Setup. Before accessing the Service, you must complete the required Service Documentation which must be accepted by Bank. The type of ACH Entries you will be allowed to originate will be only as permitted by Bank in the Service Documentation. You must also complete the required setup procedures. If you wish to access any of the Services which are available through Online Banking for Business (“OLBB”), you must also sign up for the OLBB service and complete the applicable Service Documentation and setup process and Bank must agree to provide that service to you. You are responsible for the contents of all setup instructions delivered to Bank. Bank is not responsible for detecting errors contained in any instructions or Entry Data, and Bank is entitled to rely on the information contained in your instructions. You must maintain at least one demand deposit Account with Bank to receive the Service.
Setup. Parties mutually agree to meet and fulfill implementation requirements as specified and set forth as follows. To facilitate a fast and effective implementation, eLuma will be responsible for the following:
1. Assigning an implementation specialist who will lead the implementation project, ensure that eLuma and Partner tasks are completed in a timely manner, and make sure all parties are coordinated so that Services and Software access can begin as quickly as possible.
2. Assigning an information technology specialist who will assist with Site(s) configuration and setup in accordance with the Services and Software outlined in this Agreement.
3. Recruiting, hiring, onboarding and credentialing, training, and staffing the Partner needs as outlined in this Agreement.
4. Providing a copy of the fully executed Agreement, signed W-9, and the Professional and General Liability insurance to Partner upon request.
5. Training adult supervisors (also known as “Facilitators”) and creating a one-page document for each Site’s Facilitator including, but not limited to:
1. Logging into the eLuma’s software system.
2. Turning on the webcam, microphone, and audio.
3. Basic troubleshooting webcam, microphone, and audio issues.
4. How to contact the technical support team.
6. Creating the therapy schedule with the support of the Partner, or support the Partner in creating the therapy schedule.
7. Ordering and shipping Equipment upon the request of the Partner and invoicing in accordance with this Agreement. To facilitate a fast and effective implementation, Partner shall be responsible for the following before or during the implementation process with eLuma:
1. Assigning a main point of contact (“Implementation Champion”) for the Partner during implementation. The Implementation Champion will ensure that Partner tasks are completed in a timely manner and that the implementation stays on schedule.
2. Assigning an Information Technology specialist and providing a phone number and email in order to set up working computers, webcams, microphones, audio, and/or Ethernet connections at each Site used in conjunction with Services.
3. Providing caseload information including, but not limited to the number of Students requiring Services, minutes of Services, and group therapy session size. (Note: groups sessions are not to exceed four (4) Students at a time and no more than two (2) Students per computer.)
4. Providing the name(s) of each Site and Facilitator for each Site where Service...
Setup. Prior to initiating the Program and in advance of each calendar year, HQY shall provide a planned contribution file form (the "Planned Contribution File Form") to Employer. Employer will complete the form in accordance with HQY's instructions and the standard file specifications provided to Employer by HQY from time to time, setting forth the then-available features and options of the Program. The Program shall be designed, implemented and administered in accordance with the elections made in the Planned Contribution File Form delivered to HQY by Employer. HQY shall rely on the Planned Contribution File Form delivered by Employer in developing the systems and platform for the administration of the Program. Once delivered to HQY, the Planned Contribution File Form shall be an irrevocable election by Employer (for the calendar year identified in the form) for HQY to design, implement and administer the Program in accordance with elections made therein. HQY shall have no responsibility for liabilities, penalties, or claims that result from Employer’s failure to provide timely and accurate Planned Contribution File Forms. Employer will deliver the Planned Contribution File Form to HQY at least weekly or in conjunction with the Eligibility File if an Account Holder must be added or removed from participation in the Program or the Annual HSA Balance Booster Amount of an Account Holder must be changed. “Annual HSA Balance Booster Amount” shall mean, with respect to each Account Holder, the dollar amount of planned HSA contributions Employer shall make available to such Account Holder as a contribution to such Account Xxxxxx’s HSA outside of the normal contribution cycle through the Program in a calendar year as conclusively set forth on the Planned Contribution File Form delivered to HQY by Employer from time to time. Employer shall promptly reimburse HQY for any costs incurred by the HQY as a result of any inaccurate, incomplete or erroneous data included in the Planned Contribution File Form. Employer shall provide HQY with timely, accurate and complete information regarding the methodology used by Employer to determine each Account Holder's Annual HSA Balance Booster Amount as well as where Account Holders may locate information regarding Employer's policies and procedures relating to the Program Information (collectively, "Program Information") in accordance with HQY's instructions and the standard file specifications provided to Employer by HQY from time to ...
Setup. Merchant will be solely responsible for the installation of such Equipment and any alterations necessary for such installation. Processor will not be liable for any delay or incompletion of an installation of Equipment. Merchant will be responsible for maintaining and paying for electrical power and a secured phone line or other secure internet connection to be used solely by the Equipment to communicate with Processor.
Setup. 3.1 The Supplier will undertake the Setup Work (if any) on the following basis:
(a) the Customer must meet its obligations in relation to Setup Work as described in part 1 of the Appendix;
(b) the Customer acknowledges that the Setup Work has been determined based in part on information provided to the Supplier by the Customer. Where that information is incomplete or inaccurate, or where the Customer’s requirements otherwise change before or during the course of the Setup Work, the Supplier will notify the Customer of any resulting changes to the Setup Work and may charge for the additional work at its standard hourly rates.
Setup. Have your wireless network card (device) associate with the SSID “EAAVENDOR.” Input the password (will be issued the password in your 2023 check-in packet). After association, you should have an IP address automatically assigned and can begin using the service. If you have difficulty connecting, please contact the EAA Wi-Fi Help Desk number at (000) 000-0000 for assistance or to report an issue.
Setup. The target is a development board mounting an ATmega328P 8-bit microcon- troller working at 16 MHz clock frequency. We are storing random data (8-bit values) in flash memory using a memcpy() operation (in a random address each time). During that operation, we measure the power consumption of the device with a Tektronix CT1 current probe attached to a 20 GS/s digital oscilloscope
Table 1. Portable template attack experiments using Device 1 (D1) for profiling. POI Rank (D1 vs D1) Rank (D1 vs D2) Rank (D1 vs D3) Rank (D1 vs D4) (XxXxxx Waverunner 9104) triggered by the microcontroller, which rises a GPIO signal when the internal computation starts. Each power trace is formed by 400 samples taken at 1 GHz with 8-bit resolution. As an attacker, our goal is to obtain the exact 8-bit value loaded in flash memory using template attacks. A set of np profiling traces are taken from the profiling device(s) (storing random 8-bit values) and labeled with the stored value. The traces are preprocessed by aligning them and applying the aforementioned standardization technique. Then, a SOST function is ran in order to find possible POIs. 256 templates are built by computing the mean and co-variance matrix for each labeled group (in the selected POIs), using the pooled matrix optimization method. In the attack- ing phase a set of na power traces of the attacking device storing a fixed 8-bit value are taken. Then the multivariate model is applied and the 8-bit value is guessed using the maximum likelihood principle. Each label will obtain a confi- dence value and the 256 labels will be ranked. We consider the attack successful when the correct candidate obtains a rank of 25 or less (the correct candidate is in the top 10% of candidates). We assume that then, the correct value could be guessed using (optimized) brute force.
Setup. We evaluate our approach on Chinese-English alignment and translation tasks. The training corpus consists of 1.2M sentence pairs with 32M Chinese words and 35.4M English words. We used the SRILM toolkit (Xxxxxxx, 2002) to train a 4-gram language model on the Xinhua portion of the English GIGAWORD cor- pus, which contains 398.6M words. For alignment evaluation, we used the Tsinghua Chinese-English word alignment evaluation data set.1 The evalu- ation metric is alignment error rate (AER) (Och and Ney, 2003). For translation evaluation, we used the NIST 2006 dataset as the development set and the NIST 2002, 2003, 2004, 2005, and 2008 datasets as the test sets. The evaluation metric is case-insensitive BLEU (Xxxxxxxx et al., 2002). We used both phrase-based (Xxxxx et al., 2003) and hierarchical phrase-based (Chiang, 2007) translation systems to evaluate whether our approach improves translation performance. For the phrase-based model, we used the open-source toolkit Moses (Xxxxx and Xxxxx, 2007). For the hierarchical phrase-based model, we used an in- house re-implementation on par with state-of-the- art open-source decoders. We compared our approach with two state-of- the-art generative alignment models:
1. GIZA++ (Och and Ney, 2003): unsupervised training of IBM models (Xxxxx et al., 1993) and the HMM model (Xxxxx et al., 1996) us- ing EM,
2. BERKELEY (Liang et al., 2006): unsuper- vised training of joint HMMs using EM. For GIZA++, we trained IBM Model 4 in two directions with the default setting and used the grow-diag-final heuristic to generate symmetric alignments. For BERKELEY, we trained joint HMMs using the default setting. The hyper- parameter of posterior decoding was optimized on the development set. We used first-order HMMs for both word alignment and phrase segmentation. Our joint alignment and segmentation model were trained using the Viterbi EM algorithm for five iterations. Note that the Chinese-to-English and English-to- Chinese alignments are generally non-identical but share many links (see Figure 1(c)). Then, we used the grow-diag-final heuristic to generate symmetric alignments.
Setup. We evaluated our approach on Chinese-English and English- French machine translation tasks. For Chinese-English, the training corpus from LDC con- sists of 2.56M sentence pairs with 67.53M Chinese words and 74.81M English words. We used the NIST 2006 dataset as the validation set for hyper-parameter optimization and model se- lection. The NIST 2002, 2003, 2004, 2005, and 2008 datasets were used as test sets. In the NIST Chinese-English datasets, each Chinese sentence has four reference English transla- tions. To build English-Chinese validation and test sets, we simply “reverse” the Chinese-English datasets: the first En- glish sentence in the four references as the source sentence and the Chinese sentence as the single reference translation. For English-French, the training corpus from WMT 2014 consists of 12.07M sentence pairs with 303.88M English words and 348.24M French words. The concatenation of news-test-2012 and news-test-2013 was used as the valida- tion set and news-test-2014 as the test set. Each English sen- tence has a single reference French translation. The French- English evaluation sets can be easily obtained by reversing the English-French datasets. We compared our approach with two state-of-the-art SMT and NMT systems:
Setup. For our attack we use the Pin˜ata2 development board by Riscure as our target. The CPU on the board is a Cortex-M4F, working at a clock speed of 168 MHz. The CPU has a 32-bit Harvard architecture with a three-stage pipeline. The board is programmed and modified such that it can be targeted for SCA.
