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International Joint Conference on Neural Networks (IJCNN)

作者： Kaili Zhou Yiyun Xing Yushui Geng Jing Zhao Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

This study proposes a cross-modal retrieval technique, which employs an Attention Embedded Variational AutoEncoder (AE-VAE) to tackle the problem of reduced retrieval accuracy resulting from data noise and data absence in cross-modal retrieval tasks. First, the variational auto-encoder(VAE) is used as an infrastructure to transform modal data into latent variables by encoding and decoding different modal features, thus preserving the semantic structure of the raw modal data and suppressing noise and missing information in the data. Secondly, to better capture the important semantic features of image and text modalities, an improved multi-attention module is introduced in the encoder. In addition, a cross-modal alignment module is designed to further align the latent variables of different modalities in the latent space to solve the confusion problem and improve the accuracy and robustness of cross-modal retrieval. Finally, experiments are conducted on four public datasets, CUB, SUN, AwA1 and AwA2, and the experimental results show that our proposed model is effective in improving the accuracy and robustness of retrieval.

关键词： Accuracy Uncertainty Noise Semantics Transforms Probabilistic logic Robustness

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Semantic-Enhanced Attention Network for Image-Text Matching

Semantic-Enhanced Attention Network for Image-Text Matching

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International Conference on Computer Supported Cooperative Work in Design

作者： Huanxiao Zhou Yushui Geng Jing Zhao Xishan Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Image-text matching is an important task in cross-modal information processing, which consists of evaluating the similarity between images and text. However, the data of the two modalities have different distributions and representations, which cannot be directly compared. Therefore, the two modalities need to be processed separately. Most existing image-text matching methods extract image fragments and text fragments separately, using an attention mechanism to establish relationships between regions in the image and words in the text. Subsequently, they aggregate the similarity of these images and text fragments. Regardless, not all fragment alignment is meaningful, and irrelevant fragment alignment will bring redundancy and reduce retrieval accuracy. In addition to that, there are contextual relationships between image regions and between text words, which should also be taken into account. In this paper, we simultaneously consider these two aspects and propose a Semantic-Enhanced Attention Network (SEAN). It first focuses on each modality, mining contextual relationships between fragments within each modality and aggregating contextual information into visual and textual embeddings. Then, we compute the similarity between all image regions and text fragments, focusing all attention on the region-word pairs with the highest similarity. Finally, we summarize these to infer the similarity between the image and text. Our method achieves competitive results on two generalized image text retrieval datasets, Flickr30K and MS-COCO.

关键词： Visualization Attention mechanisms Federated learning Computational modeling Aggregates Semantics Redundancy

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Zhiqiang Zhang Yongqiang Jiang Qian Gao Zhipeng Wang Key Laboratory of Computing Power Network and information Security Ministry of Eaucation Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Jinan China

Existing multimodal recommendation research still faces some challenges, such as not being able to fully mine the implicit relevance information of neighbor nodes, and the unreasonable weight allocation to imbalanced nodes. To address the aforementioned challenges, this paper introduces a new multimodal recommendation model called NS-MAR+. Specifically, the model firstly constructs a neighbor similarity graph convolutional network to capture the implicit relevance information and reasonably assigns the attention weights through the graph attention mechanism. Secondly, the model introduces a modal alignment and fusion mechanism by using a multilayer perceptron (MLP) to map image and text features into a shared space for comparison and fusion. In addition, the model constructs a user co-interaction graph and an item semantic graph based on the original information and performs graph convolution operations to enhance the preference information of users and items, and to better capture the interactions and internal features between users and items. Finally, MLP is employed to aggregate user and item representations and predict personalized recommendation rankings. To validate the experiment's efficacy, this paper compares with several leading multimodal recommendation models on public datasets with a performance improvement of 1% to 3%. The experimental outcomes indicate that the model in this paper has good superiority and accuracy in multimodal recommendation tasks.

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RALB-HC: A resource-aware load balancer for heterogeneous cluster

RALB-HC: A resource-aware load balancer for heterogeneous cl...

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作者： Ahmed, Usman Aleem, Muhammad Noman Khalid, Yasir Arshad Islam, Muhammad Azhar Iqbal, Muhammad Department of Computing Mathematics and Physics Faculty of Engineering and Science Western Norway University of Applied Sciences Bergen Norway Islamabad Pakistan Department of Computer Science Capital University of Science and Technology Islamabad Pakistan SWJTU-Leeds Joint School Southwest Jiaotong University Chengdu China

In the heterogeneous computing environment, programmers map the applications either on CPUs or GPUs. However, this default mapping process does not produce improved results, particularly on the heterogeneous clusters. If one resource of the cluster is more compute capable, then most of the scheduling schemes favor that powerful device. In this scenario, the scheduling schemes overload the powerful resources while making all other compute resources remain under utilized. This load imbalance problem results in higher energy consumption and increased execution time. In this research, a novel Resource-Aware Load Balancer for the Heterogeneous Cluster (RALB-HC) is proposed that distributes workload based on resources computing capabilities and applications computing needs. The RALB-HC uses supervised machine learning approach to classify applications using the static code-features. The RALB-HC framework comprises of two phases: (1) job mapping based on the availability of the resources and (2) the resource-aware load balancing to achieve the higher resource utilization ratio. The experimental results on a large set of real-world and synthetic workloads show that the RALB-HC reduces execution time by 31.61%, increased resource utilization ratio by 67.8% and improved throughout 147.35% as compared to baseline scheduling schemes. © 2019 John Wiley & Sons, Ltd.

关键词： Scheduling

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Comparison of Robust Estimators for Detecting Outliers in Multivariate Datasets 28

Comparison of Robust Estimators for Detecting Outliers in Mu...

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28th Simposium Kebangsaan Sains Matematik, SKSM 2021

作者： Mutalib, Sharifah Sakinah Syed Abd Satari, Siti Zanariah Yusoff, Wan Nur Syahidah Wan Centre for Mathematical Sciences College of Computing Applied Universiti Malaysia Pahang Lebuhraya Tun Razak Sciences Pahang Kuantan Gambang26300 Malaysia Faculty of Computer Media and Technology Management University College TATI Jalan Panchur Telok Kalong Terengganu Kemaman24000 Malaysia

Detecting outliers for multivariate data is difficult and does not work by visual inspection. Mahalanobis distance (MD) has been a classical method to detect outliers in multivariate data. However, classical mean and covariance matrix in MD suffer from masking and swamping effects. Masking effects happened when outliers are not identified and swamping effects happened when inliers are identified as outliers. Hence, robust estimators have been proposed to overcome these problems. In this study, the performance of a new robust estimator named Test on Covariance (TOC) is tested and compared with other robust estimators which are Fast Minimum Covariance Determinant (FMCD), Minimum Vector Variance (MVV), Covariance Matrix Equality (CME) and Index Set Equality (ISE). These five robust estimators' performance is being tested on five real multivariate datasets. Brain and weight, Hawkins-Bradu Kass, Stackloss, Bushfire and Milk datasets were used as these five real datasets are well-known in most outlier detection studies. Results show that TOC has proven to be able in detecting outliers, does not have a masking effect and has the same performance as other robust estimators in all datasets. © Published under licence by IOP Publishing Ltd.

关键词： Covariance matrix

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A Stochastic Flight Problem Simulation to Minimize Cost of Refuelling

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Computers, Materials & Continua 2021年第10期69卷 849-871页

作者： Said Ali Hassan Khalid Alnowibet Miral H.Khodeir Prachi Agrawal Adel F.Alrasheedi Ali Wagdy Mohamed Department of Operations Research and Decision Support Faculty of Computers and Artificial IntelligenceCairo UniversityGiza12613Egypt Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia Department of Mathematics and Scientific Computing National Institute of Technology HamirpurHimachal Pradesh177005India Department of Operations Research Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt

Commercial airline companies are continuously seeking to implement strategies for minimizing costs of fuel for their flight routes as acquiring jet fuel represents a significant part of operating and managing expenses for airline activities.A nonlinear mixed binary mathematical programming model for the airline fuel task is presented to minimize the total cost of refueling in an entire flight route *** model is enhanced to include possible discounts in fuel prices,which are performed by adding dummy variables and some restrictive constraints,or by fitting a suitable distribution function that relates prices to purchased *** obtained fuel plan explains exactly the amounts of fuel in gallons to be purchased from each airport considering tankering strategy while minimizing the pertinent cost of the whole flight *** relation between the amount of extra burnt fuel taken through tinkering strategy and the total flight time is also considered.A case study is introduced for a certain flight rotation in domestic US air transport *** mathematical model including stepped discounted fuel prices is *** problem has a stochastic nature as the total flight time is a random variable,the stochastic nature of the problem is realistic and more appropriate than the deterministic *** stochastic style of the problem is simulated by introducing a suitable probability distribution for the flight time duration and generating enough number of runs to mimic the probabilistic real *** similar real application problems are modelled as nonlinear mixed binary ones that are difficult to handle by exact ***,metaheuristic approaches are widely used in treating such different optimization *** this paper,a gaining sharing knowledge-based procedure is used to handle the mathematical *** algorithm basically based on the process of gaining and sharing knowledge throughout the human *** generated simulation runs of

关键词： Stochastic flight problem cost of refuelling ferrying strategy tankering discounted prices simulation procedure nonlinear mixed binary model metaheuristic algorithm gaining-sharing knowledge-based algorithm

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia Computer Science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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IoT-based Smart Parking System using Android Application

IoT-based Smart Parking System using Android Application

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2020 International Conference on Emerging technology in computing, Communication and Electronics, ETCCE 2020

作者： Warif, Nor Bakiah Abd Azman, Mohd Izzat Syahmi Saiful Ismail, Nor-Syahidatul N Remli, Muhammad Akmal Faculty of Computer Science Information Technology Universiti Tun Hussien Onn Malaysia Department of Information Security and Web Technology Batu Pahat Johor Malaysia Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pekan Pahang Malaysia Kota Bharu Kelantan Malaysia

ISBN: (纸本)9781665419628

Since all people used vehicles as a necessity, the growing number of vehicles is increasing day by day. With the increasing number of vehicles on the road, the requirement of parking lot will also be restricted. Thus, it become a problem for the people to find a free parking space in the busy building especially in shopping complex. Internet of Things (IoT) is an interesting technology that can be applied in this paper because this technology has a capability to transfer the data over a network without requiring human intervention in almost all applications in today's society. In this paper, a user-friendly mobile application, named as Android-based Car Parking Monitoring System (ACPMS) is built to aid in locating a particular parking place. ACPMS can provide a user with the ability to check vacant parking space and locate the nearest parking lot. ACPMS obtained the parking location from the current user's position with the sensor located in the shopping complex's parking lot. ACPMS is testing in a realistic environment for the purpose of movement detection and location service to notify user using the mobile application. By considering seven test case scenarios, the combination of the ACPMS mobile application with a parking prototype kit shows that the proposed work able to solve the parking problem. © 2020 IEEE.

关键词： Internet of things

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Optimum Location of Field Hospitals for COVID-19: A Nonlinear Binary Metaheuristic Algorithm

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Computers, Materials & Continua 2021年第7期68卷 1183-1202页

作者： Said Ali Hassan Khalid Alnowibet Prachi Agrawal Ali Wagdy Mohamed Department of Operations Research and Decision Support Faculty of Computers and Artificial IntelligenceCairo UniversityGiza12613Egypt College of Science Department of Statistics and Operations ResearchKing Saud UniversityRiyadhSaudi Arabia Department of Mathematics and Scientific Computing National Institute of Technology HamirpurHimachal Pradesh177005India Department of Operations Research Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt

Determining the optimum location of facilities is critical in many fields,particularly in *** study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)*** used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear *** model is used to determine the optimum location of field hospitals for COVID-19 risk *** designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper *** this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is *** DBGSK algorithm is based on how humans acquire and share knowledge throughout their *** DBGSK algorithm mainly depends on two junior and senior binary *** two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.

关键词： Facility location nonlinear binary model field hospitals for COVID-19 gaining-sharing knowledge-based metaheuristic algorithm

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Entirety of Quantum Uncertainty and Its Experimental Verification

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Chinese Physics Letters 2021年第7期38卷 11-16页

作者： Jie Xie Li Zhou Aonan Zhang Huichao Xu Man-Hong Yung Ping Xu Nengkun Yu Lijian Zhang National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences and School of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Centre for Quantum Software and Information School of SoftwareFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNSWAustralia

As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.

关键词： observable union operators

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