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Hardware design for build a Step Width System Capture
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The procreative statistical framework of musical note structures produces a crucial role in multimedia music classification and reconstruction strategies. Another most significant thing for harmonious music composition is the rhythmic structures that provide musical performance in a harmonic form. This paper has illustrated computational music theory and allied factors to regulate what human beings can acquire, remember, and reconstruct music for sustaining intangible cultural heritage. The music strings or symbols are also imperative factors that assist the musicians as performance guidelines. To afford a syntactic outline of musical note arrangements, a stochastic model along with probabilistic context-free music grammar has been illustrated in this paper. The state transition analysis has also been incorporated in terms of transition table and diagram to demonstrate which state can move to the other one within a finite automaton depending on the behaviors of the current state and associated transition rule. Petri net has been used for modeling and simulating the projected complex music composition framework to analyze system performances. The Petri net simulation-based reachability and system efficiency have been evaluated for analyzing the effectiveness of the proposed event-driven architecture. For incorporating real data into the projected framework, the music composition and reconstruction tool has also been demonstrated. The system performance evaluation metric has shown that around 92% efficiency level has been achieved by analyzing the projected music composition model.
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This data includes all the input data for the test instances used in the experiments. The input data consists of three sets of benchmarks including OR-Lib set (40 graphs), TSP-Lib set (20 graphs) and University of Florida Sparse Matrix Collection (3 graphs) .
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CAD and material properties of the monumental atrium of the Engineering Faculty Building in Bologna
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This dataset is the complete directory of all Trygve's web pages. The web page HTML code is found from its URL. For example, the HTML for http://folk.uio.no/trygver/themes/Personal/pp-index.html is in the file at themes/Personal/pp-index.html The University of Oslo is terminating its Web service after 25 years of operation. My gigabyte of web pages have been collected over the years and will no longer be accessible over the Net. The pages are stored in this dataset and it may be possible to transfer them to another service if required. It should in any case be possible to read the dataset with an HTML reader.
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This folder contains BatClassify results files used to create Temporal Pass Plots in the accompanying article Fig. 2 (Richmond_Myotis_BatClassify_Results.csv), Fig. 3a (Richmond_Ppyg_Site_A_BatClassify_Results.csv) and Fig. 3b (Richmond_Ppyg_Site_B_BatClassify_Results.csv). All other files are used in the TPP vignette, which is provided in the supplementary material.
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Temporal activity patterns can potentially reveal useful information about behaviour, phenological changes and emergence times for bat species; however, detailed assessments of temporal activity are infrequently performed or published for bats. Passive electronic devices, such as autonomous recording units and camera traps, are increasingly being used as a means of monitoring various species, communities and habitats. Data recorded by these devices inherently contain file metadata detailing the dates and times when data capture took place. We have utilised this metadata to create the Temporal Pass Plot (TPP), which provides intuitive, yet highly detailed, visualisations of temporal bat activity over prolonged periods of time. Furthermore, TPPs are produced using a common scale based upon activity within predetermined time-blocks, enabling direct comparisons between different sites and species to be performed. TPPs reveal inter- and intra-specific differences, and seasonal changes, in temporal activity. As a relatively untapped area of research, further study is required to evaluate associations between activity patterns and different behaviours (e.g. roosting, commuting and swarming). However, if this can be achieved, the scope of assessments that could be performed with passive monitoring technologies could be significantly expanded, enabling more detailed evaluations of habitat use to be performed with minimal disturbance to the target species. Although the TPP was principally designed for the purpose of studying bat activity, it can easily be adapted for other species that can are monitored using autonomous recording devices. Article data: This folder contains the data files used to create all three Temporal Pass Plots shown in the main article. Vignette data: This folder contains all files described in the TPP vignette, which is provided in the supplementary materials of the main article.
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Origin graph files of the manuscript LiFePO4_S cathode proof. Figure 1 Cyclic Voltammetry of LiFePO4-S composite with LiPF6 electrolyte. Figure 2 Cyclic Voltammetry of LiFePO4-S composite with LiTFSI electrolyte. Figure 3 dQ/dV curves calculated from charge/discharge cycling data of the LiFePO4-S composite cathode with LiTFSI electrolyte: Lithiation in cycle 3 (a) and cycle 5 (b), and delithiation in cycle 3 (c) and cycle 5 (d). Figure 5 XRD pattern of hydrothermal carbon - LiFePO4 composite prepared in acetic acid. The pattern of LiFePO4 without carbon coating is shown as a reference. * Peak corresponding to graphitic carbon. Figure 6 Raman spectrum of hydrothermal carbon-LiFePO4 composite after thermal treatment at 650 °C. The Raman spectrum of LiFePO4 reagent without carbon coating is shown as a reference. Figure 7 dQ/dV curves calculated from charge/discharge cycling data of composite cathodes of carbon-coated LiFePO4 (prepared in acetic acid) and sulfur infiltrated in porous carbon: Lithiation in cycle 3 (a) and cycle 5 (b), and delithiation in cycle 3 (c) and cycle 5 (d).
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In computer security, network botnets still represent a major cyber threat. Concealing techniques such as the dynamic addressing and the Domain Name Generation Algorithms (DGAs) require an improved and more effective detection process. To this extent, this data descriptor presents a collection of over 30 million manually-labelled algorithmically generated domain names decorated with a feature set ready-to-use for Machine Learning analysis. This proposed data set enables researchers to move forward the data collection, organization and pre-processing phases, eventually enabling them to focus on the analysis and the production of Machine-Learning powered solutions for network intrusion detection. To be as exhaustive as possible, 50 among the most important malware variants have been selected. Each family is available both as list of domains and as collection of features. To be more precise, the former is generated by executing the malware DGAs in a controlled environment with fixed parameters, while the latter is generated by extracting a combination of statistical and Natural Language Processing (NLP) metrics.
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Griffin Walker's demo, released in 2004. It's not mastered, please be kind.
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