Reviewerly/exHarmony huggingface dataset:
Strongly First Order, Domain Independent Dependencies: The Union-Closed Case

Team Semantics generalizes Tarski’s Semantics by defining satisfaction with respect to sets of assignments rather than with respect to single assignments. Because of this, it is possible to use Team Semantics to extend First Order Logic via new kinds of connectives or atoms – most importantly, via dependency atoms that express dependencies between different assignments. Some of these extensions are more expressive than First Order Logic proper, while others are reducible to it. In this work, I provide necessary and sufficient conditions for a dependency atom that is domain independent (in the sense that its truth or falsity in a relation does not depend on the existence in the model of elements that do not occur in the relation) and union closed (in the sense that whenever it is satisfied by all members of a family of relations it is also satisfied by their union) to be strongly first order, in the sense that the logic obtained by adding them to First Order Logic is no more expressive than First Order Logic itself.
Non-monotonic Reasoning via Dynamic Consequence

We approach the concept of Pivotal Rule Consequence ( $$\textsf{PRC}$$ ) proposed in [14, 15] from a semantical perspective, resorting to model updates in Public Announcement Logic ( $$\textsf{PAL}$$ ) [17]. In doing this, we take inspiration from the notion of dynamic consequence from [3, 6]. Our perspective gains in interest since $$\textsf{PRC}$$ serves as a “bridge” from Classical Logic to Default Logic –one of the most well-known non-monotonic formalisms. We show how the internalization of $$\textsf{PRC}$$ in $$\textsf{PAL}$$ leads to clear semantics of the former, and to completeness and transfer results. Moreover, we address the case of credulous consequence in Default Logic as a particular case of $$\textsf{PRC}$$ . Interestingly, we cast credulous consequence as a model checking problem. We argue that our results open the way to use well-known semantic tools from modal logic to study properties of different non-monotonic logics.
Exploring Sensitization in the Context of Extending the Behavior of an Artificial Agent

Sensitization, a non-associative learning widely observed across phylogeny, is fundamental for adaptation and, thus, survival of living organisms. This paper investigates one form of sensitization, namely pseudo-conditioning, in order to present a new computational model inspired by its characteristics. We develop this model as part of the Iterant Deformable Sensorimotor Medium (IDSM), a recently developed abstract model of sensorimotor behavior formation. The characteristics of the presented model are studied and analyzed in the light of our long term objective of investigating new unsupervised learning mechanisms for artificial autonomous agents.
Combinatorial Flows as Bicolored Atomic Flows

We introduce combinatorial flows as a graphical representation of proofs. They can be seen as a generalization of atomic flows on one side and of combinatorial proofs on the other side. From atomic flows, introduced by Guglielemi and Gundersen, they inherit the close correspondence with open deduction and the possibility of tracing the occurrences of atoms in a derivation. From combinatorial proofs, introduced by Hughes, they inherit the correctness criterion that allows to reconstruct the derivation from the flow. In fact, combinatorial flows form a proof system in the sense of Cook and Reckhow. We show how to translate between open deduction derivations and combinatorial flows, and we show how they are related to combinatorial proofs with cuts.
Expressing Power of Elementary Quantum Recursion Schemes for Quantum Logarithmic-Time Computability

Quantum computing has been studied over the past four decades based on two computational models of quantum circuits and quantum Turing machines. To capture quantum polynomial-time computability, a new recursion-theoretic approach was taken lately by Yamakami [J. Symb. Logic 80, pp. 1546–1587, 2020] by way of schematic definitions, which constitute a few initial quantum functions and a few construction schemes, including composition, branching, and multi-qubit quantum recursion. By taking a similar step, we look into quantum logarithmic-time computability and further explore the expressing power of elementary schemes designed for such quantum computation. In particular, we introduce an elementary form of the quantum recursion, called the fast quantum recursion, which helps us capture quantum logarithmic-time computability.
Towards an Intuitionistic Deontic Logic Tolerating Conflicting Obligations

We propose a minimal deontic logic, called MIND, based on intuitionistic logic. This logic gives a very simple solution to handling conflicting obligations: the presence of two conflicting obligations does not entail the triviality of the set of norms. Moreover, the logic supports the claim that there may be no obligations at all, so that a logical truth is not obligatory. Like in intuitionistic/constructive modal logic, in this logic, the two deontic modalities Obligation and Permission are not dual, however as a difference with so-called constructive modal logic, it supports distribution of permission over disjunction, but it does not satisfy aggregation of obligations. The logic MIND is a non-normal modal logic based on intuitionistic logic and it is semantically characterised by a suitable neighbourhood semantics. We further present a simple cut-free sequent calculus for this logic. By means of this calculus we show that logic MIND is decidable and that it satisfies the disjunction property.
Additive Types in Quantitative Type Theory

Dependent type theories enable us to reason about properties of our programs, while substructural type theories enable us to reason about their resource use. Quantitative type theory seamlessly combines dependent and substructural types by employing positive semirings to keep track of variable usage and computational contexts. Existing treatments of this theory typically focus on multiplicative connectives such as functions or multiplicative pairs. In this work, we extend quantitative type theory with additive zero, unit, unions and dependent pairs, as well as annotated eliminators for various types. We then explain how these additive types can be used to better describe resource use and how they integrate with existing programming techniques. Finally, we implement an interpreter for a language based on these extensions.
Linear Algebraic Approach to Strongly Secure Ramp Secret Sharing for General Access Structures with Application to Symmetric PIR

Ramp secret sharing is a variant of secret sharing which can achieve better information ratio than perfect schemes by allowing some partial information on a secret to leak out. Strongly secure ramp schemes can control the amount of leaked information on the components of a secret. In this paper, we reduce the construction of strongly secure ramp secret sharing for general access structures to a linear algebraic problem. As a result, we show that previous results on strongly secure network coding imply two linear transformation methods to make a given linear ramp scheme strongly secure. They are explicit or provide a deterministic algorithm while the previous methods which work for any linear ramp scheme are non-constructive. In addition, we present a novel application of strongly secure ramp schemes to symmetric PIR in a multi-user setting. Our solution is advantageous over those based on a non-strongly secure scheme in that it reduces the amount of communication between users and servers and also the amount of correlated randomness that servers generate in the setup.
Skill Learning for Long-Horizon Sequential Tasks

Solving long-horizon problems is a desirable property in autonomous agents. Learning reusable behaviours can equip the agent with this property, allowing it to adapt them when performing various real-world tasks. Our approach for learning these behaviours is composed of three modules, operating in two separate timescales and it uses a hierarchical model with both discrete and continuous variables. This modular structure allows an independent training process for each stage. These stages are organized using a two-level temporal hierarchy. The first level contains the planner, responsible for issuing the skills that should be executed, while the second level executes the skill. In this latter level, to achieve the desired skill behaviour, the discrete skill is converted to a continuous vector that contains information regarding which environment change must occur. With this approach, we aimed to solve long-horizon sequential tasks with delayed rewards. Contrary to existing work, our method uses both variable types to allow an agent to learn high-level behaviours consisting of an interpretable set of skills. This method allows to compose the discrete skills easily, while keeping the flexibility, provided by the continuous representations, to execute them in several different ways. Using a 2D scenario where the agent has to catch a set of objects in a specific order, we demonstrate that our approach is scalable to scenarios with increasingly longer tasks.
Neural Question Generation for the Portuguese Language: A Preliminary Study

Question Generation (QG) is an important and challenging problem that has attracted attention from the natural language processing (NLP) community over the last years. QG aims to automatically generate questions given an input. Recent studies in this field typically use widely available question-answering (QA) datasets (in English) and neural models to train and build these QG systems. As lower-resourced languages (e.g. Portuguese) lack large-scale quality QA data, it becomes a significant challenge to experiment with recent neural techniques. This study uses a Portuguese machine-translated version of the SQuAD v1.1 dataset to perform a preliminary analysis of a neural approach to the QG task for Portuguese. We frame our approach as a sequence-to-sequence problem by fine-tuning a pre-trained language model – T5 for generating factoid (or wh)-questions. Despite the evident issues that a machine-translated dataset may bring when using it for training neural models, the automatic evaluation of our Portuguese neural QG models presents results in line with those obtained for English. To the best of our knowledge, this is the first study addressing Neural QG for Portuguese. The code and models are publicly available at https://github.com/bernardoleite/question-generation-t5-pytorch-lightning .
A Service-Oriented Framework for ETL Implementation

The development of analytical systems imposes several challenges related not only to the amount and heterogeneity of the involved data but also to the constant need to readapt and evolve to overcome new business challenges. The data modelling layer represents the mapping between the domain and technical knowledge, however, to organize raw data into a form that can be used for analytics, specific Extract, Transform and Load (ETL) processes should be applied. ETL systems are recognized as a critical and tightly coupled system component that encapsulates data-level requirements that are hard to implement and maintain. In a Big Data era, adaptability and extensibility are important characteristics to hold when developing analytical systems. Thus, to provide more consistent, reliable, flexible, and reusable ETL processes, a service-oriented implementation for ETL development is proposed.
Diabetic-Friendly Multi-agent Recommendation System for Restaurants Based on Social Media Sentiment Analysis and Multi-criteria Decision Making

Lifestyle, poor diet, stress, among other factors, strongly contribute to aggravate people’s health problems, such as diabetes and high blood pressure. Some of these problems could be avoided if some of the essential recommendations for the practice of a healthy lifestyle were followed. The paper proposes a solution designed for diabetic people to find restaurants nearby that are more suitable for their health needs. A diabetic-friendly feature that will use a set of criteria, built through a Multi-Agent System (MAS) that using the user preferences initially recorded, will provide the user with three category recommendations that potentially benefit the user lifestyle and health. The solution proposes the use of Case-Based Reasoning algorithm to enable the solution to evolve and improve in each interaction with the user. Sentiment Analysis was also used for identifying the restaurant reviews score, since this is one of the defined criteria for the solution.
Content-Based Lawsuits Document Image Retrieval

The São Paulo Court of Justice has the highest number of lawsuits of all courts. The lawsuits are composed of raster-scanned documents enclosed in unstructured volumes, of which some are unreadable document images. Natural Language Processing techniques fail to extract from some of these documents due to the low quality of images. This article proposes a methodology to automatize the retrieval of document images from lawsuit databases based on the contents of the images. We developed a hybrid algorithm for feature extraction from document images and used a distance metric to retrieve similar images. The TJSP’s database was used to validate our proposal, resulting in a system that allows finding similar images with an accuracy above eighty percent.
UlyssesSD-Br: Stance Detection in Brazilian Political Polls

Political bill comments published in digital media may reveal the issuer’s stances. Through this, we can identify and group the polarity of these public opinions. The automatic stance detection task involves viewing the text and the target topic. Due to the diversity and emergence of new bills, the challenge approached is to estimate the polarity of a new topic. Thus, this paper evaluates cross-target stance detection with many-to-one approaches in a collected Portuguese dataset of the political pool from the Brazilian Chamber of Deputies website. We proposed a new corpus for the bills’ opinion domain and tested it in several models, where we achieved the best result with the mBERT model in classification with the joint input topic and comment method. We verify that the mBERT model successfully handled cross-target tasks with this corpus among the tested algorithms.
Assessing Policy, Loss and Planning Combinations in Reinforcement Learning Using a New Modular Architecture

The model-based reinforcement learning paradigm, which uses planning algorithms and neural network models, has recently achieved unprecedented results in diverse applications, leading to what is now known as deep reinforcement learning. These agents are quite complex and involve multiple components, factors that create challenges for research and development of new models. In this work, we propose a new modular software architecture suited for these types of agents, and a set of building blocks that can be easily reused and assembled to construct new model-based reinforcement learning agents. These building blocks include search algorithms, policies, and loss functions (Code available at https://github.com/GaspTO/Modular_MBRL ). We illustrate the use of this architecture by combining several of these building blocks to implement and test agents that are optimized to three different test environments: Cartpole, Minigrid, and Tictactoe. One particular search algorithm, made available in our implementation and not previously used in reinforcement learning, which we called averaged minimax, achieved good results in the three tested environments. Experiments performed with our implementation showed the best combination of search, policy, and loss algorithms to be heavily problem dependent.
A Robust State Transition Function for Multi-agent Epistemic Systems

This paper studies belief correction and state transition for ontic actions in a multi-agent epistemic framework. When a full observer agent observes the execution of an action, he will correct his (possibly wrong) initial belief about the precondition of the action as well as his belief about his own observability. The paper shows that correcting beliefs about precondition and observability is vital for observing the effect of the action and robust state transition, highlighting the risk of yielding counter-intuitive results. The paper proposes a state transition function for ontic actions which integrates correcting beliefs for precondition, observability and realizing the effect of the action. This novel transition function does not require event update models. The paper investigates several properties of the transition function, assessing its robustness in ensuring that beliefs of agents change consistently with their degree of observability of action occurrences. Sample scenarios are provided to illustrate the novel transition function.
Lawsuits Document Images Processing Classification

Natural Language Processing techniques usually fail to classify low quality lawsuit document images produced by a flatbed scanner or fax machine or even captured by mobile devices, such as smartphones or tablets. As the courts of justice have many lawsuits, the manual detection of classification errors is unfeasible, favouring fraud, such as using the same payment receipt for more than one fee. An alternative to classifying low-quality document images is visual-based methods, which extract features from the images. This article proposes classification models for lawsuit document image processing using transfer learning to train Convolutional Neural Networks most quickly and obtain good results even in smaller databases. We validated our proposal using a TJSP dataset composed of 2,136 unrecognized document images by Natural Language Processing techniques and reached an accuracy above 80% in the proposed models.
FIT: Using Feature Importance to Teach Classification Tasks to Unknown Learners

This work introduces an interactive machine teaching approach that teaches classification tasks. But instead of assuming perfect knowledge about the learner as most machine teaching approaches do, our adaptive approach—Feature Importance Teaching (FIT)—chooses the samples to show based on a model of the learner updated online using feedback about the weights attributed to the features. We run simulations where there is a mismatch on the prior knowledge and learning model of the student and the ones assumed by the teacher. The results have shown that our teaching approach can mitigate this mismatch and lead to significantly faster learning curves than the ones obtained in conditions where the teacher randomly selects the samples or does not consider this kind of feedback from the student. We tested using data sets from two different application domains and the conclusions were the same. We also tested FIT when the student provides only the most important feature and it still outperformed the other approaches considered. We finally conducted a study with real human users, which confirmed the results obtained in the simulations.
Temporal Nodes Causal Discovery for in Intensive Care Unit Survival Analysis

In hospital and after ICU discharge deaths are usual, given the severity of the condition under which many of them are admitted to these wings. Because of this, there is an urge to identify and follow these cases closely. Furthermore, as ICU data is usually composed of variables measured in varying time intervals, there is a need for a method that can capture causal relationships in this type of data. To solve this problem, we propose ItsPC, a causal Bayesian network that can model irregular multivariate time-series data. The preliminary results show that ItsPC creates smaller and more concise networks while maintaining the temporal properties. Moreover, its irregular approach to time-series can capture more relationships with the target than the Dynamic Bayesian Networks.
Automatic Configuration of Genetic Algorithm for the Optimization of Electricity Market Participation Using Sequential Model Algorithm Configuration

Complex optimization problems are often associated to large search spaces and consequent prohibitive execution times in finding the optimal results. This is especially relevant when dealing with dynamic real problems, such as those in the field of power and energy systems. Solving this type of problems requires new models that are able to find near-optimal solutions in acceptable times, such as metaheuristic optimization algorithms. The performance of these algorithms is, however, hugely dependent on their correct tuning, including their configuration and parametrization. This is an arduous task, usually done through exhaustive experimentation. This paper contributes to overcome this challenge by proposing the application of sequential model algorithm configuration using Bayesian optimization with Gaussian process and Monte Carlo Markov Chain for the automatic configuration of a genetic algorithm. Results from the application of this model to an electricity market participation optimization problem show that the genetic algorithm automatic configuration enables identifying the ideal tuning of the model, reaching better results when compared to a manual configuration, in similar execution times.
Learning to Cooperate with Completely Unknown Teammates

A key goal of ad hoc teamwork is to develop a learning agent that cooperates with unknown teams, without resorting to any pre-coordination protocol. Despite a vast number of ad hoc teamwork algorithms in the literature, most of them cannot address the problem of learning to cooperate with a completely unknown team, unless it learns from scratch. This article presents a novel approach that uses transfer learning alongside the state-of-the-art PLASTIC-Policy to adapt to completely unknown teammates quickly. We test our solution within the Half Field Offense simulator with five different teammates. The teammates were designed independently by developers from different countries and at different times. Our empirical evaluation shows that it is advantageous for an ad hoc agent to leverage its past knowledge when adapting to a new team instead of learning how to cooperate with it from scratch.
Learning Tool for Phobia Handling Based on Virtual Reality

During their lifetime, almost everyone directly or indirectly encounters some manifestation of phobia or excessive fear and inappropriate reaction to certain stimuli. Those who do not suffer from phobia themselves certainly know someone who is limited by phobia in everyday life. In this work, an innovative education tool to manage fear caused by the phobia is presented. An important therapeutic phase is to teach a patient suffering from phobia to realize his fear and to handle this state. Virtual reality applications allow the client to be exposed to various extreme situations related to his/her phobia and observe his/her reactions in selected extreme situations. This allows for a more accurate identification of the level of phobia and the consequent improvement in patient coaching to manage phobia stress. The chapter presents the process of designing, developing, testing and implementing the VR application “PhoByeVR”, which is intended for people suffering from phobias and will serve as an educational tool for better management of phobias. This application is fully compatible with HTC Vive and focuses on arachnophobia, ophidiophobia and acrophobia. For each phobia, it consists of 4 levels and a few other instructional scenes.