Neural Relational Learning Through Semi-Propositionalization of Bottom Clauses

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis in social networks. The CILP++ system is a neural-symbolic system which can perform efficient relational learning, by being able to process first-order logic knowledge into a neural network. CILP++ relies on BCP, a recently discovered propositionalization algorithm, to perform relational learning. However, efficient knowledge extraction from such networks is an open issue and features generated by BCP do not have an independent relational description, which prevents sound knowledge extraction from such networks. We present a methodology for generating independent propositional features for BCP by using semi-propositionalization of bottom clauses. Empirical results obtained in comparison with the original version of BCP show that this approach has comparable accuracy and runtimes, while allowing proper relational knowledge representation of features for knowledge extraction from CILP++ networks

Hybrid Encryption in the Multi-User Setting

This paper presents an attack in the multi-user setting on various public-key encryption schemes standardized in IEEE 1363a, SECG SEC 1 and ISO 18033-2. The multi-user setting is a security model proposed by Bellare et al., which allows adversaries to simultaneously attack multiple ciphertexts created by one or more users. An attack is considered successful if the attacker learns information about any of the plaintexts. We show that many standardized public-key encryption schemes are vulnerable in this model, and give ways to prevent the attack. We also show that the key derivation function and pseudorandom generator used to implement a hybrid encryption scheme must be secure in the multi-user setting, in order for the overall primitive to be secure in the multi-user setting. As an illustration of the former, we show that using HKDF (as standardized in NIST SP 800-56C) as a key derivation function for certain standardized hybrid public-key encryption schemes is insecure in the multi-user setting

Short One-Time Signatures

We present a new one-time signature scheme having short signatures. Our new scheme supports aggregation, batch verification, and admits efficient proofs of knowledge. It has a fast signing algorithm, requiring only modular additions, and its verification cost is comparable to ECDSA verification. These properties make our scheme suitable for applications on resource-constrained devices such as smart cards and sensor nodes. Along the way, we give a unified description of five previous one-time signature schemes and improve parameter selection for these schemes, and as a corollary we give a fail-stop signature scheme with short signatures

How the COVID-19 pandemics inspired the development of analogical games: database review and game development

COVID-19 pandemics impacted everyone's lives. Risk of infection by the SARS-CoV-2 virus imposed severe restrictive measures, submitting the population to home isolation, daily use of face masks and restringing social encounters. In this work we present the results of a research on analogical game databases where we searched for COVID-19-themed  tabletop games, discussing which health concepts were presented on these games and what kind of structure and mechanics were used. Subsequently, we present the development of a serious board game, thought to call attention of the prevention measures to reduce the risk of infection by SARS-CoV-2 and other respiratory viruses. We discuss the rationale for the selection of game mechanics and how do they fit on the health concepts that we wanted to reinforce. The product is a print and play serious game that will be available as an open educational resource

Cycles of Police Reform in Latin America.

yesOver the last quarter century post-conflict and post-authoritarian transitions in Latin America have been accompanied by a surge in social violence, acquisitive crime, and insecurity. These phenomena have been driven by an expanding international narcotics trade, by the long-term effects of civil war and counter-insurgency (resulting in, inter alia, an increased availability of small arms and a pervasive grammar of violence), and by structural stresses on society (unemployment, hyper-inflation, widening income inequality). Local police forces proved to be generally ineffective in preventing, resolving, or detecting such crime and forms of “new violence”3 due to corruption, frequent complicity in criminal networks, poor training and low pay, and the routine use of excessive force without due sanction. Why, then, have governments been slow to prioritize police reform and why have reform efforts borne largely “limited or nonexistent” long-term results? This chapter highlights a number of lessons suggested by various efforts to reform the police in Latin America over the period 1995-2010 . It focuses on two clusters of countries in Latin America. One is Brazil and the Southern Cone countries (Chile, Argentina, and Uruguay), which made the transition to democracy from prolonged military authoritarian rule in the mid- to late 1980s. The other is Central America and the Andean region (principally El Salvador, Guatemala, Honduras, Peru, and Colombia), which emerged/have been emerging from armed conflict since the mid- 1990s. The chapter examines first the long history of international involvement in police and security sector reform in order to identify long-run tropes and path dependencies. It then focuses on a number of recurring themes: cycles of de- and re-militarization of the policing function; the “security gap” and “democratization dilemmas” involved in structural reforms; the opportunities offered by decentralization for more community-oriented police; and police capacity to resist reform and undermine accountability mechanisms

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models

<p>Abstract</p> <p>Background</p> <p>Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM).</p> <p>Results</p> <p>We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function.</p> <p>Conclusions</p> <p>The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.</p

Fast relational learning using bottom clause propositionalization with artificial neural networks

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-IL2P, to perform learning from numerical vectors. C-IL2P uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy