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SSRN 2023年

作者： Woldehana, Yared Dereje Yimer, Mohammed Abebe School of Computing and Informatics Dilla University Dilla Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

Counterfeiting money is nearly as old as money itself and is a common problem around the globe. Even though counterfeiting is often motivated by financial gain, the ability of counterfeiting to have significant economic consequences has led to its use as a political weapon or terrorism. In Ethiopia, counterfeit banknotes remain a persistent problem, and the existing methods for their detection primarily rely on manual inspection of the security features, creating awareness, and hardware-based systems, such as bill counters and detector machines. However, hardware-based systems are expensive, while manual inspection is a time-consuming, subjective process and prone to errors. Relying on manual inspection, awareness, and machine-based techniques is inadequate. Only a handful of studies have been conducted on the current Ethiopian banknotes. This study presents a novel combination of two deep learning approaches known as transfer and similarity learning for classifying counterfeit and genuine Ethiopian banknotes. The experimental result indicates the Inception and Vgg16 models scored equal classification accuracy scores of 0.9925 and a classification loss of 0.0025 on the testing dataset. Despite having a similar score, the Vgg16 shows the probability of an unknown class on the test dataset. The inception similarity model having a better distance score of 0.335824 made it a better model than Vgg16. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Learning-Aided Evolutionary Search and Selection for Scaling-up Constrained Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Liu, Songbai Wang, Zeyi Lin, Qiuzhen Li, Jianqiang Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong

The existing constrained multiobjective evolutionary algorithms (CMOEAs) still have great room for improvement in balancing populations convergence, diversity and feasibility on complex constrained multiobjective optimization problems (CMOPs). Besides, their effectiveness deteriorates dramatically when facing the CMOPs with scaling-up objective space or search space. We are thus motivated to design a learning-aided CMOEA with promising problem-solving ability and scalability for various CMOPs. In the proposed solver, two learning models are respectively trained online on constrained-ignored task and feasibility-first task, which are then used to learn the two improvement-based vectors for enhancing the search by differential evolution. In addition, the union population of parent and child solutions is divided into multiple subsets with a hierarchical clustering based on cosine similarity. A comprehensive indicator, considering objective-based performance and constraint violation degree of a solution, is developed to select the representative solution from each cluster. The effectiveness of the proposed optimizer is verified by solving the CMOPs with various irregular Pareto fronts, the number of objectives ranging from 2 to 15, and the dimensionality of search space scaling up to 1000. IEEE

关键词： Evolutionary algorithms

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Enhancing Drone Video Analytics Security Management using an AERPAW Testbed

Enhancing Drone Video Analytics Security Management using an...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Alicia Esquivel Morel Zack Murry Kevin Kostage Chengyi Qu Prasad Calyam Department of Electrical Engineering and Computer Science University of Missouri Columbia USA Department of Computing and Software Engineering Florida Gulf Coast University Florida USA

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Drone-based applications involving real-time video analytics are emerging to serve diverse situational awareness use cases ranging from precision agriculture to disaster response. Hence, there is a need to study robust security measures to ensure the integrity of information and safeguard communication, as well as data transmission in drone video analytics. In this paper, we investigate methods to enhance the security management of drone video analytics in terms of reliability and integrity within realistic settings using testbed resources in the NSF-supported AERPAW infrastructure. Specifically, we study security mechanisms to model and detect threats such as Replay, Packet Injection, and Physical Capture attacks caused by situations in dynamic and potentially adversarial network environments. In addition, we generate balanced datasets through Generative Adversarial Networks (GAN) to address challenges posed by unbalanced datasets that are common when applying machine learning models for attack detection impacting drone video analytics traffic. Our experimental environment in AERPAW involves a setup for secure communication through a MAVLink-based (open-standard) drone communication protocol that uses continuous authentication via digital signatures. Our experiment results compare the efficiency gains achieved through secure MAVLink-based communication with unsecured counterparts, examining factors such as packet encryption, digital signatures, and nonces. Further, our results provide valuable insights into the adaptability of security mechanisms for drone video analytics within realistic environments.

关键词： Protocols Visual analytics Security management Authentication Machine learning Generative adversarial networks Encryption

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AI-Powered IT Project Management: Analyzing the Effectiveness of Advanced Project Management Tools to Ensure Project Efficiency

AI-Powered IT Project Management: Analyzing the Effectivenes...

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IEEE Southeastcon

作者： Jiban Krisna Das Ifeoluwa Elegbe Lord Coffie Rojeena Khadka Lei Chen Yiming Ji Dept. of Information Technology Georgia Southern University Statesboro GA USA College of Computing and Software Engineering Kennesaw State University

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The integration of Artificial Intelligence (AI) into project management has significantly transformed traditional methodologies, particularly within the Information Technology (IT) sector. This study investigates the effectiveness of AI-powered tools, specifically Asana, Jira, and *** to improve project management efficiency through automation, resource optimization, and decision-making processes. Using a systematic literature review and quantitative analysis, the research highlights the capabilities and limitations of these tools in the management of complex IT projects. The study findings reveal that AI-powered platforms facilitate real-time tracking, predictive analytics and dynamic resource allocation, which collectively contribute to reduced project timelines and improved outcomes. However, challenges such as data quality, integration complexities, and the need for skilled personnel remain prevalent. The study was summarized with strategic recommendations for organizations to incorporate automation, leverage predictive analytics, and improve data integrity, thus maximizing the benefits of AI in project management. This research underscores the critical role of AI in modernizing project management practices, which ultimately leads to increased productivity and project success rates in the IT domain.

关键词： Automation Navigation Project management Organizations Predictive models Real-time systems Complexity theory Resource management Predictive analytics Optimization

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Time Series Fusion Model for Predicting Electric power tools and equipment 6

Time Series Fusion Model for Predicting Electric power tools...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Xue, Liang Ling, Xinghong Yang, Lei Yang, Yang Computing Science and Artificial Intelligence College Suzhou City University Suzhou China College of Computer Science and Software Engineering Hohai University Nanjing China Suzhou Key Lab of Multi-modal Data Fusion and Intelligent Healthcare Suzhou City University Suzhou China

ISBN: (纸本)9798331541699

To effectively address the issue of inadequate accuracy and timeliness in industrial data classification, this paper proposes a fusion classification model based on time series (DT-RF-MLP) for loss prediction of electrical work equipment. Firstly, data information is extracted from the dataset and features are constructed based on the data information to enhance the diversity of data features. Secondly, redundant and highly correlated features are removed by quantifying the correlation degree between the features to simplify the model inputs. On this basis, a fusion classification model is constructed and comparative experiments are conducted with a single model to validate the advantages of the fusion model in terms of prediction performance. The experimental results demonstrate that the model markedly enhances prediction accuracy while concurrently reducing the duration of the model run. © 2024 IEEE.

关键词： Prediction models

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Recursive Generation of Multi-Qubits Control Gate to Facilitate the Grover's Quantum Algorithm in Large Scale Database Search 24

Recursive Generation of Multi-Qubits Control Gate to Facilit...

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Wang, Wen-Li Hussain, Shahid Rondinelli, Nathaniel V. Tang, Mei-Huei Rehman Khan, Saif Ur Penn State University Behrend Department of Computer Science and Software Engineering EriePA United States Gannon University Department of Computer Information Science EriePA United States Shifa Tameer-e-Millat University Department of Computing Islamabad44000 Pakistan

ISBN: (纸本)9798350365658

Context: Grover's algorithm is a powerful tool in the realm of quantum computing because it offers exponential speedup over classical linear searching for unstructured / unsorted search problems. However, it is challenging to construct efficient and scalable circuits for a multi-input control gate in the algorithm, particularly with a huge number of qubit inputs. Problem: Traditional circuit construction methods often require manual composition for the multi-input gate from basic quantum gates, which can be time consuming and error prone, thus hindering practical implementation. Moreover, the number of simple gates in the composition grows exponentially with the addition of each qubit. Method: This research provides an approach to recursively generate the oracle using Toffoli gates as the fundamental units to enhance scalability and reusability while maintaining effectiveness. The methodology leverages IBM Qiskit and Quantum Lab to simulate quantum computation to prove the practicality and feasibility. Result: The findings from the experiments between recursively and non-recursively generated circuits suggest that our recursive approach can offer comparable accuracy and computational complexity compared to the non-recursive approach. However, it tends to require a larger number of basic quantum gates due to a lack of circuit optimization. Conclusion: The results showcase the applicability of utilizing recursion as a speedy and economical alternative to generate circuits when implementing the Grover's algorithm. It not only presents potential scalability benefits, but also creates an avenue for further research on test verification and performance improvement to the computing community when running in actual quantum hardware. © 2024 IEEE.

关键词： Qubits

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Tree Regularization for Visual Explanations of Cancer Cell Classification

Tree Regularization for Visual Explanations of Cancer Cell C...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Muhammad Umair Raza Jie Chen Li Wang Adil Nawaz Victor C.M. Leung Jianqiang Li College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The challenge of interpretability remains a significant barrier to adopting deep neural networks in healthcare domains. Although tree regularization aims to align a deep neural network’s decisions with a single axis-aligned decision tree, however, relying on one tree for all inputs often leads to sub-optimal performance and interoperability. To address this limitation, we propose an enhanced tree regularization method that integrates a post-hoc visual explainable model such as Grad-CAM. This approach guides the deep model to be well-approximated by decision trees tailored to the salient regions identified by Grad-CAM in the input space. We rigorously validate the effectiveness of this framework on two cancer cell datasets: CNMC, which focuses on acute lymphoblastic leukemia cells, and ISBI2016, which comprises benign and malignant skin lesions. The results demonstrate that the proposed method delivers simpler and more interpretable explanations without compromising accuracy, thereby advancing the interpretability of deep learning models in critical healthcare applications.

关键词： Visualization Accuracy Decision making Artificial neural networks Predictive models Skin Decision trees Lesions Biological cells Cancer

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Data Augmentation for Emotion Detection in Small Imbalanced Text Data

Data Augmentation for Emotion Detection in Small Imbalanced ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Anna Koufakou Diego Grisales Ragy Costa de jesus Oscar Fox Department of Computing and Software Engineering Florida Gulf Coast University Fort Myers Florida USA

Emotion recognition in text, the task of identifying emotions such as joy or anger, is a challenging problem in NLP with many applications. One of the challenges is the shortage of available datasets that have been annotated with emotions. Certain existing datasets are small, follow different emotion taxonomies and display imbalance in their emotion distribution. In this work, we studied the impact of data augmentation techniques precisely when applied to small imbalanced datasets, for which current state-of-the-art models (such as RoBERTa) under-perform. Specifically, we utilized four data augmentation methods (Easy Data Augmentation EDA, static and contextual Embedding-based, and ProtAugment) on three datasets that come from different sources and vary in size, emotion categories and distributions. Our experimental results show that using the augmented data when training the classifier model leads to significant improvements. Finally, we conducted two case studies: a) directly using the popular chat-GPT API to paraphrase text using different prompts, and b) using external data to augment the training set. Results show the promising potential of these methods.

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A modified grey wolf optimizer with multi-solution crossover integration algorithm for feature selection

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Journal of Ambient Intelligence and Humanized computing 2025年第1期16卷 329-345页

作者： Ihsan, Muhammad Din, Fakhrud Zamli, Kamal Z. Ghadi, Yazeed Yasin Alahmadi, Tahani Jaser Innab, Nisreen Faculty of IT Department of Computer Science and IT University of Malakand Khyber Pakhtunkhwa Chakdara18800 Pakistan Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Department of computer science and software engineering Al Ain University Abu Dhabi12555 United Arab Emirates Department of Information SystemsCollege of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia Department of Computer Science and Information SystemsCollege of Applied Sciences Al-Maarefa University Diriyah Riyadh13713 Saudi Arabia

Feature selection helps eradicate redundant features which is essential to mitigate the curse of dimensionality when a machine-learning model deals with high-dimensional datasets. Grey Wolf Optimizer (GWO) is a swarm-based algorithm that simulates the wolves’ hunting behavior. Although very efficient, GWO faces some limitations which may cause premature convergence and/or local optima trapping. Moreover, GWO relies mainly on the three best wolves, limiting its potential for diverse exploration and exploitation. This work proposes an improved version of GWO namely, a modified grey wolf optimizer with multi-solution crossover integration (MGWO-MCI) algorithm. MGWO-MCI algorithm incorporates a multi-solution strategy that evolves new potential solutions in the optimization process. A crossover operation is performed between the new wolves and the existing hierarchy, reforming the position-updating process. MGWO-MCI utilizes this position-updating process using two different approaches. The first approach named MGWO-MCI-I expands the additional wolves’ role to both exploration and exploitation whereas the second approach named MGWO-MCI-II incorporates their role to exploration only. These approaches are evaluated and tested using 18 datasets and an Intrusion detection dataset NSL-KDD for feature selection. Statistically, the results are analyzed through the Wilcoxon test, which shows the superiority of MGWO-MCI-II. MGWO-MCI-II outperforms others with an accuracy of 98.6% on NSL-KDD and achieves 55.5% overall best outcomes on other datasets. Moreover, the MGWO-MCI was evaluated on two constrained optimization problems, the pressure vessel and welded beam design validating its effectiveness and adaptability in solving different optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Constrained optimization

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Differential-Privacy Capacity

Differential-Privacy Capacity

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IEEE International Symposium on Information Theory

作者： Wael Alghamdi Shahab Asoodeh Flavio P. Calmon Oliver Kosut Lalitha Sankar School of Engineering and Applied Sciences Harvard University Department of Computing and Software McMaster University ( School of Electrical Computer and Energy Engineering Arizona State University

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We formulate a fundamental limit in differential privacy under growing composition. We introduce the universal composition curve: the best privacy guarantee under repeated composition of a given privacy mechanism given only the sensitivity of the query. We define privacy capacity as the slowest growth rate of this universal composition curve among all privacy mechanisms. We show that, in the limit of large compositions, privacy capacity “single-letterizes” as a minimax KL-divergence term. Our privacy capacity formula extends previous literature results that connect differential privacy and KL-divergence via concentration theorems.

关键词： Privacy Differential privacy Sensitivity Information theory

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