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Iterative layerwise training for the quantum approximate optimization algorithm

Physical Review A 2024年第5期109卷 052406-052406页

作者： Xinwei Lee Xinjian Yan Ningyi Xie Dongsheng Cai Yoshiyuki Saito Nobuyoshi Asai Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki Prefecture 305-8577 Japan Graduate School of Science and Technology University of Tsukuba Ibaraki Prefecture 305-8577 Japan Faculty of Engineering Information and Systems University of Tsukuba Ibaraki Prefecture 305-8577 Japan Graduate School of Computer Science and Engineering University of Aizu Fukushima Prefecture 965-0006 Japan School of Computer Science and Engineering University of Aizu Fukushima Prefecture 965-0006 Japan

The capability of the quantum approximate optimization algorithm (QAOA) in solving combinatorial optimization problems has been intensively studied in recent years due to its application in the quantum-classical hybrid regime. Despite having difficulties that are innate in the variational quantum algorithms (VQA), such as barren plateaus and the local minima problem, QAOA remains one of the applications that is suitable for the recent noisy intermediate scale quantum (NISQ) devices. Recent works have shown that the performance of QAOA largely depends on the initial parameters, which motivate parameter initialization strategies to obtain good initial points for the optimization of QAOA. On the other hand, optimization strategies focus on the optimization part of QAOA instead of the parameter initialization. Instead of having absolute advantages, these strategies usually impose trade-offs to the performance of the optimization problems. One such example is the layerwise optimization strategy, in which the QAOA parameters are optimized layer by layer instead of the full optimization. The layerwise strategy costs less in total compared to the full optimization, in exchange of lower approximation ratio. In this work, we propose the iterative layerwise optimization strategy and explore the possibility for the reduction of optimization cost in solving problems with QAOA. Using numerical simulations, we found that by combining the iterative layerwise strategy with proper initialization strategies, the optimization cost can be significantly reduced in exchange for a minor reduction in the approximation ratio. We also show that in some cases, the approximation ratio given by the iterative layerwise strategy is even higher than that given by the full optimization.

关键词： Quantum algorithms Quantum benchmarking Quantum circuits

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A Diversity Inventory Monitoring System of Riparian Vegetation 4

A Diversity Inventory Monitoring System of Riparian Vegetati...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Putra, Dian Pratama Nugraha, Nanda Satya Suparyanto, Teddy Hidayat, Alam Ahmad Sudigyo, Digdo Pardamean, Bens STIPER Agricultural Institute Faculty of Agricultural Technology Yogyakarta55282 Indonesia STIPER Agricultural Institute Yogyakarta Faculty of Agricultural Technology Faculty of Forestry Yogyakarta55282 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

To strengthen conservation efforts for preserving biodiversity in a conservation area, forest inventory is important to understand the natural succession process in the area and to establish a monitoring strategy. Further, tree inventory aims to monitor the output yielded in the area. More specifically, the tree inventory in the watershed area plays a key role to achieve Sustainable Development Goals (SDG), especially in riparian zones which are also vital parts of green zones in forests. However, the traditional inventory approach is time-consuming and laborious therefore the development of an expert system to assist in inventory monitoring is required. In this study, we develop a monitoring system via a mobile application to collect, analyze and visualize tree inventory data. The application includes algorithms required to compute tree biodiversity, distribution, and richness for the given input of the data of all tree species in a conservation area. For the model validation stage, we compare the traditional inventory approach with our proposed application-based approach to compute diversity inventory in two riparian locations: Klaten Conservation Park and Wonosobo Conservation Park. After the three-day data collection in the areas, we obtain that the accuracy of reading data of our proposed system can achieve more than 90% in comparison with the manual approach. This demonstrates that the system can assist forestry workers to perform more efficient tree inventories in different locations. © 2022 IEEE.

关键词： Biodiversity

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Exploring the Use of Particle and Kalman Filters for Obstacle Detection in Mobile Robots

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2023年第4期67卷 384-393页

作者： Gyenes, Zoltán Bölöni, Ladislau Szádeczky-Kardoss, Emese Gincsainé Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Muegyetem rkp. 3. BudapestH-1111 Hungary Department of Computer Science University of Central Florida 4328 Scorpius St OrlandoFL32816-2362 United States

The present study aims to explore the adaptation of estimation methodologies, specifically Particle filters and Kalman filters, for the purpose of determining the position and velocity vector of obstacles within the operational workspace of mobile robots. These algorithms are commonly employed in the motion planning tasks of mobile robots for the estimation of their own position. The proposed methodology utilizes LiDAR sensor data to estimate the position vectors and calculate the velocity vectors of obstacles. Additionally, an uncertainty parameter can be determined using the introduced perception method. The performance of the newly adapted algorithms is evaluated through comparison of the absolute error in position and velocity vector estimations. © 2023 Budapest University of technology and Economics. All rights reserved.

关键词： Mobile robots

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Critical research trends of mobile technology-supported English language learning: A review of the top 100 highly cited articles

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Education and information technologies 2023年第5期28卷 4849-4874页

作者： Monther M Elaish Mahmood H Hussein Gwo-Jen Hwang Benghazi Libya Department of Computer Science Faculty of Information Technology University of Benghazi. Kuala Lumpur Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya. Taipei Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology. Taoyuan Taiwan Yuan Ze University.

Around the world, the number of English speakers and the significance of the English language are constantly increasing. Among various technology-supported instructional styles, Mobile Learning (M-Learning) has been recognized as a promising approach to enhance students' competencies and skills in the English language. By examining previous literature, a number of reviews have been performed to investigate the role of M-learning in the English language. However, none of these studies has highlighted the trends, opportunities, and challenges identified in the most cited articles that focused solely on the English language. Therefore, to address these limitations, this study performed a review of the top 100 most cited articles, published between 2007 and 2020, indexed by the Web of science, and addressing the English language only. The results revealed that most research in Mobile English Language Learning (M-ELL) followed an experimental design and employed a single mobile learning implementation. Additionally, the current study identified a number of research areas that require additional research attention. For example, further research is needed among students learning from home, more qualitative research is needed, and additional research is required to improve students' higher-order thinking skills. The outcomes of this study provide a reference to researchers and educators who intend to use mobile technologies in the area of language education, especially in the context of the English language.

关键词： Applications in subject areas M-ELL Mobile learning Pedagogical issues Teaching/learning strategies

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MLA:A New Mutated Leader Algorithm for Solving Optimization Problems

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computers, Materials & Continua 2022年第3期70卷 5631-5649页

作者： Fatemeh Ahmadi Zeidabadi Sajjad Amiri Doumari Mohammad Dehghani Zeinab Montazeri Pavel Trojovsky Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Graduate of Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Mathematics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

Optimization plays an effective role in various disciplines of science and *** problems should either be optimized using the appropriate method(i.e.,minimization or maximization).Optimization algorithms are one of the efficient and effective methods in providing quasioptimal solutions for these type of *** this study,a new algorithm called the Mutated Leader Algorithm(MLA)is *** main idea in the proposed MLA is to update the members of the algorithm population in the search space based on the guidance of a mutated *** addition to information about the best member of the population,themutated leader also contains information about the worst member of the population,as well as other normal members of the *** proposed MLA is mathematically modeled for implementation on optimization problems.A standard set consisting of twenty-three objective functions of different types of unimodal,fixed-dimensional multimodal,and high-dimensional multimodal is used to evaluate the ability of the proposed algorithm in ***,the results obtained from theMLA are compared with eight well-known *** results of optimization of objective functions show that the proposed MLA has a high ability to solve various optimization ***,the analysis and comparison of the performance of the proposed MLA against the eight compared algorithms indicates the superiority of the proposed algorithm and ability to provide more suitable quasi-optimal solutions.

关键词： Optimization metaheuristics leader benchmark objective function

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

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computers, Materials & Continua 2022年第1期70卷 1247-1261页

作者： J.V.Anchitaalagammai T.Jayasankar P.Selvaraj Mohamed Yacin Sikkandar M.Zakarya Mohamed Elhoseny K.Shankar Department of Computer Science and Engineering Velammal College of Engineering and TechnologyMadurai625009India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Information Technology SRMInstitute of Science and TechnologyKattankulathur603203Tamil NaduIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityP.O.Box 9004Abha61413Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt College of Computer Information Technology American University in the EmiratesDubai503000United Arab Emirates Department of Computer Applications Alagappa UniversityKaraikudiIndia

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern *** generally refers to a network of gadgets linked via wireless network and communicates via *** management,especially energy management,is a critical issue when designing IoT *** studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT *** this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT *** proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance *** proposed EECBRMmodel was validated through a series of experiments and the results were verified under several *** model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network *** simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant ***,the efficiency of the proposed model is established.

关键词： IoT environment clustering routing resource management energy efficiency

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Improving Candle Direction Classification in Forex Market Using Support Vector Machine with Hyperparameters Tuning 7

Improving Candle Direction Classification in Forex Market Us...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Oetama, Raymond Sunardi Heryadi, Yaya Lukas Suparta, Wayan Computer Science Bina Nusantara University Jakarta Indonesia Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350345711

Foreign Exchange market is the world's largest daily currency turnover. Two of the popular currencies Euro and Pound sterling traded against the US Dollar. Since the Russia and Ukraine war started in February 2022, their exchange rates decrease to the lowest rate ever. Even though the general trend is bearish, several daily candles increase for some days making challenges for forex analysts. To solve this problem, classification is applied. The data is labeled downward and upward. By utilizing Linear Kernel and Radial Basis Function (RBF) Kernel-based Support Vector Machines (SVM), the candle direction can be classified and optimized by tuning the Hyperparameters. The accuracy of candle direction classifications are highly improved. After tuning, in general, classification using Linear Models can outperform RBF Models. The best accuracy found on the Pound sterling against US Dollar by using the Linear model is 98.11% and the accuracy becomes 100% on data testing at a ratio of 70:30. Whilst for the Euro against the US Dollar, the best accuracy found the same for both Linear and RBF models on a ratio of 80:20 at 97.53%. However, on data testing, it decreases to 94.51% for Linear Model and 93.41% using RBF Model. The implication of this study is SVM can successfully classify candle direction on pairs in the Forex Market that are affected by a big event that comes for such a long period as long as the hyperparameter is tuned. © 2022 IEEE.

关键词： Support vector machines

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Disclosure Privacy information Social Media on TikTok 9

Disclosure Privacy Information Social Media on TikTok

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9th International Conference on information Management and technology, ICIMTech 2024

作者： Syamsuar, Dedy Fahrezi, Fazri Favian, Ahnaf Irwansyah, Edy Irawan, Achmad Fakhri School of Information System Bina Nusantara University Faculty of Engineering Professional Engineer Program Dept. Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy. The study successfully collected 369 responses. The empirical findings indicated that only the relationship between Perceived Personalization (PP) and Perceived Risk (PR) was not supported. However, all the remaining hypotheses were supported. These findings emphasize how several complicated variables influence social media users' intentions to disclose their privacy. When people feel in control of their privacy settings and see significant benefits, they are more likely to divulge personal information;nevertheless, perceived risks can greatly discourage users. © 2024 IEEE.

关键词： Differential privacy

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An efficient fake account identification in social media networks: Facebook and Instagram using NSGA-II algorithm

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Neural Computing and Applications 2024年第34期36卷 21487-21515页

作者： Sallah, Amine Abdellaoui Alaoui, El Arbi Hessane, Abdelaaziz Agoujil, Said Nayyar, Anand Department of Computer Science Faculty of Sciences and Techniques Moulay Ismail University of Meknes Errachidia Morocco Department of Sciences Ecole Normale Supérieure Moulay Ismail University of Meknes Errachidia Morocco École Nationale de Commerce et de Gestion Moulay Ismail University of Meknes El Hajeb Morocco Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam Graduate School Duy Tan University Da Nang550000 Viet Nam

The widespread use of online social networks (OSNs) has made them prime targets for cyber attackers, who exploit these platforms for various malicious activities. As a result, a whole industry of black-market services has emerged, selling services based on the sale of fake accounts. Because of the massive rise of OSNs, the number of fraudulent accounts rapidly expands. Hence, this research focuses on detecting fraudulent profiles on Instagram and Facebook and aims to find an optimal subset of features that can effectively differentiate between real and fake accounts. The problem has been formulated as a multiobjective optimization task, aiming to maximize the classification accuracy while minimizing the number of selected features. NSGA-II (non-dominated sorting genetic algorithm II) is employed as the optimization algorithm to explore the trade-offs between these conflicting objectives. In the current study, a novel approach for feature selection using the NSGA-II optimization algorithm to detect fake accounts is proposed. The proposed methodology relies on input data comprising features characterizing the profiles under investigation. The selected features are utilized to train a machine learning model. The model’s performance is evaluated using various metrics, including precision, recall, F1-score, and receiver operating characteristic (ROC) curve. The final prediction model achieved accuracy values ranging from 90 to 99.88%. The results indicated that the model, utilizing features selected by the NSGA-II algorithm, delivered high prediction accuracy while using less than 31% of the total feature space. This efficient feature selection allowed for the precise differentiation between fake and real users, demonstrating the model’s effectiveness with a minimal number of input variables. Furthermore, the results of experiments demonstrate that the proposed approach achieves better performance as compared to other existing approaches. This research paper focuses on e

关键词： Genetic algorithms

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