Reviewerly/exHarmony huggingface dataset:
Modifications of the Miller Definition of Contrastive (Counterfactual) Explanations

Miller recently proposed a definition of contrastive (counterfactual) explanations based on the well-known Halpern-Pearl (HP) definitions of causes and (non-contrastive) explanations. Crucially, the Miller definition was based on the original HP definition of explanations, but this has since been modified by Halpern; presumably because the original yields counterintuitive results in many standard examples. More recently Borner has proposed a third definition, observing that this modified HP definition may also yield counterintuitive results. In this paper we show that the Miller definition inherits issues found in the original HP definition. We address these issues by proposing two improved variants based on the more robust modified HP and Borner definitions. We analyse our new definitions and show that they retain the spirit of the Miller definition where all three variants satisfy an alternative unified definition that is modular with respect to an underlying definition of non-contrastive explanations. To the best of our knowledge this paper also provides the first explicit comparison between the original and modified HP definitions.
On Identifiability of BN2A Networks

In this paper, we consider two-layer Bayesian networks. The first layer consists of hidden (unobservable) variables and the second layer consists of observed variables. All variables are assumed to be binary. The variables in the second layer depend on the variables in the first layer. The dependence is characterised by conditional probability tables representing Noisy-AND or simple Noisy-AND. We will refer to this class of models as BN2A models. We found that the models known in the Bayesian network community as Noisy-AND and simple Noisy-AND are also used in the cognitive diagnostic modelling known in the psychometric community under the names of RRUM and DINA, respectively. In this domain, the hidden variables of BN2A models correspond to skills and the observed variables to students’ responses to test questions. In this paper we analyse the identifiability of these models. Identifiability is an important concept because without it we cannot hope to learn correct models. We present necessary conditions for the identifiability of BN2As with Noisy-AND models. We also propose and test a numerical approach for testing identifiability.
Normative Monitoring Using Bayesian Networks: Defining a Threshold for Conflict Detection

Normative monitoring of black-box AI systems entails detecting whether input-output combinations of AI systems are acceptable in specific contexts. To this end, we build on an existing approach that uses Bayesian networks and a tailored conflict measure called IOconfl. In this paper, we argue that the default fixed threshold associated with this measure is not necessarily suitable for the purpose of normative monitoring. We subsequently study the bounds imposed on the measure by the normative setting and, based upon our analyses, propose a dynamic threshold that depends on the context in which the AI system is applied. Finally, we show the measure and threshold are effective by experimentally evaluating them using an existing Bayesian network.
On the Cognitive Logic of Human Propositional Reasoning: Merging Ranking Functions

This paper considers whether the sequential application of a combination of a merging operator and a ranking construction operator predicts human propositional reasoning. Our formally sound approach is benchmarked on data from a psychological experiment, demonstrating that the approach is cognitively more adequate than classical logic.
Learning Sets of Probabilities Through Ensemble Methods

A possible approach to obtain set-valued predictions is to learn for each query instance a probability set (a.k.a. credal set) representing its associated uncertainty. Theoretically founded decision rules extending classical expectation and inducing a partial order between predictions can the be used to derive set-valued predictions. However, obtaining such a credal set by imprecisiating a given learning algorithm is usually computationally challenging, except for simple models such as decision trees or naive Bayes classifiers. In this paper, we propose a simple, easy to use quantile-based framework for estimating credal sets using output of ensemble methods, that can also cope with complex types of data, such as images and mixed/multimodal data, etc. Experiments are conducted to highlight the usefulness of the proposed framework.
A Comparison of Different Marginalization Operations in Simple Propagation

Simple Propagation is a message passing algorithm for exact inference in Bayesian networks. Simple Propagation is like Lazy Propagation but uses the one in, one out-principle when computing inter-clique messages passed between cliques of the junction tree. Here Lazy propagation performs a more in-depth graphical analysis of the set of potentials. Originally, Simple Propagation used Variable Elimination as the marginalization operation algorithm. In this paper, we describe how Symbolic Probabilistic Inference (SPI) can be used as the marginalization operation algorithm in Simple Propagation. We report on the results of an empirical evaluation where the time performance of Simple Propagation with SPI is compared to the time performance of Simple Propagation with Variable Elimination and Simple Propagation with Arc-Reversal. The experimental results are interesting and show that in some cases Simple Propagation with SPI has the best time performance.
Extension-Based Semantics for Incomplete Argumentation Frameworks: Grounded Semantics and Principles

Incomplete Argumentation Frameworks (IAFs) enrich classical abstract argumentation with arguments and attacks whose actual existence is questionable. The usual reasoning approaches rely on the notion of completion, i.e. standard AFs representing “possible worlds” compatible with the uncertain information encoded in the IAF. Recently, extension-based semantics for IAFs that do not rely on the notion of completion have been defined, using instead new versions of conflict-freeness and defense that take into account the (certain or uncertain) nature of arguments and attacks. In this paper, we give new insights on this reasoning approach, by adapting the well-known grounded semantics to this framework in two different versions. After determining the computational complexity of our new semantics, we provide a principle-based analysis of these semantics, as well as the ones previously defined in the literature, namely the complete, preferred and stable semantics.
Effect of Visual Support Information and Participant’s Personality on Training in Virtual Environments

In recent years, augmented reality and virtual reality have emerged as powerful technologies for education and training. The basic concept of augmented reality involves overlaying additional information on real world. In contrast, training using virtual reality technology is aimed at avoiding harm in real environments, or enhancing user engagement in immersive virtual environments. In this study, we observed participants’ performances during iterative training in three types of virtual environments with different content supporting manipulation. Rod tracking was used as the training task, in line with a previous study. Twenty-eight healthy students (21.8 ± 1.2 yrs.) voluntarily participated in this study. The participants’ performances were quantified via the number of contacts with the sides of the slit by the rod. Subjective fatigue, stress, and sense of embodiment are assessed by a questionnaire survey. The experimental results suggested that visual support information in training virtual environments enhanced the sense of self-location and body ownership. Further, the participants’ personalities were classified as Type A or Type B based on their performance in the training session. Consequently, important information about designing the virtual environment for training and experimental tasks were obtained.
Geospatial Insights into Ophiolitic Complexes in the Cimmerian Realm of the Afghan Central Block (Middle Afghanistan)

Ophiolites are remnants of oceanic crust that have been thrust onto continental crust due to tectonic processes. They are composed of mostly mafic and ultramafic rocks, which are genetically associated with gold, silver, platinum group element (PGE), chrome, manganese, titanium, cobalt, copper, and nickel deposits. The main objective of this research was to identify the spatial distribution of Mesozoic ophiolitic complexes within the Central Afghan Block in Middle Afghanistan using optical remote sensing data and spectral analyses. Distinct algorithms, such as false color composite (FCC), proposed band ratios (PBR), principal component analysis (PCA), and spectral angle mapper (SAM), were used to map the targeted ophiolitic complexes. New band ratios were proposed in this study based on the spectral properties of mafic-ultramafic minerals and rocks, which showed high efficiency. Based on the results, four different ophiolitic complexes were delineated within this study area. These complexes are consistent with previous studies. The accuracy assessment of this study showed an overall accuracy of 72.2%. The findings of this study can significantly contribute to further studies on the emplacement mechanism and paleo-Tethys history of Middle Afghanistan. Also, the spatial distribution of the ophiolitic complexes identified in this study can be used to constrain models of the tectonic evolution of the Central Afghan Block. Additionally, the identification of new band ratios for mapping ophiolitic complexes can be used in future studies of other ophiolite-bearing regions.
Research on Immersive Scenario Design in Museum Education

In the current museum boom, the social impact of knowledge dissemination through museum visits is becoming increasingly evident, and the educational function of museums has received widespread attention and support. The innovation of digital technology provides technical support for the modern transformation of museum resources, enhancing the sensory and interactive experience of visitors through immersive scenario experiences to achieve educational goals in a scenario-based manner and enhance the effectiveness of knowledge transmission. This article focuses on analyzing excellent cases of museum education in China, which have successfully incorporated immersive scenario design into their programs. The article provides a detailed analysis of the practical methods used in these programs and identifies two combined methods for designing educational activities in museums. By showcasing the successful implementation of immersive scenario design in museum education, this article underscores the value and potential of this approach for enhancing the educational impact of museums in the modern era and suggests future developments in museum educational activities.
AMbER - Adaptive Instructional Systems as a Use Case for the Holistic Assessment Platform

Adaptive instructional systems support learner and teacher to deter-mine which learning material would be best at a specific time to reach the learner’s goals. The learner’s current state, personality traits, and learning history are important data for a system and a teacher to support the learning process. For a beneficial use of an adaptive instructional system, the effectiveness, efficiency, and the satisfaction of the user must be tracked and evaluated to optimize the learning outcome. The human factors analysis platform AMbER uses objective and subjective measures to evaluate the user state, the efficiency, and effectiveness of the learning process. The modular architecture of the assessment platform AMbER leads to highly flexible dashboards, that can be adapted to the specific requirements of the use case. Assessing adaptive instructional systems is one of many possible scenarios to make use of AMbER. An adaptive instructional system scenario is used as an example, to showcase the benefits of the assessment platform.
Pólya’s Methodology for Strengthening Problem-Solving Skills in Differential Equations: A Case Study in Colombia

The formation of students is integral to education. Strengthening critical thinking and reasoning are essential for the professionals that today’s world needs. For this reason, the authors of this article applied Pólya’s methodology, an initiative based on observing students’ difficulties when facing mathematical problems. The present study is part of the qualitative and quantitative research paradigm and the action research methodology. In this study, the inquiry process was inductive, the sample is non-probabilistic, and the data interpretation strategy is descriptive. As a case study, six students were enrolled onto a differential equations course at the Universidad Autónoma de Bucaramanga. A didactic process was designed using information and communication technologies (ICTs) in five sequences that address first-order differential equation applications. As a result of the pedagogical intervention, problem-solving skills were strengthened. All this was based on asking the right questions, repeated reading, identifying and defining variables, mathematization, communication, and decomposing the problem into subproblems. This research study seeks to set a precedent in the Latin American region that will be the basis for future studies.
GAINS: Comparison of Genetic AlgorIthms and Neural AlgorithmS for Video Game Playing

In this paper we place ourselves in the context of sub-symbolic Artificial Intelligence. We aim at comparing two well known methods of learning (Neural Networks and Genetic Algorithms) for video game playing. The setting of video game playing (here we chose Super Mario Bros) is of particular interest because of the challenges it brings in terms of data collection. The data is challenging in nature due to its size (in our case the small number of levels in the game - thus fundamentally different from big data approaches) and its heterogeneity (in our case the different levels used to simulate non deterministic games). The non determinism aspect is key because we demonstrate it to be the main root cause of performance decline.
$$\varepsilon $$-Almost collision-flat universal hash functions and mosaics of designs

Abstract We introduce, motivate and study $$\varepsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ε</mml:mi> </mml:math> -almost collision-flat universal (ACFU) hash functions $$f:\mathcal X\times \mathcal S\rightarrow \mathcal A$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>f</mml:mi> <mml:mo>:</mml:mo> <mml:mi>X</mml:mi> <mml:mo>×</mml:mo> <mml:mi>S</mml:mi> <mml:mo>→</mml:mo> <mml:mi>A</mml:mi> </mml:mrow> </mml:math> . Their main property is that the number of collisions in any given value is bounded. Each $$\varepsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ε</mml:mi> </mml:math> -ACFU hash function is an $$\varepsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ε</mml:mi> </mml:math> -almost universal (AU) hash function, and every $$\varepsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ε</mml:mi> </mml:math> -almost strongly universal (ASU) hash function is an $$\varepsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ε</mml:mi> </mml:math> -ACFU hash function. We study how the size of the seed set $$\mathcal S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>S</mml:mi> </mml:math> depends on $$\varepsilon ,|\mathcal X |$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>ε</mml:mi> <mml:mo>,</mml:mo> <mml:mo>|</mml:mo> <mml:mi>X</mml:mi> <mml:mo>|</mml:mo> </mml:mrow> </mml:math> and $$|\mathcal A |$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>|</mml:mo> <mml:mi>A</mml:mi> <mml:mo>|</mml:mo> </mml:mrow> </mml:math> . Depending on how these parameters are interrelated, seed-minimizing ACFU hash functions are equivalent to mosaics of balanced incomplete block designs (BIBDs) or to duals of mosaics of quasi-symmetric block designs; in a third case, mosaics of transversal designs and nets yield seed-optimal ACFU hash functions, but a full characterization is missing. By either extending $$\mathcal S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>S</mml:mi> </mml:math> or $$\mathcal X$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>X</mml:mi> </mml:math> , it is possible to obtain an $$\varepsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ε</mml:mi> </mml:math> -ACFU hash function from an $$\varepsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ε</mml:mi> </mml:math> -AU hash function or an $$\varepsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ε</mml:mi> </mml:math> -ASU hash function, generalizing the construction of mosaics of designs from a given resolvable design (Gnilke et al. in Des. Codes Cryptogr. 86(1):85–95, 2017). The concatenation of an ASU and an ACFU hash function again yields an ACFU hash function. Finally, we motivate ACFU hash functions by their applicability in privacy amplification.
Hypergraphs in Logic Programming

Heterogeneous data is a significant topic in today’s context, necessitating the development of AI tools. Logic programming is a powerful approach for extracting information from datasets, enabling the interpretation of natural language as logical rules. This paper introduces a novel representation of logic normal programs, which include negated variables, using labeled hypergraphs. This representation provides a comprehensive characterization of the program, capturing all available information and relationships among variables in a specific hypergraph. Such characterization is highly advantageous, particularly for computing program consequences and models through hypergraph theory.
PETS: Predicting Efficiently Using Temporal Symmetries in Temporal PGMs

Time in Bayesian Networks is concrete: In medical applications, a timestep can correspond to one second. To proceed in time, temporal inference algorithms answer conditional queries. But the interface algorithm simulates iteratively into the future making predictions costly and intractable for applications. We present an exact, GPU-optimizable approach exploiting symmetries over time during answering prediction queries by constructing a matrix for the underlying temporal process. Additionally, we construct a vector capturing the probability distribution at the current timestep. Then, we can time-warp into the future by matrix exponentiation. We show an order of magnitude speedup over the interface algorithm. The work-heavy preprocessing step can be done offline, and the runtime of prediction queries is significantly reduced. Now, we can handle application problems that could not be handled before.
How to Design a Successful Training Application with Used Mobile Augmented Reality

Augmented Reality (AR) has been found useful and with strong affordances in education and training due to its potential to make the learning process easier, decrease the time for training, and increase motivation. Research has already highlighted the importance of UX when designing and developing mobile augmented reality. In this study we propose a framework within a UX perspective to design and implement a mobile augmented reality application. The framework is inspired by Donald Norman’s six UX design principles. We used the framework to design and develop an AR mobile application for training field engineers in service and maintenance of a medical analyzer in the medical industry. Sixteen field engineers participated in the study. The evaluation of the developed AR application was inspired by the technology acceptance model, including items in ease of use, user satisfaction, usefulness, supplemented with items in visual interface, and learnability. The findings revealed that all the field engineers expressed positive feedback in terms of being able to see, train, and practice on the medical analyzer via the mobile augmented reality. A reason for the positive evaluation, could be due the high degree of effort to include the users at a very early stage and throughout the entire design process. Further, much effort was made to design the AR application based upon an explicit understanding of the users, tasks, and environments. However, future work is needed to create significant evidence of and insight into the training and learning outcomes of AR.
Curiously: Supporting Young Children and their Caregivers Create Meaningful Learning Adventures in Nature

Our planet is facing critical challenges and systemic changes that demand an urgent attention to educational and technological solutions that empower people to take care and protect our home. But, how can we protect something that we don’t love? Curiously provides resources and support to help caregivers and families with young children (ages 2–6) create playful and engaging learning adventures in nature together, promoting meaningful emotional bonding. It includes a mockup of an app that helps parents and caregivers plan such adventures and, eventually, will include a functionality that allows them to share their ex-presences with other families while developing a community that deepens the engagement with the platform and develops emotional bonding with the natural world. This paper describes in detail the systematic design process that was followed, including design insights from the literature and from user-soaked interactions.
Private Set Intersection Based on Lightweight Oblivious Key-Value Storage Structure

At this stage, the application of Private Set Intersection (PSI) protocols is essential for smart homes. Oblivious Key-Value Stores (OKVS) can be used to design efficient PSI protocols. Constructing OKVS with a cuckoo hashing graph is a common approach. It increases the number of hash functions while reducing the possibility of collisions into rings. However, the existing OKVS construction scheme requires a high time overhead, and such an OKVS applied to PSI protocols would also have a high communication overhead. In this paper, we propose a method called 3-Hash Garbled Cuckoo Graph (3H-GCG) for constructing cuckoo hash graphs. Specifically, this method handles hash collisions between different keys more efficiently than existing methods, and it can also be used to construct an OKVS structure with less storage space. Based on the 3H-GCG, we design a PSI protocol using the Vector Oblivious Linear Evaluation (VOLE) and OKVS paradigm, which achieves semi-honest security and malicious security. Extensive experiments demonstrate the effectiveness of our method. When the set size is 218–220, our PSI protocol is less computationally intensive than other existing protocols. The experiments also show an increase in the ratio of raw to constructed data of about 7.5%. With the semi-honest security setting, our protocol achieves the fastest runtime with the set size of 218. With malicious security settings, our protocol has about 10% improvement in communication compared with other existing protocols.