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35th European Modeling and Simulation Symposium, EMSS 2023

作者： Izco, Irene Serrano-Hernandez, Adrian Sawik, Bartosz Faulin, Javier GILT-OR Group Institute of Smart Cities Dept. Statitistics Computer Science and Mathematics Public University of Navarra Campus Arrosadia s/n Pamplona31006 Spain Department of Business Informatics and Engineering Management AGH University of Science and Technology Mickiewicza 30 Kraków30059 Poland

ISBN: (纸本)9788885741881

The disruptions experienced by the processes in the last mile delivery during the SARS-CoV-2 pandemic raised the dilemma of up-to-date last mile approaches for Urban Logistics (UL) issues. Self-Collection Delivery Systems (SCDS) have been proved to be an improvement for all the players of the SC, providing flexibility of time-windows and reducing overall mileage, delivery time and, consequently, gas emissions. Differing from previous works involving hybrid modeling for automated parcel lockers (APL) network design, this paper brings a System Dynamics Simulation Model (SDSM) to forecast online shopping demand in the Spanish city of Pamplona. A bi-criteria Facility Location Problem (FLP) is solved by means of an e-constraint method, where e is defined as the level of coverage of the total demand. The experiment run considers 90% of demand coverage, in order to obtain the most complex network possible. The simulation and demand forecast was carried out using Anylogic simulation software and the optimization procedure makes use of the Java-based CPLEX API solver. © 2023 The Authors.

关键词： Complex networks

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JARVIS-Leaderboard:a large scale benchmark of materials design methods

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npj Computational Materials 2024年第1期10卷 2280-2296页

作者： Kamal Choudhary Daniel Wines Kangming Li Kevin F.Garrity Vishu Gupta Aldo H.Romero Jaron T.Krogel Kayahan Saritas Addis Fuhr Panchapakesan Ganesh Paul R.C.Kent Keqiang Yan Yuchao Lin Shuiwang Ji Ben Blaiszik Patrick Reiser Pascal Friederich Ankit Agrawal Pratyush Tiwary Eric Beyerle Peter Minch Trevor David Rhone Ichiro Takeuchi Robert B.Wexler Arun Mannodi-Kanakkithodi Elif Ertekin Avanish Mishra Nithin Mathew Mitchell Wood Andrew Dale Rohskopf Jason Hattrick-Simpers Shih-Han Wang Luke E.K.Achenie Hongliang Xin Maureen Williams Adam J.Biacchi Francesca Tavazza Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD20899USA Department of Materials Science and Engineering University of Toronto27 King’s College CirTorontoONCanada Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Lewis-Sigler Institute for Integrative Genomics Princeton UniversityPrincetonNJ 08544USA Ludwig Institute for Cancer Research Princeton UniversityPrincetonNJ 08544USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Globus University of ChicagoChicagoIL 60637USA Data Science and Learning Division Argonne National LabLemontIL 60439USA Institute of Nanotechnology Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Institute of Theoretical Informatics Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Department of Chemistry and Biochemistry and Institute for Physical Science and Technology University of MarylandCollege ParkMD 20742USA Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY12180USA Department of Materials Science and Engineering University of MarylandCollege ParkMD20742USA Department of Chemistry and Institute of Materials Science and Engineering Washington University in St.LouisSt.LouisMO63130USA School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignUrbanaIllinois 61801USA Materials Research Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Theore

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).

关键词： rigorous perfect enable

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Analysis of Cyber Security Threats in Payment Gateway technology 3

Analysis of Cyber Security Threats in Payment Gateway Techno...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Sharma, Hari Prapan Krishna, Somanchi Hari Prakash, R Vijay Tiwari, Mohit Tiwari, Tripti Kumar, Madarapu Naresh GLA University Institute of Business Management Uttar Pradesh Mathura India Vignana Bharathi Institute of Technology Department of Business Management Aushapur Village Ghatkrsae Mandal Malkajgiri Medchal Dist501301 India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering A 4 Rothak Road Paschim Vihar Delhi India BVIMR Department of Management Studies A 4 Rothak Road Paschim Vihar Delhi India National Informatics Centre Ministry of Electronics and Information Technology Delhi India

ISBN: (纸本)9798350399264

There has been a meteoric rise in the use of online payment systems recently. Multiple e-payment systems exist, each designed to provide the highest degree of security while maintaining the greatest possible convenience for online shoppers. Cyber-attack techniques, on the other hand, are developing at the same rapid pace as security mechanisms. The authors of this paper examine the history of electronic payment systems, from the development of the corresponding language through the emergence of today's standard electronic payment systems. It also reveals a lack of security measures and approaches to fixing the problem. The current survey research makes a significant contribution by outlining the state of the electronic payment system framework and the possibilities it presents for the development of e-commerce in the future. The rates of suspicious purchases, which will serve as a yardstick in the creation of a trustworthy e-payment system, have been briefly discussed and analyzed. © 2023 IEEE.

关键词： Cybersecurity

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Mixed-traffic Intersection Management using Traffic-load-responsive Reservation and V2X -enabled Speed Coordination

Mixed-traffic Intersection Management using Traffic-load-res...

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International Conference on Intelligent Transportation

作者： Nicholaus D. Yosodipuro Ehsan Javanmardi Jin Nakazato Yasumasa Tamura Xavier Défago Manabu Tsukada Department of Creative Informatics Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan Department of Computer Science School of Computing Tokyo Institute of Technology Tokyo Japan

Vehicle-to-everything (V2X) communication enables connected autonomous vehicles (CAVs) to share information and generate optimal decisions. The networking abilities of CAVs have led to the development of unsignalized autonomous intersection management (AIM) methods that leverage CAVs to significantly improve traffic flows. However, AIM methods assume 100% CAV market penetration, which is currently unrealistic owing to the gradual adoption of CAVs. Therefore, CAVs must share road usage with nonconnected vehicles (NCVs). Thus, we propose a mixed-traffic intersection management method that considers NCVs while ensuring high traffic flow, called traffic-load-responsive reservation for intersection management (TLRRIM). In TLRRIM, the roadside unit (RSU) first classifies vehicles and groups them into clusters before selecting a reservation cluster to cross an intersection. The reservation cluster selection considers both traffic load and crossing urgency. In addition, the RSU utilizes V2X-enabled speed coordination (VESC) for CAVs within the reservation cluster to further improve traffic flow, while utilizing traffic lights to guide NCVs. Simulation-based experiments using OpenCDA and CARLA showed that TLRRIM can increase throughput and reduce waiting time by up to 89.63% and 60.71 %, respectively, compared with the fixed-time signaling method. Moreover, adding VESC can increase throughput by 12.21 % and reduce waiting time by 10.80%, further enhancing traffic flow.

关键词：

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CEASEFIRE: An AI-Powered System for Combating Illicit Firearms Trafficking

CEASEFIRE: An AI-Powered System for Combating Illicit Firear...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Cani, Jorgen Mademlis, Ioannis Mancuso, Marina Paternoster, Caterina Adamakis, Emmanouil Margetis, George Chambon, Sylvie Crouzil, Alain Lechelek, Loubna Dede, Georgia Evangelatos, Spyridon Lalas, George Mignet, Franck Linardatos, Pantelis Kentrotis, Konstantinos Gierszal, Henryk Tyczka, Piotr Karagiorgou, Sophia Pantelis, George Stavropoulos, Georgios Votis, Konstantinos Papadopoulos, Georgios Th. Harokopio University of Athens Department of Informatics and Telematics Greece Università Cattolica del Sacro Cuore - Transcrime Italy Institute of Computer Science Foundation for Research and Technology Hellas Greece Universite de Toulouse Cnrs Irit France Netcompany-Intrasoft Thales France Exus Software Single Member Limited United Kingdom Itti Poland Ubitech Ltd Centre for Research and Technology Hellas Greece

ISBN: (纸本)9798350362480

Modern technologies have enabled illicit firearms trafficking to partially merge with cybercrime, while also allowing its off-line aspects to become increasingly complex. The online trade of firearms, their components, 3D blueprints and illicit substances carried out by criminals on both the surface Web and dark Web is increasingly difficult to address as a consequence of the exponential growth in the amount of information disseminated on the Internet. On the other hand, law enforcement agencies are confronted with significant challenges that require the development of sophisticated technological solutions capable of processing large volumes of data, identifying relevant information in a timely manner and creating networks of connections between potential criminal groups. This article presents a real-world practical system, namely the CEASEFIRE one, powered by advanced artificial intelligence technologies that can assist law enforcement personnel in addressing the above described challenges. © 2024 IEEE.

关键词： Crime

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Automated Indonesian Essay Scoring and Holistic Feedback Using Bidirectional Encoder Representations for Transformers

Automated Indonesian Essay Scoring and Holistic Feedback Usi...

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International Conference on Electrical, Telecommunication and computer Engineering (ELTICOM)

作者： Amalia Amalia Maya Silvi Lydia Rabiah Abdul Kadir Faradhila Aulia Utami Tanjung Dewi Sartika Br Ginting Dani Gunawan Department of Computer Science Universitas Sumatera Utara Medan Indonesia Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Malaysia Department of Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331531249

ISBN: (纸本)9798331531256

Developing an effective and accurate Automated Essay Scoring (AES) system for Indonesian essays involves overcoming several challenges, including limited datasets and issues related to grading efficiency and feedback generation. This study addresses these challenges by proposing an automated Indonesian essay grading and feedback system utilizing Bidirectional Encoder Representations from Transformers (BERT). The research employed a thorough process, including the translation of the ASAP dataset from the Kaggle competition titled “The Hewlett Foundation” into Indonesian, analysis and refinement of the dataset to include only relevant features, and fine-tuning using the pre-trained IndoBERT embeddings. To assess the relevance between the essay prompts and student responses, text similarity methods were applied outside the fine-tuning process. The final score is derived from the combination of the fine-tuned score and the relevance score. The model achieved a perfect accuracy of 1 and a strong kappa score of 0.82 during training. The alignment of the final score predictions with human feedback is validated by a Quadratic Weighted Kappa (QWK) score of 0.9, indicating a high level of agreement. This demonstrates that the automated scoring system effectively matches human judgment, highlighting its efficacy in evaluating essay quality.

关键词： Training Electric potential Accuracy Translation Computational modeling Bidirectional control Transformers Encoding Communications technology Testing

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Random Load Balancing in Mini-NDN Using Modified Inherent Topology 9

Random Load Balancing in Mini-NDN Using Modified Inherent To...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Pratama, Cendika Sahid Arrachman, Muhammad Iqbal Nurrasyid, Muhammad Shafly Zaki Hamidi, Eki Ahmad Negara, Ridha Muldina Ahdan, Syaiful Mayasari, Ratna Syambas, Nana Rachmana School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Uin Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

Named Data Networking (NDN) is a newly developing networking method that focuses on information, compared to TCP/IP, which focuses on hosts. This study proposed a solution to the growing complexity of computer networks. The fundamental problem will still be able to be found, one of them being difficulty in accessing a node as data traffic is piling up. One of the solutions is load balancing, which is implementing a system that will redirect packets to different service providers to ease up the load and reduce the number of packets that are dropped due to timeouts. This paper analyses the impact of load balancing in an extreme case emulated environment using Mini-NDN and random load balancing strategy. From the results gathered, implementing random load balancing to the consumer nodes decreases-and in some cases eliminates-dropped packets. In addition, implementing load balancing stabilizes packets' RTT (Round Trip Time) but increases the average RTT compared to using best route forwarding in an unloaded environment. © 2023 IEEE.

关键词： Topology

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Lugsam: A Novel Framework for Integrating Text Prompts to Segment Anything Model (Sam) for Segmentation Tasks of Icu Chest X-Rays

SSRN

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SSRN 2024年

作者： Ramesh, Dhanush Babu Sridhar, Rishika Iytha Upadhyaya, Pulakesh Kamaleswaran, Rishikesan Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA United States

Chest radiography is a commonly utilized imaging technique for acquiring Chest X-Ray (CXR) images due to its cost-effectiveness and its role in diagnosing lung-related disorders. Nevertheless, interpreting CXR images can be challenging, and the process of separating the lung field from CXR images can be a valuable tool for assessing and diagnosing lung diseases. While various segmentation methods exist, this study primarily focuses on META’s latest Segment Anything Model (SAM). SAM is an Artificial Intelligence (AI) model designed to segment objects within an image. This research aims to harness SAM’s capabilities for segmenting CXR images. Additionally, we explore the potential of another novel model called Grounding DINO. Grounding DINO is a zero-shot object detection model that utilizes a Swin (Shifted Windows) transformer for extracting image features and BERT (Bidirectional Encoder Representations from Transformers) for extracting textual information. It is primarily employed to detect objects in an image based on a provided text prompt, creating bounding boxes around the objects when certain text and box thresholds are met. These bounding boxes are then used as prompts for SAM to generate segmentation masks. The proposed framework has been assessed on CXRs obtained from patients at Emory Hospital in Atlanta, Georgia, USA and further evaluated using NIH clinical center’s CXR image dataset. © 2024, The Authors. All rights reserved.

关键词： Object detection

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Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge

Building Safer Autonomous Agents by Leveraging Risky Driving...

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2021 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2021

作者： Rana, Ashish Malhi, Avleen Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India Bournemouth University Department of Computing and Informatics Bournemouth United Kingdom

ISBN: (纸本)9781665432085

The highway-env reinforcement learning tasks provides a good abstract testbed for designing driving agents for specific driving scenarios like lane changing, parking or intersections etc. But, generally these driving simulation environments often restrict themselves to safer and precise trajectories. However, we clearly know that real driving tasks often involve very high risk collision prone unexpected situations. Hence, the autonomous model-free driving agents prepared in these environments are blind to certain low probability traffic collision corner cases. In our study we systematically focus on generating adversarial driving collision prone scenarios with dangerous driving behavior and heavy traffic in order to create robust autonomous agents. In our experimentation we train model free learning agents with additional collision prone scenario simulations and compare their efficacy with regular simulation based agents. Ultimately, we create a causal experimentation setup which successfully accounts for the performance improvements across different driving scenarios by utilizing learning from risky driving situations. © 2021 IEEE.

关键词： Autonomous agents

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Comprehensive Analysis and Remediation of Insecure Direct Object References (IDOR) Vulnerabilities in Android APIs

Comprehensive Analysis and Remediation of Insecure Direct Ob...

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Cryptography, informatics, and Cybersecurity (ICoCICs), IEEE International Conference on

作者： Semi Yulianto Roni Reza Abdullah Benfano Soewito Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Informatics Engineering National Institute of Science and Technology Jakarta Indonesia Computer Science Department Binus Graduate Program. Master of Computer Science Bina Nusantara University Jakarta Indonesia

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience.

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