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Computing, Networking, Telecommunications & engineering Sciences Applications (CoNTESA), International Conference on

作者： Domagoj Tuličić Marko Horvat Sandra Lovrenčić Department of Computer System and Networks University of Applied Science in Information Technology Zagreb Croatia Department of Applied Computing University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Department of Theoretical and Applied Foundations of Information Sciences University of Zagreb Faculty of Organization and Informatics Varaždin Croatia

Swarm intelligence is a class of nature-inspired metaheuristic algorithms, that is specifically derived from biological systems in nature with an emphasis on their social interactions. These algorithms have been primarily developed to solve complex combinatorial optimization problems and are extremely popular among researchers in metaheuristic algorithms. This is evident from the numerous published scientific papers describing algorithms inspired by biological systems, ranging from insects such as ants and bees to the largest animals on the planet like whales. All these algorithms share some common concepts and relationships that can be explicitly identified, named, and formalized. These shared concepts and relationships can be organized and represented through the construction of an ontology. The goal of this paper is to present a formal ontology that standardizes vocabulary, as well as a set of rules enabling precise communication and reasoning about these algorithms. Ultimately, this will improve our understanding, analysis, research processes, and utilization of these computational approaches.

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A Simulation-Optimization Model for Automated Parcel Lockers Network Design in Urban Scenarios in Pamplona (Spain), Zakopane, and Krakow (Poland)

A Simulation-Optimization Model for Automated Parcel Lockers...

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Simulation Winter Conference

作者： Bartosz Sawik Javier Faulin Adrian Serrano-Hernandez Aitor Ballano Department of Business Informatics and Engineering Management AGH University of Science and Technology Krakow POLAND Department of Statistics Institute of Smart Cities Computer Science and Mathematics Public University of Navarre Pamplona SPAIN

ISBN: (纸本)9781665476621

The constant rise of e-commerce coupled with extremely fast deliveries is a significant contributor to saturate city centers' mobility. To address this issue, the development of a convenient Automated Parcel Lockers (APLs) network improves last-mile distribution by reducing the number of transportation vehicles, the distances driven, and the delivery stops. This article aims to define and compare APL networks in the cities of Pamplona (Spain), Zakopane and Krakow (Poland). Thereby, a bi-criteria weighted-sum simulation optimization model is developed for a representative year for the aforementioned cities. The simulation forecasts the e-commerce demands whereas the optimal APL network is obtained with a bi-criteria maximum APL revenues and minimum network costs. Meaningful results are obtained from the multi-criteria hybrid model outcomes as well as from the cities comparison. These outcomes suggest efficient APLs networks considering cultural and demographic factors for a massive use of APLs in high-demand periods.

关键词： Costs Urban areas Transportation Predictive models Electronic commerce Cultural differences Optimization

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Security Enhancement for the Network Amalgamation using Machine Learning Algorithm

Security Enhancement for the Network Amalgamation using Mach...

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2020 International Conference on Smart Electronics and Communication, ICOSEC 2020

作者： Anithaashri, T.P. Ravichandran, G. Institute of Computer Science and Engineering Saveetha School of Engineering Department of Innovative Informatics Simats Chennai India Amet University Chennai India

ISBN: (数字)9781728154619

ISBN: (纸本)9781728154619

Accessing the secured data through the network is a major task in emerging technology. Data needs to be protected from the network vulnerabilities, malicious users, hackers, sniffers, intruders. The novel framework has been designed to provide high security in data transaction through computer network. The implant of network amalgamation in the recent trends, make the way in security enhancement in an efficient manner through the machine learning algorithm. In this system the usage of the biometric authenticity plays a vital role for unique approach. The novel mathematical approach is used in machine learning algorithms to solve these problems and provide the security enhancement. The result shows that the novel method has consistent improvement in enhancing the security of data transactions in the emerging technologies.. © 2020 IEEE.

关键词： Network security

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Optimization of K-Nn classification in human gait recognition 5

Optimization of K-Nn classification in human gait recognitio...

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5th International Conference on informatics and Computing, ICIC 2020

作者： Pratama, Fandy Indra Budianita, Avira Wahid Hasyim University Department of Informatics Engineering Semarang Indonesia Muhammadiyah Kudus University Department of Computer Science Kudus Indonesia

ISBN: (纸本)9781728180472

Gait recognition is one of the few introductory biometrics used in security systems to identify humans. In this research, optimizing the accuracy by improving the extraction features of previous research. The gait dataset used in this research is GEI segmentation data from Casia Dataset. The proposed method for improving the GEI segmentation feature results is the Gabor Wavelet and PCA methods. The feature extraction results using Gabor Wavelet and PCA methods are capable of generating different information from each object. Then obtained accuracy results based on the confusion matrix from the KNN classification method. The average result of accuracy from all points of view recognition using KNN method is 98, 50%. So that Gabor Wavelet, PCA, and KNN can produce higher accuracy than previous research. © 2020 IEEE.

关键词： Extraction

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Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

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Computational Intelligence and Neuroscience 2022年第0期2022卷 4690936页

作者： Veeramakali, T. Shobanadevi, A. Nayak, Nihar Ranjan Kumar, Sumit Singhal, Sunita Subramanian, Manoharan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Sri Venkateswara College of Engineering Technology Chittoor India Indian Institute of Management Kozhikode India Department of Computer Science and Engineering Manipal University Jaipur Jaipur303007 India Department of Computer Science School of Informatics and Electrical Engineering Hachalu Hundesa Campus Ambo University Ambo Ethiopia

A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a person's abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-attacks. Many clinical researchers have done extensive study on this topic. The most recent studies on this topic are summarized, and an overarching framework is provided. When it comes to the methods and datasets that make up the data collection, the feature presentation and algorithm selection layers provide an overview of the various types of algorithm selections available. The categorization and evaluation of diseases and disorders has been one of the major advantages of machine learning in medical. Because it was harder to predict, it rendered it more controllable. It might range from difficult-to-find cancers in the early stages to certain other illnesses spread through the bloodstream. In healthcare, we may pick methods in machine learning depending on reliable outcomes. To do so, we must run the findings through each method. The major issue arises during information training and validation. Because the dataset is so large, eliminating mistakes might be difficult. The providers, other characteristics, various algorithms, data labelling techniques, and assessment criteria are all presented and contrasted in depth. Detecting anomalous users in medical social networks, on the other hand, is a work in progress. The result evaluation layer provides an explanation of how to evaluate and mark up the results of the various algorithm selection layers. Finally, it looks forward to more study in this area. © 2022 T. Veeramakali et al.

关键词： Diseases

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RetinaRegNet: A Zero-Shot Approach for Retinal Image Registration

arXiv

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arXiv 2024年

作者： Sivaraman, Vishal Balaji Imran, Muhammad Wei, Qingyue Muralidharan, Preethika Tamplin, Michelle R. Grumbach, Isabella M. Kardon, Randy H. Wang, Jui-Kai Zhou, Yuyin Shao, Wei Department of Electrical and Computer Engineering University of Florida GainesvilleFL32610 United States Department of Medicine University of Florida GainesvilleFL32610 United States Department of Computational and Mathematical Engineering Stanford University StanfordCA94305 United States Department of Health Outcomes and Biomedical Informatics University of Florida GainesvilleFL32610 United States Iowa City VA Center for the Prevention and Treatment of Visual Loss Department of Internal Medicine University of Iowa Iowa CityIA52242 United States Iowa City VA Center for the Prevention and Treatment of Visual Loss Department of Ophthalmology and Visual Sciences Department of Electrical and Computer Engineering University of Iowa Iowa CityIA52242 United States Department of Computer Science and Engineering University of California Santa Cruz Santa CruzCA95064 United States Intelligent Clinical Care Center University of Florida GainesvilleFL32610 United States

We introduce RetinaRegNet, a zero-shot image registration model designed to register retinal images with minimal overlap, large deformations, and varying image quality. RetinaRegNet addresses these challenges and achieves robust and accurate registration through the following steps. First, we extract features from the moving and fixed images using latent diffusion models. We then sample feature points from the fixed image using a combination of the SIFT algorithm and random point sampling. For each sampled point, we identify its corresponding point in the moving image using a 2D correlation map, which computes the cosine similarity between the diffusion feature vectors of the point in the fixed image and all pixels in the moving image. Second, we eliminate most incorrectly detected point correspondences (outliers) by enforcing an inverse consistency constraint, ensuring that correspondences are consistent in both forward and backward directions. We further remove outliers with large distances between corresponding points using a global transformation-based outlier detector. Finally, we implement a two-stage registration framework to handle large deformations. The first stage estimates a homography transformation to achieve global alignment between the images, while the second stage uses a third-order polynomial transformation to estimate local deformations. We evaluated RetinaRegNet on three retinal image registration datasets: color fundus images, fluorescein angiography images, and laser speckle flowgraphy images. Our model consistently outperformed state-of-the-art methods across all datasets. The accurate registration achieved by RetinaRegNet enables the tracking of eye disease progression, enhances surgical planning, and facilitates the evaluation of treatment efficacy. Our code is publicly available at: https://***/mirthAI/RetinaRegNet. © 2024, CC BY-NC-SA.

关键词： Image registration

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Investigating How Employees’ Cybersecurity Behaviour is Affected by Their Knowledge of Cybersecurity Policy

Investigating How Employees’ Cybersecurity Behaviour is Aff...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Shyamasundar Tripathy Chintala Lakshmana Rao Vikas Kumar Pai H Adity Deepak Kumar Mimoh Jindal Koneru Lakshmaiah Education Foundation KL Business School Vaddeswaram Guntur Andhra Pradesh India School of Law GITAM (Deemed to be University) Visakhapatnam India National Informatics Centre New Delhi Department of Computer Science & Engineering Graphic Era Deemed to be Universty Dehradun Uttarakhand India National Informatics Centre Delhi India

Society currently faces more security threats in the cyberspace which is most harmful due to the cyberattacks development responsible for the complexity and volumes growth of mobile applications as well as Internet technologies. By conceptually defined as the domains for employee's cyber security behaviour as well as the development and the testing of operational measures, this study has advanced cyber security behaviour and research for awareness and investigation are added to the body of published literature on cybersecurity. Based on survey data from 579 corporate managers and professionals, the investigation for the impact of cyber security policy and awareness. The presented hypotheses are tested using ANOVA techniques and structural equation modeling. The findings indicate that employees familiar with the information and security terms and conditions of the company can have the ability to handle the jobs related to cyber security than the employees who do not have enough knowledge. Additionally, the study shows that an organizational environment as favourable for investigating that employee for the impact of cyber security policy that has positive impact on the cyber security behaviour.

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Accelerating Build Dependency Error Detection via Virtual Build 22

Accelerating Build Dependency Error Detection via Virtual Bu...

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Proceedings of the 37th IEEE/ACM International Conference on Automated Software engineering

作者： Rongxin Wu Minglei Chen Chengpeng Wang Gang Fan Jiguang Qiu Charles Zhang School of Informatics Xiamen University China The Hong Kong University of Science and Technology China Ant Group China Xiamen Meiya Pico Information Co. Ltd. China Department of Computer Science and Engineering HKUST China

ISBN: (纸本)9781450394758

Build scripts play an important role in transforming the source code into executable artifacts. However, the development of build scripts is typically error-prone. As one kind of the most prevalent errors in build scripts, the dependency-related errors, including missing dependencies and redundant dependencies, draw the attention of many researchers. A variety of build dependency analysis techniques have been proposed to tackle them. Unfortunately, most of these techniques, even the state-of-the-art ones, suffer from efficiency issues due to the expensive cost of monitoring the complete build process to build dynamic dependencies. Especially for large-scale projects, such the cost would not be affordable. This work presents a new technique to accelerate the build dependency error detection by reducing the time cost of the build monitoring. Our key idea is to reduce the size of a program while still preserving the same dynamic dependencies as the original one. Building the reduced program does not generate a real software artifact, but it yields the same list of dependency errors and meanwhile speeds up the process. We implement the tool VirtualBuild and evaluate it on real-world projects. It is shown that it detects all the dependency errors found by existing tools at a low cost. Compared with the state-of-the-art technique, VirtualBuild accelerates the build process by 8.74 times, and improves the efficiency of error detection by 6.13 times on average. Specifically, in the large-scale project LLVM that contains 5.67 MLoC, VirtualBuild reduces the overall time from over four hours to 38.63 minutes.

关键词： Dependency error build maintenance build system

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SMSE: A Serverless Platform for Multimedia Cloud Systems

arXiv

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arXiv 2022年

作者： Denninnart, Chavit Salehi, Mohsen Amini Laboratory School of Computing and Informatics University of Louisiana LafayetteLA70503 United States Laboratory Computer Science and Engineering Department University of North Texas United States

Along with the rise of domain-specific computing (ASICs hardware) and domain-specific programming languages, we envision that the next step is the emergence of domain-specific cloud platforms. Considering multimedia streaming as one of the most trendy applications in the IT industry, the goal of this study is to develop SMSE, the first domain-specific serverless platform for multimedia streaming. SMSE democratizes multimedia service development via enabling content providers (or even end-users) to rapidly develop their desired functionalities on their multimedia contents. Upon developing SMSE, the next goal of this study is to deal with its efficiency challenges and develop a function container provisioning method that can efficiently utilize cloud resources and improve the users’ QoS. In particular, we develop a dynamic method that provisions durable or ephemeral containers depending on the spatiotemporal and data-dependency characteristics of the functions. Evaluating the prototype implementation of SMSE under real-world settings demonstrates reducing both the containerization overhead, and the makespan time of serving multimedia functions (by up to 30%) in compare to the function provision methods being used in the general-purpose serverless cloud systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Containers

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Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine

arXiv

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arXiv 2023年

作者： Chen, Emma Kansal, Aman Chen, Julie Jin, Boyang Tom Reisler, Julia Rachel Kim, David A. Rajpurkar, Pranav Department of Biomedical Informatics Harvard Medical School United States Department of Computer Science Stanford University United States Department of Emergency Medicine Stanford University School of Medicine United States Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University United States

We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a comprehensive benchmark for evaluating foundation models in Emergency Medicine using a dataset of 100K+ continuously monitored Emergency department visits from 2020-2022. MC-BEC focuses on clinically relevant prediction tasks at timescales from minutes to days, including predicting patient decompensation, disposition, and emergency department (ED) revisit, and includes a standardized evaluation framework with train-test splits and evaluation metrics. The multimodal dataset includes a wide range of detailed clinical data, including triage information, prior diagnoses and medications, continuously measured vital signs, electrocardiogram and photoplethysmograph waveforms, orders placed and medications administered throughout the visit, free-text reports of imaging studies, and information on ED diagnosis, disposition, and subsequent revisits. We provide performance baselines for each prediction task to enable the evaluation of multimodal, multitask models. We believe that MC-BEC will encourage researchers to develop more effective, generalizable, and accessible foundation models for multimodal clinical data. © 2023, CC BY.

关键词： Emergency rooms

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