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? Applications 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? Selection 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 p...
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 Utility Server #1 Optical Network Utility Server #N with wireless (ZigBee, WiMAX, WiFi, etc.) or wired technologies, and distributed over many cities, or villages. Moreover, the physical security of the NAN gateway can xG xG NAN gateway NAN gateway be protected as it is located inside substation and locked from outsider access as recommended by [14] [30]. Note #1 ZigBee ZigBee HHAANN##11 Neighbourhood Area ZigBee Network (NAN) #1 HHAANN##NN Neighbourhood Area Network (NAN) #N #N ZigBee HHAANN##NN that the main focus of our scheme is to establish a secure and efficient communication between the two ends (i.e., smart meter and NAN). We assumed that the NAN gateway is securely connected with the utility server (US). Smart Meter HHAANN##11
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. Government institutions make planning, policy, and decision making as reg- ular activities. Information technology constributes greatly to these activi- ties, introducing in the last decade new application and paradigms such as e-Government, e-Commerce, and e-Education. Increasingly, e-government decision making needs gathering information from many sources or depart- ments. Decisions can be carried out at many government levels including national, province, district and sub-district. Each level has di erent needs in sources, types, and detail of information. For example, a district gov- ernment may need information about the length and condition of road for budget planning and maintenance, while a province may use tra c density information for economic activity evaluation. A national government level requires road classi cation types, which can be pay road, toll road, highway, etc, for tax calculation. Moreover, decision made at one level can be based on information de xxx at the other levels. Interoperability and mediation approach are needed to allow information sharing among the levels. In many applications, data have high correlation with geographic infor- mation. With the rapid development in GIS, more and more geographical database have been developed by di erent programs and applications. Un- fortunately, data sharing and acquisition still are big challenges for the de- velopment of GIS applications. There is a large amount of geographical data stored in di erent places and in di erent formats. However, data reuse by new applications and data sharing are hindered by the heterogeneity among existing system, heterogeneity data modeling concepts, data encoding tech- niques and storage structures, etc [14] This situation is even worse in large developing countries like Indonesia where large amount of spatial data are stored in various paper and digital formats. For example, to develop a deci- sion making system for land transportation the Indonesian government must consider information from departments such as the Ministry of Internal Af- fair, the Police Department, and the Ministry of Public Work. Indonesia is composed of 13,000 islands among which the population is not equally distributed. As a result economic, social, and transport activities are very diverse. The other problems of land transportation, are related to ood- ing disaster during rain session, and very high movement of people in short period time such as New Year. Internet allows e cien...
Motivation. Conversations are a natural form for humans to seek information, and there are decades of study on formal dialogues and interactions of users with reference librarians. The natural next step is to design automated systems that are ‘virtual librarians’, eliciting information needs, correcting misconceptions, and providing the right amount of information at the right time across all possible domains. Multi-turn conversations should also become more natural in the digital environment today due to the increasing variety of devices that are accessible anytime/anywhere (perhaps without screen or keyboard), the maturity of speech interfaces, and recent developments in general representation learning. Today’s digital assistants are only capable of very basic “conversations”, which usually means a single user question (“What’s the weather like today?” or “When does my flight leave tomorrow?”), followed by a single system answer. In contrast, this research direction will lead to multi-turn, multi-user, multi-task and multi-domain conversational information seeking systems.
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...
Motivation. People seek to satisfy various information needs that involve acquiring knowledge and/or making decisions, such as learning about world affairs from reading news articles, understanding their medical problems and possible treatments, or training for a job. Invariably, retrieval systems fall short of the best possible outcome, or even user expectations. The user may have had to expend more effort than ideally needed, or ended up with information that is inaccurate, biased, or lacking utility. In order to successfully accomplish such knowledge-seeking and decision-making tasks, users often need more support than that currently offered by information systems. This support needs to be offered at different stages in the information seeking process, starting even before an information need is expressed: a search system should be aware of the context of the user in which the information need is to be placed and of the user’s existing skills and knowledge. If a more complex task is to be accomplished (such as gathering different forms of evidence for a decision involving multiple constraints or aspects), the system may help by scaffolding the task at every step, as needed by the user. The system needs to be aware of biases of the user and/or the search results and take those into account when presenting these results to end up with the best possible outcome. Similarly, the user should be made more aware of the broader context in which the returned information exists. Ideally, a system should also be aware of and be able to competently deal with distractions or lack of motivation of the user. While these demands on a retrieval system in a sense have always existed, it is more pertinent than ever that these are incorporated in the information retrieval process. Technology is much better suited now to help fulfill these requirements on the one hand, and on the other, there is greater scope for the user to end up more misinformed after a search than before. To give an example, search systems (and related algorithms, such as ranking algorithms employed by social media systems) contributed to large amount of misinformation during the 2016 presidential election cycle in US politics. Finally, as learning is supplemented more and more with online technology, improved methods for getting students the right information for their learning goals could help increase student engagement, curiosity, and retention, as well as, in the longer-term, enable better knowledge transfer to...
Motivation. While there has been much publication by both companies and academic groups in this area, trends in search interfaces as well as techniques that connect online with offline evaluation mean there are rich new opportunities for researchers to contribute to this critical area of evaluation.
Motivation. There are new IR applications launched every day (e.g. online shops, enterprise search, domain- specific information services), which often require substantial investments. IR systems are com- plex: made up of pipelines of heterogeneous components. They are used together with other technologies, for example, natural language processing, recommender systems, dialogue systems, etc., and they serve complex user tasks in a wide range of contexts. However, each new instanti- ation of these applications can only be evaluated retrospectively. There is a growing need to predict the expected performance of a method for an application before it is implemented and to have more sophisticated design techniques that allow us to ensure that IR systems meet expected performance in given operational conditions. We cannot postpone any further the development of techniques for explaining and predicting performance, if we wish to be able to improve and make more effective the way in which we design IR systems in order to keep pace with the challenges the systems have to address.
