Evaluation Metrics. Given two monolingual corpora E and F , we sup- pose there exists a ground truth parallel corpus G and denote an extracted parallel corpus as D. The quality of an extracted parallel corpus can be mea- sured by F1 = 2|D ∩ G|/(|D| + |G|).
Evaluation Metrics. Evaluation is important for all NLP techniques, to assess to what extent the method is working. As in this project we are mainly dealing with the evaluation of NER, we will discuss the different evaluation metrics relevant to this technique 28 CHAPTER 2. BACKGROUND Prediction Label True tp fn False fp tn and give examples within this context. Most metrics involve calculations of per- centages between correctly and incorrectly classified items. In the case of NER, we predict a label for each token. That predicted label is compared to the true label, and we can then put each prediction in one of the following categories: True positive (tp). When a token is part of an entity, and the predicted label is the correct entity. True negative (tn). When a token is not part of an entity, and the predicted label is also not part of an entity. False negative (fn). When a token is part of an entity, but the predicted label is not part of an entity. More simply put: an entity that has not been recognised by the system. False positive (fp). When a token is not part of an entity, but the predicted label is an entity. More simply put: the system recognises an entity where there is none. These categories are further illustrated in table 2.1. Once we have this in- formation, we can calculate some metrics. The most used measures in machine learning in general are recall, precision and F1 score, and these are almost always used to evaluate NER too. Recall is a measure that indicates out of all the entities in a text, what per- centage have been correctly labelled as an entity. It can also be viewed as the percentage of entities that have been found. It is defined as follows: + Precision is a measure that indicates, out of all the labelled entities, what percentage has been assigned the correct label. In essence, this means that it shows that when an algorithm predicts an entity, how often it is right. It is defined as follows:
Evaluation Metrics. We use several metrics in this chapter, similar to previous work [157], which we defined earlier in Chapter 2 and are clarified again here for convenience. The evasion rate is defined as the number of adversarial examples that evade the oracle over the number of adversarial examples that evade the substitute models. furthermore, as MTDs may employ different models at prediction-time, an adversarial example may not always evade the oracle. Therefore, we use the repeat evasion rate (RER) as an additional metric for the evaluation. Recall that this measures the number of times an adversarial example evades the oracle out of 100 attempts. We also use standard ML metrics such as accuracy, f1, AUC, and false positive rate (fPR) to evaluate the models and defenses under different system conditions. We next present our experimental evaluation, where we evaluate MTDs and other defenses under different attacker scenarios. We begin with black-box results, followed by gray-box results and other experiments.
Evaluation Metrics. For understanding the added benefit of displaying HRI behaviours surrounding the motion/navigation of the robot, we primarily are interested in how these behaviours affect how humans perceive the robot, and how well these behaviours help in enabling humans to predict the robot’s behaviour (see, for example, Xxxxxxxxxxx et al., 2021) and therefore act and coordinate in a shared environment with it. Secondly, we are asking whether these behaviours also may benefit the planning process. We can evaluate this subjectively, asking users how legible they find the cues that the robot gives. To evaluate the added value of floor-projected direction cues for their robot, Xxxxxxxxxxxx et al. (2021) used Likert Scale items such as:
1. The robot’s communication to me was clear.
2. The robot moved as I expected.
3. The robot’s communication showed me its next movement.
4. The robot’s overall behaviour was reasonable.
5. The robot’s communication made me feel comfortable.
1. It was easy to predict which target the robots were moving toward.
2. The robots moved in a manner that made their intention clear.
3. The robots’ motion matched what I would have expected if I had known the target beforehand.
4. The robots’ motion was not surprising.
Evaluation Metrics. For validation, precision, recall, and F1 scores are used to estimate the effectiveness of extraction by comparing the system predicted results (before human revision) and the ground truth.
Evaluation Metrics. Given the uniqueness of our dataset, three evaluation metrics are adopted for our experiments to demonstrate the systems’ performance on FriendsQA. First, following SQuAD[22], Span Match (SM) is adapted to evaluate answer span selection, where each ap is treated as a bag-of-tokens (φ) and compared to the bag-of-tokens of ag; the macro-average F1 score across all questions is measured for the final evaluation (P : precision, R: recall): P (φ(ap), φ(ag)) + R(φ(ap), φ(ag)) SM = 1 Σ 2 · P (φ(ai ), φ(ai ))R(φ(ai ), φ(ai )) Additionally, Exact Match (EM) is also adopted to evaluate answer span selection that checks the exact span match between the gold and predicted answers, which results in a score either 1 or 0. Given the nature of FriendsQA in which each utterance is treated as a single unit in conversations, Utterance Match (UM) could serve as an effective measure to evaluate the accuracy since the model is considered to be powerful if it is always looking for answers in the correct utterance. High Utterance Match could indicate high precision of the model’s global understanding toward the dialogue. Given a prediction ap, UM mainly checks if it resides within the same utterance ug as the gold answer span ag, and is measured as i i follows: (n: # of questions): UM = 1 Σ(1 if ap ∈ ug; otherwise,0)
Evaluation Metrics. In clinical decision support, a positive case can be rare but critical. Instead of inspecting the overall accuracy, we focus on positive predictive value (PPV) and true positive rate (Sensitivity). In other words, the precision and recall of positive case are reported. The detail of each metric and its clinical impact in this use case are as follows: This indicates that if a case is detected, what is the probability of having VTE. This indicates that if a case is detected, what is the percentage of VTE patient we can detect based on prediction.
Evaluation Metrics. Plan for evaluation of results and quantitative metrics to be used in the assessment of results obtained by the use case: Post-processing and viewing of post-process is possible at the MRI scanner without hampering the low-latency processes and not disturbing the real-time control by operators. The demonstrator shall use a high-demanding application like DTI fiber tracking (see Figure 11) or compressed sensing. The dynamic analysis tool will run on a Windows platform and successfully detect software defects. The runtime analysis tool will enable high level partitioning decisions taking communication overhead in to account.
Evaluation Metrics. Plan for evaluation of results are described in the table in Section 2.3. The quantitative metrics to be used in the assessment of results are: Time measurements of running the generated code. Calculate the cost of using the code generator at project end.
