Rule sets are highly interpretable logical models in which the predicates for decision are expressed in disjunctive normal form (DNF, OR-of-ANDs), or, equivalently, the overall model comprises an unordered collection of if-then decision rules. In this paper, we consider a submodular optimization based approach for learning rule sets. The learning problem is framed as a subset selection task in which a subset of all possible rules needs to be selected to form an accurate and interpretable rule set. We employ an objective function that exhibits submodularity and thus is amenable to submodular optimization techniques. To overcome the difficulty arose from dealing with the exponential-sized ground set of rules, the subproblem of searching a rule is casted as another subset selection task that asks for a subset of features. We show it is possible to write the induced objective function for the subproblem as a difference of two submodular (DS) functions to make it approximately solvable by DS optimization algorithms. Overall, the proposed approach is simple, scalable, and likely to be benefited from further research on submodular optimization. Experiments on real datasets demonstrate the effectiveness of our method.

In this paper, we propose a new method for knowledge base completion (KBC): instance-based learning (IBL). For example, to answer (Jill Biden, lived city,?), instead of going directly to Washington D.C., our goal is to find Joe Biden, who has the same lived city as Jill Biden. Through prototype entities, IBL provides interpretability. We develop theories for modeling prototypes and combining IBL with translational models. Experiments on various tasks confirmed the IBL model's effectiveness and interpretability. In addition, IBL shed light on the mechanism of rule-based KBC models. Previous research has generally agreed that rule-based models provide rules with semantically compatible premises and hypotheses.

Financial transactions are increasingly being handled by automated programs called smart contracts. However, one challenge in the adaptation of smart contracts is the presence of vulnerabilities, which can cause significant monetary loss.

Recent years have seen the rise of statistical program learning based on neural models as an alternative to traditional rule-based systems for programming by example. Rule-based approaches offer correctness guarantees in an unsupervised way as they inherently capture logical rules, while neural models are more realistically scalable to raw, high-dimensional input, and provide resistance to noisy I/O specifications. We introduce PLANS (Program LeArning from Neurally inferred Specifications), a hybrid model for program synthesis from visual observations that gets the best of both worlds, relying on (i) a neural architecture trained to extract abstract, high-level information from each raw individual input (ii) a rule-based system using the extracted information as I/O specifications to synthesize a program capturing the different observations. In order to address the key challenge of making PLANS resistant to noise in the network's output, we introduce a dynamic filtering algorithm for I/O specifications based on selective classification techniques. We obtain state-of-the-art performance at program synthesis from diverse demonstration videos in the Karel and ViZDoom environments, while requiring no ground-truth program for training.

In high-stakes sectors such as network security, IoT security, accurately distinguishing between normal and anomalous data is critical due to the significant implications for operational success and safety in decision-making. The complexity is exacerbated by the presence of unlabeled data and the opaque nature of black-box anomaly detection models, which obscure the rationale behind their predictions. In this paper, we present a novel method to interpret the decision-making processes of these models, which are essential for detecting malicious activities without labeled attack data. We put forward the Segmentation Clustering Decision Tree (SCD-Tree), designed to dissect and understand the structure of normal data distributions.

A recursive strategy is able to efficiently calculate the contributions of particular substring, pre-calculating contributions of the smaller sub-strings contained within the target string. Context-free grammars (CFG) are 4-tuples G = (V, Σ, R, S), consisting of: a set of non-terminal symbols V, a set of terminal symbols Σ (also known as an alphabet), a set of production rules R, a non-terminal starting symbol S from which all strings are generated. Production rules are simple maps permitting the swapping of non-terminals with other non-terminals or terminals. All strings generated by the CFG can be broken down into a (non-unique) tree of production rules with the non-terminal starting symbol S at its head. These are known as the parse trees and are demonstrated in Figure 3 in the main paper.

Knowledge graph reasoning, which aims at predicting the missing facts through reasoning with the observed facts, is critical to many applications. Such a problem has been widely explored by traditional logic rule-based approaches and recent knowledge graph embedding methods. A principled logic rule-based approach is the Markov Logic Network (MLN), which is able to leverage domain knowledge with first-order logic and meanwhile handle the uncertainty. However, the inference in MLNs is usually very difficult due to the complicated graph structures.

Defense Minister Gen Nakatani called Saturday for closer defense cooperation among like-minded partners in the Indo-Pacific region in order to strengthen the global rules-based order and -- in an implicit criticism of China -- act as a counter to countries seeking to erode the status quo. The Japanese defense chief used a speech before scores of his counterparts and military brass in Singapore at the Shangri-La Dialogue, Asia's leading security conference, to push for closer cooperation and coordination, "while ensuring openness, inclusiveness and transparency, with an aim of restoring a rules-based international order in the Indo-Pacific region, strengthening accountability and promoting the international public good." Nakatani said the need to unite on defense cooperation was clear, pointing to Russia's invasion of Ukraine -- a violation of the U.N. charter -- and Beijing's moves in the disputed South China Sea, including its decision to openly ignore a 2016 international arbitral tribunal ruling that dismissed the country's claim to most of the strategic waterway.

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