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Privacy Policy for users of mobile apps and web-apps based on the Staffbase platform

The Staffbase GmbH runs a platform (“Staffbase platform”) which hosts an application for smartphones, tablets, and/or a over which users get access to content and functionalities.

The following privacy policy applies to collecting, processing and usage of personal data in the context of the registration and use of the provided web-apps and mobile apps.

As defined by the German Telemedia Act and the German Federal Data Protection Act, Staffbase GmbH is the website service provider and the company responsible for data protection. If you have questions or suggestions concerning our policies on data protection, please refer to the following contact information:

Staffbase GmbH Am Walkgraben 13 09119 Chemnitz Deutschland Email: Tots Boys Anvil V Shoe Black Red DC d4lxv6

We collect, process, and use your personal data only if it is allowed or appointed by law, or if you have given your consent to do so.

For technical reasons, certain data is always passed on to us from your computer as the communication takes place on the internet. This includes the date and time you visited our website, your browser type, the browser settings, the operating system, the previous website you visited and the quantity of data transmitted.

This information will only be stored to use in the case of an error and will be deleted after 30 days. This information is usually not personal data. Furthermore, your computer also transfers your IP-Address to us, which in individual cases could be personal-related. The above-mentioned data is only used for internal system-related purposes.

In the context of the registration, it is necessary that you provide various personal data, which we will store and use. While doing so, we only require you to provide such information in order for you to be able to take advantage of your app.

In the registration process we collect and use your email address or similar registration ID and possibly ask you to choose and create a password. Additionally, you can also provide us with your name and company handle. We may offer more registration alternatives in the future, which will ask different, clearly labeled information (such as using your mobile number instead of your e-mail address). We use the above-named data to authenticate you as the legitimate user of your app.

Furthermore, we (automatically) store certain activities during your use of the app. This includes the user-ID, your login date and time, your browser type, the browser settings and the operating system. Unless you give us permission to continue to store and use your data, we delete the above-mentioned information as soon as you delete or deactivate your user account.

Our employees who maintain the appropriate privileges, and those working on behalf of Staffbase GmbH’s external IT providers, are able to view the above named personal data. Please note that based on the current state of technology, absolute security of data transfer on the internet can not be guaranteed. The user is responsible for the security of the data they transfer through the internet.

In some cases, we may transfer personal data to a third party. This only happens though, if it is required or permitted by law, or if you have given your consent.

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In accordance with legal provisions, you have the right to correct, delete, and block your personal data. Additionally, you have the right to obtain the following information from us at any time: (as appropriate) which of your personal data we store, what our purpose for storing this data is, as well as requesting the origin and recipient, or recipient category of this data.

Such inquiries can be directed to the following contact information:

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Open Access
Henry Eberle , Slawomir J. Nasuto , Yoshikatsu Hayashi
Published 14 March 2018 . DOI: 10.1098/rsos.171314
Henry Eberle
Brain Embodiment Laboratory, Biomedical Engineering, School of Biological Sciences , University of Reading , Reading RG6 6AH, UK
Slawomir J. Nasuto
Brain Embodiment Laboratory, Biomedical Engineering, School of Biological Sciences , University of Reading , Reading RG6 6AH, UK
Yoshikatsu Hayashi
Brain Embodiment Laboratory, Biomedical Engineering, School of Biological Sciences , University of Reading , Reading RG6 6AH, UK

Abstract

We present a novel way of using a dynamical model for predictive tracking control that can adapt to a wide range of delays without parameter update. This is achieved by incorporating the paradigm of anticipating synchronization (AS), where a ‘slave’ system predicts a ‘master’ via delayed self-feedback. By treating the delayed output of the plant as one half of a ‘sensory’ AS coupling, the plant and an internal dynamical model can be synchronized such that the plant consistently leads the target’s motion. We use two simulated robotic systems with differing arrangements of the plant and internal model (‘parallel’ and ‘serial’) to demonstrate that this form of control adapts to a wide range of delays without requiring the parameters of the controller to be changed.

Closed-loop control is ubiquitous precisely because it is so useful—negative feedback allows a system to remain stable in the face of disturbance and continue functioning even in a changing environment. It is no surprise, then, that negative feedback loops are found so often in living organisms, which must cope with uncertain environmental conditions—the very concept of homeostasis is predicated upon them. However, some processes such as motor control are difficult to explain: closed-loop control is highly sensitive to feedback delay, and delays in the nervous system would not seem to support the quick movements that animals like humans make routinely. Some theorists argue that this can be reconciled through ‘strong anticipation’ [ FOOTWEAR Pumps on YOOXCOM Fashionlab lSBQoC8s
], where a continuous coupling between the controlling process within the nervous system and the body itself allows otherwise delayed-feedback to be predicted. This also suggests a useful paradigm for the control of artificial systems as well, but practical examples of how a strongly anticipating system could be constructed are lacking. We have designed a framework for using the paradigm of anticipating synchronization (AS) to enable closed-loop control in the presence of uncertain or variable delays that we call the ‘sensory coupling’, which we believe displays the hallmarks of strong anticipation, and applied it to a classic control task of tracking a moving target with a simple robot arm.

Date: 05 Nov, 2015

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Date: 05 Jan, 2016

The recommendations are, on purpose, neutral to the query language (or, in fact, any technology) used. The reason for that is, that the recommendations need to work across mutiple technologies and platforms, both for different data providers, but also across time.

Having said that, the need to keep a query re-executable does not rest with any data user (and is thus not necessarily part of an external API), but with the data provider! For the user, the fact that a query is being stored and pointed to will, in most cases, be entirely transparent.

As long as a data provider stays with a given technology (say, a specific SQL dialect or a shell script cutting rows and columns from CSV files), queries can be re-executed. Upon migration of the data to a new technology (which is a major project in any case, as the entire data representation might change, or all internal access APIs need to be adapted), then the queries need to be migrated as well, as addressed in Recommendations R13 and R14. Assuming that any new data representation will need to be as granular/powerful as the preceeding one, supporting the same type of data selection methods, such a migration will be possible.

There is no need for queries to work across different SQL dialects, as they are always local to the system processing the queries in the first place.

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Date: 24 Nov, 2015

It seems like a good approach. But I don't know how hard it would be to implement with some of our data systems that badly need a solution. Would it be possible to talk with somebody about arranging a pilot implementation?

Sure - we'll be happy to help! We are also curious to learn from feedback from new pilots, different settings, to see how easy or difficult the recommendations are to adopt. This is valid for any new pilot setting. Just contact us, any of the chairs (you can reach me at [email protected] ) , or feel free to also discuss any issues discovered here in the forum.

Andreas

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I second the intention to make database usage replicable/reproducible.

However, I think the current recommendations are specific for a certain type of use case.
I see at least two types of database usage scenarios that could lead to at least two strategies of preserving queries:
scenario 1) flat data model, huge amount of data (typically from sensors or instruments), many users needing different kinds of slices of the data.
scenario 2) complex data, not in truly large amounts, modeled according to prevailing but changing insights (the data model itself changes over time).
Preservation method 1): timestamp every grain of data. It makes applications a bit more complex, but in scenario 1) this is doable and leads to huge savings of space compared to the next method.
Preservation method 2): store query results together with the queries. Make periodic snapshots of the database as a whole. It takes more space, but it is doable in scenario 2), and it keeps applications simpler compared to the previous method where every grain of data must be made time sensitive.
At the Eep Talstra Centre for Bible and Computer, we are dealing with a linguistic text database of the Hebrew Bible. It has all the hall-marks of scenario 2). Users develop queries not to define a slice, but to detect special (and rare) patterns. The web application SHEBANQ ( https://shebanq.ancient-data.org ) acts as a query saver. It provides access to multiple snapshots of the database. Users can share a query (without the promise of unchangeability), but they can also publish a query against one of the snapshots. After the act of publishing, the user gets one week to unpublish, and after that the query body and its results (against one particular snapshot of the data) get frozen. The user can still add other query bodies for other snapshots and publish them separately. Using another body for another snapshot accomodates changes in datamodel between snapshots. Of course, there is no guarantee that the query bodies of one query share the same .

The second option is definitely a valid method if the data set and the accumulated query results stay small enough to be stored repeatedly and redundantly. It is basically a trade-off between the complexity of managing the complexity of having a potentially large number of result sets, or managing the complexity of migrating the data schema plus the queries frequently.

In many (most?) cases I have personally come across so far in different research infrastructures, the data schema tends to evolve rather slowly, as any change to the data schema will usually require massive changes down the processing pipline on external APIs, internal APIs, sometimes GUIs, as well as training the users or guiding them through the changes smoothly.

In settings as you described it may,however, proove better to simply store the queries and result sets redundantly (which isn't all that different from the recommendations, in general, as you still keep the query to have provenance information on the data). the main difference I see in your scenario is on how the versioning is done, i.e. whether it is done on a record level or on a snapshot level. this has to be decided based on the usage scenario, i.e. how frequent updates are to the database, and when users will be able to see those updates in their query. In many cases, if repeatability is desired, versioning the data rather than keeping redundant copies is more efficient.

Andreas

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