745 research outputs found
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Paradigm Shift from Vague Legal Contracts to Blockchain-Based Smart Contracts
In this dissertation, we address the problem of vagueness in traditional legal contracts by presenting novel methodologies that aid in the paradigm shift from traditional legal contracts to smart contracts. We discuss key enabling technologies that assist in converting the traditional natural language legal contract, which is full of vague words, phrases, and sentences to the blockchain-based precise smart contract, including metrics evaluation during our conversion experiment. To address the challenge of this contract-transformation process, we propose four novel proof-of-concept approaches that take vagueness and different possible interpretations into significant consideration, where we experiment with popular vendors' existing vague legal contracts. We show through experiments that our proposed methodologies are able to study the degree of vagueness in every interpretation and demonstrate which vendor's translated-smart contract can be more accurate, optimized, and have a lesser degree of vagueness. We also incorporated the method of fuzzy logic inside the blockchain-based smart contract, to successfully model the semantics of linguistic expressions. Our experiments and results show that the smart contract with the higher degrees of truth can be very complex technically but more accurate at the same time. By using fuzzy logic inside a smart contract, it becomes easier to solve the problem of contractual ambiguities as well as expedite the process of claiming compensation when implemented in a blockchain-based smart contract
Mathematical Problems in Rock Mechanics and Rock Engineering
With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering
Beyond Quantity: Research with Subsymbolic AI
How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately
Applications in Home Improvement Retailer, Koctas
— It sounds like Koçtaş is a leader in the home improvement sector in Turkey, and they are focused on providing the best service and customer experience possible. They are also actively working to accelerate their digital investments and use their vast amount of customer data to innovate in the industry. One way they are using this data is by collecting and analyzing video camera images using AI. This allows them to detect humans and identify which products and shelves are most viewed in their stores. This information can then be used to optimize store layout and product placement for a better customer experience. Another way Koçtaş is innovating is through the implementation of kiosks that use Natural Language Processing (NLP) to interact with customers. These kiosks can understand and respond to questions asked by customers using AI, providing a more personalized and human-like experience. Finally, Koçtaş is using Dynamic Creative Optimization to create personalized advertisements for their customers. This method allows them to optimize the content and format of their ads based on the individual preferences and behavior of their customers, leading to more effective marketing. Overall, Koçtaş is using technology and data to drive innovation and provide a better customer experience in the home improvement industry
Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation
The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics
Context-aware intelligent decisions: online assessment of heavy goods vehicle driving risk
There is a growing interest in assessing the impact of drivers' actions and behaviours on road safety due to the numerous road fatalities and costs attributed to them. For Heavy Goods Vehicle (HGV) drivers, assessing the road safety risks of their behaviours is a subject of interest for researchers, governments and transport companies, as nations rely on HGVs for the delivery of goods and services. However, HGV driving is a complex, dynamic, uncertain and multifaceted task, mostly influenced by individual traits and external contextual factors. Advanced computational and artificial intelligence (AI) methods have provided promising solutions to automatically characterise the manner by which drivers operate vehicle controls and assess their impact on road safety. However, several challenges and limitations are faced by the current intelligence-supported driving risk assessment approaches proposed by researchers, such as: (1) the lack of comprehensive driving risk datasets; (2) information about the impact of inevitable contextual factors on HGV drivers' responses is not considered, such as drivers' physical and mental states, weather conditions, traffic conditions, road geometry, road types, and work schedules; (3) ambiguity in the definition of driving behaviours is not considered; and (4) imprecision of AI models, and variability in experts' subjective views are not considered.
To overcome the aforementioned challenges and limitations, this multidisciplinary research aims at exploring multiple sources of data including information about the impact of contextual factors captured from crucial stakeholders in the HGV sector to develop a reliable context-aware driving risk assessment framework. To achieve this aim, AI methods are explored to accurately detect drivers' driving styles, affective states and driving postures using telematics data, facial images, and driver posture images respectively. Subsequently, due to the lack of comprehensive driving risk datasets, fuzzy expert systems (FESs) are explored to fuse detected driving behaviours and perceived external factors using knowledge from domain experts.
The key findings of this research are: (1) recurrent neural networks are effective in capturing the temporal dynamics and differences between the different types of driver distraction postures and affective states; (2) there is a trade-off between efficiency and privacy in processing facial images using AI approaches; (3) the fusion of driver behaviours and external factors using FESs produces realistic, reliable and fair driving risk assessments; and (4) a hierarchical representation of a decision-making process simplifies reasoning compared to flat representations
Bridging the gap between evidence-informed and actual teaching practices of engineering educators: an AI-enhanced professional learning system
Imagine a classroom where engineering students are challenged to apply what they’re learning, where they interactively explore the complexities of authentic, level-appropriate engineering problems, supported by professors who are aware of and apply evidence-informed teaching practices. Expectations align with the engineering workplace. Learners improve their acquired knowledge and skills through experimentation and deliberate practice. They harness systems thinking as they make connections and see patterns. They are challenged to adapt to whatever scenario they face, to identify problems, think critically, generate and model effective solutions, and to make justifiable decisions. Learners experience the tension between knowing and doing engineering things. They learn firsthand, and in context, what it means to be a practicing engineer. This aspiring approach is very different from the didactic practices reported in most Canadian undergraduate engineering classrooms. The challenge, and the focus of this research, is to encourage and assist engineering educators to stretch their current teaching practices beyond what’s comfortable and customary, to those that are both evidence-informed and truly representative of engineering. This research is a blend of interdisciplinary mixed-methods and design-based research. The interdisciplinary mixed-method research integrates the findings of educational research, learning sciences, professional learning, and systems thinking. Sixteen research studies explore the experiences and practices of educators and students in the Canadian undergraduate engineering system. These findings confirm that a gulf exists between evidence-informed teaching practices and what happens in the typical undergraduate engineering classroom. They clearly establish the need for an educational development model that translates existing educational research into tangible, level-appropriate teaching practices for engineering educators at all levels of experience and skill. This foundational research leads to the design and development of this thesis' first of three contributions, the LENS (Learning Environments Nurture Success) model of engineering faculty development. This model, which is comprised of six lenses that align with an effective learning environment, offers a practical framework to support educational development and planning for all forms of delivery (face-to-face, remote, blended, or hybrid). It can be used independently, in consultation with an educational developer, or in collaboration with colleagues. It threads educator-related threshold concepts associated with learning, pedagogy, assessment, and teaching with technology through each of six lenses, and links myriad interdisciplinary research findings to facilitate the successful education of undergraduate engineering students. The second contribution of this research is a proof-of-concept intelligent Professional Learning System (iPLS). This AI-enhanced learning platform individualizes and guides the development of professional knowledge and skills. The look, feel, and functionality of this proof-of-concept iPLS is shaped by an integration of research findings in professional learning, training and development, technology-based learning, and AI in education. The final contribution of this work is an iPLS application designed to help engineering educators develop their teaching practices. It provides needs-specific recommendations based on an individual's ranking on a novice to expert continuum and achieved teaching-related thresholds. Quantitative and qualitative field test results show the combined LENS, iPLS, and engineering education application (EEA) to be a viable method by which engineering educators can stretch their teaching to include more evidence-informed teaching practices. Using the elements of an elegant design as its measure, the system is determined to be effective and robust with a minimal number of unexpected consequences
Design and development of a fuzzy explainable expert system for a diagnostic robot of COVID-19
Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability
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