Our method for mining COREF’s dialogue experi- ence involves three steps. First, we compile train- ing data: positiveinstances are derived from user utterances and negative instances are derived from the generator’s alternative realizations of commu- nicative goals inferred from user utterances. Next, we build a machine learning model to distinguish positive from negative instances, using features describing the utterance itself, the current state of the conversation and relevant facts from the dia- logue history. Finally, we apply the learned model on new NLG problems by collecting candidate paraphrases and finding the one rated most likely to be natural by the learned model.
there has to be exactly one witness that makes the verifier accept with probability at least 2/3, while all other messages make him accept with probability at most 1/3. The definition of UQMA, the unique variant of QMA is the following: there is no change for negative instances with respect to QMA, but on positiveinstances there has to be a quantum witness state |ψ i which is accepted by the verifier with probability at least 2/3, whereas all states orthogonal to |ψi are accepted with probability at most 1/3. Aharonov et al. extended the Valiant-Vazirani proof for the classical witness classes by showing that MA ⊆ RP UMA and QCMA ⊆ RP UQCMA . On the other hand, they left the existence of a similar result for QMA as an open problem.
Table 3 reports the precision (P), recall (R), and F1 score (F1) based on the number of correct de- cisions for positiveinstances. The proposed method outperformed the baseline systems, achieving 0.919, 0.888, and 0.984 of F1 scores, respectively. Porter’s stemmer worked on the Inflection set, but not on the Orthography set, which is beyond the scope of the stemming algorithms. CST’s lemmatizer suf- fered from low recall on the Inflection set because it removed suffixes of base forms, e.g., (cloning, clone) → (clone, clo). Morpha and CST’s lemma-
To develop a more robust PI model, it is impor- tant to collect both “non-trivial” positive and neg- ative instances for the evaluation corpus. To cre- ate a useful evaluation corpus, we propose a novel paraphrase acquisition method that has two view- points of balancing the corpus: positive/negative and trivial/non-trivial. To balance between posi- tive and negative, our method has a machine trans- lation part collecting mainly positiveinstances and a random extraction part collecting negative in- stances. In the machine translation part, we gen- erate candidate sentence pairs using multiple ma- chine translation systems. In the random extrac- tion part, we extract candidate sentence pairs from a monolingual corpus. To collect both trivial and non-trivial instances, we sample candidate pairs
tives. As illustrated in Figure 1, they con- centrate on discovering the false positive in- stances 1 which are suppressed or removed at last and obtain a better decision boundary (green dashed line) than without considera- tion of false positiveinstances. Nevertheless, there are still a lot of false negative instances expressing similar semantic information with positive data. These instances also provide evidence for the target relation. The incor- rect labels will weaken the discriminative ca- pability of available features and confuse the model if they stay the same. However, when we remedy the label correctly, we indeed pos- sess the optimal decision boundary (red solid line).
(instances with more information of each class) are in the region between positive and negative instances. Introduce instances in this region could increment the discrimina- tive information of positiveinstances and improve the performance of a classi ﬁ er on imbalanced data sets. However, this external region is very sensible to arti ﬁ cial instances. Inadequate instances lead to introduce noise and loss of performance in the classi ﬁ er. Different arti ﬁ cial instances can cause signi ﬁ cant differences in performance. Therefore, arti ﬁ cial instances must be generated carefully.
We can see in figure 7 that for the three col- ors shown, stemming always increased the aver- age precision for that color, but could reduce re- call. In addition from figure 8, we see that some of the colors had a large increase in average positiveinstances, while others did not. This was likely due to a case where many instances labeled with “rojo” also saw enough “roja” that it was a posi- tive instance for both. When looking at the counts per instance, we found that for the 23 instances that had the token “roj” in their stemmed descrip- tions, 16 were positive examples of both “roja” and “rojo” in the un-stemmed version. For objects like cabbages (coles) and plums (ciruelas), “roja”
Abstract: Fast pace of urbanization is often complimented with inefficient urban infrastructure, a mismatch between demand and supply of urban services and poor quality of life. These are few of the driving forces for planners to introduce grass-root level planning process as an approach to facilitate local urban governance. The purpose of the paper is to present a methodical review of literary instances on participatory planning and its successful implementation. The paper includes review of 98 articles and research works that embodies the development of the subject over time. The findings of the study are suggestive that local area planning as a subject has witnessed tremendous change since its inception in 18th century till date, in terms of topics covered and methodologies employed. The paper explicate that despite formal existence of participatory approach to planning since 18th century, the paradigm has not yet achieved its objectives in true sense. There are reported gaps in terms of stakeholder analysis, framework of comprehensive local area planning and its implementation, which offers opportunity to the researchers to explore the subject further.
Table I shows ten random test instances for the core staff rostering problem. The number of employees (E) varies between 50 and 100. The planning period (W) is 4, 6 or 8 weeks. The number of shifts (S) is 1000, 2000 or 2250. The length of a shift is a uniform random number on the interval given by L. The required working time of an employee is given by an interval, for example between 10000 and 11000 minutes. The intervals are randomly created based on the total number of working time required by the shifts. The average required working time of the employees is given by T-avg. The minimum and maximum intervals of the working times are given by I-min and I-max. The data for the test instances is available online .
Abstract: The paper focuses on technology designers’ representations and discourses about advanced driving assistance systems (ADAS). This issue has been empirically explored by means of seven in-depth interviews with academic experts in intelligent transportation systems (ITS). Two main areas are investigated: 1) the meaning of advanced driver assistance and 2) the failures in intelligent driving and the consequent need to cope with them. The overall aim is to identify dominant views about the instances of “failing” and the possibilities for control, which are inscribed in the design processes of ADAS. One of the main findings concerns the designers’ emphasis on the continuous supervising, correction, and enhancement of human functioning as the core of driver assistance. According to this view, human senses, reactions and interactivity with technology turn into subjects of continuous supervision, prevention, correction, improvement and restriction – a sort of “real-time human maintenance and repair”.
Holmerin  later improved the constant in the exponents and pointed out some technical difficulties in getting better hardness than 2 O(k 1/2 ) /2 k using certain kind of PCP reduction. Another challenge is that many approximation resistance results are obtained via reduction from Unique Games. This immediately introduces problem if we need perfect completeness because the Unique Games problem is solvable in polynomial time for satisfiable instances.
This paper addresses Weakly Supervised Object Localization (WSOL) with only image-level supervision. We model the missing object locations as latent variables, and contribute a novel self-directed optimization strategy to infer them. With the strategy, our developed Self-Directed Localization Net- work (SD-LocNet) is able to localize object instance whose initial location is noisy. The self-directed inference hinges on an adaptive sampling method to identify reliable object in- stance via measuring its localization stability score. In this way, the resulted model is robust to noisy initialized object locations which we find is important in WSOL. Furthermore, we introduce a reliability induced prior propagation strategy to transfer object priors of the reliable instances to those un- reliable ones by promoting their feature similarity, which ef- fectively refines the unreliable object instances for better lo- calization. The proposed SD-LocNet achieves 70.9% Cor- Loc and 51.3% mAP on PASCAL VOC 2007, surpassing the state-of-the-arts by a large margin.
The instance based mining, is not similar to the conventional approach. Though it can be treated as a special problem of the conventional approach, it still contains enough specific applications to be treated carefully like a complete different way of mining. As discussed in this paper in the market basket example, the problem set in this approach is not a set of transactions with the item-sets whose values are in binary 0,1 only. Instead of, limiting the item set count only into binary, this problem broadens the aspect with taking decimal values into account which contains the set of positive integers. The probable applications of this approach are described also.
This paper is organized in 5 parts as follows: In the first part we give an overview of the size, format and the density of the CNF instances which we have worked with. In the second part we present and discuss runtime statistics of the solver in its default parameter settings. In the third part the empirical parameter optimization investigations and their results are presented and discussed. Due to the indeterministic behavior of the solver in multi-thread operation mode, certain changes in the source code have been undertaken, which were seen as necessary in order to increase the significance of the influence of changes in parameter settings to the benefit of the generation of distinct results. These source-code changes will be substantiated and the out of them resultant improvements of the solver runtimes will be graphically demonstrated. In the fourth part of this paper, the innovative results of an Automatic Algorithm Configuration for the parameters of CryptoMiniSat which produced even better parameter configurations will be presented and discussed. We conclude with a summary and description of further planned investigations to optimize CMS.
revenues. The second one, instead, emphasizes generality rather than analytical tractability, allowing any kind of traffic. The above revenue maximization problem does not appear to have been studied before. Perhaps the most similar related work is by Mazzucco et al , , but those studies do not consider the variable cost of acquiring the servers nor do they take into account the penalties arising for rejecting customers in case of outage due to spot instance termination. Andrzejak et al  present a probabilistic model aiming at minimizing the budget needed to meet the performance and reliability requirements of applications running on the Cloud, while  introduces an autonomic solution that given a set of goals to optimize (e.g., monetary cost or execution time) selects the resources to best meet the specified target. Hu et al  investigate how to deliver response time guarantees in a multi- server and multi-class setting hosted on the Cloud by means of allocation policies only. Similarly, ,  and  investigate how to deliver acceptable performance levels while minimiz- ing monetary cost or electricity consumption. Stokely et al  address the resource provisioning problem in a cluster in which users bid for the available resources. Similarly, Mattess et al  consider the economics of purchasing resources on the spot market to deal with unexpected load spikes, while Chohan et al  study how to best use spot instances for speeding up MapReduce workflows and investigate how the bid price affects the likelihood of premature instance termination.
Tong  presents a system for generating sentiment timelines. This system tracks online discussions about movies and displays a plot of the number of positive sentiment and negative sentiment messages over time. Messages are classified by looking for specific phrases that indicate the sentiment of the author towards the movie, using a hand-built lexicon of phrases with associated sentiment labels. There are many potential uses for sentiment timelines: Advertisers could track advertising campaigns, politicians could track public opinion, reporters could track public response to current events, and stock traders could track financial opinions. However, with Tong’s approach, it would be necessary to provide a new lexicon for each new domain. Tong’s  system could benefit from the use of an automated method for determining semantic orientation, instead of (or in addition to) a hand-built lexicon.
The task of the Pattern Learner is to learn the patterns of occurrence of relation instances. This is an inherently supervised task, because at least some occurrences must be known in order to be able to find patterns among them. Consequently, the input to the Pattern Learner includes a small set (10-15 instances) of known instances for each target relation. Our system assumes that the seeds are a part of the target relation definition. However, the seeds need not be created manu- ally. Instead, they can be taken from the top- scoring results of a high-precision low-recall unsupervised extraction system, such as KnowItAll. The seeds for our experiments were produced in exactly this way.
Fig. 4 presents the macro-F1 scores for Laplacian SVMs using between 5% and 25% of the 79,432 unlabeled instances, and evalu- ated on the testing reference standard. The macro-F1 score is sig- niﬁcantly positively correlated with the number of unlabeled instances used for training (pearson’s q = 0.529, p-value < 0.0001). Even using a small amount of unlabeled data yielded higher perfor- mance than the supervised SVM: by adding just 5% of the unla- beled data (4000 notes) the Laplacian SVM achieved a mean macro-F1 of 0.756 (compared to 0.741 for the supervised SVM). The mean macro-F1 score for the Laplacian SVM trained with 25% of the 79,432 unlabeled instances was 0.763; this is lower than the score achieved for our evaluation (0.773, Table 1), but higher than that achieved by the linear SVM (0.741). Different random samples of the unlabeled instances result in different estimates of the intrinsic geometry, and hence in different separating hyper- planes for the Laplacian SVM.
Bayesnet is a reputable classifier for clinical dataset and it is used in classification problems. This is a postulated classifier which provides posterior probabilities for the classes in desired featured instances. The classification accuracy is generally evaluated in this classifier and the attribute values are assigned to the class target using conditional probability. In this paper, we assume that the class variable status is a binary variable, with 1 as positive value and 0 as negative value and the set of feature attributes are denoted by X; x is used to cite a specific instance of the dataset. The joint probability distribution pr(X, Y) is noted with feature attribute