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Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data

There is an increasing focus on the part of academic institutions, funding agencies, and publishers, if not researchers themselves, on preservation and sharing of research data. Motivations for sharing include research integrity, replicability, and reuse. One of the barriers to publishing data is the extra work involved in preparing data for publication once a journal article and its supporting information have been completed. In this work, a method is described to generate both human and machine-readable supporting information directly from the primary instrumental data files and to generate the metadata to ensure it is published in accordance with findable, accessible, interoperable, and reusable (FAIR) guidelines. Using this approach, both the human readable supporting information and the primary (raw) data can be submitted simultaneously with little extra effort. Although traditionally the data package would be sent to a journal publisher for publication alongside the article, the data package could also be published independently in an institutional FAIR data repository. Workflows are described that store the data packages and generate metadata appropriate for such a repository. The methods both to generate and to publish the data packages have been implemented for NMR data, but the concept is extensible to other types of spectroscopic data as well.

INTRODUCTION
Recent studies have examined the rationale for greater sharing of primary research data among scientists, including in chemistry. 1 A number of benefits have been suggested including research integrity and replicability and reuse by peers. Some prominent cases of data manipulation and fraudulent data have been uncovered in recent years, 2,3 the expectation is that upon publishing the primary research data associated with an experiment, it becomes more difficult to manipulate data in a way that cannot be detected. The ease with which data can be manipulated or manufactured varies by technique, and although some attempts can be detected by human inspection, others would undoubtedly escape even simple algorithmic techniques. Upon the discovery of manipulated data, the crystallography community recently recommended that CIF data can be accompanied by structure factors in an attempt to reduce fraud. 4 Structure factors had been considered of minimal importance once the CIF format for crystal structures became standard for publishing and sharing crystal data, but it is now realized that these parameters are less susceptible to manipulation than the processed data recorded in the CIF files. Because of the cultural norms in the crystallography community, the expected deposit of CIF files has resulted in relatively straightforward methods to store both CIF and structure factors, to validate the formats and the data using tools such as checkCIF, and to display the data in both publications and interactively via online publication.
Another proposed benefit of making primary data available is the utilization of artificial intelligence/machine learning (AI/ ML) approaches to process data, to enhance existing methods for the prediction of properties of chemical structures, to develop new materials, to discover new drugs, and so on. 5 Ultimately, the goal of these AI/ML techniques would be to understand the science well enough to propose new scientific theories. Whether the latter is feasible, the use of AI/ML techniques requires a large quantity of data in an understandable and reusable format.
Archiving and sharing of analytical raw data are also pivotal for the development of new signal processing algorithms aimed at extracting information that has been elusive to existing methods. For instance, although the Fourier transform (FT) method has proved to be the gold standard for the processing of NMR data because of its computational performance and robustness, it also has well-known limitations such as poor resolving power, leakage artifacts (i.e. Gibbs oscillations), and phase and baseline distortions. There are presently many other alternatives to the FT method that can overcome these deficiencies or new ones that might be developed in the future. Those new methods could potentially uncover new information (i.e., new signals invisible to old methods) that can be used to, for example, prove or discard a chemical structure as inferred with different methods. Obviously, this can be only done if the primary acquired data is preserved. Furthermore, even raw data is generally stored with some loss of information. For example, NMR data is acquired by accumulating many scans to improve the signal-to-noise ratio. However, only the final averaged FID is kept, whereas all the individual transients are thrown away. Although this is usually not a concern, there are some advanced processing techniques that could be used to take advantage of the information contained within the individual scans. 6 For experimental data other than that for crystal structures, instruments have the ability to export in standard formats. For spectroscopic data, JCAMP-DX is a veritable common export format for data sharing, although it has yet to achieve widespread adoption. 7 Although the format has some disadvantages in the nonstandard ways in which instrument manufacturers have added custom parameters, it is still useful for understanding the basic data. However, there are two disadvantages with using export formats for spectroscopic or instrumental data. First, the exported data may be processed data and hence subject to manipulation. Second, the steps to generate the data objects for publications either as selected lists of peaks for the experimental section in the body of the article or as figures in the body of the article or in the supporting information are labor intensive and result in some loss of information. Once those traditional publishable objects have been prepared, it would be an additional human effort to generate and organize the machine-readable data files for submission as supporting information. Although many publications allow submission of supporting information data files, most publications do not require the raw data. There have been few other incentives for researchers to do the extra work to prepare the data files for publication, but most do not. This situation is changing. For instance, a new NMR file format has been recently proposed, NMReData, 8 although its scope is limited to structure characterization of small molecules (i.e., NMR assignments). This format is essentially an extension of the existing structure data format (SDF) and includes the socalled NMR record, which is a compressed folder that, in addition to the NMReDATA file, contains all related 1D and 2D spectra including the raw data such as the FIDs and all the acquisition and processing parameters.
Funding agencies are starting to issue policies that require researchers to preserve and share the research data collected during the course of a research grant. Government funding agencies, for example in the United States, U.K., EU, and Australia, as well as private foundations, such as the Howard Hughes Medical Institute, Wellcome Trust, and Gates Foundation, are mandating that research data in support of journal articles be published in a reusable form. In addition, while data repositories have been common in discipline specific areas for many years and in fact have appeared and disappeared, more generic data repositories such as Figshare,9 Data Dryad, 10 and Dataverse 11 are now available for researchers to store their experimental details. Data journals are also being established to allow researchers to publish and get credit for data collection. These include, for example, Data Science, 12 Nature Scientific Data, 13 MethodsX, 14 and Softwar-eX. 15 When publishing in these data journals, researchers can get credit in the form of a citation for data and/or software publication and at the same time also get credit for publishing a traditional article describing the results and conclusions of the research. Community norms are still emerging as to how these publications will be treated in terms of scientific recognition.
The rigor with which researchers actually comply with funding agency mandates is now a subject of much debate in the research data community. Neylon has proposed that the focus shifts from development of data sharing policies to culture change. 16 His argument is that although policies are important, they focus on changing individual behavior. Given the barriers to data sharing, such as lack of understanding of how to go about sharing data and the need to commit extra human and funding resources to sharing data, a change in culture to promote data sharing would help to motivate researchers to change their individual behavior.
Even though one might agree that an ideal state would be for all primary research data in support of scientific publication to be made publicly available, there are still constraints even in a digital environment with vast amounts of inexpensive storage available. A typical organic synthesis article might include three or four NMR experiments for each of 30−40 substances. Generating the supporting information file consisting of graphical representations of the NMR data with headings, structure diagrams, and sampling parameters is particularly time-consuming. Furthermore, such documents can grow to be very large, there are examples now of supporting information approaching or even exceeding 1000 pages. 17 An alternative approach would facilitate ways in which the researcher can organize and store the FID files as a package for submission as a dataset or a fileset. Because the FID files are the basis for both the data package and the graphical SI data, a workflow that allowed for the generation of both forms of SI at the same time would remove one of the barriers to submission of full/ raw data. Although this approach is consistent with the general concept that content should be stored in canonical form and rendered as appropriate at the time of delivery to the user, 18 it also achieves one of the hallmarks of publicationthat the reader has the option if they wish for a static representation of what the author intended, while at the same time, the interested reader, whether human or machine, has access to both a more interactive and/or a lossless version of the same data.

RESULTS AND DISCUSSION
The Mpublish project aims to address the twin challenges of increasing the findability, accessibility, interoperability, and reusability (FAIR) of spectroscopic research data 19 and facilitating the preparation of such data for publication using automated workflows. The first two sections will describe two key features of the Mpublish project, that is, • an automated workflow for the author to prepare the data for publication and

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Article • a signing system for the publisher, which allows the reader to review the data while addressing reuse and interoperability of the data. The last section will describe how deposition in the Imperial College Data Repository ensures data FAIRness.

Preparation of Primary Research Data for Publication Using an Automated Mpublish Workflow.
The Mpublish author workflow has been implemented as a plugin of the Mnova software platform 20 and is now being integrated into the Mgears automation system within Mnova. Although this limits its use to researchers and scientists that have licensed access to the Mnova software for the processing of their NMR, LC/GC/MS, and optical spectroscopic primary data, the Mnova software does have the advantage that it is widely adopted by the chemistry research community.
The Mpublish workflow generates a complete package ready to be submitted to a specific journal from primary datasets (.mnova or raw data files) that has been satisfactorily processed, analyzed, and annotated within the Mnova platform (see further details on the Mpublish author workflow in Supporting Information and Figure S1). Using the Mpublish user interface, the author will be able to • easily select the data they want to publish, either by following a system of file masks, which match the organization of the data on disk, or by following a system of multiselection or a specific metadata field on the spectral database ( Figure S2), • choose a journal template, • enter author information (name, organization, etc.).
Subsequent execution of the Mpublish author functionality will run an automated process (implemented as an Mnova script based on ECMAScript) 21 and will generate • individual spectrum figures with the corresponding file title, acquisition parameters, and peak list (multiplicity, chemical shifts) templated to the selected journal ( Figure 1), • an ODT (open document text) file including all the processed data (figure, peak list, etc.), and • a folder containing the primary raw data used to generate these processed and analyzed data (subfolder and files renamed and reorganized to identify the datasets and match them with the titles included in the automatically generated aforementioned ODT zip file).
The automatically generated ODT file 22 can be edited and used to prepare the final supporting information (SI) file intended for submission along with the manuscript and along with inclusion of experimental procedures and other appropriate data. The preparation of NMR data for the SI document is thus greatly facilitated by use of the Mpublish automation.
Overall, the advantages of the Mpublish workflow are • simultaneous generation of a folder including the NMR data (zip file) along with an SI document (ODT file) presenting and describing the data (figure, peak list) in line with a selected journal template, • easy modification of the SI document (to satisfy referee comments, to change the template) by using the Mpublish user interface, which will allow to select/ remove data and modify author information and journal template, and • generation of primary data available in machineactionable form, enabling data mining and aggregation.

Signing System Allowing Reusability and
Interoperability of Primary Research Data. A second aspect of the Mpublish project is the ability for the publisher (e.g., the data repository owner) to cryptographically "sign" a dataset, which will be possible for the reader to review using

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Article Mnova software, without the cost associated with the software license. This will allow the reader to analyze the research data more extensively than with a static PDF.
2.2.1. Publisher's Perspective. To enable this feature, the publisher has to provide Mestrelab with a public key, which will be used to sign the data and compose an Mnova publication text file (mnpub). The mnpub file will point to the primary research data (mnova or zip file) and hold both the signature and the public key information. An mnpub file will have to be created for each dataset that the publisher would like to make freely available for review using Mnova software. To this end, the publisher must use an RSA key pair: (i) the public key is sent to MestreLab to allow them to generate a certificate file (.mncrt), which is then made available to the publisher and (ii) the publisher uses their private RSA key to "sign" the dataset, which generates a signature. Overall, the mnpub file will be composed with the signature, the certification file (mncrt) and a URL to the signed dataset (see example of mnpub file). 22 The process is simple and can be executed with very little overhead by the publisher (see Supporting Information for further details). The procedure can be fully automated as a part of the submission process and will not require any additional input from the authors. For example, as described in the last Section 2.3, the procedure can be implemented in a data repository submission web service where the authors only need to upload an mnova file or a zip file, with the mnpub file generated automatically by the web service.
2.2.2. Reader's Perspective. The reader can freely download Mnova software (a purchased license is only needed for general use) and open the .mnpub file. This file will be read by Mnova and verified with the public key information contained in the mnpub file. If the verification is successful, the dataset pointed by the URL field of the mnpub file will be retrieved for use within Mnova with full functionality, without the need for a license. The reader will be able to review, reprocess, and reanalyze the research primary data, benefiting from all the functionality available within the Mnova software package, albeit only for the digitally signed dataset. This effectively makes such signed data open for review and reproduction independently of access to licensed (in this example analytical chemistry) software applications. The impact of such submission of primary research data associated with articles is that it becomes available for detailed review by a wide range of stakeholders such as article reviewers, editors, data curators, publishers, peers, the general public and artificial intelligence, and machine learning systems. Metadata associated with the primary research data as generated by the original instrument and then uploaded to the relevant repository also facilitates subsequent automated or workflow machine processing of the data on as large an aggregated scale as needed.
2.3. FAIR-Enabling Mpublish Data 23 by Deposition in a Data Repository. Although the Mpublish procedure provides an excellent mechanism for making complete NMR data readily and appropriately available, the procedure also needs to include mechanisms for ensuring it adheres as completely as possible to the FAIR data principles (findable, accessible, interoperable, and reusable). 11 Indeed, enclosing the data with the rich toolkit provided by Mnova addresses in large measure the I of FAIR. The other attributes can be accomplished by deposition into a data repository, a process that also includes generating metadata and registering it appropriately to ensure standardized publication. Here, we look at the steps taken to achieve this as implemented in the Imperial College Mpublish pilot project. 24,25 2.3.1. Data Granularity. The first task is to decide upon appropriate granularity for the metadata. Mnova files (generated from Mnova 11.0+ or using the automated Mpublish workflow described above) can be generated for entire collections of spectra in a single file containing many molecules, or they can be restricted to just a single molecule, containing if necessary spectra for different experiments and nuclei. We strongly favor the latter approach, because metadata for single molecules can be generated far more easily and transparently, and we believe also more usefully than for molecule collections. This is largely based on the availability of the InChI molecular identifier 26 and the possibility of generating it for a specific molecule using a simple machine processable workflow procedure. We in fact apply it using OpenBabel 27 in which every file submitted to the data repository is automatically screened by OpenBabel, and if molecular content is identified, then both an InChI string and an InChI key are generated from it.
In our recommendations, every NMR spectrum submitted for deposition is accompanied by a suitable molecular connection table in the form of standard files such as the MDL molfile (.mol) or the ChemDraw .cdx or .cdxml files ( Figure S5). This is essential if raw filesets such as folder collections produced directly by instruments are submitted (in the form of a compressed ZIP archive). Connection tables can also be embedded in the Mnova files resulting from the primary processing of such instrumental data, but currently, OpenBabel is not capable of extracting such information from this format and hence in practice, these too require separate inclusion of a molecule description file. We suggest that the best practice is in fact to provide both the raw instrument archive as a .zip archive (for reusers who choose to use different software) together with the Mnova superset, which includes both the FID and all analysis of the far more "userfriendly" frequency domain spectrum (including solvent reference, annotations, assignments, phase and baseline correction, etc.). Optionally, other processed data formats such as the open standard JCAMP-DX or NMReData noted above can also be provided for further interoperability and even a visual format such as PDF to allow readers the option of access to the traditional format currently found in most supporting information files.

Metadata.
Further metadata is also generated in a workflow manner (Figure S5), as implemented in the data repository. 25 A minimal set would include the following: 1. The ORCID identifier for the depositor along with any further such identifiers for appropriate collaborators. For the repository, 28 we use ORCID as an intrinsic part of the login process and so its inclusion in the metadata records is automatic, as is the affiliation of the depositor. 2. The deposition date and time are also recorded automatically, together with the identity of the formal publisher of the data, which can be the research institution as in the example here. 3. A title and description are recorded, these being part of the Dublin Core metadata elements. 29 Although these can have any value, we favor including the systematic name of the molecule as part of the title field and a brief description of the experimental procedures used to prepare the compound in the description field.

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Article 4. We include a license for reuse and if necessary, reanalysis, in our case the CC0 creative commons license, 30 this directly addressing the R of FAIR. 5. A much less frequently applied but what we consider crucial metadata component is a declaration of a socalled resource map enabling object reuse and exchange (ORE), 31 which addresses the A of FAIR. This in turn would allow any machine-driven automated procedures for retrieving the data files on a scale larger than a human would adopt. 6. A media type is also declared for specific files in any uploaded fileset. For the Mpublish project, these include chemical/x-mnova for the basic Mnova files and chemical/x-mnpub, which is automatically generated using the cryptographic keys, which control the singleuse license on which Mpublish is based.
Once assembled, the metadata is then formulated against the DataCite metadata schema (V 4.1). 32 The metadata specific to the molecule is accommodated in the <subjects> element of the schema, an extensible component where subject-specific terms can be appropriately defined. An example is shown in Figure 2 where the subjectScheme attribute is included to avoid clashes with any other domains, which may accidentally use the same vocabulary. In this example, an InChI identifier is included. To assist molecule discovery, we normally include data for a single molecule so that only one InChI identifier need be included and deprecate including data for multiple different molecules in the same dataset.
With this approach, control over the dictionary used in the <subject> element is highly desirable, because uncontrolled and undeclared extensions would inhibit the deployment of discovery tools. To this end, a Data Interest Group in chemistry has been established and regular meetings scheduled to promote communal agreement. 33 The first proposal includes that shown in Figure 2. Other more NMR specific extensions could in future include, for example, a richer set of NMR-related metadata declaration, such the NMR nucleus studied, the accepted name of any pulse sequence used in the experiment, solvent, temperature, and so forth. Workflows specific to extract this information automatically would also need to be developed. The repository used to construct this demonstrator also supports the concept of hierarchical collections, which serve to organize the data associated with a project into subcollections. Subject element metadata for such top-level collections would not normally be included if they contain no explicit molecule-based datasets.
The DataCite registration API is then used to register the metadata with DataCite, 34 a global aggregator of such content. In return, DataCite responds with a persistent identifier for the metadata taking the form of a Digital Object Identifier or DOI. This is deliberately consistent with the DOI schemes used for journal articles, allowing the development of synergies between the two. The metadata itself can be downloaded as an XML file from the landing page for any collection or dataset using a link, which includes the DOI of the data: https://data.datacite.org/ application/vnd.datacite.datacite+xml/10.14469/hpc/4751 If a more readily human-readable presentation is desired; then, the option of a style-based transform of this file is available. We also anticipate text-based presentations of the metadata will become available directly from the equivalent DataCite page, as invoked by: https://search.datacite.org/ works?query=id:10.14469/hpc/4751 An example of early use of Mpublish deposition in a published article 18 illustrates how the article can formally cite the data (as references 35 35   subject:XZYDALXOGPZGNV-UHFFFAOYSA-M+AND+media.media_type:chemical/x-gaussian* shows how the media type can be combined with two Boolean AND operations to restrict the search to datasets where a specified InChIKey has also been registered and is also accompanied by a Gaussian computation output. This search only specifies that both media types must be present in the fileset but not necessarily that the two are related. 3. https://search.datacite.org/works?query=contributors. nameIdentifiers.nameIdentifier:*0000-0002-8635-8390+AND+media.media_type:chemical/x-mnpub* illustrates how the search can be combined with a restriction to those Mpublish datasets associated with a specific researcher as defined by their ORCID. 4. We note that the above syntax is not human-friendly and hence the need for development of more accessible forms of the search interface that can take advantage of these rich searches. 5. https://app.dimensions.ai/discover/publication?search_ text=10.14469 allows the discovery of datasets assigned the DOI prefix (Imperial College) linked to journal publications registered with the Crossref metadata store, as one example of the synergies noted above. The inclusion of an ORE resource map in the collected metadata allows the results of a search of the above type to then be scripted to allow direct retrieval of specific files on as large a scale as necessary. It is important to note that knowing just the DOI of any published fileset does not automatically

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Article enable this, a DOI normally points to what is called a landing page from which further parochial navigation is required to allow access to individual files is needed. Normally, a human performs this navigation (because it is rarely standard in any predictable sense), but with data, it is essential to have a formal declaration that a machine can traverse automatically. Thus, the A of FAIR ideally relates both to visual access by a human and also to applications such as data mining by machines. One example 18 of the use of an ORE map to automatically retrieve and display data based only on knowledge of the DOI and the desired media type is based on the JSmol molecular visualizer. Such a feature could in principle also be developed for more specific spectroscopic tools such as Mnova.
2.3.4. FAIR Compliance. FAIR compliance can be evaluated using an objective, automated, and community-governed framework. 36 The evaluation 37 for the repository used here 25 is based on the features described above. We recommend that any repository providing access to FAIR-enabled data such as described here should be submitted to such an evaluation.

CONCLUSIONS
The Mpublish tools available within the Mnova NMR analysis program allow the workflow generation of publication-ready supporting information (SI) files, which can be submitted to a publisher and/or a data repository. Both the original primary/ raw data and the generated SI files can be integrated with cryptographic license keys to allow access to individual datafiles without the requirement of first obtaining full commercial licenses. Further workflows can be used to generate a set of standardized metadata describing the datasets. The registration of such metadata with a global aggregating agency such as DataCite in return for association with a persistent identifier (a DOI) can transform the data into a rich resource where all four attributes of FAIR data are at least partially addressed. These include the provenance of the data and unique descriptors of the molecule associated with the spectra to enhance findability via rich searches of the indexed metadata. A metadata resource map allows rich (machine) access, a toolkit facilitates interoperability, information is included in the metadata about any associated data relating to other aspects such as computational simulations and models and an appropriately declared license facilitates reuse. We suggest this model for spectroscopic publication could serve as a starting point for extension to many other forms of molecular spectroscopy and instrumentally generated primary or raw data.
Although ideally FAIR data might imply software agnostic and software independent tools, realistically the generation of FAIR data must take into account tools available now for researchers to prepare and publish research data. The methods described above do not purport to be a complete solution, they are merely an attempt to suggest some usable tools that can achieve the goals of FAIR data now for a specific type of data and to describe an approach, which could be extended to other types of data by other researchers in related domains.


“There’s something compelling about real life”: Technologies of security and acceleration on Chaturbate

Adult webcam platforms, or sexcams, can be considered platforms for the laboring of affect: machines that exploit, accelerate, and capitalize on it. As expected, the primary source of value is the broadcast of sexual performances. However, this article argues, the extraction of value on sexcam platforms relies as well on some of the early established conventions of webcamming, such as the perception of real-time and real-life. The location and quality of the shows are relevant for these reasons, along with the various sorts of personal interactions between the audience and performers. While some of these interactions resemble personal or human ones, the characteristics and scale of exchange that the platform enables, with thousands of viewers connected at the same time demanding the attention of one performer, require new technologies of assistance that involve humans and software—and some entanglements in between. Those technologies are located in the tension of generating value by accelerating exchanges while preserving the attributes that give them value in the first place. This article identifies some of the actors involved and investigates how they contribute to this double articulation.

A Machine of Affect
Once peripheral phenomena on the Web, camming websites-such as Chaturbate, MyFreeCams, or LiveJasminhave been gaining social and economic importance in the last years, attracting millions of visitors every day (Cunningham et al., 2018). Among other factors, crucial for this popularity is the technical and economic model used by these sites. Like other enterprises that profit from the current version and spread of the on-demand or gig economy, camming websites allow people to informally provide low-cost services and user-generated content through a networked digital platform, with minimum restrictions and lack of contractual ties. Platforms, generally speaking, take advantage of existing markets by mediating between users and providers and becoming the field where exchanges take place (Gillespie, 2010;Srnicek, 2016). However, under the appearance "of flat and frictionless digital labor markets" (van Doorn, 2017), on-demand platforms are more than intermediaries: they enact an economic and political vision, a technical model, and a discourse. Technological hybrids, platforms can be considered assemblages that "pull things together into temporary higher-order aggregations" (Bratton, 2015) with unpredictable results. Fact and fiction, the platforms explored here hold bodies of different natures, creating the conditions for their entanglements and profiting from their exchanges.
By promoting circulation through online sexual performances, personal interactions, and monetary exchanges, sexcam platforms are-paraphrasing Patricia Ticineto Clough (2013)-machines for "the laboring of affect": machines that exploit, accelerate, and capitalize on it. As expected, this extraction is conveyed mainly through sexual shows and emotional work (Hochschild, 2012)-understood here as specific types of reproductive labor (Federici, 2012). However, webcamming is a genre with its own conventions. Following Raymond Williams (1977), a convention is "an established relationship, or ground of a relationship, through which a specific shared practice [. . .] can be realized." Albeit historical, conventions are often naturalized, and the "inclusions and exclusions, the styles and the ways of seeing, that specific conventions embody and ratify" become invisible.
By focusing on the JenniCam-the first webcam featuring a human-part one of this article examines how the conventions of the genre, its zones of intensity and ways to actualize affect, were outlined in the late 1990s. The second part explores some of the technologies that Chaturbate.com-at this time one of the most popular sexcam platforms-uses for replicating those conventions in a larger (and accelerated) scale. The technologies examined here are chat moderators, apps, and bots. Drawing freely upon Foucault's (2007) concept of technologies of security, I see those actors as technologies located in the tension of generating value by accelerating exchanges while trying to preserve the attributes that give them value in the first place.
This article draws upon a broader research project about work on sexcam platforms that I have been conducting since 2014. For doing this, I followed a research-creation methodological approach (Chapman & Sawchuk, 2012;Manning, 2008) combining art-based research, 1 sensory ethnography (Pink, 2009), statistical analysis, and inductive content analysis. Perhaps unsurprisingly, the most relevant insights did not come from using a particular method but from the interconnections among them-including overlaps, conflicts, and disconnections. As Gilles Deleuze explains, "[w]hat defines [the multiplicity] is the AND, as something which has its place between the elements" (as cited in Coleman & Ringrose, 2013a). Collaterally, an interdisciplinary approach incited connections among corpus from different realms, as it can be seen in the section on bots and apps toward the end of this article. Looking for the early established zones of intensity on webcams and their current incarnations, I explored textual and visual information from two different times (1990-2003 and 2014-2018) from public broadcastings and chat logs, dedicated online forums, blog and social media posts, and Web archives.

"A Real-Time Look Into the Real Life of a Young Woman"
In 1996, Jennifer Ringley, a 19-year-old college student and Web designer, installed a camera on the top of her computer and started broadcasting through the internet, one image every 15 min. Ringley's was not the first webcam. Before hers, a coffee pot and a fish tank were available for public viewing on the internet, also revolving around matters of contemplation and control as in a fascinated surveillance. The medium was undeniably the message: the first webcams not only transmitted something but also the evidence of that possibility. Ringley, however, would consolidate the genre and would be part of the history of the internet forever when she directed the camera to herself, in her room. With that gesture, the JenniCam was born, and it would be alive for the next 7 years. Ringley's was not the last webcam, either. Her action put in motion a multitude of webcammers, most of them women, that would change notions of representation, entrepreneurship and privacy, in and out of the internet.
As defined by Ringley, the JenniCam was "a real-time look into the real life of a young woman" ("jen-ni-cam," 1999). Despite the small number of images updated per hour, Ringley was lucid in establishing real-time as the first condition of the JenniCam. Real-time is the time of the internet, a reparatory term that denies any mediation but requires an intensification that can only be brought out by technology (Doane, 2006). Real-time is a "sufficiently immediate" time (White, 2006) that "entails the promise of an experience of the now" (Weltevrede et al., 2014). As such, real-time was "the new crack" (Lovink, 2011): the condition for the internet version of a common present, the coincidence that demonstrates that there was someone out there, a zone of intensity.
The JenniCam emerged between the "deep anxieties about the integrity of identity online" (Maguire, 2018) and "the techno dreams of cyberspace as a parallel virtual reality" (Lovink, 2011). Cyberspace, imagined with psychedelic colors and populated by cyborgs, was suddenly just a 320px by 240px grainy (but reassuring) image of a blonde young woman brushing her teeth. Fascinating for thousands of newly connected viewers, it became one of the most popular websites of the early Web. If this fascination started due to its novelty and as a "response to difference" (Smit, 2000), its circulation would produce affect and attract more viewers due to its "stickiness" (Ahmed, 2004). As Ringley elaborated, "I think it's human to not want to be alone, [a]nd with JenniCAM they put it in the corner of their monitor and it's like having someone in the next room" (Allen, 1999)-even if their vital signs update every 15 min.
A crucial aspect of the JenniCam was the real life that it featured-its realness (Paasonen, 2011). Visitors of the JenniCam would commonly find Ringley sitting in front of her computer, petting her cat or eating ice-cream out of the container. Less common but most sensational were the brief moments in which she was naked or having sex. Ringley wanted to "show people that what we see on TV-people with perfect hair, perfect friends, perfect livesis not reality. I'm reality" (as cited in Senft, 2008). Again, not just life but a real one: the evidence that something was happening somewhere right now, without time for ornaments and challenging "the 'cheesy' conventions of commodified entertainment" (Andrejevic, 2004a). As Ringley stated, "there's something compelling about real life that staging it wouldn't bring to the medium" ("JenniCam-Frequently Asked Questions," 1999). Again, the medium seemed to be the message. But what did it say?
The real life of the webcam, what Ringley was bringing into being, was a particular one: the real life of a young woman. Although in her time the gesture was mostly framed as exhibitionism (Dazed, 2016;Shalit, 1998), Ringley was direct in stating that "the cam has been there long enough that now I ignore it" ("JenniCam -Frequently Asked Questions," 1999). Indeed, the camera has been there long enough. Following Raymond William's (1997) framework, Ringley did not make history by establishing a new subject but a new matter: a young woman in a domestic space in charge of her representation in the domestic space-through the internet. Like other self-declared camgirls of that period, Ringley's social experiment engaged explicitly "with the concept of being looked at" (Maguire, 2018). Or, as Mark Andrejevic (2004a) puts it, performing the "work of being watched." Being observed was not a source of discomfort to avoid but a familiar space to explore and inhabit. There, the unassumed performer and the connected audience could meet, building meaning and representation together through interactions (Senft, 2008;White, 2006) and the "affective dynamics of looking" (Coleman & Ringrose, 2013b). Through those dynamics, camgirls and audiences participated in the creation of a particular type of representation and the establishment of a "confession culture" (Senft, 2008) with a long-standing impact, challenging beliefs about the political of the personal.
Unlike other webcammers that pursued a more explicit performative or pornographic engagement with the webcam (Snyder, 2000), Ringley soon committed to what Senft (2008) calls "theatrical authenticity," construct that would define the genre onward. That authenticity entailed a refusal to seek monetary gain out of the JenniCam. "I feel like a traitor," she said when implementing a paid subscription model for subsidizing streaming costs ("JenniCAM Guests," 1997). The internet was going to be free, in different senses, and the means of production would be horizontally distributed-we were told. However, despite being the only one in charge of what would become a popular media show, Jennifer Ringley was rarely portrayed as a Web pioneer or media producer but mostly as "a curiosity" (Andrejevic, 2004b). Ringley herself contributed to this narrative by presenting the JenniCam as a "sort of window into a virtual human zoo" (Ringley, 2000), without highlighting the intense labor it required in terms of setup, maintenance and care for the audience-on camera and through a separate IRC chat. Drawing upon Terri Senft (2008), self-entrepreneurship and emotional labor shaped what the real life of a young woman meant.
"The interesting thing is not me, the interesting thing is the camera" Ringley said (Hagenbaugh, 1997). Not only the camera, though. Albeit commonly confused, the JenniCam was not Jennifer Ringley, nor the camera alone. Ringley was explicit on the hybrid nature of the compound: "[w]hen cosmic forces collide, and I situate myself in the scope of the camera lens, the resulting digital representation is known as JenniCam" (Ringley, 2000). As a new construction, the JenniCam questioned "many identity categories often thought to be stable" (Jimroglou, 1999). Private and public, of course, but also agency and oppression, collective and individual, human and machine. Like a well-behaved cyborg, the JenniCam was "resolutely committed to partiality, irony, intimacy, and perversity" (Haraway, 1991), qualities registered closely-one frame at a time.
Fans often speculated that Ringley would die in front of her camera. In 2003 however, after 7 years of transmissions, Ringley closed "the most publicized, the most enduring, and arguably the most endearing" (Smith, 2005) webcam ever. Unannounced yet foreseeable, the JenniCam had started to slow down when Ringley got a job that took her away from the camera nine hours per day. A decisive factor for its closure, though, was PayPal's cancelation of her account "because the frontal nudity on her Web site violates the company's acceptable use policy" ("Voyeur Website JenniCam to go dark," 2003). Innovating in a field that would be lucrative beyond measure but commercially unsustainable for her as a personal project, the JenniCam experienced the internal tensions of the network where everyone was equal, but some were more equal than others.

An Accelerated Togetherness
The closing of the JenniCam "marked the end of an era" (Senft, 2008). Among other factors, camgirls were no longer a novelty, webcams more accessible, and the confessional culture spread to other media. Around 10 years later, the webcammer as a massive phenomenon reappeared with the rise of the on-demand platforms that allow people to provide low-cost services and user-generated content in an informal way through the internet. However deviating from Ringley's concept, the zones of intensity of the JenniCam and the bodies it assembled have influenced webcamming onward.
If conventions are established relationships, they are habits, zones that carry a particular intensity. The early conventions of the JenniCam have had an enduring impact on who is observed on the webcam and how, where this subject is, what they are doing, what the spectacle is about, how affect is actualized. On current sexcam platforms such as Chaturbate, one of the most popular sexcam platforms and where thousands of webcammers broadcast sexual performances, work is often seen as "an irritation" and shows appear to be "the products of leisure, not of work" (Hofer, 2014). Because monetary compensations seem to be voluntary, people who ask for money are contemptuously called "token-whores" and direct associations with sex work are avoided. Streaming from what looks like their own rooms (and labeled as such), webcam performers state clear that, more than working, they are having fun: "I repeat myself but it's important for me to let you know how much I enjoy my time online with you!," a popular performer declares (Lewis, 2017). Even in those that are called "studio farms"-webcam studios that manage several performers at the same time-rooms are often disguised as bedrooms and professional traits masked, trying to sustain credibility in the realness of the performances. As in amateur pornography, the tensions "between work and the home, labor and pleasure, public and private spheres" (Hofer, 2014) produce a particular quality and shape the perceptions of authenticity, intimacy, and leisure. The real life of the JenniCam, where people work but appear not to be and the gaze seems unlimited, is still real on current sexcams.
The perception of immediacy of the webcam, its particular realtimeness (Weltevrede et al., 2014), is always relevant but continuously redefined. If the JenniCam displayed a new frame every 15 min, current technologies allow and promote different habits of vision, with ranges between 24 and 60 frames per second. Although with a different expression, the expectation of simultaneity and confirmation that someone is out there persist. Not just out there, though. In a one-to-many model of broadcasting, performers are supposed to take care of thousands of viewers at once. This augmented emotional work requires long-lasting sexual performances, entertaining, attention, and conversation to the members of the audience. "I prefer girls like you who interact with us. If I want to see images of women having sex, there [are] millions of places on the web for that. The interaction is what makes this interesting," a participant states during a transmission . Personal interactions are not limited to the timeframe of the shows but spread to other social media platforms that extend and intensify the work (Berg, 2016;Richardson, 2018;van Doorn & Velthuis, 2018). The audience collaborates as well in this construction because rooms in sexcams, as in the JenniCam, are often not only sources of transmissions but places of reunion and togetherness, where they meet and socialize with and around the performer.
The togetherness that happens in sexcam rooms is accelerated through technologies. These technologies allow to increase the number of viewers and their exchanges, with the performer and among them. Following Sara Ahmed (2010), this circulation produces affect (or actualizes it), sustaining the compound and making it sticky. This stickiness will attract even more viewers and new components to its viscosity, making compelling for several actors to invest in this circulation. The platform, for example, rather than trying to balance the uneven distribution of the audience, regularly implement new technologies for speeding up the circulation of the most popular rooms. In this way, while the most popular ones will be displayed on the front page of the website and will attract even more viewers, most of the performers will hardly be noticed in further pages. This newly accelerated togetherness, however, will require new technologies of assistance.

Technologies of Security (and Acceleration): the Simple, the Compound, and the Spurious
The sexcam platforms discussed in this article-like other platforms that provide user-generated content and informal services-extract value from qualities such as intimacy and authenticity (Liang et al., 2018). Still, a context of thousands of simultaneous exchanges and requests toward one performer creates a complex scenario for maintaining those perceptions. The intensity of those properties, that something compelling about real life that gives value to the platform have to be simplified for managing its circulation.
The technologies of assistance for managing circulation on sexcam platforms could correspond to what Foucault (2007) calls "technologies of security." Technologies of security are a wide range of norms and practices that operate by simplifying the social to a limited set of behaviors. They try to prevent the disruption of the system and create a milieu-where circulation can be accelerated. In the case of Chaturbate, a variety of actors collaborate sustaining its accelerated togetherness. These mechanisms are located in the tension of increasing affective value by accelerating exchanges while preserving the aura of authenticity that gives it value in the first place. The actors involved in those technologies are human, machines, and some entanglements in between. Among them, this article will comment on the role of moderators and automated components such as bots and apps.

Moderators
In its most common form, moderators ("mods") are fans of the performer who have received permission for managing the chat, generally as a recognition of their commitment. As in gaming platforms (Taylor, 2018), being a mod is considered an honor and a duty: a promotion that reduces the distance to the performer and creates a special position among other fans. During the transmissions, moderators have direct communication with the performer and can mediate with the audience if problems arise ("What is a mod?," n.d.). Although the role usually has no monetary retribution, some moderators obtain access to privileged content (private shows or videos, for example) or receive extra attention from the performers through social media or text messages. Their role consists mostly of protecting the chat from disruptive behavior, silencing users and spambots, as well as encouraging donations to the performer. Because a big part of the attraction of sexcams is the togetherness, this "positive chatroom environment" where nobody seems to be working ("How do I fulfill my duties as a mod?," n.d.), mods prevent performers from being distracted with administrative tasks-including asking for money. As promoted by a Chaturbate's affiliated site, moderators contribute to the atmosphere of the room in several capacities: Avoiding disruption, moderators help to maintain "live streams as functioning social and communicative spaces" (Taylor, 2018) and are de facto technologies of security. However, as such, they have to keep the circulation between the optimal and the acceptable (Foucault, 2007). Therefore, it is not only about control but also modulation. As explained by a moderator, "[a] good mod is an asset. A bad mod will kill your room so fast you'll wonder what's happened" (perseus1967cb, 2016). A bad mod is characterized as overprotective and controlling, a person that "will silence anyone & everyone he/she doesn't like." This behavior will "tend to have a chilling effect on comments and activity in the room, which most models definitely shouldn't want" (Buttler, 2016). Armed with emoticons, prewritten messages, and mute buttons, moderators exert and incarnate some of the technologies that the accelerated intimacy the site exploits require. But they do not only execute robotic maintenance tasks: They can be, and often are, replaced by bots.

Bots
In the context of Chaturbate, apps and bots are open-source software instructions that extend the capabilities of the rooms, serving as tools for handling and attracting audiences. Apps and bots are written in the same language but receive either name according to their degree of complexity, and they are made (or adapted) by independent developers, usually fans of the performers or performers themselves. Enacting the "programmability" of platforms (Mckelvey, 2011), apps do not modify them permanently but actualize for a moment some of their potential (Bratton, 2015). Accordingly, bots on Chaturbate can enable contests, display messages, and ask for money. They can also perform less visible duties like blocking the ability to chat for users without tokens or banning the use of specific words, such as the ones expressing demands, denoting spam or that can come across as inappropriate.
Bots are primarily visible in the chat window of the room, principally used by the audience to communicate with the performers and among them. Unless someone is typing in behalf of the performer, the performer's side of the chat shows mostly informative texts produced by bots, such as the rules of the room, the tip menu, and the received donations. In this way, bots on Chaturbate not only facilitate the management of a broad audience but also alleviate the performers of their most repetitive communications. As a performer declares, "I'm sorry but when rooms get busy it's very difficult to thank everyone personally. I have missed several tippers due to very active chatting. I would rather have something acknowledge every tip then have someone feel unappreciated" (kmoore, n.d., emphasis added). Messages produced by humans and machines render in the chat in a similar way, where the distinction between them not only does not seem to be relevant but it is erased actively to accelerate circulation.
Bots in Chaturbate, however, resist a regular classification. Although these bots are bots in a chat, they are not precisely chatbots or socialbots that intend to pass as autonomous entities (human beings or not) as in other social media sites. If to exist socialbots need a face and a brain, a profile and a socialbot software (Boshmaf et al., 2011), bots on Chaturbate do not need a different persona other than the performer. Following this definition, they could be considered cyborgs: a hybrid category describing "bot-assisted humans or humanassisted bots" (Chu et al., 2010a). Still, these entities are easier to grasp by borrowing a taxonomy for the nineteenthcentury automata.
In 1821, Robert Willis proposed three categories for describing automatons: "the simple, the compound, and the spurious-or, those depending on mechanism alone, those moved by machinery but also in communication with a human agent, and those controlled solely by a human agent 'under the semblance only of mechanism'" (Sussman, 1999, p. 74). In this mode, bots displaying notices could be considered simple ones and the bots displaying messages as performers could be considered compounds of machines and humans. But there are some spurious ones as well. Indeed, some bots display messages prewritten by the performer as if they were chatbots. Designed "to be stealthy" (Boshmaf et al., 2011), they try, however, not to pass as humans but as chatbots. Hence, they have bot names (Zax, Liz) and introduce themselves unapologetically as such, autonomous agents without responsibility for their words. The following text is part of the code of a bot, showing possible replies that would appear randomly in reaction to some words previously defined: (universitysmiles, n.d.) These bots are described as tools for "models who aren't naturally passive-aggressive but find it appropriate in response to demanding users" (universitysmiles, n.d.) or as "a bitchy AI for models who don't like being bitchy" (codeanon, n.d.), helping performers to maintain that friendly environment where payment is not an obligation. Although pretending otherwise, these bots are not autonomous entities. Yet most of the performers, addressing thousands of viewers at the same time, are not independent either. By sending messages that could or could not have been written by the performer, more than deceiving the audience, bots contribute to the formation of a kind of robo-sociality (Bakardjieva, 2017): a sociality simplified to accelerate its circulation while keeping some of the characteristics that actualize affect.

Apps
The most popular apps on Chaturbate allow the audience to trigger the vibration of sex-toys remotely through monetary donations. The services are often advertised in terms of "give me pleasure with your tips!" and performers who use those apps correlate the amount of money received with the intensity, duration, and pattern of the vibrations. In line with the tradition of the automata and coin-operated machines, the audience expects to perceive the effect of their tips on the performer's body. Their reactions are a source of public entertainment and a sort of video game where the audience collaborates in the orgasm of the performer. Due to its high gratification level ("why tip if it is not going to show" a member asks in a chat), this type of application became popular quickly, changing the landscape of Chaturbate. They standardized shows and increased the number of monetary exchanges and viewers on the site, contributing as much to the acceleration as to the homogenization of the platform. Conventions of the genre, such as the authenticity and simultaneity of the JenniCam, do not disappear but change their expressions.
With the use of these apps, the evidence of a connection between audience and performers is no longer only by chatting but through the reactions that monetary donations seem to produce on the performer's body. Conversational and entertaining skills, before valuable assets in webcam platforms, lose their importance and allow performers to handle bigger audiences, facilitating the emergence of webcammers that do not speak English-even if English remains as the primary language of the platform. Monetary donations acquire a more significant and literal potential with the use of these apps, which leads to an acceleration in the circulation of currency on the platform. This acceleration, however, does not necessarily entail speed: A more passive attitude of the performers, waiting to be affected by the vibrations, brings back some of the slowness of the JenniCam.
These new forms of authenticity and togetherness received criticisms: "[s]ince models use this kind of toy, no more real shows. Models are waiting in front of cam without doing anything. Really annoying. Furthermore, most of them simulate pleasure. What a fake!" (geni_xxx, n.d.). In fact, besides the exaggeration-or not-of the performances of pleasure, the setup of these sex-toys is a complex process that involves the app, a specific browser, and a phone. A user in the same forum complains: "I refuse to tip models using this toy because there is NO SURE WAY TO KNOW if they are using the correct apps necessary to make the toy work properly or just faking a reaction." And another person replies: "who really cares if they are faking it as long as the person who is tipping enjoys the reaction." As with the JenniCam, sexcams challenge standard classifications: The simple, the compound, and the spurious-and the affective viscosity where these categories dissolve.
Setting "the stage for actions to unfold" (Bratton, 2015), the platform allows the materialization of a new assembled body: an overlap of the compound automaton and the spurious one, a mix of humans and machinery-and its impersonation. As with the Industrial Revolution's automata (Sussman, 1999), this fiction does not surge in a void but in a technoeconomic landscape where it could exist, where capital not only reproduces global processes in bodies-as feminist scholars have recalled-but fabricates ones that can affect and be affected by thousands. The automaton is not only a figure here: if a body is formed by relations of forces and velocities (Parisi & Terranova, 2000), these apps facilitate to re-configure the bodies of the performers by embedding capital in their structure, taming, and making them able to react to stimuli sent by thousands of unknown viewers. Likewise, this new generated speed of circulation modifies the bodies of the audience as well, giving them the power to affect the body of a stranger remotely, bounded to this assemblage by the sticky qualities of the accelerated affect.

Conclusion
Sexcam platforms are machines for the laboring of affect: machines that exploit, accelerate, and capitalize on it. This extraction entails the exploitation of zones of intensity with always new technologies. However, despite the novelty of these technologies, some webcamming conventions established more than 20 years ago are still relevant for its extraction of value. Following Raymond William's (1977) characterization of conventions as established relationships, this article understands them as zones of intensity. From the beginning of webcamming with the JenniCam, in 1996, those conventions rely on the perception of simultaneity, intimacy, and authenticity-among others. In the large and accelerated scale of the platform, extracting value from these perceptions require the assistance of a particular set of technologies.
The technologies mentioned above are located in the tension of generating value by accelerating exchanges while preserving (or mimicking) the attributes that give them value in the first place. These technologies are equated here to what Foucault (2007) calls "technologies of security": a wide range of norms and practices that operate by simplifying the social to a limited set of behaviors. This article identifies three of them: moderators, apps, and bots.
An exploration of the technologies of security of the platform shows an array of assemblages of human and nonhuman actors-and their impersonations. While some of those assemblages (such as bots) have received a cyborg denomination (Chu et al., 2010b), this article proposes the adoption of a taxonomy for the Industrial Revolution's automata (Sussman, 1999) for a better understanding of such constructs. An unveil of the compound and spurious creatures of the platform contributes to problematize the technocratic aspects of its discourse. It shows as well that acceleration is not only an increase of speed but, involving movement and displacement (Massumi, 2017), entails the transformation of actors and structures.
A critical examination of the zones of intensity of the webcam and their genealogies reveals the uneven landscape of the platform. Instead of the flatness that the term evokesor even Bratton's (2015) "stack" -the platform seems to be pullulated by a collection of provisional structures and abandoned assemblages. This analysis shows, once again, that the machine that labors affect is not neutral but an opportunistic one that profits from, and perpetuates, pre-existent zones of intensity-such as biases and inequalities. Engaging in what Anna Tsing (2015) calls salvage accumulation, these machines can co-opt and exploit those pre-existent zones, seeking to impose their logic of extraction.
Finally, this article contributes to illustrate the non-linear history of media. If media and media technologies are "not a fixed substance but [. . .] a realm of affects, potentials, and energetics" (Parikka, 2010), it can be expected to have different temporalities, pockets of intensity that seem to be inactive but, as potential, can always be actualized.


extfieldsofstudy:
Physics
Computer Science

s2fieldsofstudy:
Computer Science
Physics
Engineering

text:
The Software System Implementation of Speech Command Recognizer Under Intensive Background Nosie

A device for speech command recognition is designed and implemented in this paper. The adaptive filtering technique is adopted for speech enhancement by using two microphones for sampling the background noise and noisy speech signal correspondently. The connected-word speech recognition algorithm based on HMM is employed for speech command recognition. The experiment shows that the equipment can work well under the background noise of 90dB.

Introduction
Speech command recognition technology for limited vocabulary based on conjunction recognition technology has good robustness and high recognition rate and it is widely used in many fields of information query and voice control.However, speech recognition technology is sensitive to environmental noise when it is applied, especially in strong noise environment, which has a significant impact on speech endpoint detection and feature extraction.Therefore, in a speech recognition system, speech enhancement technology is usually used to reduce the effect of noise on speech signal.The purpose of speech enhancement technology is to try to recover clean speech signals from noisy speech.Traditional speech enhancement methods mainly include parameter estimation methods such as comb filter (estimation of pitch period parameters), wiener filter, kalman filter (estimation of signalto-noise ratio parameters) and parameter estimation-based speech enhancement methods such as spectral subtraction and adaptive filtering noise cancellation.In addition, there are voice enhancement methods based on microphone arrays and methods based on statistics and in-depth learning.Considering the size limitation of microphone array in application, the noise of application scene may be non-stationary, and the type and statistical characteristics of noise are unknown.In practical applications, we consider using non-parametric speech enhancement methods.
In non-parametric methods, spectral subtraction will produce serious spectral distortion under the condition of low signal-to-noise ratio, which will affect the performance of speech recognition. Therefore, we choose the adaptive filtering noise cancellation method of dual microphones as our speech enhancement method.In speech recognition model, we adopt HMM model based on segment length distribution, which has good adaptability and can achieve high recognition rate of command words under the condition of accurate speech endpoint detection. Finally, we transplant the speech enhancement algorithm and speech recognition algorithm to ADSP-21469 platform, and realize the speech recognition task in strong noise environment.  Figure 1 is the block diagram for the realization of speech recognition algorithm.In the front of the speech recognition system, a speech enhancement scheme based on adaptive noise cancellation with dual microphones is used. The microphone labeled Mic_S in the system is close to the mouth to collect voice command signals containing environmental noise. Another microphone labeled Mic_N is set up to collect environmental noise.Under the condition of 90 dB ambient noise, the output signal-tonoise ratio of the microphone used to collect voice is about 0 dB under the condition of short talk.These two microphones acquire signals through adaptive filters to achieve noise cancellation function. The signal-to-noise ratio can be increased by more than 20 dB, which meets the requirement of speech signal-to-noise ratio of follow-up command word speech engine.The speech recognition module of command words used in the system includes endpoint detection, speech feature extraction and template search matching. The final recognition result is sent to the controller system through the data communication interface. Here we introduce the functions and implementation algorithms of the main modules of the system in detail.

( )
Nn  noise path difference and adaptive filter delay in microphone output.The purpose of this method is to make the adaptive filtering noise cancellation algorithm better adapt to various noise environments.
The output of speech enhancement  Figure 2, the adaptive filter is in transverse form.Its input signal is ( ) In this system, the Mic_S microphone for voice acquisition is close to the speaker's mouth, and the Mic_N for background noise acquisition is relatively far from the speaker's mouth.Because of the near-field effect of speech, the signal-to-noise ratio of speech collected by Mic_S microphone is much larger than that collected by Mic_N microphone. That is to say , the signal collected by Mic_N microphone in strong noise environment can be approximately considered as environmental noise.In this case, the condition of formula (3) is approximately satisfied.
According to formula (3), when estimating the minimum mean square value, the following is derived.
In formula (5), ( ) n  is the step-size coefficient of the algorithm, and gradient operator  is concerned with finding partial derivatives of W . Its vector form is expressed as follows.
Notice that  When the value of M is small, for example 01 M.  , the misalignment coefficient has the following approximate relationship.

 
It can be seen from formula (8) that the larger the step size coefficient  , the larger the misalignment coefficient, which will eventually lead to greater output error of the filter, leading to instability.The analysis of filter [1] shows that if signal ( ) is set as a constant, it will affect the stability and convergence speed of LMS algorithm in practical application.To solve this problem, many improved algorithms have been proposed. We use the improved normalized LMS algorithm (NLMS).The NLMS algorithm takes into account the change of input signal amplitude and normalizes the calculation of iteration step coefficients.Its iteration formula is as follows.
W n e n X n T n X n X NLMS algorithm can be seen as optimizing u again on the basis of formula (7), so that the posterior error of filter coefficients calculated from formula (7) is minimized when applied to transverse filters [1] .
Experiments show that the NLMS algorithm takes into account the stability and convergence speed of adaptive filtering, and the algorithm is relatively simple and the computational complexity is small.

Speech endpoint detection algorithm
After noise cancellation, the signal-to-noise ratio (SNR) of speech signal is enhanced, which can satisfy the basic requirement of endpoint detection for SNR, so that the position of speech signal can be detected more accurately from background noise.
Experiments show that the speech recognizer is sensitive to the initial position error of speech. The software of the recognizer uses a double threshold detection method based on short-term average energy to determine the starting point of speech more accurately.Since voice always has a high energy voiced sound after the beginning of speech, a higher threshold T h is set to judge the location of voice 0 N . In order to avoid the loss of data with less energy at the beginning of voice segment, a threshold T i lower than T h is set to detect the real voice starting position 1 N .As shown in Figure 3, the double threshold detection method is based on the correct estimation of background noise energy. In endpoint detection algorithm, cyclic buffer is used to cache voice data.In order to estimate the background noise of speech signal after speech enhancement, the algorithm calculates the energy of input signal of one frame (frame length is 20ms) every 10ms interval, and caches the speech data in a circular queue of 500 frames (5 seconds). In speech detection, the minimum energy of all data frames in the cyclic queue is searched as the background noise energy T noise of the current speech. Setting two thresholds 20dB TT h noise .When detecting the location 0 N where the frame energy of speech is higher than the threshold value of T h , it is considered that a speech event is detected, and the frame energy value of speech is traced back from that time until the location where the frame energy is less than the threshold of T i is found, which is the real starting time of speech 1 N .In addition, in order to prevent misjudgement in retrospective speech starting point, it is agreed that the maximum number of retrospective frames should not exceed 20 frames. When the 0 N position is detected, the voice pronunciation begins. The frame energy of the voice lasts more than 0.5 seconds and falls again below the threshold value T i . This is the end of the voice pronunciation, that is the 2 N position in Figure 3.The method of speech detection based on energy threshold alone works well in quiet environment. However, when there is background noise, this method is prone to interference, resulting in speech error judgment. For this reason, the analysis of pitch features is added in this system. Its principle is that there are voiced components in the pronunciation of Chinese syllables.A remarkable feature of voiced tone is that there is a relatively stable fundamental frequency, so we add a pitch search algorithm to the algorithm.We search for pitches in the range of 3-13 ms pitch period. If more than five pitch frames are found in succession, the voice will be recognized as voice, otherwise it will be considered as interference signal. Using pitch features to detect speech greatly improves the stability of the endpoint detection algorithm.

Command word recognition algorithms and implementation scheme
In the recognition system, the sampling frequency of speech signal is 16 KHz, the adaptive filter uses 320-point FIR transverse filter, and the speech microphone channel inserts 160-point delay to automatically adapt to the change of the two-channel noise delay path environment. After speech enhancement, the speech endpoint is judged, and then the speech recognition features are extracted.Speech signal is pre-emphasized with a high-pass filter ( ) . The speech signal is analyzed by using Hamming window. The frame length is 20 ms and the frame is shifted to 10 ms. The speech feature vector is a 45-dimensional feature vector composed of normalized energy and 14-dimensional MFCC coefficients, together with their first-order and second-order difference coefficients. The system uses DDBHMM [2] model as the basic recognition framework, and uses two Gauss functions of total covariance matrix to form GMM model to describe the distribution of observation vectors.Considering the flexibility and stability, the system uses half-syllable (each Chinese syllable can be divided into Initial and Final parts) as the modeling primitive for word stitching. The system uses 100 Initial models and 164 Final models. Each Initial model is described by two states, and each Final model is described by four states. There are 856 states in the whole model. The training of the HMM model used the soliton full syllable database (1254 syllables) of 100 people (50 male voices + 50 female voices) and the connection word database (699 words) of 100 people (50 male voices + 50 female voices). The two databases covered all the connections between the commonly used syllables and syllables in Chinese.HMM model training is carried out on PC in advance, and the trained model is stored in Flash chip through the simulator development interface. When recognizing DDBHMM model, the frame synchronization search algorithm based on Viterbi decoding can be used directly to recognize the DDBHMM [2] model without considering the correlation of segment length between states, the correlation between observation vectors and the uniform distribution of segment length. If the recognition algorithm is searched directly according to the MLSS recognition algorithm of DDBHMM model, the calculation of the Gauss probability model of the total covariance matrix is very large. Therefore, a two-stage search strategy is adopted.
The first stage is the fast search stage.The state codebook of HMM uses a single Gauss diagonal matrix to find the top N candidate entries with the highest matching degree.The second stage is precise search stage, using GMM model of total covariance matrix.The likelihood distance is calculated


text:
Research on Hodoop Job Scheduling Algorithms Based on Dynamic Fusion

Adaptive genetic algorithm and improved ant colony algorithm are combined to solve Hadoop job scheduling problem. Firstly, the global search ability of the adaptive genetic algorithm is used to generate the list of resources allocated by the task. When the search speed of the genetic algorithm gradually decreases, the optimal integration time of the adaptive genetic algorithm and the ant colony algorithm is determined dynamically. The initial pheromone distribution of the ant colony algorithm is generated from the optimal solution solved by the adaptive genetic algorithm. Improve the target node selection strategy of ant colony algorithm, consider the success rate of completing tasks of nodes, and accelerate the speed of ant colony algorithm to solve the optimal solution. Simulation results show that compared with genetic algorithm and ant colony algorithm, hybrid genetic algorithm takes less time, and the more tasks, the more obvious advantages.

Introduction
Hadoop [1] is an open source big data processing framework of Apache foundation, The core part consists of HDFS(distributed file system) and MapReduce (parallel computing model) modules [2]. HDFS can efficiently complete the efficient storage of large-scale data sets. MapReduce divides the work to be processed by the application into several small pieces, making it easy for developers to realize distributed application programmers. The combination of the two allows users to write distributed programs at the top without having to know the details at the bottom. Users can make full use of the advantages of clustering for distributed and high-speed storage and computing. The combination of HDFS and MapReduce makes Hadoop more powerful [3].
Job scheduling is an important factor affecting the performance of Hadoop platform. Many experts and scholars have adopted particle swarm optimization (pso) [4], ant colony optimization (aco) [5] and genetic algorithm (ga) [6][7]to solve the Hadoop cluster job scheduling problem. Literature [8] aimed at the slow convergence speed and redundant operation problem of genetic algorithm, adopted dual objective function and optimal solution retention strategy to improve job scheduling efficiency. Literature [9]applies the adaptive job scheduling scheme to the ant colony algorithm and makes use of the advantage of positive feedback of ant colony algorithm to improve the efficiency of Hadoop cluster job scheduling. Literature [10]firstly classifies tasks by using resource signatures, and then iterates the configuration scheme by using genetic algorithm, which significantly improves the utilization rate of the cluster. The Hadoop job scheduling algorithm based on mixed genetic algorithm adopted in this paper does not need the administrator to preset values, so as to enhance the dynamic adaptability and improve the overall performance of the platform.

Hadoop job scheduling problem model
Hadoop job scheduling problem is that N tasks are executed on K task-tracker nodes, each Task can only be assigned to execute on one node, and the optimal scheduling sequence of these N tasks to execute on K nodes is to be solved. To describe the Hadoop job scheduling problem model, the following conventions are made: is a task node, set ij ETG as subtask i P on the j T expected execution time, Then the assignment relation of corresponding tasks on nodes can form an n-by-k matrix, the calculation formula is as follows: If m tasks are assigned to the JTH task-tracker node for execution, then the time(j) when node j completes all tasks is: (2) After all tasks are allocated to each resource node for execution, the execution time used is the maximum value of time(j). Therefore, the total execution time is:

Basic idea of algorithm
The basic idea of this algorithm is to adopt dynamic cohesion strategy to mix Adaptive Genetic Algorithm(AGA) with Adaptive Colony Optimization Algorithm(ACO), form a hybrid optimization algorithm(AGA-ACO). The idea of combining AGA with ACO dynamically is：Before the best time ta, the distribution of pheromone conducted by AGA is taken, then the best solution is generated with ACO. The dynamic strategy is as follows ： (1) Set the minimum generation Genemin and the maximum generation Genemax；(2) Calculate the evolution rate of the best solution Genemin-rati of offspring the in the generation of evolution and set the minimum evolution rate of offspring based on Genemin-rati；(3)if the evolution rate of offspring is less than Genemin-rati in the continuous generations, it means that optimization speed in GA is low so as to terminate the GA and run AA.

Adaptive genetic algorithm
3.2.1. Genetic coding and fitness function. Real number coding scheme was used in this passage, the allocation of resources is represented by a list of real numbers, the length of the chromosome is the actual number of tasks. Suppose you have 10 tasks to perform on four task-tracker nodes, task sets TotalTime is the aim function, TotalTime more smaller, the fitness of chromosomes is greater.

3.2.2.
Adaptive genetic strategy . The adaptive genetic algorithm's adjustment strategies for crossover probability and mutation probability are as follows: Original crossover probability is c p ,adjusted crossover probability c P is: f is the fitness value of the optimal chromosome involved in crossover before crossover operation, 2 c f is the fitness value of the optimal chromosome obtained after crossing.
The original probability of variation was m p , the adjusted probability of variation is m P : By adjusting the weighting factor to focus on the selection of certain types of nodes, the weight of pheromone corresponding to CPU processing capacity can be appropriately increased for cpuintensive tasks, so that nodes with strong CPU processing capacity can be selected more easily.
The estimated execution time of tasks on each node obtained by genetic algorithm is converted into the pheromone

Pheromone update.
If the expected execution time of a task on node I is within the specified range, the node is considered to be a valid node. Ant will be affected by pheromone concentration during traveling, and pheromone update is determined by whether the node is a valid node and relevant information brought by backward ant. When task is assigned to the node at 1 t time, the value of the node pheromone is updated to: ( 1 t i  is the size of the node pheromone.  is the regulatory factor. At the time 2 t ,the task assigned by the node is successfully executed and released, or the task assigned by the node fails, the pheromone is updated to:  10. According to equations (7) and (8), the pheromone on the node is initialized; 11. Set parameters and end conditions of ant colony algorithm; 12. Before searching with the ant colony algorithm, first check the task list. If the task list is empty, then randomly place the ants on the node, Otherwise, place m forward-facing ants at the recommended node, and select the next node j according to formula (11), put j into k tabu ; 13. If node j is a valid node, the backward ant is generated at node , and pheromone update is carried out according to equations (9) and (10). If the optimal solution is found successfully, the node is filled into the task list.
14. According to the backward ant information, the success rate of each node and the global pheromone are updated.
15. If it meets the ending condition of ant colony algorithm, then the end of the algorithm is achieved; otherwise, go to step 12.

Combining self-adaptive genetic algorithm with ant algorithm
1. Set the time for dynamic fusion：(1)reach the maximum generation Genemax;(2) Genedie is less Geneminratio by a predefined value.
2.Set the initial pheromone of the ant algorithm: We use the initial distribution of GA. Pheromone on the path ( i,j) is defined as follows:

Analysis of experimental results
The simulated environment was 20 resource nodes, and 30-100 tasks were taken as examples for testing. Simulation experiments were carried out on self-genetic algorithm (GA), ACO and AGA-ACO. In order to test the effectiveness of the algorithm, the control parameters of GA, ACO and AGA-ACO are set the same, and the running time and iteration times of the algorithm are compared respectively. The minimum set of tasks is shown in Table 1.
In order to compare the execution time and cost of AGA, ACO and AGA-ACO in executing larger task sets, a larger task set is set up as shown in Table 2. The control parameters of the three algorithms are the same, and the execution time and execution cost are compared respectively. Expectation cost [10,50] As can be seen from figure 1, when the number of tasks is relatively small, GA has a strong search ability and takes a short time, but with the increase of the number of tasks, the time increases rapidly. ACO algorithm rapidly converges to the optimal solution with the accumulation of pheromones, and the time increase is smaller than AGA algorithm. For AGA-ACO algorithm, after combining the advantages of the two algorithms, the advantages of the algorithm are not obvious when the number of tasks is small. As the number of tasks increases, the advantages of the algorithm become prominent.    Figure 2 shows the AGA, the ACO and AGA -the ACO three algorithms in different task the number of iterations required to obtain a optimal solution of the comparison, the comparison result shows that the number of iterations required AGA algorithm, so the algorithm is the most timeconsuming, AGA -the ACO rapidly in early use of the global search ability of GA algorithm to obtain the initial pheromone distribution, the optimal fusion point of reusing the ACO algorithm of positive feedback mechanism, finding the optimal solution quickly the number of iterations required less than GA and ACO algorithm.

Conclusion
Aiming at the Hadoop job scheduling problem, this paper integrates the adaptive genetic algorithm and ant colony algorithm to solve the optimal solution of task allocation. In the early stage, AGA's global fast search ability is used; in the later stage, ACO's positive feedback mechanism is used to form complementary advantages. In the process of algorithm implementation, the resource capacity of the node and the estimated time of task completion are considered comprehensively, and then the task is allocated to the most suitable node for execution. Compared with the three common schedulers of Hadoop, this algorithm improves the efficiency of job scheduling and saves the cost of administrator management node.