic profile. Data mining in Smart Healthcare is now considerably more effective and efficient, thanks to machine learning. Using complex algorithms, machine learning models can analyze vast amounts of data markedly faster and more correctly than conventional methods. Healthcare experts may not immediately notice hidden patterns and connections in data that machine learning algorithms can find. As a result, machine learning can assist healthcare practitioners in making decisions that are better informed, improving patient outcomes. © 2023 IEEE.

关键词： Diagnosis

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The Smart Development of Maximum Distance Rendezvous Point Model For Commercial Scheduling of Complex Networks 2

The Smart Development of Maximum Distance Rendezvous Point M...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Geetha, S. Mahesh, T.R. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The maximum distance rendezvous point for commercial scheduling is a point at which two or more commercial flights can meet for transfer of passengers and/or cargo. This rendezvous point is usually located in an area that is geographically convenient and cost-effective for both the airline and the customer. In order to determine the maximum distance rendezvous point, the airlines must consider a variety of factors, such as the distance between the airports, the availability of ground transportation to and from the airport, the number of passengers and cargo being transferred, the number of flights arriving and departing, the cost of fuel, the safety of the airspace, and the weather. The rendezvous point must also be able to accommodate the size of the aircraft involved, as well as the number of passengers and cargo that need to be transferred. This can be accomplished by having a designated area close to the airport that is large enough to accommodate the aircraft, as well as any necessary ground transportation. The rendezvous point should also be located in an area that is free of congestion and in a safe airspace. In addition, the area should have adequate security measures in place to ensure passenger and cargo safety. This can include security checkpoints, as well as trained personnel to monitor the area. © 2023 IEEE.

关键词： Airports

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Optimization of Cooperative RelayingMolecular Communications for Nanomedical Applications

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computer Modeling in engineering & sciences 2024年第2期138卷 1259-1275页

作者： Eman S.Attia Ashraf A.M.Khalaf Fathi E.Abd El-Samie Saied M.Abd El-atty Konstantinos A.Lizos Osama Alfarraj Heba M.El-Hoseny Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61111Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)OsloNorway Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer science The Higher Future Institute for Specialized Technological StudiesEl ShoroukEgypt

Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)***,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is *** this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift *** fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)*** the previous work,the probability of bit error is expressed in a closed-form *** is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano *** simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.

关键词： Nanomedical system molecular communication cooperative relay optimization

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Automating Documentation Maintenance: Implementing Hyperlink Testing via GitHub Actions 1

Automating Documentation Maintenance: Implementing Hyperlink...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Charan, N. R V College of Engineering Computer Science Department Bangalore560059 India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518578

This paper addresses the testing phase of the Software development life cycle, mainly focusing on identifying the broken links in documentation. Broken links cause serious issues that can bring down the integrity of the documentation, so it is required to regularly check for broken links in documentation. Current methods for detecting broken hyperlinks in documentation often rely on manual checks, which can be time-consuming and prone to human error. This paper introduces a practical approach for automatically detecting broken hyperlinks in documentation files using GitHub Actions. The goal is to automate the identification of links with 404 error (i.e. Page not found) and invalid path reference issues, ensuring high-quality documentation in collaborative software projects. By incorporating this method into continuous integration and continuous Deployment workflows, it can automatically detect broken hyperlinks before integrating and publishing new documents, thus maintaining the documentation integrity. © 2025 IEEE.

关键词： Integration testing

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Leveraging LLM Reasoning with Dual Horn Programs 27th

Leveraging LLM Reasoning with Dual Horn Programs

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27th International Symposium on Practical Aspects of Declarative Languages, PADL 2025

作者： Tarau, Paul Department of Computer Science and Engineering University of North Texas Denton United States

ISBN: (纸本)9783031849237

Dual Horn clauses mirror key properties of Horn clauses. We revisit Dual Horn clauses as enablers of a form of constructive negation that supports goal-driven forward reasoning and is valid both intuitionistically and classically. In particular, we explore the ability to falsify hypotheses in the context of a background theory expressed as a Dual Horn clause program. With Dual Horn clause programs, by contrast to negation as failure, the variable bindings in their computed answers provide explanations for the reasons why a statement is successfully falsified. We devise a compilation scheme from Dual Horn clause programs to Horn clause programs, ensuring their execution with no performance penalty and we design the embedded SymLP language to support combined Horn clause and Dual Horn clause programs. As a (motivating) application, we cast LLM reasoning chains into propositional Horn and Dual Horn clauses that work together to constructively prove and disprove goals and enhance Generative AI with explainability of reasoning chains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Application programs

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An Efficient Algorithm for the 2-Central Path Problem

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International Journal of Computational Geometry and Applications 2024年第1-2期34卷 43-61页

作者： Huang, Ziyun Zhu, Yongding Xu, Jinhui Department of Computer Science and Software Engineering Penn State Erie The Behrend College Erie PA United States Department of Computer Science and Engineering State University of New York at Buffalo Buffalo NY United States

In this paper we consider the following 2-Central Path Problem (2CPP): Given a set of m polygonal curves = {P1,P2,...,Pm} in the plane, find two curves Pu and Pl, called 2-central paths, that best represent all curves in . Despite its theoretical interest and a wide range of practical applications, 2CPP has not been well studied. In this paper, we first establish criteria that Pu and Pl ought to meet in order for them to best represent . In particular, we require that there exists parametrizations fu(t) and fl(t) (t [a,b]) of Pu and Pl respectively such that the maximum distance from {fu(t),fl(t)} to curves in is minimized. Then an efficient algorithm is presented to solve 2CPP under certain realistic assumptions. Our algorithm constructs Pu and Pl in O(nmlog4n2α(n)α(n)) time, where n is the total complexity of (i.e., the total number of vertices and edges), m is the number of curves in , and α(n) is the inverse Ackermann function. Our algorithm uses parametric search technique and is considerably faster than arrangement-related algorithms (i.e. ω(n2)) when m ≪ n as in most real applications. © 2024 World Scientific Publishing Company.

关键词： 2-central path Minkowski sum parametric search

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Recent Trends in Brain Tumor Detection Using Deep Learning Based Diagnostic Models Assisted with Medical Imaging

Recent Trends in Brain Tumor Detection Using Deep Learning B...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Batra, Salil Singh, Gursharan Chakraborty, Gouri Shankar Malhi, Simarjit Singh Mahajan, Makul Singh, Amanpreet Lovely Professional University Department Of Computer Science And Engineering Phagwara India

ISBN: (纸本)9798350366846

Detection of brain tumors is a crucial aspect of processing biomedical images. These tumors are one of the prime reasons for death and disability, and effective treatment requires early identification. Varied types of medical images like Magnetic Resonance Imaging (MRI), CT Scan images, X-ray are frequently used to detect tumors in brain. Most of the time, these images are examined by the radiologists manually which is a time taking process and there are chances to have erroneous results. To make the process automatic and accurate, deep learning techniques came to the picture where detection models developed by different researchers are being used to detect brain tumors with reliable and promising performances. This paper examines deep learning methods available for brain tumor detection, assessing their performance on publicly available datasets and identifying research gaps and limitations in existing approaches. It also discusses the potential impact of deep learning and future research directions. © 2024 IEEE.

关键词： Tumors

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