After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple discovered the Raven Paradox (RP). Then, Carnap used the logical probability increment as the confirmation measure. So far, many confirmation measures have been proposed. Measure F among them proposed by Kemeny and Oppenheim possesses symmetries and asymmetries proposed by Elles and Fitelson, monotonicity proposed by Greco et al., and normalizing property suggested by many researchers. Based on the semantic information theory, a measure b* similar to F is derived from the medical test. Like the likelihood ratio, b* and F can only indicate the quality of channels or the testing means instead of the quality of probability predictions. And, it is still not easy to use b*, F, or another measure to clarify the RP. For this reason, measure c* similar to the correct rate is derived. The c* has the simple form: (a-c)/max(a, c); it supports the Nicod Criterion and undermines the Equivalence Condition, and hence, can be used to eliminate the RP. Some examples are provided to show why it is difficult to use one of popular confirmation measures to eliminate the RP. Measure F, b*, and c* indicate that fewer counterexamples' existence is more essential than more positive examples' existence, and hence, are compatible with Popper's falsification thought.

Since data collection and analysis are difficult, time consuming and costly, we are always looking for a way to optimum use of data to achieve the correct decision that can be referred to diagnose and experiment of diseases in healthcare organizations [3]. In addition, common method such as angiography [5,6] in experimenting and diagnosing diseases is costly and have adverse effects for patients as healthcare resear chers are trying to utilize methods that avoid the high cost as well as the adverse effects of previous methods, which can be performed by using computer - aided disease diagnose methods means machine learning. Whereas, da ta mining process by utilizing machine learning science and database management knowledge [1] has become a robust tool for data analysis and management of health industry data which ultimately leads to knowledge extraction. It should be noted that, with the progress of technology in t he healthcare especially, healthcare industry 4.0, human lifetime has become progressive and more comfortable [ 7 ] . In this new generation, with the development of new medical devices, equipment and tools, new knowledge can be gained in the field of disease diagnosis.

Climate change impacts and adaptations are the subjects to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.

An improved online learning algorithm for general fuzzy min-max neural network Thanh Tung Khuat Advanced Analytics Institute University of T echnology Sydney Sydney, Australia thanhtung.khuat@student.uts.edu.au Abstract --This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max neural network (GFMM) to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries. These drawbacks lower its classification performance, so an improved algorithm is proposed in this study to address the above limitations. The proposed approach does not use the contraction process for overlapping hyperboxes, which is more likely to increase the error rate as shown in the literature. The empirical results indicated the improvement in the classification accuracy and stability of the proposed method compared to the original version and other fuzzy min-max classifiers. In order to reduce the sensitivity to the training samples presentation order of this new online learning algorithm, a simple ensemble method is also proposed. I NTRODUCTION Artificial neural networks (ANNs) are one of the most widely used methods for dealing with classification problems as well as real-world applications [1]. However, the main disadvantage of the original ANNs is that they do not have the capability of giving explanations of their predictive results to humans explicitly. This drawback restricts the widespread use of the ANNs for critical domains such as healthcare and criminal justice [2]. In a recent study, Rudin [2] has highlighted that there is a high demand for interpretable models to substitute black-box models in assisting decision-makers in areas with the requirement of high safety and trust.

Deep learning has achieved great successes in many important areas to dealing with text, images, video, graphs, and so on. However, the black-box nature of deep artificial neural networks has become the primary obstacle to their public acceptance and wide popularity in critical applications such as diagnosis and therapy. Due to the huge potential of deep learning, interpreting neural networks has become one of the most critical research directions. In this paper, we systematically review recent studies in understanding the mechanism of neural networks and shed light on some future directions of interpretability research (This work is still in progress).

Modern applications of artificial intelligence in decision support systems, plan generation systems require reasoning with imprecise a nd uncertain information. Logical frameworks based on bivalent reasoning are not suitable for such applications, because the set {0, 1} cannot capture the vagueness or uncertainty of underlying proposition. Though fuzzy log ic-based systems can represent imprecise linguistic information by ascribi ng membership values to attributes (or truth values to propositions) taken fr om the interval 1 [0,1], but this graded valuation becomes inadequate if the p recise membership can not be determined due to some underlying uncerta inty. This uncertainty may arise from lack of complete information or f rom lack of reliability of source of information or lack of unanimity amon g rational agents in a multi-agent reasoning system or from many other reasons . This uncertainty with respect to the assignment of membership degr ees is captured by assigning a range of possible membership values, i.e. by a ssigning an interval.

Despite the recent successes of deep learning, such models are still far from some human abilities like learning from few examples, reasoning and explaining decisions. In this paper, we focus on organ annotation in medical images and we introduce a reasoning framework that is based on learning fuzzy relations on a small dataset for generating explanations. Given a catalogue of relations, it efficiently induces the most relevant relations and combines them for building constraints in order to both solve the organ annotation task and generate explanations. We test our approach on a publicly available dataset of medical images where several organs are already segmented. A demonstration of our model is proposed with an example of explained annotations. It was trained on a small training set containing as few as a couple of examples.

As in any interaction process, misunderstandings, ambiguity, and failures to correctly understand the interaction partner are bound to happen in human-robot interaction. We term these failures 'conflicts' and are interested in both conflict detection and conflict resolution. In that, we focus on the robot's perspective. For the robot, conflicts may occur because of errors in its perceptual processes or because of ambiguity stemming from human input. This poster presents a brief system overview, and details Here, we briefly outline the project's motivation and setting, introduce the general processing framework, and then present two kinds of conflicts in some more detail: 1) a failure to identify a relevant object at all; 2) ambiguity emerging from multiple matches in scene perception.

This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developing quite separately in the last three decades. Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding. Then some methodologies combining reasoning and learning are reviewed (such as inductive logic programming, neuro-symbolic reasoning, formal concept analysis, rule-based representations and ML, uncertainty in ML, or case-based reasoning and analogical reasoning), before discussing examples of synergies between KRR and ML (including topics such as belief functions on regression, EM algorithm versus revision, the semantic description of vector representations, the combination of deep learning with high level inference, knowledge graph completion, declarative frameworks for data mining, or preferences and recommendation). This paper is the first step of a work in progress aiming at a better mutual understanding of research in KRR and ML, and how they could cooperate.

Evidential clustering is an approach to clustering in which cluster-membership uncertainty is represented by a collection of Dempster-Shafer mass functions forming an evidential partition. In this paper, we propose to construct these mass functions by bootstrapping finite mixture models. In the first step, we compute bootstrap percentile confidence intervals for all pairwise probabilities (the probabilities for any two objects to belong to the same class). We then construct an evidential partition such that the pairwise belief and plausibility degrees approximate the bounds of the confidence intervals. This evidential partition is calibrated, in the sense that the pairwise belief-plausibility intervals contain the true probabilities "most of the time", i.e., with a probability close to the defined confidence level. This frequentist property is verified by simulation, and the practical applicability of the method is demonstrated using several real datasets.