Motivation Clause Samples
The Motivation clause outlines the underlying reasons or objectives for entering into the agreement. It typically provides context by describing the parties' intentions, such as fostering collaboration, achieving a specific business goal, or addressing a mutual need. By clearly stating the purpose behind the contract, this clause helps ensure that all parties share a common understanding of the agreement's aims, which can aid in interpreting the contract and resolving potential disputes about its intent.
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Motivation. How important were each of the following possible reasons in your decision to go to university? Not important Somewhat important Important Very important motiv1 To prepare for a specific job or career ☐ ☐ ☐ ☐ motiv2 To satisfy my intellectual curiosity ☐ ☐ ☐ ☐ motiv3 To earn more money than if I didn’t go ☐ ☐ ☐ ☐ motiv4 To get a broad education ☐ ☐ ☐ ☐ motiv5 I am more likely to get a job with a degree ☐ ☐ ☐ ☐ motiv6 The satisfaction of doing challenging academic work ☐ ☐ ☐ ☐ motiv7 To apply what I will learn to make a positive difference in society or my community ☐ ☐ ☐ ☐ motiv8 I didn’t have anything better to do ☐ ☐ ☐ ☐ motiv9 To get a more fulfilling job than I probably would if I didn’t go ☐ ☐ ☐ ☐ motiv10 To meet my family’s expectations ☐ ☐ ☐ ☐ motiv11 Learning new things is exciting ☐ ☐ ☐ ☐ motiv12 Most of my friends are going ☐ ☐ ☐ ☐ motiv13 To meet new people ☐ ☐ ☐ ☐ motiv14 The chance to participate in varsity athletics ☐ ☐ ☐ ☐ motiv15 To explore whether university is right for me ☐ ☐ ☐ ☐ motiv16 Other reason (please specify below): ☐ ☐ ☐ ☐ motivtxt motivtop Which one was the most important to you? How many universities besides <university name> did you apply to? app1 in Canada: app2 in other countries: app3 Did you apply to a college or CEGEP? Yes ☐ No ☐ app4 Is <university name> your first choice? Yes ☐ No ☐ [If app4 = “No” branch to apptxt, otherwise branch to the Selection section.] Apptxt What was your first choice university? How important were each of the following in your decision to choose <university name>? Not important Somewhat important Important Very important sel1 I wanted to live close to home ☐ ☐ ☐ ☐ sel2 I wanted to live away from home ☐ ☐ ☐ ☐ sel3 It offered a place in residence ☐ ☐ ☐ ☐ sel4 Cost of university residence ☐ ☐ ☐ ☐ sel5 Cost of tuition and fees ☐ ☐ ☐ ☐ sel6 It has the program I want to take ☐ ☐ ☐ ☐ sel7 The program I want has a co-op, practicum or other work experience ☐ ☐ ☐ ☐ sel8 The program I want offers study/work experience abroad ☐ ☐ ☐ ☐ sel9 The academic reputation of the university ☐ ☐ ☐ ☐ sel10 It has a good reputation for campus life ☐ ☐ ☐ ☐ sel11 It offered a scholarship ☐ ☐ ☐ ☐ sel12 It offered other financial assistance ☐ ☐ ☐ ☐ sel13 The size of the university suits me ☐ ☐ ☐ ☐ sel14 The city/town it’s in ☐ ☐ ☐ ☐ sel15 Availability of public transportation ☐ ☐ ☐ ☐ sel16 It’s where my friends are going ☐ ☐ ☐ ☐ sel17 It’s where my family wanted me to go ☐ ☐ ☐ ☐ sel18 The chance to participate in varsity a...
Motivation. □ Used positive reinforcements with students; motivated and encouraged students to achieve. □ At times used positive reinforcement with students; inconsistent in encouragement of students. □ Little or no use of positive reinforcement or encouragement to succeed.
Motivation. The one-way authentication schemes, and the high computational and communicational costs can raise concern in two-way smart energy communications. Moreover, in the SEN communication, the integrity of messages is equally important as other security properties, since message integrity provides assurance that the messages are not been altered/forged in transit (or from the origin), as suggested by the National Institute Standards Technology (NIST) [27]. A loss of integrity may cause destruction of information and may lead to incorrect decision in smart energy network. However, the most of recently proposed schemes (e.g., [17], [19], [20], [21]), are vulnerable where an attacker can violate message
Motivation. Why is this Project Important?
Motivation. Byzantine agreement (BA) and secure multi-party computation (MPC) are two fundamental and widely explored problems in distributed computing and cryptography. The general problem of MPC allows a set of n parties to correctly carry out an arbitrary computation, without revealing anything about their inputs that could not be inferred from the computed output [45, 46]. Such guarantees must hold even when a subset of the parties are corrupted and actively deviate from the protocol specification. BA can be seen as an instance of MPC, in which the function to evaluate guarantees agreement on a common output [42, 44] and privacy is not a requirement. Protocols for BA are often used as building blocks within larger constructions, including crucially in MPC protocols, and have received renewed attention in the context of blockchain protocols (starting with [38]). There are two prominent communication models in the literature when it comes to the design of such primitives. In the synchronous model, parties have synchronized clocks and messages are assumed to be delivered within some (publicly known) delay ∆. Protocols in this setting achieve very strong security guarantees: under standard setup assumptions, BA [22, 30] and MPC [4, 5, 7, 15, 18, 19, 21, 25, 26, 28, 43] are achievable even when up to t < n/2 parties are corrupted. However, the security of synchronous protocols is often completely compromised as soon as the synchrony assumptions are violated (for example, if even one message is delayed by more than ∆ due to unpredictable network delays). This is particularly undesirable in real- world applications, where even the most stable networks, such as the Internet, occasionally experience congestion or failures. In the asynchronous model, no timing assumptions are needed, and messages can be arbitrarily delayed. Protocols designed in this model are robust even in unpredictable real-world networks, but the security guarantees that can be achieved are ⋆ This work was partially carried out while the author was at ETH Zürich. significantly weaker. For example, protocols in this realm can only tolerate up to t < n/3 corruptions [8, 14, 24]. As a consequence, when deploying protocols in real-world scenarios, one has to decide be- tween employing synchronous protocols —risking catastrophic failures in the case of unforeseen network delays —or settling for the weaker security guarantees of asynchronous protocols.
Motivation. What motivated you to establish this fund? (Select all that apply) ☐ Involve multiple generations in giving ☐ Simplify my annual charitable giving ☐ Benefit the community ☐ Learn about and support an issue or community ☐ Reduce tax burden ☐ Other:
Motivation. Lacks initiative, performs only as directed. Rarely shows initiative. Occasionally initiates action. Frequently shows initiative. Exceptionally ambitious and a self-starter.
Motivation. The classical IR evaluation model was designed to evaluate the performance of the IR system with respect to just one interaction instance: the response that the system provides to one query put to that system. The model has been extended in various ways, to differential effect. Test collections have used a surprisingly wide range of labeling criteria: topical relevance, home-page- for, key page, spam, opinionated, a-venue-I-would-go-to, novelty, and others. ▇▇▇▇▇▇▇▇▇ assumes an atomic preference criterion: that is, an individual document’s preference label is defined with respect to the document and topic only. Atomicity allows us to build test collections scalably because documents can be labeled in a single pass. Other kinds of criteria for building test collections should be explored. For other atomic qualities we need to understand how to define them, how to develop labeling guidelines that are understandable enough for separate sites to label items comparably, how to measure the consistency and reliability of those labels, and how to measure the impact of label disagreements. As research problems these questions deserve more attention. Although there have been serious attempts to design methods to evaluate system support for information search sessions, these have uniformly failed. There are various reasons for this failure. The atomic criterion of relevance, basic to the model, does not easily apply to the evaluation of the success of a whole session, and the presence of human beings, having varied intentions during the information search session, making individual decisions during the search session, and having varied individual characteristics, has made comparability of performance of different systems with different persons, as required by the classic model, seemingly impossible. Extending the Cranfield model into full interactions is hard because it violates the atomicity criterion. To consider an interaction where a user starts from different queries, encounters docu- ments differently, and moves towards completion of the task along multiple paths, a test collection would need, at a minimum, to define the relevance of each document with respect to all docu- ments already seen. Without constraining this within some sort of structure, there would be an exponential number of relevance judgments needed. Taking a further step and allowing the user’s understanding of the task to evolve and criteria for successful completion of that task to change duri...
Motivation. While there is a history of strong research on efficiency issues in the IR community, much attention has focused on a few established and often narrow problems and setups motivated by standard IR architectures and systems, leaving many other cases largely unexplored. There have also been a number of emerging changes in modern search systems that call for new directions and approaches. In particular, efficiency researchers need to look at issues related to Multi-Stage Search Systems (MSSs), which are increasingly being deployed with machine-learned models, and examine the end-to-end performance of methods under MSSs. There are also opportunities for further efficiency improvements that require the application of machine learning and data mining techniques, including methods that learn index structures, learn how to optimize queries, or that estimate query distributions and optimize for these. There might also be ways to completely bypass the currently used index structures via neural nets, structures from the Combinatorial Pattern Matching community, or FPGA-based systems. Finally, new emerging IR applications also require attention. In summary, it is time to reconsider some basic assumptions, and to step away (at least partially) from the comfortable world of inverted lists and simple ranking functions that so many (often strong) papers have addressed.
Motivation. Why does it matter for IR? IR systems often capture associations between entities and/or properties, and depending on the semantic connotations of such relationships they might lead to reinforcing current stereotypes about various groups of people, propagating and amplifying harm. For example, these associations may originate from the data used to train the ranking models, which may not provide enough coverage for all possible associations such that they can all be learned. Certain groups of individuals may be over- or under-represented in the data, which could be a reflection of greater societal disparities (e.g., unequal access to health care can result in unequal representation in health records) or of the types of people who are able to contribute content, including the rate at which these contributions are made (e.g., women tend to be over- represented in Instagram data, but under-represented in StackOverflow data). Representation is also affected by the quality of the tools used to capture the data. For example, it is more difficult to do facial recognition of dark-skinned people in video surveillance footage because of limitations with how cameras are calibrated. As a result, an image retrieval system might fail to properly identify images related to darker-skinned people, while an image assessment system might flag them more often for security interviews, or to scrutinize them in more detail. What makes this specific to IR? Given the ubiquitous usage of IR systems, often broadly construed (e.g., search, recommendation, conversational agents), their impact — negative included — is potentially wide ranging. For instance, research has shown that people trust more sources ranked higher in the search results, but the ranking criteria may rather rely on signals indicative of user satisfaction, than on those indicative of factual information. For consequential searching tasks, such as medical, educational, or financial, this may raise concerns about the trade-offs between satisfying users and providing reliable information. The SIGIR community has the responsibility to address fairness, accountability, confidentiality and transparency in all aspects of research and in the systems built in industry. Similar respon- sibility issues are addressed in related fields, however, there are specific issues in IR stemming from the characteristics of, and reliance on document collections and the often imprecise nature of search and recommendation tasks. IR has a stro...