feature identification
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2022 ◽  
Vol 31 (2) ◽  
pp. 1-25
Ryan Williams ◽  
Tongwei Ren ◽  
Lorenzo De Carli ◽  
Long Lu ◽  
Gillian Smith

IoT firmware oftentimes incorporates third-party components, such as network-oriented middleware and media encoders/decoders. These components consist of large and mature codebases, shipping with a variety of non-critical features. Feature bloat increases code size, complicates auditing/debugging, and reduces stability. This is problematic for IoT devices, which are severely resource-constrained and must remain operational in the field for years. Unfortunately, identification and complete removal of code related to unwanted features requires familiarity with codebases of interest, cumbersome manual effort, and may introduce bugs. We address these difficulties by introducing PRAT, a system that takes as input the codebase of software of interest, identifies and maps features to code, presents this information to a human analyst, and removes all code belonging to unwanted features. PRAT solves the challenge of identifying feature-related code through a novel form of differential dynamic analysis and visualizes results as user-friendly feature graphs . Evaluation on diverse codebases shows superior code removal compared to both manual feature deactivation and state-of-art debloating tools, and generality across programming languages. Furthermore, a user study comparing PRAT to manual code analysis shows that it can significantly simplify the feature identification workflow.

2022 ◽  
pp. 146906672110733
Sean Sebastian Hughes ◽  
Marcus M. K. Hughes ◽  
Rasmus Voersaa Jonsbo ◽  
Carsten Uhd Nielsen ◽  
Frants Roager Lauritsen ◽  

Beer is a complex mix of more than 7700 compounds, around 800 of which are volatile. While GC-MS has been actively employed in the analysis of the volatome of beer, this method is challenged by the complex nature of the sample. Herein, we explored the possible of using membrane-inlet mass spectrometry (MIMS) coupled to KNIME to characterize local Danish beers. KNIME stands for Konstanz Information Miner and is a free open-source data processing software which comes with several prebuilt nodes, that, when organized, result in data processing workflows allowing swift analysis of data with outputs that can be visualized in the desired format. KNIME has been shown to be promising in automation of large datasets and requires very little computing power. In fact, most of the computations can be carried out on a regular PC. Herein, we have utilized a KNIME workflow for data visualization of MIMS data to understand the global volatome of beers. Feature identification was not possible as of now but with a combination of MIMS and a KNIME workflow, we were able to distinguish beers from different micro-breweries located in Denmark, laying the foundation for the use of MIMS in future analysis of the beer volatome.

2021 ◽  
Vol 44 (4) ◽  
pp. 458-470
E. A. Kozyreva ◽  
A. A. Rybchenko ◽  
S. Demberel

Water flows with significant flow rate feature a high destructive force and can lead to catastrophic consequences. Fluvial processes caused by uneven distribution of rain precipitation over the area pose risks to the developed inland foothill territories. The purpose of this study is to carry out a quantitative morphometric analysis of the territory in order to identify the formation features of flood flows. The analysis and ranking of catchment basins are performed using a basin approach. On the basis of SRTM images and the use of stock cartographic material in the GIS program the authors have built specialized electronic maps that allow to obtain quantitative parameters reflecting the morphometry of the basins under analysis including basin geometry, drainage network and terrain relief. On example of the Ulaanbaatar agglomeration territory it is shown how initial morphometric parameters of basins and watercourses (length, width, area, perimeter, erosion dissection, drainage network density, terrain relief coefficient, Melton coefficient, etc.) form the features of flood flow. For developed territories, the initial data on the catchment basin morphometry constitute the basis for compilation of specialized maps to be used in planning and construction. The combination of morphometric indicators on the territory of the Ulaanbaatar agglomeration indicates that there is possibility of large flood formation and development of dangerous mudstone flows in some catchment basins.

2021 ◽  
Vol 133 ◽  
pp. 103528
Marcos Leandro Hoffmann Souza ◽  
Cristiano André da Costa ◽  
Gabriel de Oliveira Ramos ◽  
Rodrigo da Rosa Righi

2021 ◽  
Vol 11 (23) ◽  
pp. 11086
Luna Ngeljaratan ◽  
Mohamed A. Moustafa

This paper describes an alternative structural health monitoring (SHM) framework for low-light settings or dark environments using underexposed images from vision-based sensors based on the practical implementation of image enhancement algorithms. The proposed framework was validated by two experimental works monitored by two vision systems under ambient lights without assistance from additional lightings. The first experiment monitored six artificial templates attached to a sliding bar that was displaced by a standard one-inch steel block. The effect of image enhancement in the feature identification and bundle adjustment integrated into the close-range photogrammetry were evaluated. The second validation was from a seismic shake table test of a full-scale three-story building tested at E-Defense in Japan. Overall, this study demonstrated the efficiency and robustness of the proposed image enhancement framework in (i) modifying the original image characteristics so the feature identification algorithm is capable of accurately detecting, locating and registering the existing features on the object; (ii) integrating the identified features into the automatic bundle adjustment in the close-range photogrammetry process; and (iii) assessing the measurement of identified features in static and dynamic SHM, and in structural system identification, with high accuracy.

2021 ◽  
Sen Huang ◽  
Linna Li ◽  
Dongwang Zhong ◽  
Li He ◽  
Jianfeng Si

In the blasting demolition processs of high-rise structures, the impact of blasting vibration to the environment and objects to be protected must be effectively controlled, so the blasting vibration signal is deeply analyzed [1]. In this paper, the blasting vibration signal of a chimney is analyzedbased on HHT. The blasting vibration signal is denoised by Empirical Mode Decomposition (EMD)-wavelet threshold, then using Hilbert-Huang Transform (HHT) [2] the measured blasting vibration waveform Hilbert spectrum, marginal spectrum and instantaneous energy graph are draw to analyze the chimney blasting vibration. The results show that the denoising effect of EMD-wavelet threshold is good for blasting vibration signal [3]. HHT method has a good feature identification ability when processing vibration signals, and can reflect the characteristics of data more comprehensively and accurately, which provides convenience for the study of vibration signal data.

Brady G. Anderson ◽  
Alexander Raskind ◽  
Hani Habra ◽  
Robert T. Kennedy ◽  
Charles R. Evans

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Yunfei Chen ◽  
Yang Liu ◽  
Xintao Fan

In order to solve the problem of large signal acquisition error caused by radio wave multipath effect in indoor environment, firstly, the signal source carried on the motion platform is collected for spectrum signal, and the signal processed by wavelet threshold denoising algorithms extracted and stored for spectrum feature extraction. Then, after data training and identification, the signal source is input into the system in random mode for identification. The experimental results show that the improved fuzzy clustering algorithm (FCA) is 12.7% higher than the spectrum envelope extraction method (SEEM) in the recognition rate of spectrum characteristics of different modes of signal source.

2021 ◽  
Kemel Jouini

<p>My thesis deals with dependency relations in the structure of sentences in Arabic and how properties of verbal morphology and associated lexical items dictate how sentences are derived. I adopt the probe-goal-Agree Minimalist view that variation between languages (even those that are closely related, such as Standard Arabic and Tunisian Arabic) is due to the 'feature structure' of functional elements that enter into the derivation.  In particular, the essential architecture of sentences expressing the dependency relations verbs and associated elements have with the 'functional' portion of sentences (i.e., tense/modality properties) is universal in that these dependency relations will be expressed on the basis of the same feature structure cross-linguistically. However, this architecture still allows for the kind of parametric variation that exists even between closely related languages.  In this context, I am interested in the status of subject-verb agreement configurations, in both VSO and SVO word orderings, and wh- and other A’-dependencies in Standard Arabic (with comparisons to some modern spoken varieties of Arabic, where appropriate). The analysis is shown to extend to other V-raising languages of the Semitic/Celtic type with ‘basic’ VSO word ordering. A possible extension of the analysis to the V2 phenomenology is also discussed and the major role played by the raising of V-v to T and the raising of T to Agr(s) or T to Fin is highlighted.  An important aspect of my analysis is a proper understanding of the dependency relations involved in the derivation of the relevant sentences where the role of the CP domain projections, verb-movement, feature identification and/or feature valuation along with clause type is essential for interpretation at the interface at the output of syntax. In this feature-based analysis of parametric and micro-parametric variation, I show that variation between typologically similar and typologically different languages is minimal in that it is limited to the interaction of feature combinations in the derivation of sentences.  These feature combinations concern the feature structure of the T-node in relation to the position where T is spelled out at the interface. In particular, T raises to Agr(s) or to Fin in some languages and/or structures. Such raising processes are important in subject-verb agreement configurations cross-linguistically involving combinations of T-features and D-features, which would differ in interpretability (i.e., interpretable vs. uninterpretable) as the basis for feature valuation. Similar feature combinations also drive the raising processes in wh-dependencies with some F-feature (mainly related to ‘focus’) interacting with the T-features of Fin.  I propose that two modes of licensing of these feature combinations are at work. The first mode of licensing is the basic head-head agreement relation. This agreement relation is the basis for verb-movement to the functional field above vP/VP in V-raising languages. The second mode of licensing is the Spec-head agreement relation, brought about by the Merge (internal or external) of D(P) elements in A-dependencies and the Merge of wh-elements in A’-dependencies.  In dependency relations other than subject-verb agreement and wh-dependencies, I propose that the licensing of these feature combinations is strictly a question of ‘identification’ via head-head agreement whereby a feature on a functional head does not need to be valued, but it still needs to be ‘identified’ for the well-formedness of the C-(Agr[s])-T dependency. This is the case of the interpretable D-feature of the Top node in Topic-comment structures and the interpretable F-feature of the two functional head nodes, Mod(al) and Neg, in relation to the T-features of Fin in a V-raising language like Standard Arabic.</p>

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