Languages are powerful solutions to coordination problems: they provide stable, shared expectations about how the words we say correspond to the beliefs and intentions in our heads. Yet language use in a variable and non-stationary social environment requires linguistic representations to be flexible: old words acquire new ad hoc or partner-specific meanings on the fly. In this paper, we introduce a hierarchical Bayesian theory of convention formation that aims to reconcile the long-standing tension between these two basic observations. More specifically, we argue that the central computational problem of communication is not simply transmission, as in classical formulations, but learning and adaptation over multiple timescales. Under our account, rapid learning within dyadic interactions allows for coordination on partner-specific common ground, while social conventions are stable priors that have been abstracted away from interactions with multiple partners. We present new empirical data alongside simulations showing how our model provides a cognitive foundation for explaining several phenomena that have posed a challenge for previous accounts: (1) the convergence to more efficient referring expressions across repeated interaction with the same partner, (2) the gradual transfer of partner-specific common ground to novel partners, and (3) the influence of communicative context on which conventions eventually form.

In the framework of evidence theory, data fusion combines the confidence functions of multiple different information sources to obtain a combined confidence function. Stock price prediction is the focus of economics. Stock price forecasts can provide reference data. The Dempster combination rule is a classic method of fusing different information. By using the Dempster combination rule and confidence function based on the entire time series fused at each time point and future time points, and the preliminary forecast value obtained through the time relationship, the accurate forecast value can be restored. This article will introduce the prediction method of evidence theory. This method has good running performance, can make a rapid response on a large amount of stock price data, and has far-reaching significance.

Because of the efficiency of modeling fuzziness and vagueness, Z-number plays an important role in real practice. However, Z-numbers, defined in the real number field, lack the ability to process the quantum information in quantum environment. It is reasonable to generalize Z-number into its quantum counterpart. In this paper, we propose quantum Z-numbers (QZNs), which are the quantum generalization of Z-numbers. In addition, seven basic quantum fuzzy operations of QZNs and their corresponding quantum circuits are presented and illustrated by numerical examples. Moreover, based on QZNs, a novel quantum multi-attributes decision making (MADM) algorithm is proposed and applied in medical diagnosis. The results show that, with the help of quantum computation, the proposed algorithm can make diagnoses correctly and efficiently.

This is clearly demonstrated by the performance of BALD. To be specific, the BNNs trained with BALD have accuracies ranging from 70 90%, but for the models-under-test M-FashionMNIST and M-MNIST-ES (average & bad models), the metric estimation accuracies range from 90 100% - which are much higher than the BNNs' accuracies. For our proposed method ALT-MAS, with the models-under-test M-FashionMNIST, M-MNIST-ES, the behaviours are similar to those of BALD. That is, the metric estimation accuracies are always higher than the BNNs accuracies, especially for per-class metrics. It is worth noting that, for the per-class metrics, even though the BNNs accuracies by ALT-MAS are much lower than the BNNs by BALD, but the metric estimations by ALT-MAS are much higher than by BALD. This asserts the motivation of our sampling approach, that is, the BNN only needs to accurately predict the data points that contribute to the metric estimation. On the other hand, with the good model-under-test M-MNIST, due to our data augmentation training strategy, the BNN accuracies by ALT-MAS are much higher than those of BALD, and thus, the metric estimations by ALT-MAS are also more accurate than those by BALD. Figure 2: The accuracy of the BNN, for each combination of model-under-test (M-MNIST, M-FashionMNIST, & M-MNIST-ES) and metric set. Plotting mean and standard error over 3 repetitions (Best seen in color).

Machine Learning (ML) techniques are becoming an invaluable support for network intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats. Typically, ML algorithms are exploited to classify/recognize data traffic on the basis of statistical features such as inter-arrival times, packets length distribution, mean number of flows, etc. Dealing with the vast diversity and number of features that typically characterize data traffic is a hard problem. This results in the following issues: i) the presence of so many features leads to lengthy training processes (particularly when features are highly correlated), while prediction accuracy does not proportionally improve; ii) some of the features may introduce bias during the classification process, particularly those that have scarce relation with the data traffic to be classified. To this end, by reducing the feature space and retaining only the most significant features, Feature Selection (FS) becomes a crucial pre-processing step in network management and, specifically, for the purposes of network intrusion detection. In this review paper, we complement other surveys in multiple ways: i) evaluating more recent datasets (updated w.r.t. obsolete KDD 99) by means of a designed-from-scratch Python-based procedure; ii) providing a synopsis of most credited FS approaches in the field of intrusion detection, including Multi-Objective Evolutionary techniques; iii) assessing various experimental analyses such as feature correlation, time complexity, and performance. Our comparisons offer useful guidelines to network/security managers who are considering the incorporation of ML concepts into network intrusion detection, where trade-offs between performance and resource consumption are crucial.

A. Relevance of Model Explanations in Real-World Contexts Complex estimation and decision-making tasks have traditionally been analyzed and judged by human experts. Hence, decisions have typically been able to be complemented with human-interpretable justifications, when needed, as experts can normally explain the line-of-thought that led to their own decision-making. However, in the past two decades, algorithmic decision-making has spread increasingly to many relevant societal contexts. Despite the notable enthusiasm for the potential benefit that this type of technology can bring, the underlying methods used are typically not inherently transparent, in the sense that they do not readily provide human-interpretable justifications for their decisions [1]. Moreover, in recent years there is a trend where the most successful algorithms, particularly in complex tasks like machine vision and natural language processing, tend to rely on highly complex models, which has led to a further increase in tension between accuracy and interpretability [2]. Relevant societal contexts where algorithmic decision systems have gained substantial traction include medical diagnosis and treatment [3], counter-terrorism [4], criminal justice [5], and risk assessments for credits and insurance [6]. In such impactful contexts, there is a legitimate need for providing human-interpretable explanations along with the estimations and decisions made. Indeed, lack of interpretability has become a barrier to the adoption of machine learning-based systems in many institutions and companies. Hence the value of complementing ML models with human-interpretable accounts of the statistical rationals behind their estimations, in a way that human decision-makers can more easily understand machine estimations, and even integrate their statistical rationals with qualitative information and human expert judgements.

In recent years, the use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise. Although these models can often bring substantial improvements in the accuracy and efficiency of organizations, many governments, institutions, and companies are reluctant to their adoption as their output is often difficult to explain in human-interpretable ways. Hence, these models are often regarded as black-boxes, in the sense that their internal mechanisms can be opaque to human audit. In real-world applications, particularly in domains where decisions can have a sensitive impact--e.g., criminal justice, estimating credit scores, insurance risk, health risks, etc.--model interpretability is desired. Recently, the academic literature has proposed a substantial amount of methods for providing interpretable explanations to machine learning models. This survey reviews the most relevant and novel methods that form the state-of-the-art for addressing the particular problem of explaining individual instances in machine learning. It seeks to provide a succinct review that can guide data science and machine learning practitioners in the search for appropriate methods to their problem domain.

Interactions between several features sometimes play an important role in prediction tasks. But taking all the interactions into consideration will lead to an extremely heavy computational burden. For categorical features, the situation is more complicated since the input will be extremely high-dimensional and sparse if one-hot encoding is applied. Inspired by association rule mining, we propose a method that selects interactions of categorical features, called Random Intersection Chains. It uses random intersections to detect frequent patterns, then selects the most meaningful ones among them. At first a number of chains are generated, in which each node is the intersection of the previous node and a random chosen observation. The frequency of patterns in the tail nodes is estimated by maximum likelihood estimation, then the patterns with largest estimated frequency are selected. After that, their confidence is calculated by Bayes' theorem. The most confident patterns are finally returned by Random Intersection Chains. We show that if the number and length of chains are appropriately chosen, the patterns in the tail nodes are indeed the most frequent ones in the data set. We analyze the computation complexity of the proposed algorithm and prove the convergence of the estimators. The results of a series of experiments verify the efficiency and effectiveness of the algorithm.

Parametric statistical models are commonly used in many sub-fields of bioinformatics [1], [2]. For simplicity and computational concerns, bioinformatic scientists prefer to use differentiable and unconstrained statistical models than non-differentiable and constrained ones. For example, in pseudotime analysis (see section 3), in [3], the authors propose to regress gene expression on pseudotime using cubic B-spline so that an analytical solution is available. Other authors suggest to replace B-spline with a generalized linear model and a gradient-based method is applied to find maximum likelihood estimation [4]. In zero imputation problem, the authors construct a Gamma-Normal mixture model so that parameters can be estimated analytically [5]. In [6], the authors propose an unconstrained LASSO-type objective function and optimize it with a convex optimization algorithm. However, in real applications, it is common to impose constraints on parameters for interpretability. Besides, analytically solutions are not always available and the likelihood function is not differentiable or convex if discrete parameters are contained. Thus, constrained models without desirable mathematical properties can be more realistic and interpretable in many cases.

Active learning is a framework for supervised learning to improve the predictive performance by adaptively annotating a small number of samples. To realize efficient active learning, both an acquisition function that determines the next datum and a stopping criterion that determines when to stop learning should be considered. In this study, we propose a stopping criterion based on error stability, which guarantees that the change in generalization error upon adding a new sample is bounded by the annotation cost and can be applied to any Bayesian active learning. We demonstrate that the proposed criterion stops active learning at the appropriate timing for various learning models and real datasets.