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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Min Jiang Zhongqiang Huang Guiying Jiang Minghui Shi Xiangxiang Zeng Department of Cognitive Science and Fujian Key Laboratory of Brain-inspired Computing Technique and Applications (Xiamen University). an algorithm engineer of Sangfor Technologies Company. College of Foreign Languages of Xiamen University Department of Computer Science of Xiamen University

Motion generation is one of the most important and challenging problems in multi-legged robot research. Most of the existing methods show a good fulfillment of the requirements of robots in structured environments. However, it still faces many challenges to generate motions effectively and quickly for multi-legged robot works in complex environments. In this paper, we put forward a method which converts the motion generation problem into a Multi-objective Optimization Problem (MOP), which will make the robot not only run as fast as possible, but also save energy, and then use a distribution estimation algorithm, the trend prediction model method, to obtain motions for a six-legged robot. Experiments show that this method is effective.

关键词： Legged locomotion Optimization Mathematical model Estimation Robot kinematics Analytical models

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Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data

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computer Systems science & Engineering 2023年第12期47卷 2741-2757页

作者： Abdelwahed Motwakel Hala J.Alshahrani Jaber S.Alzahrani Ayman Yafoz Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and *** of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so *** is expressed in several means,like facial and speech expressions,gestures,and written *** recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)*** article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this *** presented DHODBN-EC model aims to examine the existence of distinct emotion classes in *** the introductory level,the DHODBN-EC technique pre-processes the tweets at different ***,the word2vec feature extraction process is applied to generate the word embedding *** emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class ***,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN *** extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.

关键词： Deer hunting optimization deep belief network emotion classification Twitter data sentiment analysis english corpus

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Dart Games Optimizer with Deep Learning-Based Computational Linguistics Named Entity Recognition

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Intelligent Automation & Soft computing 2023年第9期37卷 2549-2566页

作者： Mesfer Al Duhayyim Hala J.Alshahrani Khaled Tarmissi Heyam H.Al-Baity Abdullah Mohamed Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed IEldesouki Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl Aflaj16828Saudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityP.O.Box 145111Riyadh4545Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Computational linguistics is an engineering-based scientific *** deals with understanding written and spoken language from a computational ***,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue *** Entity Recognition(NER)is a fundamental task in the data extraction *** concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and ***,the NER mechanism identifies and removes more types of entities as per the *** significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER *** conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)*** this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for *** presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named *** the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec *** the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this *** last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s *** earlier studies integrated the DGO mechanism with the CGRU model for *** exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes *** experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.

关键词： Named entity recognition deep learning natural language processing computational linguistics dart games optimizer

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Improving semantic relevance for sequence-to-sequence learning of chinese social media text summarization

arXiv

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arXiv 2017年

作者： Ma, Shuming Sun, Xu Xu, Jingjing Wang, Houfeng Li, Wenjie Su, Qi Moe Key Laboratory of Computational Linguistics Peking University School of Electronics Engineering and Computer Science Peking University Department of Computing Hong Kong Polytechnic University School of Foreign Languages Peking University

Current Chinese social media text summa-rization models are based on an encoderdecoder framework. Although its generated summaries are similar to source texts literally, they have low semantic relevance. In this work, our goal is to improve se-mantic relevance between source texts and summaries for Chinese social media sum-marization. We introduce a Semantic Rel-evance Based neural model to encourage high semantic similarity between texts and summaries. In our model, the source text is represented by a gated attention encoder, while the summary representation is produced by a decoder. Besides, the similarity score between the representa-tions is maximized during training. Our experiments show that the proposed model outperforms baseline systems on a social media corpus. Copyright © 2017, The Authors. All rights reserved.

关键词： Social networking (online)

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Structural Analysis of Histological Images to Aid Diagnosis of Cervical Cancer

Structural Analysis of Histological Images to Aid Diagnosis ...

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Brazilian Symposium on computer Graphics and Image Processing (SIBGRAPI)

作者： Gisele Helena Barboni Miranda Junior Barrera Edson Garcia Soares Joaquim Cezar Felipe Department of Computing and Mathematics Faculty of Philosophy Sciences and Languages of Ribeirão Preto USP Brazil Department of Computer Science Institute of Mathematics and Statistics USP Brazil Department of Pathology Faculty of Medicine of Ribeirão Preto USP Brazil

The use of computational techniques in the processing of histopathological images allows the study of the structural organization of tissues and their pathological changes. The overall objective of this work includes the proposal, the implementation and the evaluation of a methodology for the analysis of cervical intraepithelial neoplasia (CIN) from histopathological images. For this purpose, a pipeline of morphological operators were implemented for the segmentation of cell nuclei and the Delaunay Triangulation were used in order to represent the tissue architecture. Also, clustering algorithms and graph morphology were used to automatically obtain the boundary between the histological layers of the epithelial tissue. Similarity criteria and adjacency relations between the triangles of the network were explored. The proposed method was evaluated concerning the detection of the presence of lesions in the tissue as well as the their malignancy grading.

关键词： Image segmentation Lesions Vectors Cancer Clustering algorithms Image reconstruction computer architecture

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A Survey of Lung Nodules Detection and Classification from CT Scan Images

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computer Systems science & Engineering 2024年第6期48卷 1483-1511页

作者： Salman Ahmed Fazli Subhan Mazliham Mohd Su’ud Muhammad Mansoor Alam Adil Waheed Faculty of Computing and Technology IQRA UniversityIslamabad44000Pakistan Faculty of Engineering and Computer Science National University of Modern LanguagesIslamabad44000Pakistan Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad44000Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan

In the contemporary era,the death rate is increasing due to lung ***,technology is continuously enhancing the quality of *** improve the survival rate,radiologists rely on Computed Tomography(CT)scans for early detection and diagnosis of lung *** paper presented a detailed,systematic review of several identification and categorization techniques for lung *** analysis of the report explored the challenges,advancements,and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning(DL)*** findings also highlighted the usefulness of DL networks,especially convolutional neural networks(CNNs)in elevating sensitivity,accuracy,and specificity as well as overcoming false positives in the initial stages of lung cancer *** paper further presented the integral nodule classification stage,which stressed the importance of differentiating between benign and malignant nodules for initial cancer ***,the findings presented a comprehensive analysis of multiple techniques and studies for nodule classification,highlighting the evolution of methodologies from conventional machine learning(ML)classifiers to transfer learning and integrated ***,while accepting the strides formed by CAD systems,the review addressed persistent challenges.

关键词： Lung nodules computed tomography scans lung cancer deep learning

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Annotating user-defined abstractions for optimization 06

Annotating user-defined abstractions for optimization

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： D. Quinlan M. Schordan R. Vuduc Qing Yi Lawrence Livermore National Laboratory Center for Applied Scientific Computing Livermore CA USA Institute of Computer Languages University of Technology Vienna Vienna Austria Department of Computer Science University of Texas San Antonio San Antonio TX USA

ISBN: (纸本)9781424400546

Although conventional compilers implement a wide range of optimization techniques, they frequently miss opportunities to optimize the use of abstractions, largely because they are not designed to recognize and use the relevant semantic information about such abstractions. In this position paper, we propose a set of annotations to help communicate high-level semantic information about abstractions to the compiler, thereby enabling the large body of traditional compiler optimizations to be applied to the use of those abstractions. Our annotations explicitly describe properties of abstractions that are needed to guarantee the applicability and profitability of a broad variety of such optimizations, including memoization, reordering, data layout transformations, and inlining and specialization

关键词： Optimizing compilers Design optimization Scientific computing Laboratories Profitability computer languages computer science Costs US department of Energy Containers

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A Model Driven Approach for the Alignment of Business and Information Systems Models

A Model Driven Approach for the Alignment of Business and In...

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Mexican International Conference on computer science (ENC)

作者： Valeria de Castro Juan M. Vara Mesa Elisa Herrmann Esperanza Marcos Kybele Research Group Department of Computing Languages and Systems II School of Computer Science Engineering Rey Juan Carios University Madrid Spain Kybele Research Group Rey Juan Carlos University Madrid Spain

ISBN: (纸本)9780769534398

This paper focuses on the development of service-oriented information systems. One of the main challenges facing service-oriented computing is providing methodologies that support the specification and design of IS behavior, and allowing software engineers to move from the earlier stages of business analysis to its final step of implementation. Model Driven approaches provide with models, transformations between them and code generators to address software development. This kind of approaches provides a conceptual structure that brings together diagrams used by business managers and analysts and those used by software developers, and is capable of organizing them in such a way that the requirements specified in one diagram can be traced through more detailed derived ones. In this paper we present a Model Driven approach which deals with the alignment between high level business models (the business view) and behavioural models of the service-oriented information system (the information system view). The work presents, by means of a case study, models proposed for modeling both, the views and model transformation between them.

关键词： computer science

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Teaching information security management using an incident of intellectual property leakage

arXiv

引用

arXiv 2021年

作者： Ahmad, Atif Maynard, Sean B. Motahhir, Sameen Alshaikh, Moneer School of Computing and Information Systems The University of Melbourne ParkvilleVIC Australia Center for Digital Humanities Department of Languages and Linguistics Information Technology University Lahore Pakistan Department of Cybersecurity College of Computer Science and Engineering University of Jeddah Saudi Arabia

Case-based learning (CBL) is a powerful pedagogical method of creating dialogue between theory and practice. CBL is particularly suited to executive learning as it instigates critical discussion and draws out relevant experiences. In this paper we used a real-world case to teach Information Security Management to students in Management Information Systems. The real-world case is described in a legal indictment (T-mobile USA Inc v. Huawei Device USA Inc. and Huawei Technologies Co. LTD) alleging theft of intellectual property (trade secrets) and breaches of contract concerning confidentiality and disclosure of sensitive information. The incident concerns a mobile phone testing robot (Tappy) developed by T-mobile USA to automate testing of mobile phones prior to launch. T-mobile alleges Huawei stole the technology by copying the robot's specifications and stealing parts and software to develop its own testing robot. The incident scenario is interesting as it relates to a business asset that has both digital and physical components that has been compromised through an unconventional cyber-physical attack facilitated by insiders. The scenario sparked an interesting debate among students about the scope and definition of security incidents, the role and structure of the security unit, the utility of compliance-based approaches to security, and the inadequate use of threat intelligence in modern security strategies. Copyright © 2021, The Authors. All rights reserved.

关键词： Intellectual property

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A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica

arXiv

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arXiv 2021年

作者： Calderón-Ramírez, Saúl Murillo-Hernández, Diego Rojas-Salazar, Kevin Elizondo, David Yang, Shengxiang Moemeni, Armaghan Molina-Cabello, Miguel De Montfort University United Kingdom PARMA Research Group Instituto Tecnológico de Costa Rica Costa Rica School of Computing Engineering Instituto Tecnológico de Costa Rica Costa Rica School of Computer Science University of Nottingham United Kingdom Department of Computer Languages and Computer Science University of Málaga Spain

The implementation of deep learning based computer aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of diagnosing patients. However, training a deep learning model requires a considerable amount of labeled images, which can be expensive to obtain as time and effort from clinical practitioners is required. A number of publicly available datasets have been built with data from different hospitals and clinics. However, using models trained on these datasets for later work on images sampled from a different hospital or clinic might result in lower performance. This is due to the distribution mismatch of the datasets, which include different patient populations and image acquisition protocols. The scarcity of labeled data can also bring a challenge towards the application of transfer learning with models trained using these source datasets. In this work, a real world scenario is evaluated where a novel target dataset sampled from a private Costa Rican clinic is used, with few labels and heavily imbalanced data. The use of two popular and publicly available datasets (INbreast and CBIS-DDSM) as source data, to train and test the models on the novel target dataset, is evaluated. The use of the semi-supervised deep learning approach known as MixMatch, to leverage the usage of unlabeled data from the target dataset, is proposed and evaluated. In the tests, the performance of models is extensively measured, using different metrics to assess the performance of a classifier under heavy data imbalance conditions. It is shown that the use of semi-supervised deep learning combined with fine-tuning can provide a meaningful advantage when using scarce labeled observations. We make available the novel dataset for the benefit of the community. © 2021, CC BY.

关键词： Mammography

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