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Complete The Last Mile: Delivery Problems in the Quantum Computing

IEEE Nanotechnology Magazine 2023年第2期17卷 9-14页

作者： Liao, Pei Yen Bhie, Santos Jay Ou, Chia-Ho Chen, Chih-Yu Chang, Ching-Ray National Taiwan University Department of Mathematics Taipei Taiwan Chung Yuan Christian University Department of Electrical Engineering Taoyuan Taiwan National Pingtung University Department of Computer Science and Information Engineering Pingtung Taiwan Chung Yuan Christian University Quantum Information Center Taoyuan Taiwan

In social networks, delivery-related concerns are perennially significant and challenging. Another noteworthy and captivating development is the development of quantum computing or quantum-inspired computing. Combining these two fields is not only timely but natural and necessary in the current environment of data explosion. The current strengths and challenges of quantum computing were put together in addressing delivery problems as an example and provide a collection and analysis of existing approaches to last-mile delivery. The purpose of this paper is to help people to understand how to use quantum computing to quickly solve delivery problems and find the potential niche market in highly competitive big data environments. © 2007-2011 IEEE.

关键词： Directed graphs

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PerfMon: Measuring Application-Level Performance in a Large-Scale Campus Wireless network

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China Communications 2023年第3期20卷 316-335页

作者： Weizhen Dang Tao Yu Haibo Wang Jing’An Xue Fenghua Li Jilong Wang Institute for Network Sciences and Cyberspace Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology(BNRist) Beijing 100084China Huawei Technologies Beijing 100085China Zhongguancun Laboratory Beijing 100094China

WiFi has become one of the most popular ways to access the ***,in large-scale campus wireless networks,it is challenging for network administrators to provide optimized access quality without knowledge on fine-grained traffic characteristics and real network *** this paper,we implement PerfMon,a network performance measurement and diagnosis system,which integrates collected multi-source datasets and analysis *** on PerfMon,we first conduct a comprehensive measurement on application-level traffic patterns and behaviors from multiple dimensions in the wireless network of T university(TWLAN),which is one of the largest campus wireless *** we systematically study the application-level network *** observe that the application-level traffic behaviors and performance vary greatly across different locations and device *** performance is far from satisfactory in some *** diagnose these problems,we distinguish locations and device types,and further locate the most crucial factors that affect the *** results of case studies show that the influential factors can effectively characterize performance changes and explain for performance degradation.

关键词： WiFi traffic patterns network manage-ment performance measurement network diagnosis

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Residual Convolutional Graph Neural network with Subgraph Attention Pooling

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Tsinghua science and Technology 2022年第4期27卷 653-663页

作者： Yutai Duan Jianming Wang Haoran Ma Yukuan Sun Information and Communication Engineering Department Tiangong UniversityTianjin 300387China Computer Science Department Tiangong UniversityTianjin 300387China Software Engineering Department Tiangong UniversityTianjin 300387China Center for Engineering Intership and Training Tiangong UniversityTianjin 300387China

The pooling operation is used in graph classification tasks to leverage hierarchical structures preserved in data and reduce computational ***,pooling shrinkage discards graph details,and existing pooling methods may lead to the loss of key classification *** this work,we propose a residual convolutional graph neural network to tackle the problem of key classification features ***,our contributions are threefold:(1)Different from existing methods,we propose a new strategy to calculate sorting values and verify their importance for graph *** strategy does not only use features of simple nodes but also their neighbors for the accurate evaluation of its importance.(2)We design a new graph convolutional layer architecture with the residual *** feeding discarded features back into the network architecture,we reduce the probability of losing critical features for graph classification.(3)We propose a new method for graph-level *** messages for each node are aggregated separately,and then different attention levels are assigned to each node and merged into a graph-level representation to retain structural and critical information for *** experimental results show that our method leads to state-of-the-art results on multiple graph classification benchmarks.

关键词： graph neural network graph pooling information loss

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The Prospects of Quantum Computing for Quantitative Finance and Beyond

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IEEE Nanotechnology Magazine 2023年第2期17卷 31-37页

作者： Chang, Yen-Jui Sie, Ming-Fong Liao, Shih-Wei Chang, Ching-Ray National Taiwan University Department of Physics Taipei10617 Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Chung Yuan Christian University Quantum Information Center Taoyuan City320 Taiwan

Quantum computation holds the promise of highly efficient algorithms, provides exponential speedups for specific technologically significant problems, plays the most promising role in quantum technological progress, and has transformative potential in numerous industry sectors. In quantum computing, finance is recognized as one of the most beneficial areas because of the potential for many financial cases that can be solved by quantum algorithms suitable for noisy-intermedia-scale quantum(NISQ) computers. The benefits of quantum computing are colossal time and computational memory reduction with which the computational tasks are performed, which leads to the accuracy of the computations. This paper presents a comprehensive summary of quantum computing for traditional and Blockchain financial applications. © 2007-2011 IEEE.

关键词： Semantic Web

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A distinguished-bit tracking knowledge-based query tree for RFID tag identification

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computer Communications 2024年 218卷 166-175页

作者： Lin, Chih-Chung Lai, Yuan-Cheng Hailemariam, Zelalem Legese Department of Computer Information and Network Engineering Lunghwa University of Science and Technology No. 300 Sec. 1 Wanshou Rd. Guishan District Taoyuan City 333326 Taiwan Department of Information Management National Taiwan University of Science and Technology No. 43 Sec. 4 Keelung Road Taipei 106 Taiwan

—Many RFID systems own the information of RFID tags in the database, so the reader knows how many tags will be identified and which tags’ IDs might appear. How the RFID anti-collision protocols take advantage of this knowledge is a challenge. Some previous anti-collision protocols, including Query Tree with Shortcutting and Couple Resolution (QTSC) and Bit-tracking Knowledge-based Query Tree (BKQT), solved this task with the knowledge. We propose one novel protocol, Distinguished-bit Tracking Knowledge-based Query Tree (DKQT), which integrates the knowledge with the bit-tracking technique. Utilizing the knowledge and knowing the collided-bit locations, DKQT adopts a distinguishable technique, which can identify the tags that have a distinguished-bit representing that only one tag has a unique “0” or “1” in a bit location of the tag ID, to speed up the tag identification. Innovatively, adopting a distinguishable technique, DKQT forms an n-ary tree where a node has some leaves that are the tags having distinguished-bits. Thus, DKQT using the distinguishable technique can recognize multiple appearing tags in a collision by traversing this n-ary tree. The simulation results show that DKQT can reduce the number of slots by 36.94 % and 15.58 %, and the identification time by 37.47 % and 17.86 %, compared to the previous knowledge-based protocols, QTSC and BKQT, respectively. © 2024 Elsevier B.V.

关键词： Distinguished-bit Knowledge-based tree RFID anti-collision Tag identification

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Bamboo Forest Growth Optimization Algorithm for Night Image Enhancement 15th

Bamboo Forest Growth Optimization Algorithm for Night Image...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Shi, Hao-Jie Pan, Jeng-Shyang Chu, Shu-Chuan Kong, Lingping Snášel, Václav College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (纸本)9789819700677

The Bamboo Forest Growth Optimization(BFGO) algorithm is an algorithm proposed by the growth law of bamboo forest, which has the advantages of fast convergence and not easily falling into local optimum. In this paper we have experimented the BFGO with several other population intelligence optimization algorithms on 23 benchmark test functions and proved its superiority. Also we apply the bamboo forest optimization algorithm to nighttime image enhancement in this paper, and the algorithm achieves better results on this application. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

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computers, Materials & Continua 2023年第7期76卷 1151-1172页

作者： Jahanzaib Latif Shanshan Tu Chuangbai Xiao Anas Bilal Sadaqat Ur Rehman Zohaib Ahmad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of Technology100124BeijingChina College of Information Science Technology Hainan Normal UniversityHainan ProvinceHaikou571158China Department of Computer Science University of SalfordManchesterUK Faculty of Information Technology Beijing University of Technology Chaoyang District BeijingChina

Glaucoma is a progressive eye disease that can lead to blindness if left *** detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized *** study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)*** proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global *** enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for *** evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,*** results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of *** study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.

关键词： Glaucoma detection grey golf optimization support vector machine feature extraction image classification

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Development and Analysis of a Versatile Dataset of Speech, Real and Synthesized, of Arabic Learners 3

Development and Analysis of a Versatile Dataset of Speech, R...

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3rd International Conference on Computing and information Technology, ICCIT 2023

作者： Alsulaiman, Mansour Algabri, Mohammed Mathkour, Hassan Bencherif, Mohamed A. Muhammad, Ghulam Al-Gahtani, Saad Faisal, Mohammed Mekhtiche, Mohamed Amine Alfakih, Taha King Saud University College of Computer and Information Sciences Computer Engineering Department Riyadh Saudi Arabia AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia King Saud University College of Computer and Information Sciences Computer Science Department Riyadh Saudi Arabia Riyadh Saudi Arabia King Saud University Arabic Language Institute Saudi Arabia Center of Ai & Robotics Kuwait

ISBN: (纸本)9798350321487

computer-Aided Pronunciation Training (CAPT) systems are gaining popularity recently due to the advancements in deep neural networks (DNN) and machine learning and the availability of databases of speech of language learners. Unfortunately, research in Arabic CAPT systems suffer from the lack of CAPT datasets compared to other languages. In this paper, we present the details of and the ideas used in the development of a versatile dataset of speech, real and synthesized, of Arabic Learners. To develop the dataset, we utilized an existing Arabic speech corpus, King Saud University Speech Database (KSU-SD). KSU-SD's main application was a speaker recognition system, but it was designed to be useful in other applications such as CAPT systems. KSU-SD includes a large number of speakers from diverse nationalities (Saudis, Arabs, and Non-Arabs). KSU-SD contains the recording of about 60 non-native speakers from more than 20 nationalities, hence we selected it to build the non-native Arabic-CAPT corpus. The developed corpus consists of transcribed, segmented, and annotated speech, which makes it suitable for building Arabic CAPT systems. In addition to presenting the details of developing the dataset, we also present an analysis of the text and speech errors of the dataset. The dataset was verified by many CAPT systems. © 2023 IEEE.

关键词： Deep neural networks

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Novel Positive Multi-Layer Graph Based Method for Collaborative Filtering Recommender Systems

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Journal of computer science & Technology 2022年第4期37卷 975-990页

作者： Bushra Alhijawi Ghazi AL-Naymat Department of Computer Science Princess Sumaya University for TechnologyAmman 11941Jordan Artificial Intelligence Research Center College of Engineering and Information TechnologyAjman UniversityAjman 20550U.A.E.

Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, and coverage. We propose a novel graph-based collaborative filtering method, namely Positive Multi-Layer Graph-Based Recommender System (PMLG-RS). PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations. The positive multi-layer graph consists of two connected layers: the user and item layers. PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node. A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets, MovieLens-100K, MovieLens-Last, and Film Trust. The results demonstrate the superiority of PMLG-RS and its high capability in making relevant, novel, and diverse recommendations for users.

关键词： recommender system collaborative filtering graph theory path search novelty coverage diversity

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