Experiments Sample Clauses
Experiments. 6.1. Experiments will not be verified or approved by us. We shall not be responsible for any Experiment, any content contained within any Experiment, and/or the results of and/or conclusions drawn from any Experiment.
6.2. You acknowledge that we take no warranty for any problems in the data collection or for any errors in the software.
6.3. You agree that you make your participants aware of the fact that some Experiments accessed through the Tool have been created, and are run by, third party researchers and not us. Additionally, where necessary, you inform participants that Experiments have not been verified or approved by us and that we shall not be responsible for any Experiment, any content contained within any Experiment, ones participation in an Experiment, or the results of and/or conclusions drawn from any Experiment.
6.4. Experiments must not:
6.4.1. contain any material which is defamatory of any person;
6.4.2. contain any material which is obscene, offensive, hateful or inflammatory;
6.4.3. promote sexually explicit material;
6.4.4. promote violence;
6.4.5. promote discrimination based on race, gender, religion, nationality, disability, sexual orientation or age;
6.4.6. infringe any copyright, database right or trade mark of any other person;
6.4.7. be likely to deceive any person in a way that might bring about serious harm;
6.4.8. be made in breach of any legal duty owed to a third party, such as a contractual duty or a duty of confidence;
6.4.9. promote any illegal activity;
6.4.10. be threatening, abuse or invade another’s privacy, or cause substantial annoyance, inconvenience or needless anxiety;
6.4.11. be likely to harass, upset, embarrass, alarm or substantially annoy any other person;
6.4.12. be used to impersonate any person, or to misrepresent your identity or affiliation with any person;
6.4.13. give the impression that they emanate from us, if this is not the case; or
6.4.14. advocate, promote or assist any unlawful act such as (by way of example only) copyright infringement or computer misuse.
6.5. You agree that you will conduct Experiments ethically and treat participants in the Experiments with respect and inform the participant at the outset of the Experiment of the nature of the Experiment and the participant’s right to withdraw his or her participation in the Experiment at any time;
6.6. We reserve the right, without liability or prejudice to other rights to you, to disable your access to any material or terminate any Experi...
Experiments. In this section, we evaluate our approach in two tasks: phrase alignment (Section 4.1) and machine translation (Section 4.2). , | ×
Experiments. Upon AgrEvo's agreement to provide compensation for Experiments approved by the JRC pursuant to Section 2.4, Lynx will use commercially reasonable and diligent efforts to perform Experiments on samples of Crops and model species as agreed by the Parties provided by AgrEvo, as specified and coordinated by the JRC, and to keep AgrEvo informed as to the progress of Experiments being performed. AgrEvo will compensate Lynx for such work as set forth in Article 4. Within ten (10) days of the completion of a set of Genotyping Experiments for a particular Crop, Lynx shall deliver the Genotyping Results produced in such Genotyping Experiments to AgrEvo. The results produced in Experiments other than Genotyping Experiments shall be delivered by Lynx to AgrEvo at such times as are otherwise agreed in writing by the Parties.
Experiments. We investigated whether the correct diagnosis is among the most probable diagnoses if the knowledge of one of the agents contains an error. In the experiments, we gen- erated 8000 systems each to be diagnosed by three agents. We chose three agents since this is the smallest number to make one diagnosis significantly more probable if one of the agents disagree with the others, while using more agents would have simplified the diagnostic problem. Each gener- ated system consisted of 40 components, each with one out- put and two inputs. An input was either connected to one of the four system inputs or to an output of a randomly cho- sen component without causing cycles. The normal behavior of a component was a modulo n adder for each of the three agents each using a different perspective. Besides, a component had faulty behaviors, namely ab and two specific faulty behaviors f1 and f2. In both fault modes f1 and f2, a fault value was added mod- ulo n to the output of the component. These faults values were randomly chosen for each combination of a compo- nent, a fault mode and an agent. Finally, for every compo- nent c, the same value was used for the probabilities of the fault modes f1 and f2 and the probability that a behavior mode is incorrect. To create a diagnostic problem, in each generated sys- tem one component was chosen to be the broken compo- nent and one of the fault mode f1 or f2 was selected for the component. In one of the three perspectives, however, the component behaved according to the other fault mode, i.e. the knowledge of the agent using this perspective was in- correct in the current situation.
Experiments. In order to fulfill the above purposes, the Joint Trade Board shall have the authority to experiment with revisions to work rules, use of tools, spray and overtime Section of this Agreement in order to recapture repaint work and work being done non-union and to obtain jurisdiction of new products and processes in our industry. All experiments shall be carefully documented and results made available to the ASSOCIATION and the UNION.
Experiments. In this section, we explore the behavior of the proposed negotiation model in different scenar- ios. The proposed framework has been imple- mented in genius (Xxx et al., 2012), a simulation framework for automated negotiation that allows researchers to test their frameworks and xxxxxx- xxxx against state-of-the-art agents designed by other researchers. Recently, genius has become a widespread tool that increases its repository of ne- gotiating agents with the annual negotiation com- petition (Baarslag et al., 2012). In order to assess the performance of the pro- posed negotiation approach, we have performed dif- ferent experiments. All of the experiments have been carried out in the negotiation domain (or case study) introduced in Section 2.4. The first exper- iment (Section 6.1) studies the performance of the proposed model when facing single opponent agent. The comparison is carried out in scenarios with dif- ferent degrees of team’s preference dissimilarity. In the second experiment, we study the performance of our negotiation team model when facing another negotiation team in bilateral negotiations. In the third experiment (Section 6.3) we study how the Bayesian weights wA and wop, which control the importance given to the preferences of the team and the opponent in the unpredictable partial offer pro- posed to teammates, impact the performance of the proposed model when team members employ the Bayesian strategy. Finally, we conduct an experi- ment to study the effect of team members’ xxxxx- vation utility on the performance of the proposed negotiation model (Section 6.4).
Experiments. In the present section we describe implementation details and measurement results for CR.
Experiments. Our experimental setup is once again as described in Chapter 2. For this model, in addition to measuring parsing F1, we also measure how well the word alignments match gold-standard annotations according to AER and F1. In our syntactic MT experiments, we investigate how the two relevant components of the model (English parse trees and word alignments) affect MT performance individually and in tandem.
Experiments. We have applied our method to several original data sets (coming from factored numbers) and show that this gives good results. We have carried out two types of experiments. First we assumed that the complete data set is given and we wanted to know if the simulation gave the same oversquareness when simulating the same number of relations as contained in the original data set. As input for the simulation we used
Experiments. In this section, we illustrate how our framework can be used to support secure programming, for both the sandboxing and constant-time scenarios, w.r.t. the contracts from §III. ct Tooling: To automate our analysis we adapted Spectec- tor [11], which can already check SNI for the J·)spec contract, to support checking SNI and wSNI w.r.t. all the contracts from §III, i.e., J · )seq , J · )seq, J · )spec, J · )seq-spec. arch ct ct ct-pc • Existing constant-time approaches (type systems [26], ct ct