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Experiment on Automatic Functional Requirements Analysis with the EFRF's Semantic Cases

Experiment on Automatic Functional Requirements Analysis wit...

引用

2016 ieee international conference on progress in informatics and computing

作者： Yinglin Wang Jianzhang Zhang School of Information Management and Engineering Shanghai University of Finance and Economics

Requirements engineering is crucial for a single custom-built project lifecycle and requirements reuse within Software Product Line(SPL). Natural language requirements specifications are informal, imprecise and ambiguous and cannot be easily analyzed by engineers. Extended Functional Requirements Frame(EFRF) [1] is a helpful intermediate representation and it can map the free style requirements text to formal specifications, which can be effectively processed by analysis tools. In this paper we propose an approach to automatically extract EFRF from natural language Software Requirements Specifications(SRSs). The approach employs machine learning, natural language process(NLP) and semantic analysis techniques to extract EFRF's semantic cases in the functional requirements sentences. With a 10-fold cross validation setting, we evaluate our method on a manually annotated corpus. Our approach achieves a promising performance. In addition, we train an extraction model on Ecommerce system requirements dataset and then tested it on the auto-maker system requirements sentences. The cross-domain extraction results show that our method is domain independent and robust to some extent.

关键词： requirements engineering functional requirements extraction EFRF semantic analysis

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High-Dimensional Real Parameter Clonal Selection Memory Algorithm

High-Dimensional Real Parameter Clonal Selection Memory Algo...

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2016 ieee international conference on progress in informatics and computing

作者： Dan Song Xiaoping Fan School of Information Science and Engineering Central South University Department of Computer and Communication Hunan Institute of Engineering Department of Information Management Hunan University of Finance and Economics

For numerical high-dimensional real parameter optimization problem, a new clonal selection memory algorithm is proposed in the paper by introducing a short-term memory mechanism into the optimization algorithm. Through non-genetic information storage and guiding the subsequent evolution the algorithm can effectively increase the local convergence rate. On the other hand, the search depth threshold setting, supplement operator and crossover operator makes the algorithm can effectively escape local optimum traps. Combined with other intelligent algorithms, the simulation experiment of the 20 and 30 dimensional standard test functions show that the new algorithm has obvious advantages in convergence speed, convergence precision and global convergence. Furthermore, the simulation experiment of the 100 and 200 dimensional standard test functions shows that, the new algorithm shows better global convergence speed, convergence precision and stability.

关键词： numerical optimization high-dimensional real parameter intelligent algorithm

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Bounded Asymmetric Gaussian Mixture Model with Spatial Constraint for Image Segmentation

Bounded Asymmetric Gaussian Mixture Model with Spatial Const...

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2016 ieee international conference on progress in informatics and computing

作者： Juanfen Sun Zexuan Ji School of Computer Science and Engineering Nanjing University of Science and Technology

Accurate image segmentation is an important issue in image processing, where spatially constrained Gaussian mixture models play an important part and have been proven to be effective. However, most GMM based methods suffer from limited noise robustness, over-smoothness for segmentations, and lack of flexibility to fit data. In order to address these issues, in this paper,we propose a rough set bounded asymmetric Gaussian mixture model with spatial constraint for image segmentation. First, we partition the target image into three rough regions with two adaptively computed thresholds. Second, a new bounded indicator function is constructed to determine the bounded support regions of the observed data. Third, two novel prior factors are proposed by incorporating the spatial information among neighborhood pixels, which are constructed based on the prior and posterior probabilities of the within-and between-clusters, and considers the spatial direction. We compare our algorithm to state-of-theart segmentation approaches in both synthetic and real images to demonstrate the superior performance of the proposed algorithm.

关键词： Image segmentation Asymmetric Gaussian mixture model Markov random field Rough Set theory EM algorithm

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Research on the Pricing Decision of the Resources of Industry Chain in the Internet of Things

Research on the Pricing Decision of the Resources of Industr...

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2016 ieee international conference on progress in informatics and computing

作者： Shihai Tian Weinan Sun Harbin University Of Science And Technology

Resource pricing is an important tache of the internet of things industry chain. Firstly, the complex relationship in the transaction process of each subject of resources pricing is analyzed based on the supernetwork theory. The pricing relationship structure is designed, on the basis of considering single level and multi-level network. Secondly, the resource pricing supernetwork model of the Internet of things industry chain is constructed. At the same time, the variational inequality is used to optimize the action and target of different levels of transaction subject, coordinate profit distribution, and propose the supply equilibrium state of the resource pricing supernetwork model, so that each subject can achieve optimal profits. Finally, according to the improved projection algorithm, the resource pricing supernetwork model is simulated and the equilibrium solution is obtained. The result verifies the validity of the model. It provides an important reference value for the resource pricing decision of the Internet of things industry chain.

关键词： internet of things supernetwork resource pricing variational inequality

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Adaptive Fine-Tuning and Feature Enhancement of SAM for Remote Sensing Instance Segmentation

Adaptive Fine-Tuning and Feature Enhancement of SAM for Remo...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Zhixin Zhao Yangguang Liu Lingwu Meng Haochen Zhang Mingyang Zhu Liang Xiao School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Remote sensing image instance segmentation aims to accurately separate independent objects and automatically identify land attributes. Recent advancements in large models have propelled self-supervised learning, especially with the segment anything model (SAM). Although SAM can generate instance segmentation masks without additional training, its application to remote sensing images still faces challenges, such as a severe reliance on manual prior prompts, inaccuracies in detail segmentation, and insufficient generalization capability. Therefore, to enhance the generalization capability of SAM in the field of remote sensing images, we propose a SAM-based fine-tuning model called AFFE-SAM. This model combines adaptive low-rank (AdaLoRA) and variable adapter (MiMi-Adapter) fine-tuning methods. Additionally, it improves mask quality in the decoder by employing adaptive multi-scale feature enhancement. We conducted experiments on two datasets, obtaining mAP scores of 66.5 for the NWPU VHR-10 dataset and 67.1 for the SSDD dataset. The evaluation results validate the effectiveness of our method.

关键词： Instance segmentation Training Adaptation models Self-supervised learning Propulsion Decoding Sensors Object recognition informatics Remote sensing

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Personalized Learning Design of Ideology and Politics of Distance Education Courses Based on Big Data

Personalized Learning Design of Ideology and Politics of Dis...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Junyan Zhao Xubiao Yang Qian Qiao Liqiong Chen School of Network Education East China University of Science and Technology Shanghai China College of Air Transport Shanghai University of Engineering Science Shanghai China School of Social and Public Administration East China University of Science and Technology Shanghai China Shanghai Institute of Technology Shanghai China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

As an important position in education, distance education is a new subject faced by the education community under the new situation. In view of the problem of "islandization" in the process of ideological and political education in distance education based on big data technology, this paper proposes a personalized learning service framework for distance education students' ideological and political courses, including data management, data analysis layer and service provision. At first, a large amount of remote education course ideological and political data is collected through the network, database and various mobile platforms. After the data is preprocessed, knowledge graphs and data mining techniques are used to mine the ideological and political elements of the course and develop personalized ideological and political learning program. Distance education curriculum ideology and politics are supported to teach students according to their aptitudes, and focus on students' individualized ideology and politics learning, then improve the teaching effect of distance education ideology and politics lessons.

关键词： Data analysis Databases conferences Big Data Educational courses Data mining informatics

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TransRESCAL: A Dense Feature Model for Knowledge Graph Completion

TransRESCAL: A Dense Feature Model for Knowledge Graph Compl...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Panfeng Chen Yisong Wang Quan Yu Qing Fan Guizhou University Guiyang China Qiannan Normal University for Nationalities Qiannan China Jingchu University of Technology Jingmen China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

Knowledge Graph Completion (KGC) is a hot-spot in artificial intelligence research. Dozens of hundreds of models are devised to this end. The translation-based and factorization- based approaches are very popular ones for their efficiency and simplicity. TransE and RESCAL are the canonical ones in both research lines. However, the simplicity also limits the expressivity of the model to achieve better performance. To improve the results, we couple the two approaches to form a more expressive one named TransRESCAL. It is capable of distinguishing head from tail entity embedding due to TransE has the ability to be capable of doing this. Therefore, the proposed model is capable of clarifying the fact that the position of head and tail entity embeddings in score function of factorization-based models. It is beneficial for further understanding the model for KGC task. Hence, the proposed model is not only capable of capturing structural feature by TransE but also semantic ones by RESCAL. It potentially captures denser features than every single model, which is a competitive alternative to the deep neural network (DNN)-based models for KGC tasks.

关键词： Measurement Knowledge engineering Semantics Neural networks Mathematical model Task analysis informatics

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Design of a RESTful middleware to enable a web of medical things

Design of a RESTful middleware to enable a web of medical th...

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EAI international conference on Wireless Mobile Communication and Healthcare (Mobihealth)

作者： N. Philip T. Butt D. Sobnath R. Kayyali S. Nabhani-Gebara B. Pierscionek I. Chouvarda V. Kilintis P. Natsiavas N. Maglaveras A. Raptopoulos Faculty of Science Engineering and Computing Kingston University Surrey UK Lab of Medical Informatics Aristotle University Thessaloniki Greece Exus Athens Greece

In this paper, we consider the design methodology of a mobile patient hub for the remote self-management of COPD patients. The patient hub design forms a part of the WELCOME system. WELCOME is a current EU project that aims to design and develop a new mobile health system to provide integrated care for COPD patients with comorbidities. The approach adopted for this research is based on the Web of Things architecture with RESTful principles as the enabler of communications. The proposed patient hub architecture design is based on three layers: an application layer, a middleware layer and the sensors layer. This paper presents the detail of the initial design of the middleware and an analysis of the architecture in the context of the system's requirements.

关键词： Mobile communication Computer architecture ieee 802.11 Standards Web servers Intelligent sensors

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Formal property verification in a conformance testing framework

Formal property verification in a conformance testing framew...

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ACM and ieee international conference on Formal Methods and Models for Co-Design (MEMOCODE)

作者： Houssam Abbas Hans Mittelmann Georgios Fainekos School of Electrical Energy and Computer Engineering Arizona State University Tempe AZ U.S.A. School of Mathematical and Statistical Sciences Arizona State University Tempe AZ U.S.A. School of Computing Informatics and Decision Systems Arizona State University Tempe AZ U.S.A.

In model-based design of cyber-physical systems, such as switched mixed-signal circuits or software-controlled physical systems, it is common to develop a sequence of system models of different fidelity and complexity, each appropriate for a particular design or verification task. In such a sequence, one model is often derived from the other by a process of simplification or implementation. E.g. a Simulink model might be implemented on an embedded processor via automatic code generation. Three questions naturally present themselves: how do we quantify closeness between the two systems? How can we measure such closeness? If the original system satisfies some formal property, can we automatically infer what properties are then satisfied by the derived model? This paper addresses all three questions: we quantify the closeness between original and derived model via a distance measure between their outputs. We then propose two computational methods for approximating this closeness measure. Finally, we derive syntactical re-writing rules which, when applied to a Metric Temporal Logic specification satisfied by the original model, produce a formula satisfied by the derived model. We demonstrate the soundness of the theory with several experiments.

关键词： Computational modeling Testing Integrated circuit modeling Educational institutions Software packages Switches Measurement

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Refined Learning of Hidden Markov Models with a Modified Baum-Welch Algorithm and Informative Components

Refined Learning of Hidden Markov Models with a Modified Bau...

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The 2010 ieee international conference on progress in informatics and computing

作者： Michael G.Madden School of Engineering and Informatics National University of Ireland Galway GalwayIreland

Methods for refined learning of hidden Markov models(HMMs) have been studied for decades,taking either structural or parametric *** paper discusses using informative components of state parameters to refine the training of *** the purpose of HMM learning is to decompose a high-dimensional observation space into latent states,we may still end up with high dimensional state models.A problem with such high dimensional state models is that if they are learned with limited amounts of training data,this will bias the likelihood during training,particularly when there are outliers in the observation *** propose a modified Baum-Welch algorithm to estimate the HMM parameters over informative components selected with nearly sufficient statistics, using relative entropy *** method produces a modified likelihood estimation in reduced dimensions,which decreases the computation complexity and preserves near-optimality of the HMM that is *** variations on the algorithm are discussed,one that deals with general state model and another that considers a special Gaussian *** approach is evaluated with a problem of using pen tip trajectory for handwriting character verification and *** data sets are studied,one with low dimensionality and the other with high dimensionality.A comparison is performed of the results of using HMMs trained without and with informative state *** both data sets,we found that our approach yields improvements in *** error rate varies with relative entropy change is also evaluated.

关键词： hidden Markov model Baum-Welch algorithm relative entropy handwriting recognition

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