Data Sets Sample Clauses

Data Sets. Each Party hereby agrees that, for data sets that it uses to demonstrate its product (PTC Products in the case of PTC, and the RA Products in the case of RA) it will, if allowed under its agreement with the provider(s) of the data set, provide such data sets to the other Party solely for purposes of the other Party demonstrating the Combined Offering.
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Data Sets. Each Party hereby agrees that, for data sets that it uses to demonstrate its product (PTC Products in the case of PTC, and the RA Products in the case of RA) it will, if allowed under its agreement with the provider(s) of the data set, provide such data sets to the other Party solely for purposes of the other Party demonstrating the Combined Offering. 2. SUPPORT SERVICES DEFINITIONS 3.1 “Level 1 Support” means the resolution of Customer inquiries relating to the Combined Offerings in real time or off-line without assistance from the other Party except as otherwise agreed. 3.2. “Level 2 Support” means the technical expertise the one Party provides to the other’s technical support case managers concerning inquiries regarding the Combined Offering by phone, web-based support interface or other agreed-upon means (“Official Means”) that is necessary to resolve off-line a Customer inquiry, when Level 1 Support does not resolve the customer inquiry and when the applicable technical support representative who takes the call generating such inquiry finds it necessary to elevate the inquiry to the applicable Party’s technical support case manager for resolution off-line, who in turn finds it necessary to contact the applicable other Party to obtain from such other Party the technical expertise necessary to resolve such Customer inquiries. 3.3. “Error” is defined in the Strategic Alliance Agreement. 3.4.
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Data Sets. The Data Set is the intellectual property of the Licensor and its List Owners. The Licensee acknowledges this ownership of the Intellectual Property by the Licensor and agrees that it is an original work that has been created, developed and maintained by the Licensor and its List Owners who have spent considerable time and expense in its compilation and authorship. The loss or abuse of this Intellectual Property could therefore constitute a considerable financial loss to the Licensor and the List Owners and that the Licensee agrees that they will not commit, or cause to be committed, any act that will damage the value of the Intellectual Property in the Data Sets. It is the responsibility of the Licensee to determine the fitness for a particular purpose of the Data Set. The Licensor offers no guarantee as to the fitness for a particular purpose of the Licensed Data Set and the maximum extent of the liability of the Licensor to the Licensee is the License fee paid for the Data Set under the Contract. The Licensee agrees to the following additional usage terms for the Data Set Licensed:
Sample 1
Data Sets. Application or algorithm mapping to hardware demands the appropriate data set on the design and testing procedure. Dataset should be representative accordingly to the algorithm and the way the final user will use this algorithm, in order to help designer to make the right decisions. An inappropriate data set can mislead the designer to decisions that will make the final system without the desired functionality or with low performance. Data sets are used at three design phases: at profiling, simulation and verification phase. These Data Sets are the same for the hardware and software designs but analyzed in different manner. At profiling phase the designer must analyze the algorithm or the application in order to find the most computationally demanding part. If the data set is not proper, then the designer can focus accidentally on a different part of the code than he or she should, and as a result he/she will map to hardware an inappropriate part. The resulting system will not achieve high performance as the hardware part will not be accelerating the most demanding aspect of the algorithm. At simulation phase, the data set has to be representative and to cover every state of the algorithm. If all states are not covered, then the system cannot be tested correctly and it will probably fail at run time. At verification phase, proper data sets lead to proper functional verification. If the data set does not cover all cases then the system will not have been properly verified and at run time it may produce wrong results. Many times these results are very difficult to be found. The well-known 1994Intel Pentium FDIV division bug is the most the famous such case. One solution could be to have a data set that will exhaustively test the algorithm. Such a solution is completely inapplicable in practically all cases, as due to state explosion the profiling, simulation and verification phases will take too long for testing. Such Datasets are the same used in software to show proper functionality.
Sample 1
Data Sets. The implementation of the Count-Min algorithm focused on the efficient mapping of the method on a hardware-based platform. We used a frequently used real-life data set, i.e. the worldcup‟98 [37] (wc‟98), for the algorithmic analysis, the performance evaluation and the validation of the output results. The wc‟98 data set consists of all HTTP requests that were directed within a period of 92 days to the web-servers hosting the official world-cup 1998 website. It contains a total of 1.089 billion valid requests. Each request was indexed using the web-page URLas a key. We created point queries over the Count-Min sketch data structure estimating the popularity of each web-page by counting the frequency of its appearances.
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Data Sets. As described above, the Exponential Histogram is a method that can efficiently offer a probabilistic solution to the counting problem. For the testing and the evaluation of our implemented system we used, again, the real-life data set from the Worldcup ‟98 [37] (wc‟98). We streamed the data into the EH data structure. During the streaming process, we created and made queries over the EH data structure about the number of appearances of specific valid requests. The results that we took as answers were cross validated vs. the answers from Java implementation that we used as basis for our EH implementation.
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Data Sets. The QualiMaster project will use high volume financial data streams. We tested and evaluated the correlation software-based system by using real data from the stock market. The stock prices are provided by an API from the SPRING that provides access to real time quotes and market depth data to the consortium.
Sample 1
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Data Sets. For testing the LDA implementation we collected and prepared data sets consisting of text files where each line is a bag of terms from a document within the particular dataset. Following data sets are available: ● Flickr emotions dataset (47MB): This dataset consist of a Flickr image metadata crawl where emotional tags like “angry”, “happy” were used as queries. Each document in this dataset is a concatenation of a particular image title, tags and description. ● Global warming dataset (64MB): This dataset is a cropped dataset to a Wikipedia article about global warming. Cropping is a technique where given a set of documents (the Wikipedia article in our case) as a seed, a set of similar documents can be selected. Thereby key phrases are extracted from the initial set and used as text queries (to Wikipedia again in our case) to obtain more similar documents and expand the initial set. Thus the dataset consist of Wikipedia articles related to the topic “global warming”. Each document in this dataset is a paragraph from one of the articles. ● Xxxxx dataset (75MB): This dataset was constructed in exact the same way as the predecessor but with the Wikipedia article about “Xxxxx” as a seed. - ● Newsgroups dataset (9.3MB): Newsgroups: The 20-Newsgroups dataset was originally collected by X. Xxxx . It consists of 19,997 newsgroup postings and is usually divided into 7 categories for supervised learning covering different areas: “alt” - atheism, “comp”- computer hardware and software, “misc”- things for sale, “rec” - baseball, hockey, cars, and bikes, “sci” - cryptography, medicine, space, and electronics, “soc” - Christianity, and “talk” - religion, politics, guns, and the middle east. The number of postings for these categories varies from 997 to 5,000. ● CS Proceedings dataset (30MB): We collected scientific publications within the Computer Science domain. We gathered 2,957 scientific documents from proceedings of the conferences in the following areas: “Databases” (VLDB, EDBT), Data mining (KDD,ICDM), “E-Learning” (ECTEL, ICWL), “IR” (SIGIR,ECIR), “Multimedia” (ACM MM, ICMR), “CH Interfaces” (CHI, IUI), and “Web Science” (WWW, HYPERTEXT). The number of publications for these categories varies from 179 to 905. We limited our selection to conferences that took place in the years 2011 and 2012 and publications with more than four pages and we removed references and acknowledgment sections. ● WWW proceeding dataset (26MB): We collected conference proceedings from t...
Sample 1
Data Sets. The table below presents updated information proving the compliance of the project with Article 29 of the Grant Agreement. It contains information about the project publications and the research data needed to validate the results presented in the deposited scientific publications. Completed information about publications themselves are available in D7.6. Type of scientific publication Title of the scientific publication DOI Authors Title of the journal or equivalent Place of publication Year of publicati on Peer- review Is/Will open access provided to this publication Publication Repository Dataset Repository Publication in Conference proceeding/W orkshop Real-Time Connectivity Capabilities of Cellular Network for Smart Grid Applications n/a Xxxx Xxxxxxxxx, Xxxxx Xxxxxxx, German Xxxxxxxx Xxxxxxx EuCNC 2018 conference Ljubljana 2018 YES n/a n/a n/a Publication in Conference proceeding/W orkshop Reasoning on Adopting OPC UA for an IoT- Enhanced Smart Energy System from a Security Perspective 10.1109/C BI.2018.10 060 Xxxxxx Xxxxxxxxxxx 2018 IEEE 20th Conference on Business Informatics (CBI) Vienna 2018 YES Yes - Green OA n/a n/a Publication in Conference proceeding/W orkshop Novel power electronics and used EV batteries in grid optimisation xxxx://xxx.xx g/10.5281/ zenodo.32 05125 Xxxxxxxxx Xxxx- Xxxxxxxx INVADE Black Sea 2018 Workshop Varna 2018 NO n/a n/a n/a Publication in Conference proceeding/W orkshop Low Voltage Grid Operation Scheduling with Uncertainties xxxx://xxx.xx g 10.1007/97 8-3-030- 20055- 8_47 Xxxxxx Xxxxxx, Xxxxxx Xxxxxxx- Xxxxxxxx, Xxxx Xxxxxxx SOCO- International Conference on Soft Computing Models in Industrial and Sevilla 2019 YES Yes- Green OA xxxx://xxx.xxxxx x.xxx/00000/0 6678 Confidential Environmental Applications Publication in Conference proceeding/W orkshop Methodology for the sizing of a hybrid energy storage system in low voltage distribution grids 10.1109/M PS.2019.8 759696 Xxxxxxxx Xxxxxx- Xxxxxxxxxx, Xxxxxxxxx Xxxx-Xxxxxxxx, Xxxxxxx Xxxxxx, Xxxxxx Xxxxxxx- Xxxxxxx, Xxxxx Xxxxxxx, Xxxxx Xxxxxxx-Xxxxxxxxx A: International Conference on Modern Power Systems. "Proceedings of 2019 8th International Conference on Modern Power Systems (MPS) Cluj- Napoca 2019 YES Yes xxxxx://xxx.xxx/ 10.5281/zenod o.3240014 xxxxx://xxxxxxxxx.xxx. edu/handle/2117/337124 Publication in Conference proceeding/W orkshop Resolvd - renewable penetration levered by efficient low voltage distribution grids. Specifications and use case analysis xxxx://xx.xx ...
Sample 1

Related to Data Sets

  • Data Services In lieu of any other rates or discounts, the Customer will receive a discount of 20% for the following Data Services: Access: Standard VBS2 Guide local loop charges for DS1 and DS-3 Access Service.

  • Data Security The Provider agrees to utilize administrative, physical, and technical safeguards designed to protect Student Data from unauthorized access, disclosure, acquisition, destruction, use, or modification. The Provider shall adhere to any applicable law relating to data security. The provider shall implement an adequate Cybersecurity Framework based on one of the nationally recognized standards set forth set forth in Exhibit “F”. Exclusions, variations, or exemptions to the identified Cybersecurity Framework must be detailed in an attachment to Exhibit “H”. Additionally, Provider may choose to further detail its security programs and measures that augment or are in addition to the Cybersecurity Framework in Exhibit “F”. Provider shall provide, in the Standard Schedule to the DPA, contact information of an employee who XXX may contact if there are any data security concerns or questions.

  • Data Storage Where required by applicable law, Student Data shall be stored within the United States. Upon request of the LEA, Provider will provide a list of the locations where Student Data is stored.

  • Data Segregation a. DSHS Data must be segregated or otherwise distinguishable from non-DSHS data. This is to ensure that when no longer needed by the Contractor, all DSHS Data can be identified for return or destruction. It also aids in determining whether DSHS Data has or may have been compromised in the event of a security breach. As such, one or more of the following methods will be used for data segregation.

  • Privacy and Data Security (a) The parties will keep confidential any information regarding the Company, Nationwide, the Variable Accounts, and Contract Owners received in connection with providing services and meeting their respective obligations hereunder, except: (a) as necessary to provide the services or otherwise meet their respective obligations under this Agreement; (b) as necessary to comply with applicable law; and (c) information regarding the Variable Accounts which is otherwise publicly available. The parties will maintain internal safekeeping procedures to safeguard and protect the confidentiality of the data transmitted to another party or its designees or agents in accordance with Section 248.11 of Regulation S-P (17 CFR 248.1–248.30) (“Reg S-P”) and any other applicable federal or state privacy laws and regulations, including without limitation 201 CFR 17.00 et seq. and applicable security breach notification regulations (collectively “Privacy Laws”). Each party shall use such data solely to effect the services contemplated herein, and none of the parties will directly, or indirectly through an affiliate, disclose any non-public personal information protected under Privacy Laws (“Non-public Personal Information”) received from another party to any person that is not an affiliate, designee, service provider, or agent of the receiving party and provided that any such information disclosed to an affiliate, designee, service provider, or agent will be under the same or substantially similar contractual limitations on use and non-disclosure and will comply with all legal requirements. The Company will not use information, including Non-public Personal Information, directly or indirectly provided to it by Nationwide or its designees or agents pursuant to this Agreement for the purpose of marketing to Contract Owners or any other similar purpose, except as may be agreed by the parties hereto. Except for confidential information consisting of Non-public Personal Information, which will be governed in all respects in accordance with the immediately preceding sentence, confidential information does not include information which (i) was publicly known and/or was in the possession of the party receiving confidential information (“Receiving Party”) from other sources prior to the Receiving Party’s receipt of confidential information from the party disclosing confidential information (“Disclosing Party”), or (ii) is or becomes publicly available other than as a result of a disclosure by the Receiving Party or its representatives, or (iii) is or becomes available to the Receiving Party on a non-confidential basis from a source (other than the Disclosing Party) which, to the best of the Receiving Party’s knowledge, is not prohibited from disclosing such information to the Receiving Party by a legal, contractual, or fiduciary obligation to the Disclosing Party, or (iv) describes the fees payable to Nationwide under this Agreement.

  • Data Subjects The categories of Data Subjects who we may collect Personal Data about may include the following where they are a natural person: the Customer, the directors and ultimate beneficial owner(s) of the Customer, your customers, employees and contractors, payers and payees. You may share with Airwallex some or all of the following types of Personal Data regarding Data Subjects: • full name; • email address; • phone number and other contact information; • date of birth; • nationality; • public information about the data subject; • other relevant verification or due diligence documentation as required under these terms; and • any other data that is necessary or relevant to carry out the Agreed Purposes.

  • Communications and Computer Lines Tenant may install, maintain, replace, remove or use any communications or computer wires and cables (collectively, the "Lines") at the Project in or serving the Premises, provided that (i) Tenant shall obtain Landlord's prior written consent, use an experienced and qualified contractor reasonably approved by Landlord, and comply with all of the other provisions of Articles 7 and 8 of this Lease, (ii) an acceptable number of spare Lines and space for additional Lines shall be maintained for existing and future occupants of the Project, as determined in Landlord's reasonable opinion, (iii) the Lines therefor (including riser cables) shall be (x) appropriately insulated to prevent excessive electromagnetic fields or radiation, (y) surrounded by a protective conduit reasonably acceptable to Landlord, and (z) identified in accordance with the "Identification Requirements," as that term is set forth hereinbelow, (iv) any new or existing Lines servicing the Premises shall comply with all applicable governmental laws and regulations, (v) as a condition to permitting the installation of new Lines, Tenant shall remove existing Lines located in or serving the Premises and repair any damage in connection with such removal, and (vi) Tenant shall pay all costs in connection therewith. All Lines shall be clearly marked with adhesive plastic labels (or plastic tags attached to such Lines with wire) to show Tenant's name, suite number, telephone number and the name of the person to contact in the case of an emergency (A) every four feet (4') outside the Premises (specifically including, but not limited to, the electrical room risers and other Common Areas), and (B) at the Lines' termination point(s) (collectively, the "Identification Requirements"). Landlord reserves the right to require that Tenant remove any Lines located in or serving the Premises which are installed in violation of these provisions, or which are at any time (1) are in violation of any Applicable Laws, (2) are inconsistent with then-existing industry standards (such as the standards promulgated by the National Fire Protection Association (e.g., such organization's "2002 National Electrical Code")), or (3) otherwise represent a dangerous or potentially dangerous condition.

  • Data Security and Privacy Plan As more fully described herein, throughout the term of the Master Agreement, Vendor will have a Data Security and Privacy Plan in place to protect the confidentiality, privacy and security of the Protected Data it receives from the District. Vendor’s Plan for protecting the District’s Protected Data includes, but is not limited to, its agreement to comply with the terms of the District’s Bill of Rights for Data Security and Privacy, a copy of which is set forth below and has been signed by the Vendor. Additional components of Vendor’s Data Security and Privacy Plan for protection of the District’s Protected Data throughout the term of the Master Agreement are as follows:

  • Data Encryption Contractor must encrypt all State data at rest and in transit, in compliance with FIPS Publication 140-2 or applicable law, regulation or rule, whichever is a higher standard. All encryption keys must be unique to State data. Contractor will secure and protect all encryption keys to State data. Encryption keys to State data will only be accessed by Contractor as necessary for performance of this Contract.

  • Data Security and Unauthorized Data Release The Requester and Approved Users, including the Requester’s IT Director, acknowledge NIH’s expectation that they have reviewed and agree to manage the requested controlled-access dataset(s) and any Data Derivatives of controlled-access datasets according to NIH’s expectations set forth in the current NIH Security Best Practices for Controlled-Access Data Subject to the GDS Policy and the Requester’s IT security requirements and policies. The Requester, including the Requester’s IT Director, agree that the Requester’s IT security requirements and policies are sufficient to protect the confidentiality and integrity of the NIH controlled-access data entrusted to the Requester. If approved by NIH to use cloud computing for the proposed research project, as outlined in the Research and Cloud Computing Use Statements of the Data Access Request, the Requester acknowledges that the IT Director has reviewed and understands the cloud computing guidelines in the NIH Security Best Practices for Controlled-Access Data Subject to the NIH GDS Policy. The Requester and PI agree to notify the appropriate DAC(s) of any unauthorized data sharing, breaches of data security, or inadvertent data releases that may compromise data confidentiality within 24 hours of when the incident is identified. As permitted by law, notifications should include any known information regarding the incident and a general description of the activities or process in place to define and remediate the situation fully. Within 3 business days of the DAC notification, the Requester agrees to submit to the DAC(s) a detailed written report including the date and nature of the event, actions taken or to be taken to remediate the issue(s), and plans or processes developed to prevent further problems, including specific information on timelines anticipated for action. The Requester agrees to provide documentation verifying that the remediation plans have been implemented. Repeated violations or unresponsiveness to NIH requests may result in further compliance measures affecting the Requester. All notifications and written reports of data security incidents and policy compliance violations should be sent to the DAC(s) indicated in the Addendum to this Agreement. NIH, or another entity designated by NIH may, as permitted by law, also investigate any data security incident or policy violation. Approved Users and their associates agree to support such investigations and provide information, within the limits of applicable local, state, tribal, and federal laws and regulations. In addition, Requester and Approved Users agree to work with the NIH to assure that plans and procedures that are developed to address identified problems are mutually acceptable and consistent with applicable law.

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