A framework for automatic clustering of semantic models

In Linguistics the terms model and modelling have a vast array of meanings, which depends on the purpose and the object, and the type of the scientific research. The article is dedicated to the investigation of a special procedure of semantic processes modelling, deducing and substantiating the notion “evolutional semantic model”, the content and operational opportunities of which differ drastically from the essence and purpose of the known from the scientific literature phenomenon of the same name. In the proposed research this variety of modelling is oriented towards the description of the dynamics of the legal terms content loading, the estimation of possible vectors of the semantic evolution on the way of its terminalization/determinalization. The evolutional model of semantics has here as its basis the succession of sememes or series of sememes, the order of which is determined with accounting of a number of parameters. The typical schemes of the meaning development, illustrated by the succession of sememes, are considered to be the models of semantic laws (evolutional semantic models = EMS). Their function is the explanation of the mechanism and the order of the stages of the semantic evolution of the system of the words which sprung from one root on the way of its legal specialization, and, therefore, the proposed in the paper experience of semantic laws modelling differs from the expertise of the “catalogue of semantic derivations”, proposed by H. A. Zaliznjak, which doesn’t have as its purpose the explanation of meaning displacements, and from the notion of semantic derivation, models of derivation, dynamic models, worked out by O. V. Paducheva, which also only state such a displacement, without proving its reality. Key words: evolutional semantic model (EMS), modelling, semantic law, sememe, pre(law).

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Detecting linguistic idiosyncratic interests in autism using distributional semantic models

10.3115/v1/w14-3206 ◽

2014 ◽

Cited By ~ 4

Author(s):

Masoud Rouhizadeh ◽

Emily Prud'hommeaux ◽

Jan van Santen ◽

Richard Sproat

Keyword(s):

Semantic Models ◽

Distributional Semantic Models

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Formative-semantic models of adjective compounds in English and Serbian

Вопросы языкознания ◽

10.31857/s0373658x0003837-9 ◽

2017 ◽

pp. 42-64

Author(s):

Vladimir Jovanović ◽

Keyword(s):

Semantic Models

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A Hybrid Method Using Evolutionary and a Linear Integer Model to Solve the Automatic Clustering Problem

Learning and Nonlinear Models ◽

10.21528/lnlm-vol13-no2-art2 ◽

2015 ◽

Vol 13 (2) ◽

pp. 73-91 ◽

Cited By ~ 1

Author(s):

Marcelo Dib Cruz ◽

Luiz Satoru Ochi

Keyword(s):

Hybrid Method ◽

Clustering Problem ◽

Automatic Clustering

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Segmentation of SAR Image using Fuzzy C-Means and Filters

Science & Technology Journal ◽

10.22232/stj.2020.08.01.11 ◽

2020 ◽

Vol 8 (1) ◽

pp. 84-90

Author(s):

R. Lalchhanhima ◽

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Debdatta Kandar ◽

R. Chawngsangpuii ◽

Vanlalmuansangi Khenglawt ◽

...

Keyword(s):

Clustering Algorithm ◽

State Of The Art ◽

Speckle Noise ◽

Synthetic Aperture Radar Image ◽

Synthetic Aperture ◽

Sar Image ◽

Spatial Filters ◽

Fuzzy C Means ◽

Automatic Clustering ◽

Intensity Information

Fuzzy C-Means is an unsupervised clustering algorithm for the automatic clustering of data. Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore the segmentation process can not directly rely on the intensity information alone but must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use the fuzzy nature of classification for the purpose of unsupervised region segmentation in which FCM is employed. Different features are obtained by filtering of the image by using different spatial filters and are selected for segmentation criteria. The segmentation performance is determined by the accuracy compared with a different state of the art techniques proposed recently.

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ESRFuzzer: an enhanced fuzzing framework for physical SOHO router devices to discover multi-Type vulnerabilities

Cybersecurity ◽

10.1186/s42400-021-00091-9 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Yu Zhang ◽

Wei Huo ◽

Kunpeng Jian ◽

Ji Shi ◽

Longquan Liu ◽

...

Keyword(s):

Web Server ◽

Test Cases ◽

Communication Model ◽

Home Office ◽

Security Vulnerabilities ◽

Testing Environment ◽

Semantic Models ◽

Recovery Mechanisms ◽

General Mode ◽

Soho Router

AbstractSOHO (small office/home office) routers provide services for end devices to connect to the Internet, playing an important role in cyberspace. Unfortunately, security vulnerabilities pervasively exist in these routers, especially in the web server modules, greatly endangering end users. To discover these vulnerabilities, fuzzing web server modules of SOHO routers is the most popular solution. However, its effectiveness is limited due to the lack of input specification, lack of routers’ internal running states, and lack of testing environment recovery mechanisms. Moreover, existing works for device fuzzing are more likely to detect memory corruption vulnerabilities.In this paper, we propose a solution ESRFuzzer to address these issues. It is a fully automated fuzzing framework for testing physical SOHO devices. It continuously and effectively generates test cases by leveraging two input semantic models, i.e., KEY-VALUE data model and CONF-READ communication model, and automatically recovers the testing environment with power management. It also coordinates diversified mutation rules with multiple monitoring mechanisms to trigger multi-type vulnerabilities. With the guidance of the two semantic models, ESRFuzzer can work in two ways: general mode fuzzing and D-CONF mode fuzzing. General mode fuzzing can discover both issues which occur in the CONF and READ operation, while D-CONF mode fuzzing focus on the READ-op issues especially missed by general mode fuzzing.We ran ESRFuzzer on 10 popular routers across five vendors. In total, it discovered 136 unique issues, 120 of which have been confirmed as 0-day vulnerabilities we found. As an improvement of SRFuzzer, ESRFuzzer have discovered 35 previous undiscovered READ-op issues that belong to three vulnerability types, and 23 of them have been confirmed as 0-day vulnerabilities by vendors. The experimental results show that ESRFuzzer outperforms state-of-the-art solutions in terms of types and number of vulnerabilities found.

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