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14th IEEE Integrated STEM Education Conference, ISEC 2024

作者： Samaras, Ted Marlowe, Thomas J. Herbert, Katherine G. Anu, Vaibhav K. Hagiwara, Sumi Robila, Stefan A. Instructional Technology Coach Franklin Township Public Schools United States Seton Hall University Department of Mathematics and Computer Science United States School of Computing Montclair State University United States College for Education and Engaged Learning Montclair State University United States

ISBN: (纸本)9798350352801

In the ever changing environment of education, the skills learned and applied in computer and data science (CADS) have expanded beyond the reach of siloed, single-subject teaching in the classroom. The need for interdisciplinary learning and constructs have increased as real-world experiences have become a necessity and time has become a scarcity. The progression toward this reality has been accelerated in a post-pandemic world. This paper reviews literature regarding this topic, looks at barriers impacting student learning, and provides resources to assist educators in weaving plugged, unplugged, and hybrid components into their interdisciplinary lessons to amplify the impact of CADS. It also provides a scenario for and a vision of learning if educators can utilize an interdisciplinary approach to their lesson and unit plans. The sample template provided focuses on a middle and high school education model where multiple teachers are responsible for teaching the same students in a team. The curriculum discussed in the template is based on topics covered in the New Jersey State Standards for eighth grade students, but is flexible enough to be molded for the needs of various grade levels and different state and local standards. By using multimodal techniques and generating greater synergy through interdisciplinary lesson constructs, more robust learning environments are created for both students and educators. © 2024 IEEE.

关键词： Data science

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Algorithmic Perspective of Strongly Possible Keys and Functional Dependencies

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Informatica (Slovenia) 2024年第4期48卷 507-512页

作者： Alattar, Munqath Sali, Attila ITRDC University of Kufa Iraq Alfréd Rényi Institute of Mathematics Budapest Hungary Department of Computer Science Budapest University of Technology and Economics Budapest Hungary

It is common to encounter missing values in database tables. For an incomplete table, a possible world can be obtained by replacing any missed value with a value from the attribute (infinite) domain. A possible key (possible functional dependency) is satisfied in an incomplete table"T" if there exists a possible world of"T" that satisfies the key (the functional dependency) constraint. If all possible worlds of"T" satisfy the key (functional dependency), then we say that"T" satisfies a certain key (functional dependency). The concept of strongly possible worlds was introduced recently that considers only the active domain (the set of values that are already appearing in each attribute in the table), in a way that a strongly possible world is obtained by replacing any missing value with a value from the corresponding attributes active domain. So, a strongly possible key spKey (functional dependency spFD) is satisfied by a table"T" if there exists a strongly possible world that satisfies the key (functional dependency). In this paper, we investigate the approximation measures of spKeys and spFDs when the strongly possible constraint is not satisfied by a given table. The measure g3 represent the ratio of the minimum number of tuples that need to be removed so that the table satisfies the constraint. We introduce a new measure g5, which is the ratio of the minimum number of tuples to be added to the table so the result satisfies the constraint. Where adding new tuples with new values will extend the active domain. We prove that g3 is an upper bound of g5 for a constraint in a table. Furthermore, g3 and g5 are independent of each other, where there exist tables of some large number of tuples that satisfy g3 − g5 = pq for any rational number 0 ≤ pq © 2024 Slovene Society Informatika. All rights reserved.

关键词： approximate dependencies approximate keys database key Functional dependencies missing values

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Provably Secure Conditional-Privacy Access Control Protocol for Intelligent Customers-Centric Communication in VANET

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1747-1756页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Bashir, Ali Kashif Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BH United Kingdom Woxsen University Woxsen School of Business Hyderabad502345 India Lebanese American University Department of Computer Science and Mathematics Beirut1102 2801 Lebanon Illinois Institute of Technology Information Technology and Management ChicagoIL60555 United States

Globally, the development of Intelligent Cyber-Physical Transportation Systems (ICTS) aims to tackle several challenges, including reducing traffic accidents and fuel usage, alleviating congestion, shortening travel time, and enhancing overall transportation safety. These systems leverage advanced customer-centric communication and networked control methods, such as inter-vehicle, vehicle-to-roadside (V2R), and vehicle-to-vehicle (V2V) communication through the use of vehicular ad hoc networks (VANETs) to cover all aspects of transportation-based information. In existing systems, once devices are registered with a Trusted Authority (TA), subsequent authentication still relies on the TA's assistance. However, these devices typically remain stationary, and frequent interaction with the TA becomes impractical and costly in highly mobile VANET environments. To address this challenge, we propose a secure access control protocol with conditional privacy for VANETs. Unlike other protocols, our protocol does not require the TA's involvement during authentication between vehicles and RSUs. Additionally, our protocol leverages pseudonym mechanisms to provide conditional privacy, enabling legitimate vehicles to remain anonymous while malicious ones can be tracked. Our proposed scheme is supported by both formal and informal security analyses and has been shown to be secure against several known attacks in VANETs. Furthermore, compared to relevant studies, our protocol achieves 30.2505% of efficiency in terms of computation cost and 11.09276% of efficiency in terms of communication cost, respectively. © 1975-2011 IEEE.

关键词： Vehicular ad hoc networks

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Foundations and Applications of Information Systems Dynamics

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Engineering 2023年第8期27卷 254-265页

作者： Jianfeng Xu Zhenyu Liu Shuliang Wang Tao Zheng Yashi Wang Yingfei Wang Yingxu Dang Information Technology Service Center of People’s Court Beijing 100745China School of Information Management for Law China University of Political Science and LawBeijing 102249China School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Mathematics and Statistics Beijing Institute of TechnologyBeijing 100081China

Although numerous advances have been made in information technology in the past decades,there is still a lack of progress in information systems dynamics(ISD),owing to the lack of a mathematical foundation needed to describe information and the lack of an analytical framework to evaluate information *** value of ISD lies in its ability to guide the design,development,application,and evaluation of largescale information system-of-systems(So Ss),just as mechanical dynamics theories guide mechanical systems *** paper reports on a breakthrough in these fundamental challenges by proposing a framework for information space,improving a mathematical theory for information measurement,and proposing a dynamic configuration model for information *** this way,it establishes a basic theoretical framework for *** proposed theoretical methodologies have been successfully applied and verified in the Smart Court So Ss Engineering Project of China and have achieved significant improvements in the quality and efficiency of Chinese court *** proposed ISD provides an innovative paradigm for the analysis,design,development,and evaluation of large-scale complex information systems,such as electronic government and smart cities.

关键词： System-of-systems engineering Information theory Information measurement Information systems dynamics Judicial informatization

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on Engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Convolutional neural networks

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Ensuring Zero Trust IoT Data Privacy: Differential Privacy in Blockchain using Federated Learning

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Hussain, Altaf Akbar, Wajahat Hussain, Tariq Bashir, Ali Kashif Dabel, Maryam M. Al Ali, Farman Yang, Bailin School of Electronic and Control Engineering Chang’an University Xi’an China School of Computer Science and Technology and School of Mathematics and Statistics Zhejiang Gongshang University Hangzhou China Department of Computing and Mathematics Manchester Metropolitan University UK Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul South Korea

In the increasingly digitized world, the privacy and security of sensitive data shared via IoT devices are paramount. Traditional privacy-preserving methods like k-anonymity and ldiversity are becoming outdated due to technological advancements. In addition, data owners often worry about misuse and unauthorized access to their personal information. To address this, we propose a secure data-sharing framework that uses local differential privacy (LDP) within a permissioned blockchain, enhanced by federated learning (FL) in a zero-trust environment. To further protect sensitive data shared by IoT devices, we use the Interplanetary File System (IPFS) and cryptographic hash functions to create unique digital fingerprints for files. We mainly evaluate our system based on latency, throughput, privacy accuracy, and transaction efficiency, comparing the performance to a benchmark model. The experimental results show that the proposed system outperforms its counterpart in terms of latency, throughput, and transaction efficiency. The proposed model achieved a lower average latency of 4.0 seconds compared to the benchmark model’s 5.3 seconds. In terms of throughput, the proposed model achieved a higher throughput of 10.53 TPS (transactions per second) compared to the benchmark model’s 8 TPS. Furthermore, the proposed system achieves 85% accuracy, whereas the counterpart achieves only 49%. IEEE

关键词： Anonymity

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Deep learning for iris recognition: a review

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Neural Computing and Applications 2025年第17期37卷 11125-11173页

作者： Yin, Yimin He, Siliang Zhang, Renye Chang, Hongli Zhang, Jinghua School of Mathematics and Statistics Hunan First Normal University Hunan Changsha410205 China School of Computer Science Hunan First Normal University Changsha410205 China School of Information Science and Engineering Southeast University Nanjing210096 China College of Intelligence Science and Technology National University of Defense Technology Hunan Changsha410073 China

Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person’s lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional machine learning-based iris recognition methods, deep learning technology does not rely on feature engineering and boasts excellent performance. This paper collects 131 relevant papers to summarize the development of iris recognition based on deep learning. We introduce the background of iris recognition and the motivation and contribution of this survey. Then, we present the common datasets widely used in iris recognition. After that, we summarize the key tasks involved in the process of iris recognition based on deep learning technology, including identification, segmentation, presentation attack detection, and localization. Finally, we discuss the challenges and potential development of iris recognition. This review provides a comprehensive sight of the research of iris recognition based on deep learning. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Contrastive Learning

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Criss-Cross Attentional Siamese Networks for Object Tracking

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computers, Materials & Continua 2022年第11期73卷 2931-2946页

作者： Zhangdong Wang Jiaohua Qin Xuyu Xiang Yun Tan Neal N.Xiong College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410004China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464OKUSA

Visual object tracking is a hot topic in recent *** the meanwhile,Siamese networks have attracted extensive attention in this field because of its balanced precision and ***,most of the Siamese network methods can only distinguish foreground from the non-semantic *** fine-tuning and retraining of fully-convolutional Siamese networks for object tracking(SiamFC)can achieve higher precision under interferences,but the tracking accuracy is still not ideal,especially in the environment with more target interferences,dim light,and *** this paper,we propose crisscross attentional Siamese networks for object tracking(SiamCC).To solve the imbalance between foreground and non-semantic background,we use the feature enhancement module of criss-cross attention to greatly improve the accuracy of video object tracking in dim light and shadow *** results show that the maximum running speed of SiamCC in the object tracking benchmark dataset is 90 frames/*** terms of detection accuracy,the accuracy of shadow sequences is greatly improved,especially the accuracy score of sequence HUMAN8 is improved from 0.09 to 0.89 compared with the original SiamFC,and the success rate score is improved from 0.07 to 0.55.

关键词： Criss-cross attention object-tracking siamese-network

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Comparing UAV camera data with satellite imagery for global localization 7

Comparing UAV camera data with satellite imagery for global ...

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7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Kozachuk, Oleksandr Prystavka, Pylyp National Aviation University Faculty of Computer Science and Technology Department of Applied Mathematics Kyiv03058 Ukraine

ISBN: (纸本)9798331534141

Global visual localization is critical for UAVs operating in environments where global navigation satellite systems (GNSS) are unreliable or unavailable. While many methods, such as visual odometry (VIO), rely on optical flow for localization, they often require prior knowledge of the current coordinates. In this paper, we propose an enhanced UAV localization method using the vector of locally aggregated descriptors (VLAD) combined with a cycle generative adversarial network (GAN) architecture. Unlike traditional approaches, our method does not require knowledge of the current coordinates;it only requires a pre-downloaded flight map, enabling location determination through image matching. © 2024 IEEE.

关键词： Satellite imagery

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Target Group Influence Maximization using Reinforcement Learning Approach 10

Target Group Influence Maximization using Reinforcement Lear...

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10th International Conference on Web Research, ICWR 2024

作者： Binesh, Neda Haddadi, Mahdieh Arazkhani, Niloofar Semnan University Faculty of Mathematics Statistics and Computer Science Semnan Iran Department of Computer Engineering & Information Technology Tehran Iran

ISBN: (纸本)9798350394986

In social networks groups play a crucial role and making decisions based on majority consensus. Which influencer nodes should we select if our goal is to broadcast a subject in a target group and increase the number of active nodes in this group? Here, we study a new influence maximization (IM) problem that focuses on individuals in a target group who are activated by some relevant topic or information. Target Group Influence Maximization (TGIM) aims to select k influencer nodes in such a way that the number of activated nodes in the target group is maximized. In this paper, we study TGIM and focus on activating the majority of nodes in the target group. We propose an algorithm named Reinforcement Learning for Target Group (RLTG) based on the analysis of the influence of nodes on the target group. The algorithm uses the reinforcement learning approach to learn the optimal path from each target node to some candidate influencers. The experimental results indicate that the recommended approach outperforms known methods. © 2024 IEEE.

关键词： Reinforcement learning

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