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2024 IEEE Sustainable Smart Lighting World Conference and Expo, LS24 2024

作者： Mahmoudi, Ali Ozcelebi, Tanir Meratnia, Nirvana Eindhoven University of Technology Math. and Computer Science Dept. Eindhoven Netherlands

ISBN: (纸本)9798331540197

Recognizing human activities is necessary to achieve fully context-aware lighting in a smart home. Ambient sensors suit this task as they are ubiquitous, non-intrusive, and non-obstructive. However, each home has a unique floor plan and a unique number and placement of these sensors. This leads to heterogeneity of input data spaces for machine learning models, which hinders their use in different homes. We propose that converting the data into the semantic input space of a language model can not only unify the input space across different houses but also improve the prediction performance thanks to the model's understanding of semantics. We compared a pre-trained BERT model with one trained from scratch to test the proposed semantic conversion on three datasets from CASAS. The results show that the pre-trained model performs better on accuracy and F1-score. © 2024 IEEE.

关键词： Semantics

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Distributing Classical Information via Quantum Secret Sharing Technique 5

Distributing Classical Information via Quantum Secret Sharin...

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5th International Conference on Data Analytics for Business and Industry, ICDABI 2024

作者： Elkhouly, E.S. Abd-Rabbou, M.Y. Ali, S.I. Metwally, N. Ain Shams University Faculty of Women for Arts Science and Education Math. Dept. Cairo Egypt Al-Azhar University Faculty of Science Math. Dept. Cairo Egypt University of Bahrain College of Science Math. Dept. Bahrain

ISBN: (纸本)9798350368710

The possibility of distributing classical information between distinct users is discussed. Our technique based on transferring the e classical string is into two different quantum strings by using local operation. The obtained results show that both receivers could obtain the maximum information that encoded on the initial classical strings. However, in some case, one of them obtain the sent information completely, while the other received it partially. The obtained information depends on the initial quantum string which encodes the classical information. We expect that these results could be helpful on distributing quantum passwords and quantum key distribution © 2024 IEEE.

关键词： Quantum cryptography

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Initial State Time-Dependent Quantum Game 5

Initial State Time-Dependent Quantum Game

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5th International Conference on Data Analytics for Business and Industry, ICDABI 2024

作者： Makram-Allah, A.T.M. Abd-Rabbou, M.Y. Metwally, N. Ain Shams University Faculty of Women for Art Science and Education Math. Dept. Cairo Egypt Al-Azhar University Faculty of Science Math. Dept. Cairo Egypt University of Bahrain College of Science Math. Dept. Bahrain

ISBN: (纸本)9798350368710

The possibility of implementing a time dependent version of Eisert, Wilkens, and Lewenstein (EWL), quantum game is discussed. It is assumed that the players initial states are time dependent, while the entangling quantum gate either time dependent or independent during the game. The obtained results shows that the players payoff depend on their initial state, while The time-independent entangling gate has a minor impact on the game compared to its time-dependent counterpart. However, one can improve the lower bounds of the payoffs by using time dependent gate. Moreover, the exponential form of the entangling power is more powerful than the linear one, where the payoffs are much better. However, the maximum potential rewards in this game may be lower than those attainable in the classical game, the minimum guaranteed outcomes are much better. © 2024 IEEE.

关键词： Quantum entanglement

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Modeling Automatic Emergency Braking System Using Fuzzy Timed Petri Nets Based on the 3-Second Rule

Modeling Automatic Emergency Braking System Using Fuzzy Time...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Serji, A. Mermri, E.B. Blej, M. Mohammed Premier University Dept. of Math. and Computer Science Oujda Morocco

ISBN: (纸本)9798350327816

Automatic Emergency Braking (AEB) systems play a critical role in enhancing road safety by assisting drivers in avoiding or mitigating collisions. This paper proposes a comprehensive method to model AEB systems using Fuzzy Timed Petri Nets tailored for the 3-second rule. The 3-second rule dictates that a vehicle should maintain a safe distance of at least 3 seconds from the advanced vehicle to minimize the risk of forward collisions. It is important to note that this paper's scope is limited to applying the three-second rule under specific conditions. We focus on scenarios where the weather is clear and ideal for driving, the road conditions are standard without any anomalies, and the vehicles involved are in optimal working conditions without mechanical faults. This narrowed focus allows for more in-dept. analysis and understanding of the AEB system's performance under these parameters. By incorporating FTPN, which combines the benefits of Timed Petri Nets and fuzzy logic, we can effectively capture timing constraints and uncertainties associated with the AEB system. By integrating Artificial Intelligence (AI) technologies by combining the strengths of Petri Nets and fuzzy logic, FTPN allows us to accurately represent and infer real-time systems while effectively capturing the timing constraints and uncertainties inherent in reliable performance in critical scenarios for our system. Our model aimed at determining the decision of emergency Braking based on rules that consider the vehicle's speed, the distance to the vehicle in front, and the safety distance. An Expert System derives several rules from our database and presents them as Fuzzy Timed Production Rules (FTPR). The conversion from FTPR to FTPN is crucial in building this model since we cannot model this system without the correct formulation of the rules. The based model depicts a real-time system that responds to the surroundings and its speed to determine when it may start braking and to what extent it w

关键词： Artificial Intelligence Automatic Emergency Braking Expert System FTPN FTPR Real-Time System

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Generative-AI in E-Commerce: Use-Cases and Implementations 20

Generative-AI in E-Commerce: Use-Cases and Implementations

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Ghaffari, Shervin Yousefimehr, Behnam Ghatee, Mehdi University of Technology Dept. of Math. & Computer Sci. Amirkabir Tehran Iran

ISBN: (纸本)9798350383942

In recent years, the advancement of generative AI has profoundly influenced its application across various industries. One such industry is e-commerce where this technology can enhance both customer experience as well as merchants' productivity and profitability. In this paper, our objective is to review some of the potential use cases of generative AI technology in different areas of an online store. Specifically, we will focus on the use cases of product description generation, sentiment analysis of product reviews, and product tagging and categorization. We utilize various prompt engineering techniques to suggest exemplary implementations of these applications using large language models (LLMs). Lastly, the paper will discuss the possible risks and challenges that come with using generative AI in these contexts. © 2024 IEEE.

关键词： Sentiment analysis

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Integration between Bioinformatics Algorithms and Neutrosophic Theory

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Neutrosophic Sets and Systems 2024年 66卷 34-54页

作者： Farag, Romany M. Shams, Mahmoud Y. Aldawody, Dalia A. Khalid, Huda E. El-Bakry, Hazem M. Salama, Ahmed A. Dept. of Math and Computer Science Faculty of Science Port Said Univ. Egypt Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Egypt Telafer University Telafer Iraq Dept. of Information Systems Faculty of Computer and Information Sciences Mansoura University Egypt Dept. of Math and Computer Science Faculty of Science Port Said University Egypt

This paper presents a neutrosophic inference model for bioinformatics. The model is used to develop a system for accurate comparisons of human nucleic acids, where the new nucleic acid is compared to a database of old nucleic acids. The comparisons are analyzed in terms of accuracy, certainty, uncertainty, neutrality, and bias. The proposed system achieves good results and provides a reliable standard for future comparisons. It highlights the potential of neutrosophic inference models in bioinformatics applications. Data mining and bioinformatics play a crucial role in computational biology, with applications in scientific research and industrial development. Biological analysts rely on specialized tools and algorithms to collect, store, categorize, and analyze large volumes of unstructured data. Data mining techniques are used to extract valuable information from this data, aiding in the development of new therapies and understanding genetic relationships between organisms. Recent advancements in bioinformatics include gene expression tools, Bio sequencing, and Bio databases, which facilitate the extraction and analysis of vital biological information. These technologies contribute to the analysis of big data, identification of key bioinformatics insights, and generation of new biological knowledge. Data collection, analysis, and interpretation in this field involves the use of modern technologies such as cloud computing, machine learning, and artificial intelligence, enabling more efficient and accurate results. Ultimately, data mining and bioinformatics enhance our understanding of genetic relationships, aid in developing new therapies, and improve healthcare outcomes. © (2024), (Neutrosophic Sets and Systems). All rights reserved.

关键词： Bioinformatics

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Studying the Impact of Infrastructure on Cyclist Route Selection: Real-world Data Mining 15

Studying the Impact of Infrastructure on Cyclist Route Selec...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Raud, Merlin Hadachi, Amnir Sharma, Rajesh Bertini, Flavio University of Tartu Institute of Computer Science Tartu Estonia University of Parma Dept. of Math. Phys. and Computer Sciences Parma Italy

ISBN: (纸本)9798331508821

Due to urbanisation and increased environmental awareness, many developed countries have put much effort into promoting sustainable mobility modes. Cycling is one option to replace motorised transport in cities. However, this shift often requires the transformation of the urban environment to be more suitable for cyclists. In this paper, we study the spatial and temporal patterns of cyclists and the influence of cycling infrastructure on their route choices using OpenStreetMap street tag information. A dataset of 290,117 unique trips collected over six months in Bologna, Italy, was used to gain insight into cyclist behaviour and preferences in an urban environment. The bicycle in Bologna is used as a means of transportation mainly for short-and medium-distance trips. The analysis of this dataset revealed the routine of cyclists in different periods: peak hours in the workday morning and evening and periods with lower activities during weekends. Generally, cyclists prefer streets with good cycling infrastructure and avoid streets with heavy traffic. In the suburban area, cyclists prefer bigger roads which lead towards the city centre, while in the city centre preference is on roads which avoid narrow streets in the old town. The obtained results may help to grasp the reasons behind specific route preferences, and they can be used to find opportunities to improve cycling infrastructure for a better and safer urban environment. © 2024 IEEE.

关键词： Bicycles

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A Taxonomic Classification of Insider Threats: Existing Techniques, Future Directions & Recommendations

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Journal of Cyber Security and Mobility 2023年第2期12卷 221-252页

作者： Rauf, Usman Mohsen, Fadi Wei, Zhiyuan Dept. of Math. & Computer Science Mercy College NY United States Information Systems Group Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen9712 CP Netherlands

In the last two decades, the number of rapidly increasing cyber incidents (i.e., data theft and privacy breaches) shows that it is becoming enormously difficult for conventional defense mechanisms and architectures to neutralize modern cyber threats in a real-time situation. Disgruntled and rouge employees/agents and intrusive applications are two notorious classes of such modern threats, referred to as Insider Threats, which lead to data theft and privacy breaches. To counter such state-of-the-art threats, modern defense mechanisms require the incorporation of active threat analytics to proactively detect and mitigate any malicious intent at the employee or application level. Existing solutions to these problems intensively rely on co-relation, distance-based risk metrics, and human judgment. Especially when humans are kept in the loop for access-control policy-related decision-making against advanced persistent threats. As a consequence, the situation can escalate and lead to privacy/data breaches in case of insider threats. To confront such challenges, the security community has been striving to identify anomalous intent for advanced behavioral anomaly detection and auto-resiliency (the ability to deter an ongoing threat by policy tuning). Towards this dimension, we aim to review the literature in this domain and evaluate the effectiveness of existing approaches per our proposed criteria. According to our knowledge, this is one of the first endeavors toward developing evaluation-based standards to assess the effectiveness of relevant approaches in this domain while considering insider employees and intrusive applications simultaneously. There have been efforts in literature towards describing and understanding insider threats in general. However, none have addressed the detection and deterrence element in its entirety, hence making our contribution one of a kind. Towards the end of this article, we enlist and discuss the existing data sets. The data sets can help

关键词： Machine learning

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BERT Goes to SQL School: Improving Automatic Grading of SQL Statements

BERT Goes to SQL School: Improving Automatic Grading of SQL ...

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2023 Congress in computer science, computer Engineering, and Applied Computing, CSCE 2023

作者： Rivas, Pablo Schwartz, Donald R. Quevedo, Ernesto School of Eng. & Computer Science Baylor University Dept. of Computer Science United States School of Computer Science & Math. Marist College Dept. of Computing Technology United States

ISBN: (纸本)9798350327595

Automated grading of SQL queries is a challenging task due to the complexity of the language and the variety of acceptable solutions for a given problem. In this paper, we propose a novel approach that leverages deep learning with a BERT model to understand the syntax and semantics of SQL statements. By training BERT on a dataset of SQL queries and their corresponding grades, we create a model that can automatically grade new questions accurately. Our experiments demonstrate that the proposed methodology achieves high accuracy and consistency in grading SQL queries, outperforming existing state-of-the-art models. Furthermore, we provide an analysis of the model's explainability, revealing a new capability that can be extremely beneficial for understanding the decision-making process. Overall, our work demonstrates the potential of deep learning with BERT for improving the efficiency and accuracy of SQL query grading. © 2023 IEEE.

关键词： bert deep learning machine learning natural language processing sql

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Wireless Security and IoT Device Identification using RF Fingerprinting and Deep Learning 100

Wireless Security and IoT Device Identification using RF Fin...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Quadar, Nordine Chehri, Abdellah Debaque, Benoit Royal Military College of Canada Dept. Math. and Computer Science Kingston Canada Thales-Canada Research and Technology Center QC Canada

ISBN: (纸本)9798331517786

Enhancing the security of wireless networks involves implementing a user authentication method when the fingerprint of a network device is unknown or considered a potential threat. This technique is known as radio frequency (RF) fingerprinting. This paper presents a novel method for RF fingerprinting of Internet of Things (IoT) devices, addressing the challenges of the radio frequency spectrum. The proposed architecture integrates a feature generator module that transforms time-series I/Q samples into a multi-dimensional matrix and a deep learning module inspired by the ResNet-50-1D model. We assess the effectiveness of our approach by analyzing a real-world dataset of BT emissions obtained from 10 commercial IoT devices in two challenging indoor environments. The datasets, made publicly accessible on IEEE Dataport, were gathered using a USRP X300 software-defined radio (SDR) in both line-of-sight (LoS) and rich multipath propagation scenarios. Our method showcases excellent results in the TTS scenario and shows promise in the challenging TTD scenario, considering the complex nature of frequency hopping. The evaluation results emphasize the significance of evaluating RF fingerprinting models in various scenarios and offer valuable insights into the strengths and limitations of our approach in handling radio frequency waveforms. © 2024 IEEE.

关键词： Frequency hopping

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