Experience what its like to crush the earth beneath your gigantic feet as you make tracks in the Triassic.
Learn to fly as you glide through the Jurassic. Return to present day and earn your Jr. Paleontologist credentials, then engage in the most current dino-debates. Testify in T-Rex’s defense, save the natural history museum from an expensive fossil hoax, and scale a wall of mysteries
Figure 1: Answering questions regarding product facts and specifications
2.2 Customer Service Chatbot
The most closely related branches of work to ours are probably customer service chatbots for e-commerce websites. An example can be the Shopbot 1 of eBay. Shopbot aims at helping con- sumers narrow down the best deals from eBays over a billion listings. The bot’s main focus is to understand the user intent and then make person- alized recommendations. Unlike Shopbot, here we do not focus on making product recommen- dations. Instead we aim to develop a model for answering questions about product specifications.
We have presented an end-to-end environment for the extraction of co-occurrence networks based on criteria guided by literary research questions. This guidance not only informs the kinds of entities we are taking into account, but also the different ways of segmenting the text and even the fact that we are including non-named entities in the net- works. The given examples in Section 7 demon- strate the influence of these choices on the net- works of one and the same narrative text, thus it is important to make these decisions in close col- laboration with the domain experts who will use the results. Ultimately, relying solely on named entities can lead to highly skewed impressions of the relative importance of characters in a text, to misleading interpretations of networks and thus, of literary texts. This becomes even more dangerous when large text collections are analyzed, for which a manual inspection is simply not possible. Allow- ing interactive exploration of aggregated data (net- works) mitigates this issue: Domain experts inter- actively working with a network of a text become aware of such issues quickly. The early integration of scholarly experts even into primarily technical modules is therefore of utmost importance.
writing was not clear and when it was simply a matter of the actor not being familiar with the material. Over the course of rehearsal, I renewed my vows to the specificity of punctuation and the possibilities of interpretation. When is punctuation (the scoring of the text) vital to the music, the rhythm, the denotation of the text and when is there room for the interpretation that may not follow my intended score? Professor Moss wisely asked how I might address these issues within the script. Indeed, the playwright has the license to call attention to her intentions in various ways including the plasticity of the page; utilizing the visual composition of the letters on the page to impact and inform the experience of reading and interpreting the text. In the end, however, it is out of the playwright’s hands—directors will direct, actors will embody, and audiences will interpret—all from a myriad of subjective and individual histories.
There just isn’t enough time.
(b) To be completely rational in economic decision making, provided one does not take time into consideration, one has to take account of every factor. This would take a great deal of time.
One could not, for example, make any purchase without first searching the classifieds to see whether a better deal could be had, rather than simply heading for the nearest store.
0, otherwise (2)
Where w avg , w std and w max are the average, stan- dard deviation and maximum of all weights in the text. We use a instead of w std because in rare cases it can occur that w std > w max − w avg , which would lead to visualizations without fully opaque positions. These two options make it possible to adapt the attention visualization to fit the need of the analysis. For example, it is possible to only highlight the most important sections by increasing the threshold. On the other hand, it is also possible to highlight all segments that are slightly relevant by increasing the sensitivity and at the same time reducing the threshold.
Figure 2: Training procedure for the Initial Phonetic-Semantic Joint Embedding. After training, the the encoded vector (z in red) obtained here is used to train the SpeechBERT.
where k is the index for training audio words. This process en- ables the vector z to capture the phonetic structure information of the audio words but not semantics at all, which is not ade- quate for the goal here. So we make the vector z be constrained by a L1-distance loss (lower right) further:
The complete Cylinder weighs W newton. The plane of contact of both of its half is vertical. Determine the minimum Value of P, for which both halves of the cylinder will be in equilibrium on a horizontal plane. 5 Marks
22. i) A horizontal rod of PQRS is 12 m long, where PQ = QR = RS = 4 m. Forces of 1000, 1500, 1000 and 500 N act at P, Q, R and S respectively and action of these forces make angles 90 0 , 60 0 , 45 0 and 30 0 respectively. Find out the magnitude, direction and position of the resultant of system.
Many different types of governments exist around the world. Each nation's government establishes control over its territory and people through law. No two nations' governments and laws are exactly the same. One type of government is a democracy, where everyone has decision-making power. A democracy can either be direct or indirect. In a direct democracy, everyone gets a vote on every major decision. This type works best for small groups. There is also an indirect democracy (also called a representative democracy or republic). This is the type of government of the United States. In an indirect democracy, the people elect a few individuals to represent them. The people trust those elected officials to make decisions in their best interest.
College is fun. Remember that, and you’ll have the time of your life.
Try to settle into a rhythm with work, classes & new friends. Plus, make friends wherever you can, even if they aren’t your best friends. These people will make your first semester easier.
The incoming freshmen must work hard on their courses. Most courses that they will take are not so difficult; if they spend reasonable time, they will get good grades. But the most important thing is self-management. I think most of the freshmen did not experience dorm-life yet. It’s so easy for freshmen to go away from academic stuff. In light of drinking, playing and etc. Thus self-determination is so crucial to be successful in this “tough” CMU life.
4. This Question Booklet contains 16 pages including blank pages for rough work. After you are permitted to open the seal, please check all pages and report discrepancies, if any, to the invigilator.
5. There are a total of 65 questions carrying 100 marks. All these questions are of objective type. Each question has only one correct answer. Questions must be answered on the left hand side of the ORS by darkening the appropriate bubble (marked A, B, C, D) using ONLY a black ink ball point pen against the question number. For each question darken the bubble of the correct answer. More than one answer bubbled against a question will be treated as an incorrect response.
1 David R. Cheriton School of Computer Science, University of Waterloo
We demonstrate an end-to-endquestion an- swering system that integrates BERT with the open-source Anserini information retrieval toolkit. In contrast to most question answer- ing and reading comprehension models today, which operate over small amounts of input text, our system integrates best practices from IR with a BERT-based reader to identify an- swers from a large corpus of Wikipedia arti- cles in an end-to-end fashion. We report large improvements over previous results on a stan- dard benchmark test collection, showing that fine-tuning pretrained BERT with SQuAD is sufficient to achieve high accuracy in identify- ing answer spans.
Figure 2: The complete system for yes/no answer classification using a question and relevant snippets lap with an entity tagged and an answer for the
question. We can notice that PubTator and Ling- Pipe have a good recall with relatively low preci- sion, while Gram CNN has high recall but low pre- cision. However, the final results with the Named Entity Taggers were not aligned with our expec- tations. This is mostly because the answers for BioASQ are usually a combination of BioNERs and complementary words, making it hard to de- fine a pruning method that is able to yield satisfac- tory results. Surprisingly, a group of candidates formed of the 100 most frequent n-grams (n from 1 to 4) from the snippets’ sentences were a bet- ter candidate group than the NER approach for our supervised ranking method (with NER taggers used as features instead of candidate entities).
In addition, we would like to further explain the mech- anism of our model in comparison with the existing QA models. The difference of our proposed model stems from the density matrix representation. Such a matrix can repre- sent the mixture of the semantic subspaces, and the join- t representation of question and answer matrices can encode similarity patterns. By using the 2D-CNN, we can extract useful similarity patterns and obtain a good performance on the answer selection task. On the other hand, most existing neural network based QA models concatenates word embed- ding vectors. Based on such concatenation, the 1D convo- lution neural networks (1D-CNN for short) can be directly performed. We have carried out the above experiments for a comparison. In the future, we will systematically analyze and evaluate the above two different mechanisms in-depth.
However, if BW is used for verification purposes within the context of a service level agreement
(SLA), this threshold philosophy could soon elicit a conflict of interests. On the one hand, threshold values and their associated alerts are important indicators for quickly identifying inconsistencies and, if possible, rectifying them even before they reach a really critical level. On the other hand, the service level agreement precisely defines the threshold values, thus reducing the advance warning time to almost zero. Help here is provided by an independent set of threshold values for service level agreements in SAP End User Expe- rience Monitoring and an additional disjunct report from Interactive Reporting. This enables adequate advance warning using appropriate alerts, while at the same time providing accurate reports for the service level agreement. In this context, "accu- rate" also means that an agreement has been either upheld or broken, so only one threshold value that clearly defines this limit must be speci- fied.
[γ k + d (s + p, o) − d s 0 + p, o 0 ] + (13)
Where S is the set of KB facts and S 0 is the cor- rupted facts. In our QA task, we filter out the com- pletely unrelated facts to save time. Specifically, we first collect all the topic entities of all the ques- tions as initial set. Then, we expand the set by adding directly connected and 2-hop entities. Fi- nally, all the facts containing these entities form the positive set, and the negative facts are random- ly corrupted. This is a compromising solution due to the large scale of Freebase. To employ the glob- al information in our training process, we adop- t a multi-task training strategy. Specifically, we perform KB-QA training and TransE training in turn. The proposed training process ensures that the global KB information acts as additional su- pervision, and the interconnections among the re- sources are fully considered. In addition, as more KB resources are involved, the OOV problem is relieved. Since all the OOV resources have exact- ly the same attention towards a question, it will weaken the effectiveness of the attention model.
Neoliberalism is an economic policy characterized by state retrenchment, free trade, market liberalization, deregulation, privatization, commercialized social programs, and foreign investment. 5 Since its triumph over communism, neoliberalism marked “the end of history.” 6 This thesis examines poverty reduction and neoliberalism in the context of international development in order to illustrate how poverty is perpetuated through international policies of inclusive neoliberalism. Much like a pendulum, international development has oscillated between efforts to reduce poverty to the advancement of neoliberalism. The primary concern of this thesis is how poverty is sustained through international poverty reduction strategies that implicitly employ the tenets of neoliberalism in the pursuit of development. More specifically, this thesis illustrates the tension that is created when the logic of neoliberalism collides with the logic of inclusion as contained in poverty reduction strategies. There is a paradox whereby poverty reduction strategies deployed by international development institutions work against their purported aims when implicitly employing the tenets of neoliberalism. This thesis employs a Gramscian and Neo-Gramscian framework whereby the poor can be examined simultaneously alongside the overarching neoliberal hegemony that is complicit in their abject poverty. Assessing inclusive neoliberalism through a Gramscian framework offers a gateway to analyzing the determinants of existing structural global imbalances that sustain poverty. The thesis is argued through case studies of microfinance and conditional cash transfers internationally followed by a consideration of their application in Egypt.