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Joint 2nd International Workshop on Multilingual Semantic Web and 2nd International Workshop on Deep Learning for Question Answering and 1st International Workshop on Semantic Reasoning and Representation in IoT with CEUR-WS, MSW-DLQ-SIOT 2022

作者： Deepak, Gerard Sawarn, Shivam Priyadarshini, Sheeba Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India School of Computing DIT University Dehradun India Bengaluru India

The requirement of the job for people and employees for employers are al-ways in demand. This is due to the lack of proper infrastructure to reduce the unmatching job application for employers and inappropriate job recommendations for people. This chapter proposes a strategic framework with machine learning and knowledge integration to increase accuracy in the provided recommendations and increase the chance of getting a job offer. The usage of'user's search data intends job recommended more in liking of the users, and the machine learning helps in finding the accurate job recommendation. The machine learning technique used here is Radial Basis Function Neural Net-work for the classification and Knowledge Integrated using Analysis of Variance - Web Point Wise Mutual Information and Kullback Leibler (KL) divergence. All the job providers ads are retrieved from the top websites using beautiful soup. The proposed JRHDLSI architecture achieved an accuracy of 94.99% which outperformed the baseline models and was much superior. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Semantic Web

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GECKA3D: A 3D game engine for commonsense knowledge acquisition 29

GECKA3D: A 3D game engine for commonsense knowledge acquisit...

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29th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016

作者： Cambria, Erik Nguyen, Tam V. Cheng, Brian Kwok, Kenneth Sepulveda, Jose School of Computer Engineering Nanyang Technological University Singapore ARTIC Research Center Singapore Polytechnic Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore

ISBN: (纸本)9781577357568

Commonsense knowledge representation and reasoning is key tor tasks such as artificial intelligence and natural language understanding. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. In this paper, we introduce a novel 3D game engine for commonsense knowledge acquisition (GECKA3D) which aims to collect commonsense from game designers through the development of serious games. GECKA3D integrates the potential of serious games and games with a purpose. This provides a platform for the acquisition of re-usable and multi-purpose knowledge, and also enables the development of games that can provide entertainment value and teach players something meaningful about the actual world they live in. © 2016, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Serious games

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Data recovery for medical organs based on a joint statistical deformable model that incorporates prior knowledge of the missing data

Data recovery for medical organs based on a joint statistica...

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5th International Conference on Visual Information Engineering, VIE 2008

作者： Feng, Jun He, Qizhen Ip, H.H.S. School of Information and Technology Northwest University 710069 Xi'an China Image Computing Group Department of Computer Science City University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9780863419140

In this paper, we proposed a novel joint statistical deformable model (SDM) that incorporates prior knowledge of the missing data into the data recovery process for medical organs. Our approach formalized the prior knowledge of the missing data as the probability of the sample belonging to the SDM from the training data set, and together with the known points to provide accurate data recovery. Our experiments have shown that, the proposed approach achieves a higher accuracy than a state of the art technique for highly variable (soft tissue) organs particularly when the number of known points is small compared with that of the missing data. This results in a unique and robust framework of data recovery that is suitable for both rigid and highly flexible medical organs. ©2008 The Institution of Engineering and technology.

关键词： Recovery

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Automatic library generation for BLAS3 on GPUs

Automatic library generation for BLAS3 on GPUs

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25th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2011

作者： Cui, Huimin Wang, Lei Xue, Jingling Yang, Yang Feng, Xiaobing Institute of Computing Technology Chinese Academy of Sciences China Graduate University Chinese Academy of Sciences China School of Computer Science and Engineering University of New South Wales Australia

ISBN: (纸本)9780769543857

High-performance libraries, the performance-critical building blocks for high-level applications, will assume greater importance on modern processors as they become more complex and diverse. However, automatic library generators are still immature, forcing library developers to manually tune library to meet their performance objectives. We are developing a new script-controlled compilation framework to help domain experts reduce much of the tedious and error-prone nature of manual tuning, by enabling them to leverage their expertise and reuse past optimization experiences. We focus on demonstrating improved performance and productivity obtained through using our framework to tune BLAS3 routines on three GPU platforms: up to 5.4x speedups over the CUBLAS achieved on NVIDIA GeForce 9800, 2.8x on GTX285, and 3.4x on Fermi Tesla C2050. Our results highlight the potential benefits of exploiting domain expertise and the relations between different routines (in terms of their algorithms and data structures). © 2011 IEEE.

关键词： Program processors

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Filter-cluster attention based recursive network for low-light enhancement

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Frontiers of Information technology & Electronic Engineering 2023年第7期24卷 1028-1044页

作者： Zhixiong HUANG Jinjiang LI Zhen HUA Linwei FAN School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China Co-innovation Center of Shandong Colleges and Universities:Future Intelligent Computing Shandong Technology and Business UniversityYantai 264005China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan 250014China

The poor quality of images recorded in low-light environments affects their further *** improve the visibility of low-light images,we propose a recurrent network based on filter-cluster attention(FCA),the main body of which consists of three units:difference concern,gate recurrent,and iterative *** network performs multi-stage recursive learning on low-light images,and then extracts deeper feature *** compute more accurate dependence,we design a novel FCA that focuses on the saliency of feature *** and self-attention are used to highlight the low-light regions and important channels of the *** also design a dense connection pyramid(DenCP)to extract the color features of the low-light inversion image,to compensate for the loss of the image's color *** results on six public datasets show that our method has outstanding performance in subjective and quantitative comparisons.

关键词： Low-light enhancement Filter-cluster attention Dense connection pyramid Recursive network

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A formal test for detecting information leakage via covert channels 11

A formal test for detecting information leakage via covert c...

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7th Annual Cyber Security and Information Intelligence Research Workshop: Energy Infrastructure Cyber Protection, CSIIRW11

作者： Jaskolka, Jason Khedri, Ridha Sabri, Khair Eddin Dept. of Computing and Software Faculty of Engineering McMaster University Canada Dept. of Computer Science King Abdullah II School for Information Technology University of Jordan Jordan

ISBN: (纸本)9781450309455

With the emergence of computers in every day activities and with the ever-growing complexity of networks and network communication protocols, covert channels are becoming an eminent threat to the confidentiality of information. We propose a technique to detect confidential information leakage via covert channels. The proposed technique is based on relational algebra. It provides tests to verify the existence of a leakage of information via a monitored covert channel. The technique also provides computations which show how the information was leaked if a leakage exists. Our focus is limited to protocol-based covert channels and instances where the users of covert channels modulate the information that is being sent;either by encryption, or some other form of encoding. We discuss possible applications of the proposed technique in digital forensics and cryptanalysis. We also report on a prototype tool that allows for the automation of the proposed technique. Copyright © 2011 ACM.

关键词： Formal methods

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Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling 28

Balanced Joint Adversarial Training for Robust Intent Detect...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Cao, Xu Xiong, Deyi Shi, Chongyang Wang, Chao Meng, Yao Hu, Changjian School of Computer Science Beijing Institute of Technology Beijing China College of Intelligence and Computing Tianjin University Tianjin China Lenovo Research AI Lab. Beijing China

ISBN: (纸本)9781952148279

Joint intent detection and slot filling has recently achieved tremendous success in advancing the performance of utterance understanding. However, many joint models still suffer from the robustness problem, especially on noisy inputs or rare/unseen events. To address this issue, we propose a Joint Adversarial Training (JAT) model to improve the robustness of joint intent detection and slot filling, which consists of two parts: (1) automatically generating joint adversarial examples to attack the joint model, and (2) training the model to defend against the joint adversarial examples so as to robustify the model on small perturbations. As the generated joint adversarial examples have different impacts on the intent detection and slot filling loss, we further propose a Balanced Joint Adversarial Training (BJAT) model that applies a balance factor as a regularization term to the final loss function, which yields a stable training procedure. Extensive experiments and analyses on the lightweight models show that our proposed methods achieve significantly higher scores and substantially improve the robustness of both intent detection and slot filling. In addition, the combination of our BJAT with BERT-large achieves state-of-the-art results on two datasets. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Filling

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A distributed group signature scheme with traceable signers for mobile Ad hoc networks

A distributed group signature scheme with traceable signers ...

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2010 International Conference on computer Application and System Modeling, ICCASM 2010

作者： Zhong, Hong Shi, Runhua Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9781424472369

In this paper, we propose a distributed group signature scheme with traceable signers for mobile Ad hoc networks. In such scheme, there isn't a trusted center, and all members of Ad hoc group cooperate to generate all system parameters and all private/public keys. Any t members in Ad hoc group can collaboratively generate the group signature with the help of a designated clerk, where the signer respectively generates the partial signature by using his private key and the clerk can check the correctness of the partial signature. Furthermore, anyone can verify the validity of the group signature by the group public key and trace back to find the identities of signers from the warrant created by the clerk. What's more, it can dynamically increase the parameter values of t and n , according to the actual security needs of mobile Ad hoc networks, but the private and public keys of the whole Ad hoc group still are not changed for relatively long-term stability. © 2010 IEEE.

关键词： Mobile ad hoc networks

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Brain Tumour Diagnosis with Lightweight Federated Learning using Identically Distributed Images 1

Brain Tumour Diagnosis with Lightweight Federated Learning u...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Trivedi, Naresh Kumar Shukla, Sunil Agarwal, Ambuj Kumar Tiwari, Raj Gaurang Gautam, Vinay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Dehradun India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350330915

The innovative method was developed by bringing federated learning methods to the medical field. In this study, the author refined federated learning models with data sets containing photographs of brain tumors. The cancer research community faces a huge problem in trying to predict the development of brain tumors. This paper proposes a streamlined framework for using federated deep learning to classify brain tumor diseases while keeping private information safe. When validating federated deep learning models, this system leverages a dispersed client-server architecture to safeguard client data and IID. The system was tested in both conventional and federated classroom settings with a small and large number of users. After evaluating several pre-trained models, we chose AlexNet as our benchmark due to its high 98.53 percent accuracy. Federal learning (FL) was then used to assess IID datasets. The framework is the best choice for detecting and categorizing brain tumors early because of its improved capabilities. © 2023 IEEE.

关键词： AlexNet brain tumor deep learning federated learning MRI scans

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Hsm2: A hybrid and scalable metadata management method in distributed file systems 10th

Hsm2: A hybrid and scalable metadata management method in di...

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10th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2019

作者： Wang, Yiduo Chen, Youxu Shao, Xinyang Chen, Jinzhong Yuan, Liu Xu, Yinlong School of Computer Science and Technology University of Science and Technology of China AnHui Province Key Laboratory of High Performance Computing Hefei China East China Research Institute of Electronic Engineering Hefei China China Academy of Electronics and Information Technology Beijing China

In the bigdata era, metadata performance is critical in modern distributed file systems. Traditionally, the metadata management strategies like the subtree partitioning method focus on keeping namespace locality, while the other ones like the hash-based mapping method aim to offer good load balance. Nevertheless, none of these methods achieve the two desirable properties simultaneously. To close this gap, in this paper, we propose a novel metadata management scheme, HSM2, which combines the subtree partitioning and hash-based mapping method together. We implemented HSM2 in CephFS, a widely deployed distributed file systems, and conducted a comprehensive set of metadata-intensive experiments. Experimental results show that HSM2 can achieve better namespace locality and load balance simultaneously. Compared with CephFS, HSM2 can reduce the completion time by 70% and achieve 3.9× overall throughput speedup for a file-scanning workload. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Metadata

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