Title : AI-Driven Approach for Analyzing Construction Contracts and Missing Clauses Detection
Name : Mohab Nasser Khaled
University : Structural Engineering Department, Faculty of Engineering, Cairo University
Country : Egypt
Download AbstractAbstract
Effective drafting of construction contract conditions is essential for minimizing disputes and ensuring efficient project delivery. While prior studies have explored methods for selecting suitable contract forms, limited attention has been given to comparing multiple forms to identify missing clauses. This research proposes an integrated NLP-based framework that supports both contract form selection and completeness assessment. Guided topic modeling is used to evaluate major contractual criteria from literature across different standard forms, user-defined priorities are incorporated through AHP and TOPSIS to determine the most suitable contract conditions form. A pretrained Legal-BERT model is applied to compare the chosen form against others, detecting missing clauses through semantic similarity analysis. Multiple similarity measures, including Cosine, Manhattan, and Jaccard, are tested, with sensitivity analysis to identify optimal thresholds. Results show that Cosine similarity achieves the highest performance, with an F1 score of 91.8%, demonstrating the framework’s effectiveness in enhancing automated contract conditions assessment.
Biography
Mohab Nasser is an Assistant Lecturer in Construction Engineering and Management at Cairo University and a practicing contracts engineer specializing in contract analysis, cost estimation, and procurement evaluation. His research focuses on applying artificial intelligence and natural language processing to construction contract analysis and decision-making. He has publications mainly on automated contract clause classification and AI-supported selection of construction contract types.
Title : State Space Search for Solving Inverse Kinematics applied to Redundant Robotic Manipulators
Name : Breno Souza Nucci
University : Ph.D Candidate, Department of Systems and Energy, Universidade Estadual de Campinas
Country : Brazil
Download AbstractAbstract
This work presents an alternative method for the inverse kinematics problem when serial chain industrial robots are considered. Such method is based on an iterative search algorithm that solves the inverse kinematics by allowing that only one joint to change the robot movement at a time, in a simulation stage. This transforms the n-dimensional problem in simpler one-dimensional problems, which analytical solution for each joint is presented employing the Denavit-Hartenberg convention. The considered method does not possess internal singularities. Furthermore, due to its simplicity, it is shown experimental results obtained employing a 3-Degrees-of-Freedom redundant manipulator.
Biography
Breno S. Nucci holds a degree in Industrial Mechatronics Technology from the Faculty of Technology of Mogi Mirim “Arthur de Azevedo” (2022), a Master’s degree in Electrical Engineering from the State University of Campinas (UNICAMP) (2024), and is currently pursuing a PhD in Electrical Engineering at the same institution. He is a member of the research group at the Laboratory of Modular Robotic Systems (LSMR), affiliated with the School of Electrical and Computer Engineering at UNICAMP, with experience in electrical engineering and a focus on industrial robotics, working mainly in the areas of robotics, nonlinear systems control, neural networks, mobile robotics, trajectory control, and robotic joint control.
Title : Erasure code backup system for data security
Name : Shan-Hsiang Shen
University : Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology
Country : Taiwan
Download AbstractAbstract
With the development of distributed storage, it is critical to determine how to place the copies of data in different locations with the consideration of network performance and data security. The Named Data Networking (NDN) proposes a solution to cache data everywhere along the routing path toward data consumers, so we do not need to manually place the data copies. However, its multi-copy cache strategy wastes storage space and does not consider remote backup to defense physical hazards. It tends to place backup copies closer to each others, so some copies does not have chance to be accessed and physical hazards can destroy all copies in close geographic location. To address the issue, this paper proposes an Advanced Remote Backup Data System (ARBDS ), which brings erasure coding technology and remote backup concept to distributed storage. The erasure coding can achieve the same reliability at a smaller storage cost with less data redundancy, while remote backup concept protects data from physical hazards. With the consideration on reliability and transmission performance, we design an algorithm named erasure code backup algorithm (ECBA) to find the backup nodes with the 57% shorter average length of routing paths to the consumer compared to existing solutions, provide the 92% higher available bandwidth and keep backup copies far from each other to prevent physical hazards.
Biography
Shan-Hsiang Shen received the M.S. degree from National Chiao Tung University, R.O.C., in 2004, and the Ph.D. degree from University of Wisconsin, USA, in 2014. He is currently an Assistant Professor with the Computer Science and Information Engineering Department, National Taiwan University of Science and Technology, R.O.C. His main research interests include software-defined networking, network function virtualization, network security, and cloud computing.
Title : A Model-based Systems Engineering Approach for Identifying Cybersecurity Threats
Name : Dr. Sarah Rudder
University : Colorado State University, CO, USA
Country : USA
Download AbstractAbstract
As systems become increasingly interconnected, traditional cybersecurity threat identification strategies often fail to defend against nefarious actors and their sophisticated attacks. Given the current internet protocols (IP) meant to protect against email infiltrations such as Domain-based Message Authentication, Reporting and Conformance (DMARC), DomainKeys Identified Mail (DKIM), and Sender Policy Framework (SPF), a more comprehensive security analysis is desired for early threat identification. These protocols are intended to address specific email-based threats, however; the threat landscape being remediated is not typically incorporated into system conceptual or logical models. This presentation demonstrates a model-based systems engineering (MBSE) approach for threat modeling that leverages the ISO 21434 – “Road vehicles – Cybersecurity Engineering” additions to the Systems Modeling Language (SysML) profile. Capabilities of the extension include the ability to represent threats, attack trees, and hazards for further analysis. These elements are linked to structural and behavioral elements of the system architecture to maintain traceability throughout the lifecycle. Integrating a cybersecurity perspective into system structural and behavioral models promises to improve security posture of the overall architecture. There is an increase in visibility of threats to IPs by integrating security threats into a system model, increasing transparency among affected team members. Benefits to the audience include an enhanced understanding of email protocols, how they are shown in a system model, current analysis techniques, and threat mitigation methods.
Biographies
Sarah Rudder is a systems engineer and ontologist for Enola Technologies and a current PhD candidate at Colorado State University specializing in model-based systems engineering tools, languages, and approaches.
Title : Anomaly Detection in Heterogeneous Cybersecurity Data
Name : Amadi Chukwukwuemeka
University : Department of Computer Science Federal University of Technology Owerri, Imo State Nigeria
Country : Nigeria
Download AbstractAbstract
The increasing complexity of cybersecurity threats necessitates advanced methods to detect anomalies across diverse and heterogeneous data sources. Traditional security systems often struggle with isolated data silos, high false-positive rates, and the inability to adapt to evolving threats. Anomaly detection has emerged as a critical approach to addressing these challenges by identifying deviations from expected behaviours that may indicate malicious activities. This paper explores the application of anomaly detection techniques in heterogeneous cybersecurity data, encompassing network traffic logs, endpoint telemetry, user activity, and external threat intelligence. It examines the role of machine learning, deep learning, and statistical models in processing and correlating these diverse datasets to identify threats with improved accuracy and speed. The discussion includes challenges such as managing data diversity, scalability, and balancing sensitivity with specificity in detection. Through a review of case studies and recent advancements, the paper highlights successful implementations of anomaly detection, including hybrid approaches combining unsupervised learning with domain expertise. This work underscores the importance of anomaly detection in safeguarding digital ecosystems against increasingly sophisticated cyber threats.
Biography
Amadi Chukwukwuemeka Augustine is a cybersecurity and data analytics researcher with a strong academic and professional background in Computer Science, information security, threat intelligence, and data-driven decision-making. His work focuses on integrating and correlating heterogeneous cybersecurity data to enhance advanced threat detection and situational awareness. He has gained experience through research projects, applied analytics, and collaborative initiatives addressing real-world cybersecurity challenges. He has a authored and co-authored academic papers, contributed to conference proceedings, and presented research findings at seminars and professional forums. His interests lie in AI-driven security analytics, cyber risk management, and resilient digital infrastructures. He is motivated by the goal of bridging the gap between theory and practice, and of developing innovative solutions that strengthen cybersecurity capabilities in complex and evolving digital environments.
Title : Digital Transformation Through Enterprise Architecture and AI: A Stakeholder-Driven Framework for Public Sector and Academic Innovation
Name : Martin Lukas
University : Czech University of Life Science in Prague
Country : Czech Republic
Download Abstract
Title : Orchard Sweet Cherry Color Distribution Estimation from Wireless Sensor Networks and Video-based Fruit Detection
Name : Luis Gustavo Cossio
University : Universidad de O’Higgins
Country : Chile
Download Abstract
Title : Arabic Historical Documents Layout Analysis using Mask RCNN
Name : Latifa Jalal Aljiffry
University : King Abdualaziz university/IT
Country : Saudi Arabia
Download Abstract