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arXiv 2024年

作者： Zielinski, Sebastian Zorn, Maximilian Gabor, Thomas Feld, Sebastian Linnhoff-Popien, Claudia Institute for Informatics LMU Munich Munich Germany Quantum & Computer Engineering Delft University of Technology Delft Netherlands

A common way of solving satisfiability instances with quantum methods is to transform these instances into instances of QUBO, which in itself is a potentially difficult and expensive task. State-of-the-art transformations from MAX-3SAT to QUBO currently work by mapping clauses of a 3SAT formula associated with the MAX-3SAT instance to an instance of QUBO and combining the resulting QUBOs into a single QUBO instance representing the whole MAX-3SAT instance. As creating these transformations is currently done manually or via exhaustive search methods and, therefore, algorithmically inefficient, we see potential for including search-based optimization. In this paper, we propose two methods of using evolutionary algorithms to automatically create QUBO representations of MAX-3SAT problems. We evaluate our created QUBOs on 500 and 1000-clause 3SAT formulae and find competitive performance to state-of-the-art baselines when using both classical and quantum annealing solvers. © 2024, CC BY-NC-ND.

关键词： Combinatorial optimization

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A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction

A Heterogeneous Network-based Contrastive Learning Approach ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hu, Junwei Bewong, Michael Kwashie, Selasi Zhang, Wen Nofong, Vincent M. Wu, Guangsheng Feng, Zaiwen Huazhong Agricultural University College of Informatics Hubei Wuhan China Charles Sturt University School of Computing Mathematics & Engineering Wagga WaggaNSW Australia Charles Sturt University Artificial Intelligence & Cyber Futures Institute BathurstNSW Australia University of Mines and Technology Faculty of Engineering Tarkwa Ghana Xinyu University School of Mathematics and Computer Science China

ISBN: (纸本)9798350386226

Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving excellent performance. However, these methods typically overlook edge features when dealing with heterogeneous biomedical networks. We propose a heterogeneous network-based contrastive learning method called HNCL-DTI, which designs a heterogeneous graph attention network to predict potential/novel DTIs. Specifically, our HNCL-DTI utilizes contrastive learning to collaboratively learn node representations from the perspective of both node-based and edge-based attention within the heterogeneous structure of biomedical networks. Experimental results show that HNCL-DTI outperforms existing advanced baseline methods on benchmark datasets, demonstrating strong predictive ability and practical effectiveness. The data and source code are available at https://***/Zaiwen/HNCL-DTI. © 2024 IEEE.

关键词： Contrastive Learning

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High-temperature Corrosion of ∼ 30 Pct Porous FeCr Stainless Steels in Air: Long-Term Evaluation Up to Breakaway (vol 54, pg 2244, 2023)

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METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE 2023年第8期54卷 3370-3370页

作者： Koszelow, D. Advanced Materials Centre Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology 80-233 Gdańsk Poland Laboratory for Nuclear Materials Laboratory for Synchrotron Radiation and Femtochemistry Paul Scherrer Institut 5232 Villigen Switzerland International Centre of Electron Microscopy for Materials Science Faculty of Metal Engineering and Industrial Computer Science AGH University of Science and Technology Al. Mickiewicza 30 30-059 Krakow Poland

In this work, a long-term (up to 6000 hours) corrosion evaluation of three porous (~ 30 pct of initial porosity) ferritic iron-chromium alloys with different Cr contents (20, 22, and 27 wt pct of Cr) was carried out at 600 °C, 700 °C, 800 °C, and 900 °C in air. Mass gain measurements and SEM analyses revealed that at temperatures above 600 °C, all alloys exhibit breakaway corrosion, whereas at 600 °C, none of the alloys were heavily oxidized even after 6000 hours. Based on the results, the diffusion character of the corrosion of porous chromia-forming alloys was identified. The microstructure changes at high temperatures in porous alloys containing 22 wt pct of Cr were determined in detail by transmission electron microscopy. The proposed prediction model indicated that the lifetimes of the Fe20Cr and Fe22Cr alloys were determined as 1250 hours (± 535 hours) and 1460 hours (± 640 hours), respectively. It is in agreement with the long-term oxidation experiment. For the Fe27Cr alloy, the deviation between predicted and observed lifetimes occurs. The proposed model allows for qualitative estimation of the porous alloys’ lifetime with experimentally validated accuracy.

关键词： Chromium alloys

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Unsupervised Unlearning of Concept Drift with Autoencoders

Unsupervised Unlearning of Concept Drift with Autoencoders

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： André Artelt Kleanthis Malialis Christos G. Panayiotou Marios M. Polycarpou Barbara Hammer Faculty of Technology Bielefeld University Bielefeld Germany KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

Concept drift refers to a change in the data distribution affecting the data stream of future samples. Consequently, learning models operating on the data stream might become obsolete, and need costly and difficult adjustments such as retraining or adaptation. Existing methods usually implement a local concept drift adaptation scheme, where either incremental learning of the models is used, or the models are completely retrained when a drift detection mechanism triggers an alarm. This paper proposes an alternative approach in which an unsupervised and model-agnostic concept drift adaptation method at the global level is introduced, based on autoencoders. Specifically, the proposed method aims to “unlearn” the concept drift without having to retrain or adapt any of the learning models operating on the data. An extensive experimental evaluation is conducted in two application domains. We consider a realistic water distribution network with more than 30 models in-place, from which we create 200 simulated data sets / scenarios. We further consider an image-related task to demonstrate the effectiveness of our method.

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Improving Student Performance Prediction Using a PCA-based Cuckoo Search Neural Network Algorithm 27

Improving Student Performance Prediction Using a PCA-based C...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Ali, Maria Liaquat, Muhammad Daniyal Atta, Muhammad Nouman Khan, Abdullah Lashari, Saima Anwar Ramli, Dzati Athiar Institute of Computer Science and Information Technology The University of Agriculture Peshawar Pakistan College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia School of Electrical and Electronic Engineering University Sains Malaysia USM Engineering Campus Nibong Tebal Pulau Pinang14300 Malaysia

The ANN is a commonly used network for pattern recognition, and has been trained for various tasks such as prediction, classification, and engineering. However, this model faces challenges such as local minima and slow convergence, which have been addressed through different strategies such as combining the artificial neural network (ANN) with optimised models like the cuckoo search (CS) algorithm. However, for large datasets, the hybrid ANN-based CS algorithm can lead to overfitting. To overcome this issue, the authors propose a new algorithm called Principal Component Analysis with Cuckoo Search Neural Network (PCACSNN). The performance of this algorithm is compared to other commonly used algorithms such as ANN, backpropagation neural network (BPNN), and cuckoo search backpropagation (CSBP), using the Mean Square Error (MSE) and accuracy on classification problems. The simulations were performed on the Student Performance dataset taken from the UCIMLR. The results show that the proposed model performs better than the other models, achieving high accuracy and low MSE for both mathematics and Portuguese student datasets. For the mathematics students, the suggested model attained an accuracy of (99.32%) with MSE of 2.77E-07 for 70% training data and an accuracy of 98.52% with MSE of 2.50E-04 for 30% training data. Similarly, for the Portuguese student dataset, the proposed model obtained (99.38%) accuracy with MSE of 1.09E-08 for 70% training data and 98.72% accuracy with MSE of 1.01E-04 for 30% training data. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Accuracy Artificial Neural Network (ANN) Back Propagation Cuckoo Search (CS) Algorithm Local Minima Mathematics Mean Square Error (MSE) Portuguese Slow Convergence

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Training Learning Weights of Elman Neural Network Using Salp Swarm Optimization Algorithm 27

Training Learning Weights of Elman Neural Network Using Salp...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Mahdi, Qaedah Ali Musaeed Naji Ali, Maria Atta, Muhammad Nouman Khan, Abdullah Lashari, Saima Anwar Ramli, Dzati Athiar Institute of Computer Science and Information Technology The University of Agriculture Peshawar Pakistan College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia School of Electrical and Electronic Engineering University Sains Malaysia USM Engineering Campus Pulau Pinang Nibong Tebal14300 Malaysia

Elman Neural Network (ENN) is one of the most common type of recurrent neural network (RNN). It is frequently applied to numerous applications such as classification and predication. However, the ENN model, suffers from serious flaws like network stagnancy and delayed convergence during training. To enhance the effectiveness of the ENN and address the mention problems, numerous studies have been conducted to speed up the learning process of ENN. Many optimization techniques are being used and each algorithm has its own characteristics in terms of efficiency. Some of the algorithms inspire from nature, while others come from the swarm's combined behaviour. Different numbers of optimization methods, including Particle Swarm Optimization (PSO), Bat Algorithm, and Cuckoo Search (CS) are applied to improve the ENN's learning process. As a result, this study suggested using Salp Swarm algorithm (SSA) to address these issues and speed up the ENN algorithm's learning process. The SSA, which is based on the typical behaviour of Salp swarms, is proposed in this research as a novel optimization technique. Due to its effectiveness and Salp intelligent behaviour, a novel Salp algorithm improves the learning process of the artificial neural network (ANN), and ENN models. Therefore this study proposed Salp swarm artificial neural network (SSANN) and Salp swarm Elman neural network (SSElmanNN). Performance of the suggested models is evaluated against ANN, back propagation neural network (BPNN), along with ENN in term of accuracy and Mean Square Error (MSE). Two datasets such as IRIS and Credit Card are used for simulation. The simulation results demonstrate that the suggested models outperform the other algorithms used in this study in terms of MSE and accuracy. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： and Credit Card ANN BPNN ENN Iris MSE SSA

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Automated Surface Classification System Using Vibration Patterns - A Case Study With Wheelchairs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 884-895页

作者： Yu, Haoxiang Raychoudhury, Vaskar Saha, Snehanshu Edinger, Janick Smith, Roger O. Gani, Md Osman The University of Texas at Austin Department of Electrical and Computer Engineering AustinTX78712 United States Miami University Department of Computer Science & Software Engineering OxfordOH45056 United States Bits Pilani K.K. Birla Goa Campus Department of Computer Science & Information Systems Appcair Zuarinagar403726 India University of Hamburg Department of Informatics & Distributed Operating Systems Hamburg20146 Germany University of Wisconsin-Milwaukee Department of Rehabilitation Sciences & Technology MilwaukeeWI53706 United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD21250 United States

Finding a barrier-free accessible path through the built environment is necessary for wheelchair users. Researchers have identified the effect of surface vibration on the health of wheelchair users and proposed various solutions to identify and annotate mobility barriers. However, the effects of surface-induced vibration on accessibility are yet to be investigated. In order to address this problem, we present the WheelShare system, which uses machine learning to identify surfaces and curbs from vibration captured by accelerometer and gyroscope sensors (both smartphone and wearable device). WheelShare allows users to share their collected data via crowdsourcing. Based on this surface-specific vibration, we recognize 32 different surfaces found across the built environment (in Austria, China, France, Germany, India, and the USA for 32 human subjects) with an accuracy of 97.5%. The recognized surfaces are then grouped into different categories based on their characteristics. Our novel contributions in this article include the following. 1) We have developed a machine-learning-enabled surface recognition system that uses sensor data to capture surface-induced vibration patterns. 2) We have tested the robustness of the system for devices with only accelerometer data (such as low-end smartphones) and achieved an accuracy of 91.6%. 3) We have deployed our prototype WheelShare system for real-time surface classification in new sites that are unseen in training and testing and successfully recognized the surface characteristics. © 2020 IEEE.

关键词： Accelerometers

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Dynamic Channel Allocation via Bandit Learning for WiFi 7 Networks with Multi-Link Operation

Dynamic Channel Allocation via Bandit Learning for WiFi 7 Ne...

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IEEE Conference on Wireless Communications and Networking

作者： Shumin Lian Jingwen Tong Liqun Fu School of Informatics Xiamen University Xiamen China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The upcoming IEEE 802.11be standard, termed WiFi 7, introduces multi-link operation (MLO), enabling devices to establish multiple simultaneous connections utilizing different frequencies and channels. While MLO has the potential to boost network throughput, optimizing channel allocation in WiFi 7 networks introduces many challenges. In this paper, we propose a best-arm identification-enabled Monte Carlo tree search (BAI-MCTS) algorithm for efficient channel allocation in WiFi 7 networks. Specifically, we first employ an efficient mechanism to calculate the network throughput by capturing the essential features of the CSMA protocol. We then formulate this channel allocation problem as a multi-armed bandit (MAB) problem. However, solving this MAB problem induces high sample complexity due to the large-arm space. To overcome this challenge, we introduce BAI-MCTS by combining the BAI and MCTS techniques. Notably, BAI-MCTS has a fast convergence rate and low sample complexity. Simulation results demonstrate that the proposed algorithm outperforms the baseline algorithms in terms of the convergence rate, which is about 42.40% faster than the UCT algorithm when reaching 95% of the optimal value.

关键词： Protocols Monte Carlo methods Heuristic algorithms Simulation Channel allocation Throughput Dynamic scheduling Multiaccess communication Wireless fidelity Convergence

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Systematic Review and Propose an Investment Type Recommender System Using Investor’s Demographic Using ANFIS 8th

Systematic Review and Propose an Investment Type Recommender...

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8th International Congress on Information and Communication technology, ICICT 2023

作者： Asemi, Asefeh Asemi, Adeleh Ko, Andrea Doctoral School of Economics Business and Informatics Corvinus University of Budapest Budapest Hungary Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Corvinus University of Budapest Budapest Hungary

ISBN: (纸本)9789819932429

The development of investment recommender systems (IRSs) has increased due to advancements in technology. This study aims to present a new model for IRSs based on potential investor’s demographic data and feedback, using fuzzy neural inference solutions. Both qualitative and quantitative methods were used in this research, including a review of past studies and analysis of data through Scopus analyze tool, Voyant, and VosViewer. The proposed model combines expert’s knowledge with demographic data to present the most suitable type of investment through an adaptive neuro-fuzzy inference recommender system. The model is processed in several steps, including clustering investment data types in JMP and proposing the results through MATLAB. This study provides a framework for IRSs that can give relevant and accurate recommendations for potential and actual investors, thus enhancing their investment experience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Investments

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Polymer optical fiber sensors for monitoring the bond slip between concrete and reinforcing bars 28

Polymer optical fiber sensors for monitoring the bond slip b...

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28th International Conference on Optical Fiber Sensors, OFS 2023

作者： Pospori, Andreas Ioannou, Andreas Melo, José Varum, Humberto André, Paulo Marques, Carlos A.F. Kalli, Kyriacos Photonics and Optical Sensors Research Laboratory Cyprus University of Technology Saripolou 33 Limassol3036 Cyprus University of Porto Porto Portugal Department of Electrical and Computer Engineering Instituto de Telecomunicações Instituto Superior Técnico University of Lisbon Lisboa1049-001 Portugal I3N & Physics Department University of Aveiro Aveiro3810-193 Portugal

Polymer optical fiber sensors are developed for in-situ monitoring of bond slip between concrete and reinforcing bars. This work demonstrates the potential for early detection of structural failure, thereby enhancing ... 详细信息

ISBN: (纸本)9781957171302

关键词： Fracture mechanics

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