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As technological advancements shape the workforce, computational thinking skills (CTS) are becoming increasingly crucial. The objective of the study is to explore the potential mediating role of professional development and career planning in the association between robotic coding and software and computational thinking. The study selected 308 youths using simple random sampling and the collected data was subjected to structural equation modelling (SEM) and confirmatory factor analysis (CFA) using SmartPLS. Additionally, demographic data were analyzed using statistical package for social sciences (SPSS). The results concluded that the instrument adopted fulfilled the requirements and was valid for measuring CTS. This indicates that the instrument effectively assessed the participants’ proficiency in CTS. The findings of this study have implications for addressing the skills gaps among youths. The outcomes of this research can aid in designing educational interventions and policies that focus on developing computational thinking skills among new graduates. By fostering these skills, youths can better adapt to the demands of the rapidly evolving technological landscape and contribute effectively to the industries and job market influenced by industry 4.0. Further research on longitudinal studies may be beneficial to assess the long-term impact of CTS development initiatives on reducing skills gaps and ensuring youths are equipped for future workforce requirements [ABSTRACT FROM AUTHOR]

Recently, computational thinking (CT) has gained importance in education systems worldwide, specifically the CT training of pre-service teachers. This study conducted a systematic literature analysis (2011–2021) of 38 works on pre-service teachers' CT based on Web of Science, Science Direct, and Google Scholar databases. The results were as follows: (1) Six training methods were found, (2) CT training effectively improved pre-service teachers' CT, (3) A positive relationship was found between pre-service teachers' CT ability and the five factors affecting the ability, (4) A mode of training to improve CT ability of pre-service teachers and the relationship between CT ability and teaching methods were considered. This study suggested ideas for designing training modules of CT ability and a reference for realizing the best training effect. Finally, future research trends and a general model of training were presented as references for researchers, instructors, and policy makers to promote the CT of pre-service teachers. [ABSTRACT FROM AUTHOR]

The powers that artificial intelligence (AI) has developed are impressive, with recent success in leveraging human expertise at various stages of model development. AI can attain its full potential only if, as part of its intelligence, it also actively teams with humans to co-create solutions. Combining AI simulation with human intelligence through data convergence can improve decision-making processes and provide a capacity akin to a "teaming intelligence." This research, for the first time, introduces the concepts of Human-AI Convergence (HAC) capabilities for flood evacuation decision-making. The objective of this study was to develop a unique, computationally effective surrogate HAC system for flood evacuation decision-making that integrates the distinctive features of AI with transportation geospatial data, a river hydraulic model, and human data from X (previously Twitter) to visualize flood inundation areas and suggest re-routing. The HAC system is smartly designed to forecast flood stage levels using AI across the US Geological Survey gauging stations and combine the results with Manning's equation results and transportation data, integrated into a web-based Google Earth visualization architecture. The technology has been tested in the Lowcountry of South Carolina, where previous flooding disasters caused considerable damage to the transportation networks and increased traffic on evacuation routes. This state-of-the-art HAC system -- a flood evacuation product -- stands to advance the frontier of human-AI collaborative research in the context of real-time flood emergency management and response. [ABSTRACT FROM AUTHOR]

This document provides an overview of the development of an AI curriculum for gifted students in grades 6-10. The curriculum includes subjects such as introduction to AI, probability, supervised and unsupervised learning, clustering, decision trees, and the Naive Bayes algorithm. The curriculum does not require coding as a prerequisite and emphasizes the importance of mathematical and statistical dimensions in AI education. The document also includes resources related to AI education in Turkey, such as curriculum programs and guidelines developed by the Ministry of Education. Additionally, it discusses the foundations and configuration of Bayesian networks, including calculations of marginal probabilities, joint probabilities, and conditional probabilities. Examples and explanations are provided to aid understanding and application of these concepts. [Extracted from the article]

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Recent findings show that goals affect the way in which agents represent their environment, choose relevant actions, and subjectively evaluate outcomes as rewards. Because goals influence all aspects of decision-making and learning, developing a complete theory thereof requires that we understand how agents select their goals. While computational research on goal generation and management is in its early stages, existing studies suggest that goal selection may be framed as a value-based decision-making process, with values acquired via direct experience or inference, combined with directed and undirected selection policies. Goals play a central role in human cognition. However, computational theories of learning and decision-making often take goals as given. Here, we review key empirical findings showing that goals shape the representations of inputs, responses, and outcomes, such that setting a goal crucially influences the central aspects of any learning process: states, actions, and rewards. We thus argue that studying goal selection is essential to advance our understanding of learning. By following existing literature in framing goal selection within a hierarchy of decision-making problems, we synthesize important findings on the principles underlying goal value attribution and exploration strategies. Ultimately, we propose that a goal-centric perspective will help develop more complete accounts of learning in both biological and artificial agents. [ABSTRACT FROM AUTHOR]

Mental wellbeing of university students is a growing concern that has been worsening during the COVID-19 pandemic. Numerous studies have gathered empirical data to explore the mental health impact of the pandemic on university students and investigate factors associated with higher levels of distress. While the online questionnaire survey has been a prevalent means to collect data, regression analysis has been observed a dominating approach to interpret and understand the impact of independent factors on a mental wellbeing state of interest. Drawbacks such as sensitivity to outliers, ineffectiveness in case of multiple predictors highly correlated may limit the use of regression in complex scenarios. These observations motivate the underlying research to propose alternative computational methods to investigate the questionnaire data. Inspired by recent machine learning advances, this research aims to construct a framework through feature permutation importance to empower the application of a variety of machine learning algorithms that originate from different computational frameworks and learning theories, including algorithms that cannot directly provide exact numerical contributions of individual factors. This would enable to explore quantitative impact of predictors in influencing student mental wellbeing from multiple perspectives as a result of using different algorithms, thus complementing the single view due to the dominant use of regression. Applying the proposed approach over an online survey in a UK university, the analysis suggests the past medical record and wellbeing history and the experience of adversity contribute significantly to mental wellbeing states; and the frequent communication with families and friends to keep good relationship as well as regular exercise are generally contributing to improved mental wellbeing. [ABSTRACT FROM AUTHOR]

The brain learns representations of sensory information from experience, but the algorithms by which it does so remain unknown. One popular theory formalizes representations as inferred factors in a generative model of sensory stimuli, meaning that learning must improve this generative model and inference procedure. This framework underlies many classic computational theories of sensory learning, such as Boltzmann machines, the Wake/Sleep algorithm, and a more recent proposal that the brain learns with an adversarial algorithm that compares waking and dreaming activity. However, in order for such theories to provide insights into the cellular mechanisms of sensory learning, they must be first linked to the cell types in the brain that mediate them. In this study, we examine whether a subtype of cortical interneurons might mediate sensory learning by serving as discriminators, a crucial component in an adversarial algorithm for representation learning. We describe how such interneurons would be characterized by a plasticity rule that switches from Hebbian plasticity during waking states to anti-Hebbian plasticity in dreaming states. Evaluating the computational advantages and disadvantages of this algorithm, we find that it excels at learning representations in networks with recurrent connections but scales poorly with network size. This limitation can be partially addressed if the network also oscillates between evoked activity and generative samples on faster timescales. Consequently, we propose that an adversarial algorithm with interneurons as discriminators is a plausible and testable strategy for sensory learning in biological systems. Author summary: After raw sensory data is received at the periphery, it is transformed by various neural pathways and delivered to the sensory cortex. There, neural activity forms an internal model of the state of the external world, which is updated appropriately by new information. A goal of learning, then, is to learn how to transform information into the appropriate representational form within the brain's internal model. Here, we look to artificial intelligence for new possible theories of how the brain might learn representations that resolve issues with previously proposed theories. We describe how one particular algorithm—adversarial learning—resolves a major issue with previous hypotheses relating to recurrence. Furthermore, this algorithm resembles broad features of the organization and learning dynamics of the brain, such as wake and sleep cycles. Considering seriously how this algorithm would appear if implemented by the brain, we map its features to known physiology and make testable predictions for how neural circuits learn new representations of information. [ABSTRACT FROM AUTHOR]

In his pioneering work in the field of inductive inference, Gold (Inf Control 10:447–474, 1967) proved that a set containing all finite languages and at least one infinite language over the same fixed alphabet is not identifiable in the limit (learnable in the exact sense) from complete texts. Gold's work paved the way for computational learning theories of language and has implications for two linguistically relevant classes in the Chomsky hierarchy (cf. Chomsky in Inf Control 2:137–167, 1959, Chomsky in Knowledge of language: its nature, origin, and use, Praeger, 1986; Shieber in Linguist Philos 8:333–343, 1985; Heinz in Linguist Inq 41:623–661, 2010). Within that same framework, Angluin (Inf Control 45:117–135, 1980) provided a complete characterization for the learnability of language families. Mathematically, the concept of identification in the limit from that classical setting can be seen as the use of a particular type of metric for learning in the limit (Wharton in Inf Control 26:236–255, 1974). In this research note, I use Niyogi's extended version of a theorem by Blum and Blum (Inf Control 28:125–155, 1975) on the existence of locking data sets to prove a necessary condition for learnability in the limit of any family of languages with the Gold property in any given metric. This recovers the most general version of Gold's Theorem as a particular case. Moreover, when the language family is further assumed to contain all finite languages, the same condition also becomes sufficient for learnability in the limit. Finally, we discuss questions that are left open and outline a research program regarding language learnability in the limit from other input types and cognitive feasibility. [ABSTRACT FROM AUTHOR]

While increasing the equitable participation in computer science (CS) education at schools, most existing studies focus on the acquisition of computational skills and measurements of cognitive skills as the learning outcome. The potential benefits of developing psychosocial attributes in the process of computational learning are of less concern, particularly for the underprivileged youth with Special Educational Needs (SEN) and from low-income family. To improve the inequitable learning in school, the Project C program has been developed to integrate computational participation and mentoring scheme through social-cognitive approach for underprivileged youth. The aim of this research is to investigate the psychosocial impact of the program, and examine the interrelation between hope, sense of community, and perceived social support in computational learning. The Project C program was piloted in a special school in Hong Kong under the category of Schools for Children with Moderate Intellectual Disability, targeting 8th grade students (n = 51) with SEN (i.e., mild autism) living in low-income residential districts, and a self-reported pre/post questionnaire was administered to measure the psychosocial changes before and after the intervention. The findings reveal a statistically significant change in hope, sense of community, and perceived social support through the program, and the relation between sense of community and hope was fully mediated by the perceived social support. Based on the findings, theoretical and practical implications of social-cognitive approach to computational learning are discussed. [ABSTRACT FROM AUTHOR]

Formal models of learning from teachers need to respect certain criteria to avoid collusion. The most commonly accepted notion of collusion-avoidance was proposed by Goldman and Mathias (1996), and various teaching models obeying their criterion have been studied. For each model M and each concept class C, a parameter M-TD(C) refers to the teaching dimension of concept class C in model M--defined to be the number of examples required for teaching a concept, in the worst case over all concepts in C. This paper introduces a new model of teaching, called no-clash teaching, together with the corresponding parameter NCTD(C). No-clash teaching is provably optimal in the strong sense that, given any concept class C and any model M obeying Goldman and Mathias's collusion-avoidance criterion, one obtains NCTD(C) ≤ M-TD(C). We also study a corresponding notion NCTD+ for the case of learning from positive data only, establish useful bounds on NCTD and NCTD+, and discuss relations of these parameters to other complexity parameters of interest in computational learning theory. We further argue that Goldman and Mathias's collusion-avoidance criterion may in some settings be too weak in that it admits certain forms of interaction between teacher and learner that could be considered collusion in practice. Therefore, we introduce a strictly stronger notion of collusion-avoidance and demonstrate that the well-studied notion of Preference-based Teaching is optimal among all teaching schemes that are strongly collusion-avoiding on all finite subsets of a given concept class. [ABSTRACT FROM AUTHOR]

In 2020, it is undeniable that the existence of digital natives in tertiary education is undeniable, especially in universities. They are part of a tech-savvy generation that led to technological progress that is always connected and linked based on their wishes and needs without any hesitation of time and place. They are basically using technology without guidance, but how far they can manage to handle the consequences of the technology usage and the problems that occur is always questionable. It is undeniable that a digital native must begin with at least a basic level of computer knowledge in order to manage their education and daily life. Learning about computer science not only helps students to create programs, applications or how to handle devices, but strengthens the practise of Computational Thinking (CT). CT refers to the capacity of learners to systematically tackle unstructured tasks focused on four computing concepts such as decomposition, abstraction, pattern recognition, and algorithmic thinking. The purpose of this paper is to study the relationship between CT and Mayer’s Cognitive Theory of Multimedia Learning (CTML) on the cognitive load of the learners. This research focuses on CT concepts that will be integrated with Mayer’s CTML in designing the learning material for young digital natives. Therefore, the researcher proposes a conceptual framework that aims to comprehend how to facilitate the cruciality of CT concepts in Mayer’s CTML when designing instructional learning. The proposed conceptual framework adds value in tackling the cognitive load among students, particularly in the context of the digital native generation. This paper provides several implications and highlights for further studies through a comprehensive and wide-ranging literature review. [ABSTRACT FROM AUTHOR]

Numerous computational and learning theory models have been studied using probabilistic functional equations. Especially in two-choice scenarios, the vast bulk of animal behavior research divides such situations into two different events. They split these actions into two possibilities according to the animals' progress toward a particular decision. However, reward plays a crucial role in such experiments because, based on the selected side and the food placement, such scenarios may be classified into four distinct categories. This article aims to explore the animals' reactions to such circumstances by presenting a generic stochastic functional equation. By using the well-known fixed point theory results, we examine the existence, uniqueness, and stability of solutions to the suggested functional equation. Moreover, an example is included to emphasize the significance of our findings. [ABSTRACT FROM AUTHOR]

In the DSM-5, psychiatric diagnoses are made based on self-reported symptoms and clinician-identified signs. Though helpful in choosing potential interventions based on the available regimens, this conceptualization of psychiatric diseases can limit basic science investigation into their underlying causes. The reward prediction error (RPE) hypothesis of dopamine neuron function posits that phasic dopamine signals encode the difference between the rewards a person expects and experiences. The computational framework from which this hypothesis was derived, temporal difference reinforcement learning (TDRL), is largely focused on reward processing rather than punishment learning. Many psychiatric disorders are characterized by aberrant behaviors, expectations, reward processing, and hypothesized dopaminergic signaling, but also characterized by suffering and the inability to change one's behavior despite negative consequences. In this review, we provide an overview of the RPE theory of phasic dopamine neuron activity and review the gains that have been made through the use of computational reinforcement learning theory as a framework for understanding changes in reward processing. The relative dearth of explicit accounts of punishment learning in computational reinforcement learning theory and its application in neuroscience is highlighted as a significant gap in current computational psychiatric research. Four disorders comprise the main focus of this review: two disorders of traditionally hypothesized hyperdopaminergic function, addiction and schizophrenia, followed by two disorders of traditionally hypothesized hypodopaminergic function, depression and post-traumatic stress disorder (PTSD). Insights gained from a reward processing based reinforcement learning framework about underlying dopaminergic mechanisms and the role of punishment learning (when available) are explored in each disorder. Concluding remarks focus on the future directions required to characterize neuropsychiatric disorders with a hypothesized cause of underlying dopaminergic transmission. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is an important enabling technology for intelligent manufacturing. The DL-based industrial image pattern recognition (DLBIIPR) plays a vital role in the improvement of product quality and production efficiency. Although DL technology has been widely used in the field of natural image, industrial image often has some mixed characteristics, such as small sample, imbalance, small target, strong interference, fine-grained, temporality and semantical, which reduce the feasibility and generalization of DLBIIPR. To solve this problem, this paper provides an overview of approaches commonly used in industry by enriching the sample space and limiting the hypothesis space. In order to improve the confidence of front-line workers in using DL models, the explainable deep learning (XDL) methods are reviewed, and a case study is used to verify the effectiveness of XDL. [ABSTRACT FROM AUTHOR]

We show that the topes of a complex of oriented matroids (abbreviated COM) of VC-dimension d admit a proper labeled sample compression scheme of size d. This considerably extends results of Moran and Warmuth on ample classes, of Ben-David and Litman on affine arrangements of hyperplanes, and of the authors on complexes of uniform oriented matroids, and is a step towards the sample compression conjecture – one of the oldest open problems in computational learning theory. On the one hand, our approach exploits the rich combinatorial cell structure of COMs via oriented matroid theory. On the other hand, viewing tope graphs of COMs as partial cubes creates a fruitful link to metric graph theory. [ABSTRACT FROM AUTHOR]

Declarative process modeling formalisms—which capture high-level process constraints—have seen growing interest, especially for modeling flexible processes. This paper presents DisCoveR, an efficient and accurate declarative miner for learning Dynamic Condition Response (DCR) Graphs from event logs. We present a precise formalization of the algorithm, describe a highly efficient bit vector implementation and present a preliminary evaluation against five other miners, representing the state-of-the-art in declarative and imperative mining. DisCoveR performs competitively with each of these w.r.t. a fully automated binary classification task, achieving an average accuracy of 96.1% in the Process Discovery Contest 2019 (Results are available at https://icpmconference.org/2019/process-discovery-contest). We appeal to computational learning theory to gain insight into its performance as a classifier. Due to its linear time complexity, DisCoveR also achieves much faster run times than other declarative miners. Finally, we show how the miner has been integrated in a state-of-the-art declarative process modeling framework as a model recommendation tool and discuss how discovery can play an integral part of the modeling task and report on how the integration has improved the modeling experience of end-users. [ABSTRACT FROM AUTHOR]

The article discusses the method of mixtures and its applications in statistics. The authors explain that a mixture of test supermartingales is a test supermartingale, and this method can be traced back to Ville and Wald. They also highlight the importance of the mixture method for probability measures in Bayesian statistics. The article further explores the use of the mixture method in game-theoretic probability and online learning. The authors provide examples of different loss functions and algorithms that can be used in regression and prediction problems. [Extracted from the article]

This article examines the resource sharing functionality of the decryption platform of Europol. The platform supports European law enforcement agencies (LEAs) that are having difficulties accessing legally obtained encrypted evidence due to the lack of human and technical resources. The article investigates Europol's resource transmission role with sharing economy variables such as transaction cost and trust-building. The core argument of the article is that Europol's sharing platform has a strong potential to reduce the transaction cost of outsourcing the decryption services and diminish trust-related problems for national policing agencies by replacing private sector actors. However, for a more sustainable cybercrime investigation strategy, the decryption platform needs a new market-oriented model supported with robust vetting and evidence security mechanisms to encourage more private company participation. [ABSTRACT FROM AUTHOR]

Smoothed analysis is a powerful paradigm in overcoming worst-case intractability in high-dimensional data analysis and unsupervised learning. While polynomial time smoothed analysis guarantees have been obtained for worst-case intractable problems like tensor decomposition and learning mixtures of Gaussians, such guarantees have been hard to obtain for several other important problems in data analysis. A core technical challenge in analyzing algorithms is obtaining lower bounds on the least singular value for random matrix ensembles with dependent entries, that are given by low-degree polynomials of a few base underlying random variables. In this work, we address this challenge by obtaining high-confidence lower bounds on the least singular value of new classes of structured random matrix ensembles of the above kind. We then use these bounds to design algorithms with polynomial time smoothed analysis guarantees for the following three important problems in high-dimensional data analysis: Higher order tensor decompositions, where we generalize and analyze the so-called FOOBI algorithm of Cardoso. This allows us to obtain polynomially robust decomposition algorithms for 2 ℓ 'th order tensors with rank O (n ℓ) . Learning overcomplete hidden markov models, where the size of the state space is any polynomial in the dimension of the observations. This gives the first polynomial time guarantees for learning overcomplete HMMs in the smoothed analysis model. Robust subspace recovery, when the fraction α of inliers in the d-dimensional subspace T ⊂ R n is at least α > (d / n) ℓ for any constant ℓ ∈ Z + . This contrasts with the known worst-case intractability when α < d / n , and the previous smoothed analysis result which needed α > d / n ([33]). [ABSTRACT FROM AUTHOR]

This paper deals with a single-machine scheduling problem with a time-dependent learning effect. The goal is to determine the job sequence that minimise the number of tardy jobs. Two dominance properties, two heuristic algorithms and a lower bound to speed up the search process of the branch-and-bound algorithm are proposed. Computational experiments show that the branch-and-bound algorithm can solve instances up to 18 jobs in a reasonable amount of time, and the proposed heuristic algorithm MFLA performs effectively and efficiently [ABSTRACT FROM PUBLISHER]

This paper presents a novel, sequentially executed supervised machine learning‐based electric theft detection framework using a Jaya‐optimized combined Kernel and Tree Boosting (KTBoost) classifier. It utilizes the intelligence of the XGBoost algorithm to estimate the missing values in the acquired dataset during the data pre‐processing phase. An oversampling algorithm based on the Robust‐SMOTE technique is utilized to avoid the unbalanced data class distribution issue. Afterward, with the aid of few very significant statistical, temporal, and spectral features extracted from the acquired kWh dataset, the complex underlying data patterns are comprehended to enhance the accuracy and detection rate of the classifier. For effectively classifying the consumers into "Honest" and "Fraudster," the ensemble machine learning‐based classifier KTBoost, with Jaya algorithm optimized hyperparameters, is utilized. Finally, the developed model is re‐trained using a reduced set of highly important features to minimize the computational resources without compromising the performance of the developed model. The outcome of this study reveals that the proposed theft detection method achieves the highest accuracy (93.38%), precision (95%), and recall (93.18%) among all the studied methods, thus signifying its importance in the studied area of research. [ABSTRACT FROM AUTHOR]

Quantum computation requires large classical datasets to be embedded into quantum states in order to exploit quantum parallelism. However, this embedding requires considerable resources in general. It would therefore be desirable to avoid it, if possible, for noisy intermediate-scale quantum (NISQ) implementation. Accordingly, we consider a classical-quantum hybrid architecture, which allows large classical input data, with a relatively small-scale quantum system. This hybrid architecture is used to implement a sampling oracle. It is shown that in the presence of noise in the hybrid oracle, the effects of internal noise can cancel each other out and thereby improve the query success rate. It is also shown that such an immunity of the hybrid oracle to noise directly and tangibly reduces the sample complexity in the framework of computational learning theory. This NISQ-compatible learning advantage is attributed to the oracle's ability to handle large input features. [ABSTRACT FROM AUTHOR]

This article aims to bring contributions about the use of Pierre Rabardel's Instrumental Approach, as an effective theoretical framework in the development of studies by researchers who are interested in the analysis of technological tools and their potentialities that intervene in the subject's relationship (researcher, Professor3 and/or student), with the objects of knowledge through the mediation of these tools, aiming at mathematical learning. In the applications, we give special attention to digital technological tools, choosing the GeoGebra software as a computational learning environment, observing the theoretical elements of this approach in two dimensions, which we define as internal research and external research. We therefore propose an extension of the Instrumental Activity Situations (IAS) model in three dimensions, motivating a reflection on the role of the Professor or researcher in the primitive relations of this model in external research, during its application in the classroom. We hope that our contributions/reflections find a significant space in the development of educational research, especially in science and mathematics using Rabardel's Instrumental Approach [ABSTRACT FROM AUTHOR]

Deformations of the computational mesh, arising from optimization routines, usually lead to decrease of mesh quality or even destruction of the mesh. We propose a theoretical framework using pre-shapes to generalize the classical shape optimization and calculus. We define pre-shape derivatives and derive corresponding structure and calculus theorems. In particular, tangential directions are featured in pre-shape derivatives, in contrast to classical shape derivatives, featuring only normal directions. Techniques from classical shape optimization and calculus are shown to carry over to this framework. An optimization problem class for mesh quality is introduced, which is solvable with the use of pre-shape derivatives. This class allows for simultaneous optimization of the classical shape objectives and mesh quality without deteriorating the classical shape optimization solution. The new techniques are implemented and numerically tested for 2D and 3D. [ABSTRACT FROM AUTHOR]