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arXiv 2022年

作者： Khan, Arif Ali Ahmad, Aakash Waseem, Muhammad Liang, Peng Fahmideh, Mahdi Mikkonen, Tommi Abrahamsson, Pekka M3S Empirical Software Engineering Research Unit University of Oulu Oulu90014 Finland School of Computing and Communications Lancaster University Leipzig Germany School of Computer Science Wuhan University Wuhan China School of Business University of Southern Queensland QLD Australia Faculty of Information Technology University of Jyväskylä JyvaskylaFI-40014 Finland

Quantum computing systems rely on the principles of quantum mechanics to perform a multitude of computationally challenging tasks more efficiently than their classical counterparts. The architecture of software-intensive systems can empower architects who can leverage architecture-centric processes, practices, description languages to model, develop, and evolve quantum computing software (quantum software for short) at higher abstraction levels. We conducted a Systematic Literature Review (SLR) to investigate (i) architectural process, (ii) modeling notations, (iii) architecture design patterns, (iv) tool support, and (iv) challenging factors for quantum software architecture. Results of the SLR indicate that quantum software represents a new genre of software-intensive systems;however, existing processes and notations can be tailored to derive the architecting activities and develop modeling languages for quantum software. Quantum bits (Qubits) mapped to Quantum gates (Qugates) can be represented as architectural components and connectors that implement quantum software. Tool-chains can incorporate reusable knowledge and human roles (e.g., quantum domain engineers, quantum code developers) to automate and customize the architectural process. Results of this SLR can facilitate researchers and practitioners to develop new hypotheses to be tested, derive reference architectures, and leverage architecture-centric principles and practices to engineer emerging and next generations of quantum software. © 2022, CC BY.

关键词： Modeling languages

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Localized LRR on grassmann manifolds: An extrinsic view

arXiv

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arXiv 2017年

作者： Wang, Boyue Hu, Yongli Gao, Junbin Sun, Yanfeng Yin, Baocai Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology College of Metropolitan Transportation Beijing University of Technology Beijing100124 China Discipline of Business Analytics University of Sydney Business School University of Sydney NSW2006 Australia College of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116620 China Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing University of Technology Beijing100124 China

Subspace data representation has recently become a common practice in many computer vision tasks. It demands generalizing classical machine learning algorithms for subspace data. Low-Rank Representation (LRR) is one of the most successful models for clustering vectorial data according to their subspace structures. This paper explores the possibility of extending LRR for subspace data on Grassmann manifolds. Rather than directly embedding the Grassmann manifolds into the symmetric matrix space, an extrinsic view is taken to build the LRR self-representation in the local area of the tangent space at each Grassmannian point, resulting in a localized LRR method on Grassmann manifolds. A novel algorithm for solving the proposed model is investigated and implemented. The performance of the new clustering algorithm is assessed through experiments on several real-world datasets including MNIST handwritten digits, ballet video clips, SKIG action clips, DynTex++ dataset and highway traffic video clips. The experimental results show the new method outperforms a number of state-of-the-art clustering methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Clustering algorithms

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Augmented shortcuts for vision transformers 21

Augmented shortcuts for vision transformers

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Yehui Tang Kai Han Chang Xu An Xiao Yiping Deng Chao Xu Yunhe Wang Key Lab of Machine Perception (MOE) Dept. of Machine Intelligence Peking University and Huawei Noah's Ark Lab Huawei Noah's Ark Lab School of Computer Science Faculty of Engineering University of Sydney Central Software Institution Huawei Technologies Key Lab of Machine Perception (MOE) Dept. of Machine Intelligence Peking University

ISBN: (纸本)9781713845393

Transformer models have achieved great progress on computer vision tasks recently. The rapid development of vision transformers is mainly contributed by their high representation ability for extracting informative features from input images. However, the mainstream transformer models are designed with deep architectures, and the feature diversity will be continuously reduced as the depth increases, i.e., feature collapse. In this paper, we theoretically analyze the feature collapse phenomenon and study the relationship between shortcuts and feature diversity in these transformer models. Then, we present an augmented shortcut scheme, which inserts additional paths with learnable parameters in parallel on the original shortcuts. To save the computational costs, we further explore an efficient approach that uses the block-circulant projection to implement augmented shortcuts. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of the proposed method, which brings about 1% accuracy increase of the state-of-the-art visual transformers without obviously increasing their parameters and FLOPs.

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Machine Learning Approaches for Efficient Traffic Flow in Smart Cities

Machine Learning Approaches for Efficient Traffic Flow in Sm...

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Electrical Power engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Aniket Kulkarni P. Anitha John Yesudas Valluri M V Sunena Rose U Hemavathi Omar M. Hussein Software QA Manager Drata Inc San Diego CA USA Department of Computer Applications Kalasalingam Academy of Research and Education Srivilliputhur Tamil Nadu India Faculty of Management Studies CMS Business School Jain (Deemed-to-be University) Bengaluru India Department of Computer Science and Engineering Jain (Deemed-to-be University) Bengaluru India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Medical Technical College Medical Instrumentation Engineering Al-Farahidi University Baghdad Iraq

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning (ML) techniques can handle large numbers of parameters in massive amounts of data and dynamic behaviour over time, they are replacing analytical and statistical methods in the solving of real-world problems. This research presents a convergence of machine-learning (ML) and deep-learning (DL) calculations for traffic stream prediction; this paves the way for flexible traffic signals, which can be implemented through the use of a calculation that adjusts the timing in accordance with the anticipated traffic flow or by controlling traffic signals to some degree. The proposed ML and DL models are constructed, validated, and tested using two publicly accessible datasets. The first depicts the total number of cars subjected to checks using various sensors at six junctions over the course of 56 days. In this research, ML and DL models are created using data from four of the six junctions. The excellent performance metrics obtained by all ML and DL computations suggest that their application to intelligent traffic light management is feasible.

关键词： Measurement Power engineering Machine learning algorithms Statistical analysis Smart cities Transportation Machine learning Prediction algorithms Timing Junctions

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Knowledge Graphs for the Life sciences: Recent Developments, Challenges and Opportunities

arXiv

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arXiv 2023年

作者： Chen, Jiaoyan Dong, Hang Hastings, Janna Jiménez-Ruiz, Ernesto López, Vanessa Monnin, Pierre Pesquita, Catia Škoda, Petr Tamma, Valentina Department of Computer Science University of Manchester United Kingdom Department of Computer Science University of Oxford United Kingdom Institute for Implementation Science in Health Care University of Zurich Switzerland School of Medicine University of St. Gallen Switzerland City University of London United Kingdom SIRIUS University of Oslo Norway IBM Research Europe Dublin Ireland Université Côte d’Azur Inria CNRS I3S France LASIGE Faculdade de Ciências Universidade de Lisboa Portugal Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Department of Computer Science University of Liverpool United Kingdom

The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graph-structured. The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledge-driven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery. In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research. © 2023, CC BY.

关键词： Knowledge graph

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Predictive Analysis of Graduate Students' Employability Using Machine Learning Techniques

Predictive Analysis of Graduate Students' Employability Usin...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Endalkachew Hiwot Tamene Ayodeji Olalekan Salau Satvik Vats Keshav Kaushik Tibebu Legesse Molla Ting Tin Tin Department of Software Engineering Wollo University Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Graphic Era Deemed to be University Dehradun Uttarakhand India Amity School of Engineering and Technology Amity University Mohali Punjab India Department of Information Technology Wollo University Ethiopia Faculty of Data Science and Information Technology INTI International University Negeri Sembilan Malaysia

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Machine Learning evaluates datasets, finds patterns, and makes judgments based on data, it is essential for tackling a wide range of problems across multiple areas. The main objective of this study is to know, understand and analyze undergraduate student's employability status in Kombolcha Institute of Technology Wollo University. We prepared and cleaned the collected data to develop the predictive models. We used Random Forest and Gradient Boost ensemble Machine Learning methods. In order to explain the results of the experiment that classified students who are employed and unemployed. The study used different metrics to measure the performance of the developed model. Random Forest model accuracy is 96.7% and Gradient Boost model accuracy is 91.4%. Random Forest algorithm was discovered to perform better than other models.

关键词： Measurement Accuracy Machine learning algorithms Employment Predictive models Prediction algorithms Stakeholders Predictive analytics Random forests Monitoring

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Diff-Reg: Diffusion Model in Doubly Stochastic Matrix Space for Registration Problem

arXiv

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arXiv 2024年

作者： Wu, Qianliang Jiang, Haobo Luo, Lei Li, Jun Ding, Yaqing Xie, Jin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic National University of Singapore Singapore

Establishing reliable correspondences is essential for 3D and 2D-3D registration tasks. Existing methods commonly leverage geometric or semantic point features to generate potential correspondences. However, these features may face challenges such as large deformation, scale inconsistency, and ambiguous matching problems (e.g., symmetry). Additionally, many previous methods, which rely on single-pass prediction, may struggle with local minima in complex scenarios. To mitigate these challenges, we introduce a diffusion matching model for robust correspondence construction. Our approach treats correspondence estimation as a denoising diffusion process within the doubly stochastic matrix space, which gradually denoises (refines) a doubly stochastic matching matrix to the ground-truth one for high-quality correspondence estimation. It involves a forward diffusion process that gradually introduces Gaussian noise into the ground truth matching matrix and a reverse denoising process that iteratively refines the noisy one. In particular, we deploy a lightweight denoising strategy during the inference phase. Specifically, once points/image features are extracted and fixed, we utilize them to conduct multiple-pass denoising predictions in the reverse sampling process. Evaluation of our method on both 3D and 2D-3D registration tasks confirms its effectiveness. The code is available at https://***/wuqianliang/Diff-Reg. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Transformers in Medical Image Analysis: A Review

arXiv

引用

arXiv 2022年

作者： He, Kelei Gan, Chen Li, Zhuoyuan Rekik, Islem Yin, Zihao Ji, Wen Gao, Yang Wang, Qian Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis. © 2022, CC BY-SA.

关键词： Diagnosis

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Classification of chicken egg quality using faster region convolutional neural network

引用

AIP Conference Proceedings 2024年第1期2987卷

作者： Amira Nurul Amanda Ivan Jaya Fanindia Purnamasari Research Laboratory in Data Science Intelligent System and Software Engineering Faculty of Computer and Information Technology Universitas Sumatera Utara Medan Indonesia

Chicken eggs are one of animal product which have high nutrition with cheaper price compared to another animal product. As one of the most consumed food, the quality of chicken eggs need to be more considered. According to SNI 3926:2008 and Egg Grading Manual Booklet, the quality of chicken egg can be seen from inside and also external characteristic of egg (egg shell). The internal characteristic are the yolk, egg whites, blood stains. The external characteristic are color, texture, cleanliness, thickness of the shell. This study builds a system which can detect the quality of chicken eggs by seeing the texture and the color of the shell. The quality of that chicken eggs grouped into three levels which is Grade A, Grade B, and Grade C. The amount of data that used in this study are 60 data that divided into 480 data for training and 120 data for testing. Data were doing pre-processing before classified with Faster Region Convolutional Neural Network method with ResNet-50 as pre-trained model. Based on the result of training system, this system can classify the quality of chicken eggs with an accuracy rate of 94%. The performance of the system in classifying egg quality is very sufficient.

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Appropriation of social media for fostering effective tacit knowledge sharing: developing conceptual model

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Journal of Physics: Conference Series 2017年第1期892卷

作者： A Amidi M Jabar Y Y Jusoh R Abdullah Department of software engineering and information system Faculty of Computer Science and Information Technology University Putra Malaysia 43400 UPM Serdang Malaysia

With the rising popularity of social media in the past few years, several researches ratiocinate that this type of interactive and collaborative technology could be a beneficial tool for the sharing of tacit knowledge. Nevertheless, very few literatures have tackled the subject of how social media could facilitate tacit knowledge sharing among medical practitioners, and what are its contributions in the area. Thus, the factors that drive individuals to share tacit knowledge need to be investigated further and included in literature. Through a systematic literature review, this study proposes seven enabling conditions which could potentially facilitate the sharing of tacit knowledge. TAM was applied as a novelty in this study in investigating the factors influencing knowledge sharing via social media, whilst taking into account the mediation effects of Attitude in social media usage. This study uncovered an important correlation between virtual settings and the conversion of tacit knowledge, which affects organizational members who are not co-located physically but have a crucial need for sharing information.

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