Behavioral studies on language processing rely on the eye-mind assumption, which states that the time spent looking at text is an index of the time spent processing it. In most cases, relatively shorter reading times are interpreted as evidence of greater processing efficiency. However, previous evidence from L2 research indicates that non-native participants who present fast reading times are not always more efficient readers, but rather shallow parsers. Because earlier studies did not identify a reliable predictor of variability in (...) L2 processing, such uncertainty around the interpretation of reading times introduces a potential confound that undermines the credibility and the conclusions of online measures of processing. The present study proposes that a recently developed modulator of online processing efficiency, namely, chunking ability, may account for the observed variability in L2 online reading performance. L1 English – L2 Spanish learners’ eye movements were analyzed during natural reading. Chunking ability was predictive of overall reading speed. Target relative clauses contained L2 Verb-Noun multiword units, which were manipulated with regards to their L1-L2 congruency. The results indicated that processing of the L1-L2 incongruent units was modulated by an interaction of L2 chunking ability and level of knowledge of multiword units. Critically, the data revealed an inverse U-shaped pattern, with faster reading times in both learners with the highest and the lowest chunking ability scores, suggesting fast integration in the former, and lack of integration in the latter. Additionally, the presence of significant differences between conditions was correlated with individual chunking ability. The findings point at chunking ability as a significant modulator of general L2 processing efficiency, and of cross-language differences in particular, and add clarity to the interpretation of variability in the online reading performance of non-native speakers. (shrink)

This paper reports the first cross-study competitive test of thecore solution in side-payment games where the core is nonempty and nonunique (i.e., larger than a single point). The core was tested against five alternative theories including the Shapley value, the disruption nucleolus, the nucleolus, the 2-center, and the equality solution. A generalized Euclidean distance metric which indexes the average distance between an observed payoff vector and the entire set of predicted payoff vectors (Bonacich, 1979) was used as the measure of (...) goodness-of-fit. Analysis of data assembled from six previously reported studies (encompassing a total of 1,464 observations over 56 3-person and 4-person side-payment games) showed the core to predict less accurately than the Shapley value, disruption nucleolus, and nucleolus solutions (p < 0.01). These findings are consistent with previous empirical results that show the core to have a low level of predictive accuracy in side-payment games. (shrink)

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a (...) dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%. (shrink)

This study aims to examine the main determinants of efficiency of both conventional and Islamic Saudi banks and then choose the best fit model among machine learning prediction models, Chi-squared automatic interaction detector, linear regression, and neural network ). The data were collected from the annual financial reports of Saudi banks from 2014 to 2018. The Saudi banking sector consists of 11 banks, 4 of which are Islamic. In this study, the major financial ratios are subgrouped into the profitability (...) ratios, managerial practices, asset and loans, capital adequacy ratios, and liquidity. First, regression analysis is implemented with efficiency ratio as a dependent variable and the proxies of banks’ profitability, liquidity, asset quality, management ratios, and capital adequacy ratios as independent variables. Next, the feature selection is applied for different prediction models. Subsequently, 4 prediction models were developed to choose the best fit. The performance metrics have also been evaluated. Regression results exhibit that the efficiency of both conventional and Islamic banks is highly affected by profitability, liquidity, and managerial practices. Finally, we choose the best prediction model with the highest R2 in the training and the testing phases with/out feature selection that is the CHAID model. The best predictors of cost efficiency for Saudi banks are the capital ratios, namely, CAR total and CAR tier 1. Findings are theoretically and practically important to academics, investors, and policymakers. Policymakers can benefit from the novelty of this study in building an early warning system using the CHAID model to predict different financial distress scenarios. (shrink)

Day-to-day lives are affected globally by the epidemic coronavirus 2019. With an increasing number of positive cases, India has now become a highly affected country. Chronic diseases affect individuals with no time identification and impose a huge disease burden on society. In this article, an Efficient Recurrent Neural Network with Ensemble Classifier is built using VGG-16 and Alexnet with weighted model to predict disease and its level. The dataset is partitioned randomly into small subsets by utilizing mean-based splitting method. Various (...) models of classifier create a homogeneous ensemble by utilizing an accuracy-based weighted aging classifier ensemble, which is a weighted model’s modification. Two state of art methods such as Graph Sequence Recurrent Neural Network and Hybrid Rough-Block-Based Neural Network are used for comparison with respect to some parameters such as accuracy, precision, recall, f1-score, and relative absolute error. As a result, it is found that the proposed ERNN-EC method accomplishes accuracy of 95.2%, precision of 91%, recall of 85%, F1-score of 83.4%, and RAE of 41.6%. (shrink)

Although China’s wind industry has made great progress in recent years, the wind abandonment phenomenon caused by the unbalanced development of regional wind power is still prominent. It is particularly important for the scientific development of wind power to accurately measure the utilization efficiency of wind power and understand its regional differences in China. This study establishes the improved super-efficiency slack-based measure model and long short-term memory network models, systematically and comprehensively measures and predicts the wind power utilization (...) efficiency of 30 regions in China from 2013 to 2020, and explores regional differences in wind power utilization efficiency. Our results show the following: China’s overall wind power utilization efficiency is relatively low but has been on a steady upward trend since 2013. Regional differences are obvious, showing that the spatial distribution pattern of wind power utilization efficiency is greatest in Northeast China, followed by North China, East China, South China, Northwest China, and Central China. The “Three-North” region with abundant wind energy resources has relatively high wind power utilization efficiency and exhibits a good development trend. East China, South China, and Central China, where wind energy resources are relatively poor, have low wind power utilization efficiency, and their development trends are not stable and are more prone to change. The utilization efficiency of wind power in coastal areas is generally better than that in inland areas. There are also differences among the thirty Chinese regions studied. Inner Mongolia and Shandong have achieved real efficiency in wind power utilization efficiency, with optimal allocation of input and output, and a good development trend. The other 28 regions have varying degrees of inefficiency, and there is still room for improvement. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments (...) with the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

A distinguishing feature of neural computation and information processing is that it fits models that describe the most efficient strategies for performing different cognitive tasks. Efficiency determines a distinctive sense of teleology involving optimal performance and resource management through a specific strategy. I articulate this kind of teleology and call it efficient teleological function. I argue that efficient teleological function is compatible with mechanistic explanation and, most likely, neural computational mechanisms are efficiently functional in this sense. They are members (...) of a distinctive class of computational mechanisms whose efficiency is intertwined with their functionality. This is illustrated by widely discussed approaches to mind, such as Barlow’s efficient coding hypothesis or the ones associated with the so-called “predictive mind”, which propose that the brain employs highly efficient coding strategies to save energy resources that are critical to the organism’s survival. (shrink)

This paper proposes a nonlinear autoregressive neural network method for the investment performance evaluation of state-owned enterprises. It is different from the traditional method based on machine learning, such as linear regression, structural equation, clustering, and principal component analysis; this paper uses a regression prediction method to analyze investment efficiency. In this paper, we firstly analyze the relationship between diversified ownership reform, corporate debt leverage, and the investment efficiency of state-owned enterprises. Secondly, a set of investment efficiency (...) evaluation index system for SOE was constructed, and a nonlinear autoregressive neural network approach was used for verification. The data of A-share state-owned listed companies in Shanghai and Shenzhen stock exchanges from 2009 to 2018 are taken as a sample. The experimental results show that the output value from the NARNET is highly fitted to the actual data. Based on the neural network model regression analysis, this paper conducts a descriptive statistical analysis of the main variables and control variables of the evaluation indicators. It verifies the direct impact of diversified ownership reform on the investment efficiency of SOE and the indirect impact on the investment efficiency of SOE through corporate debt leverage. (shrink)

The lostness measure, an implicit and unobtrusive measure originally designed for assessing the usability of hypertext systems, could be useful in Virtual Reality (VR) games where players need to find information to complete a task. VR locomotion systems with node-based movement mimic actions for exploration and browsing found in hypertext systems. For that reason, hypertext usability measures, such as “lostness” can be used to identify how disoriented a player is when completing tasks in an educational game by examining steps made (...) by the player. An evaluation of two different lostness measures, global and local lostness, based on two different types of tasks, is described in a VR educational game using 13 college students between 14 and 18 years old in a first study and extended using 12 extra participants in a second study. Multiple Linear Regression analyses showed, in both studies, that local lostness, and not global lostness, had a significant effect on a post-game knowledge test. Therefore, we argued that local lostness was able to predict how well-participants would perform on a post-game knowledge test indicating how well they learned from the game. In-game experience aspects (engagement, cognitive interest, and presence) were also evaluated and, interestingly, it was also found that participants learned less when they felt more present in the game. We believe these two measures relate to cognitive overload, which is known to have an adverse effect on learning. Further research should investigate the lostness measure for use in an online adaptive game system and design the game system in such a way that the risk of cognitive overload is minimized when learning, resulting in higher retention of information. (shrink)

Conversion degree, defined as the proportion of converted‐form use out of total lemma use, has notably increased in English over the past century. For instance, track had a conversion degree of only 5% in the 1920s (95% noun, 5% verb) but rose to 35% by the 2010s (65% noun, 35% verb). This historical trend presents a communicative paradox: it favors speaker economy (articulatory ease via form reuse) seemingly at the expense of listener economy (increased ambiguity). The present study investigates this (...) paradox through the framework of efficient communication, which predicts that ambiguous forms like conversion should be licensed in predictable contexts. We tested this prediction by analyzing 639 noun‐to‐verb and 443 verb‐to‐noun conversion words from the 1920s to the 2010s. Generalized linear mixed models were fitted with surprisal estimates from three architectures: local 5‐grams, syntactic POS‐grams, and broad‐context GPT‐2. Results indicate that the historical rise in conversion degree is accompanied by increasingly predictable contexts, suggesting that contextual predictability compensates for the absence of explicit morphological marking. We further demonstrate that syntactic and lexical surprisal exert independent inhibitory effects on conversion degree. Crucially, their positive interaction indicates that the constraining effect of lexical surprisal is stronger when syntactic surprisal is low and attenuated when syntactic surprisal is high, consistent with a resource competition account. These findings portray the evolution of English conversion not as random drift, but as a dynamic optimization reflecting a negotiation between communicative efficiency and cognitive resource limitations, offering a novel information‐theoretic perspective on language change. (shrink)

The general principles of perceptuo-motor processing and memory give rise to theNow-or-Never bottleneckconstraint imposed on the organization of the language processing system. In particular, the Now-or-Never bottleneck demands an appropriate structure of linguistic input and rapid incorporation of both linguistic and multisensory contextual information in a progressive, integrative manner. I argue that the emerging predictive processing framework is well suited for the task of providing a comprehensive account of language processing under the Now-or-Never constraint. Moreover, this framework presents a (...) stronger alternative to theChunk-and-Passaccount proposed by Christiansen and Chater (2016), as it better accommodates the available evidence concerning the role of context (in both the narrow and wider senses) in language comprehension at various levels of linguistic representation. Furthermore, the predictive processing approach allows for treating language as a special case of domain-general processing strategies, suggesting deep parallels with other cognitive processes such as vision. (shrink)

The debate between the amodal and the grounded views of cognition seems to be stuck. Their only substantial disagreement is about the vehicle or format of concepts. Amodal theorists reject the grounded claim that concepts are couched in the same modality-specific format as representations in sensory systems. The problem is that there is no clear characterization of format or its neural correlate. In order to make the disagreement empirically meaningful and move forward in the discussion we need a neurocognitive criterion (...) for representational format. I argue that efficient coding models in computational neuroscience can be used to characterize modal codes: These are codes which satisfy special informational demands imposed by sensory tasks. Additionally, I examine recent studies on neural coding and argue that although they do not provide conclusive evidence for either the grounded or the amodal views, they can be used to determine what predictions these approaches can make and what experimental and theoretical developments would be required to settle the debate. (shrink)

The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision-making, and motor control. This article provides an up-to-date introduction to the two most influential theories within this framework: predictive coding and active inference. The first half of the paper (Sections 2–5) reviews the evolution of predictive coding, from early ideas about efficient coding (...) in the visual system to a more general model encompassing perception, cognition, and motor control. The theory is characterized in terms of the claims it makes at Marr's computational, algorithmic, and implementation levels of description, and the conceptual and mathematical connections between predictive coding, Bayesian inference, and variational free energy (a quantity jointly evaluating model accuracy and complexity) are explored. The second half of the paper (Sections 6–8) turns to recent theories of active inference. Like predictive coding, active inference models assume that perceptual and learning processes minimize variational free energy as a means of approximating Bayesian inference in a biologically plausible manner. However, these models focus primarily on planning and decision-making processes that predictive coding models were not developed to address. Under active inference, an agent evaluates potential plans (action sequences) based on their expected free energy (a quantity that combines anticipated reward and information gain). The agent is assumed to represent the world as a partially observable Markov decision process with discrete time and discrete states. Current research applications of active inference models are described, including a range of simulation work, as well as studies fitting models to empirical data. The paper concludes by considering future research directions that will be important for further development of both models. (shrink)

This chapter addresses predictive policing, which is a term that refers to a range of crime-fighting approaches that use crime mapping data and analysis, and, more recently, social network analysis, big data, and predictive algorithms. The rise of predictive policing, especially in many police jurisdictions in large cities in the USA, has raised the spectre of the surveillance society in which citizens can be arrested by police for crimes they have not yet committed on the basis of (...) evidence that they will commit them. Speaking generally, predictive policing faces several problems. Some of these are problems for predictive policing even in its own terms of contributing to crime reduction. Others are moral problems, about whether predictive policing violates moral rights or is unjust. These two types of problems are interconnected. Ultimately, the expanding use of biometric facial recognition databases and other emerging technologies in law enforcement as part of predictive policing should be clearly and demonstrably justified in terms of efficiency and effectiveness in the service of specific law enforcement purposes rather than by general appeals to community security or safety. Moreover, it should comply with moral principles constitutive of liberal democracy, such as the principle that individuals have a moral right to freedom from state interference absent prior evidence of violation of its laws. (shrink)

This paper describes a theory that explains both the creativity and the efficiency of people's conceptual combination. In the constraint theory, conceptual combination is controlled by three constraints of diagnosticity, plausibility, and informativeness. The constraints derive from the pragmatics of communication as applied to compound phrases. The creativity of combination arises because the constraints can be satisfied in many different ways. The constraint theory yields an algorithmic model of the efficiency of combination. The C3 model admits the full (...) creativity of combination and yet efficiently settles on the best interpretation for a given phrase. The constraint theory explains many empirical regularities in conceptual combination, and makes various empirically verified predictions. In computer simulations of compound phrase interpretation, the C3 model has produced results in general agreement with people's responses to the same phrases. (shrink)

Medical predictions, for example, concerning a patient's likelihood of survival, can be used to efficiently allocate scarce resources. Predictions of patient behaviour can also be used—for example, patients on the liver transplant waiting list could receive lower priority based on a high likelihood of non‐adherence to their immunosuppressant medication regimen or of drinking excessively. But is this ethically acceptable? In this paper, we will explore arguments for and against behavioural predictions, before providing novel empirical evidence on this question. Firstly, we (...) note that including behavioural predictions would lead to improved transplant outcomes. Fairness could also require prioritising those predicted to engage in healthier behaviours: consistent with using behavioural predictions in other contexts such as psychiatry and substance misuse. Conversely, behavioural predictions may be judged too inaccurate or discriminatory, or it may be thought unfair to deprioritise based on future behaviour. In part two, we performed an online survey of 172 UK adults. When presented with possible factors relevant to liver allocation, most thought predictions of higher medication adherence (78.6%) and lower future alcohol use (76.5%) should be used but not predictions of lower future criminality (24.7%) and higher societal contribution (21.2%). Randomising participants into two groups, 69.8% of participants found deprioritising a patient based on their predicted medication adherence acceptable (91.9% found a nonbehavioural prediction acceptable). We did not identify an ethically relevant difference between behavioural predictions and other medical predictions already used in organ allocation. Our sample of participants also appeared to support behavioural predictions in this context. (shrink)

Predictive coding models of brain processing propose that top-down cortical signals promote efficient neural signaling by carrying predictions about incoming sensory information. These “priors” serve to constrain bottom-up signal propagation where prediction errors are carried via feedforward mechanisms. Depression, traditionally viewed as a disorder characterized by negative cognitive biases, is associated with disrupted reward prediction error encoding and signaling. Accumulating evidence also suggests that depression is characterized by impaired local and long-range prediction signaling across multiple sensory domains. This review (...) highlights the electrophysiological and neuroimaging evidence for disrupted predictive processing in depression. The discussion is framed around the manner in which disrupted generative predictions about the sensorium could lead to depressive symptomatology, including anhedonia and negative bias. In particular, the review focuses on studies of sensory deviance detection and reward processing, highlighting research evidence for both disrupted generative predictions and prediction error signaling in depression. The role of the monoaminergic and glutamatergic systems in predictive coding processes is also discussed. This review provides a novel framework for understanding depression using predictive coding principles and establishes a foundational roadmap for potential future research. (shrink)

Predictive coding – sometimes also known as ‘predictive processing’, ‘free energy minimisation’, or ‘prediction error minimisation’ – claims to offer a complete, unified theory of cognition that stretches all the way from cellular biology to phenomenology. However, the exact content of the view, and how it might achieve its ambitions, is not clear. This series of articles examines predictive coding and attempts to identify its key commitments and justification. The present article begins by focusing on possible confounds (...) with predictive coding: claims that are often identified with predictive coding, but which are not predictive coding. These include the idea that the brain employs an efficient scheme for encoding its incoming sensory signals; that perceptual experience is shaped by prior beliefs; that cognition involves minimisation of prediction error; that the brain is a probabilistic inference engine; and that the brain learns and employs a generative model of the world. These ideas have garnered widespread support in modern cognitive neuroscience, but it is important not to conflate them with predictive coding. (shrink)

Ockham’s razor is the principle that, all other things being equal, scientists ought to prefer simpler theories. In recent years, philosophers have argued that simpler theories make better predictions, possess theoretical virtues like explanatory power, and have other pragmatic virtues like computational tractability. However, such arguments fail to explain how and why a preference for simplicity can help one find true theories in scientific inquiry, unless one already assumes that the truth is simple. One new solution to that problem is (...) the Ockham efficiency theorem, which states that scientists who heed Ockham’s razor retract their opinions less often and sooner than do their non-Ockham competitors. The theorem neglects, however, to consider competitors following random strategies and in many applications random strategies are known to achieve better worst-case loss than deterministic strategies. In this paper, we describe two ways to extend the result to a very general class of random, empirical strategies. The first extension concerns expected retractions, retraction times, and errors and the second extension concerns retractions in chance, times of retractions in chance, and chances of errors. (shrink)

We identify two pragmatic problems in temporal reasoning, the qualification problem and the extended prediction problem, the latter subsuming the infamous frame problem. Solutions to those seem to call for nonmonotonic inferences, and yet naive use of standard nonmonotonic logics turns out to be inappropriate. Looking for an alternative, we first propose a uniform approach to constructing and understanding nonmonotonic logics. This framework subsumes many existing nonmonotonic formalisms, and yet is remarkably simple, adding almost no extra baggage to traditional logic. (...) We then use this framework to propose a solution to the qualification problem. We also outline a solution to the extended prediction problem, and speculate on a possible connection between the above discussion and the concept of causation. (shrink)

Rheumatoid arthritis is a progressive systemic autoimmune disease characterized by inflammation of the joints and surrounding tissues, which seriously affects the life of patients. The Sharp/van der Heijde method has been widely used in clinical evaluation for the RA disease. However, this manual method is time-consuming and laborious. Even if two radiologists evaluate a specific location, their subjective evaluation may lead to low inter-rater reliability. Here, we developed an efficient model powered by deep convolutional neural networks to solve these problems (...) and automated the overall scoring on hand X-rays. The depthwise separable convolution technique is used based on ResNet-50 due to the high resolution of hand X-rays. An inverted residual block is introduced to devise a ResNet-Dwise50 model to enhance the efficiency of the model. The model was trained and tested using bilateral posteroanterior images of 3818 patients. The experiment results show the ResNet-Dwise50 model achieved an MAE of 14.90 and RMSE of 22.01 while ensuring high efficiency. There was no statistical difference between the average scores given by two experienced radiologists and predicted scores from our model. (shrink)

In this longitudinal study we examined the stability of exploratory play in infancy and its relation to cognitive development in early childhood. We assessed infants' ( N = 130, mean age at enrollment = 12.02 months, SD = 3.5 months; range: 5–19 months) exploratory play four times over 9 months. Exploratory play was indexed by infants' attention to novelty, inductive generalizations, efficiency of exploration, face preferences, and imitative learning. We assessed cognitive development at the fourth visit for the full (...) sample, and again at age three for a subset of the sample ( n = 38). The only measure that was stable over infancy was the efficiency of exploration. Additionally, infants' efficiency score predicted vocabulary size and distinguished at-risk infants recruited from early intervention sites from those not at risk. Follow-up analyses at age three provided additional evidence for the importance of the efficiency measure: more efficient exploration was correlated with higher IQ scores. These results suggest that the efficiency of infants' exploratory play can be informative about longer-term cognitive development. (shrink)

The main issue addressed here concerns the central notion of a forward internal model, through which efficient control and planning are linked together and to the related online predictive error processing. The existence of such a model has strong implications in action production and may question Glover's model.

Heterogeneous information networks can naturally simulate complex objects, and they can enrich recommendation systems according to the connections between different types of objects. At present, a large number of recommendation algorithms based on heterogeneous information networks have been proposed. However, the existing algorithms cannot extract and combine the structural features in heterogeneous information networks. Therefore, this paper proposes an efficient recommendation algorithm based on heterogeneous information network, which uses the characteristics of graph convolution neural network to automatically learn node information (...) to extract heterogeneous information and avoid errors caused by the manual search for metapaths. Furthermore, by fully considering the scoring relationship between nodes, a calculation strategy combining heterogeneous information and a scoring information fusion strategy is proposed to solve the scoring between nodes, which makes the prediction scoring more accurate. Finally, by updating the nodes, the training scale is reduced, and the calculation efficiency is improved. The study conducted a large number of experiments on three real data sets with millions of edges. The results of the experiments show that compared with PMF, SemRec, and other algorithms, the proposed algorithm improves the recommendation accuracy MAE by approximately 3% and the RMSE by approximately 8% and reduces the time consumption significantly. (shrink)

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Machine Learning has become a popular tool in a variety of applications in criminal justice, including sentencing and policing. Media has brought attention to the possibility of predictive policing systems causing disparate impacts and exacerbating social injustices. However, there is little academic research on the importance of fairness in machine learning applications in policing. Although prior research has shown that machine learning models can handle some tasks efficiently, they are susceptible to replicating systemic bias of previous human decision-makers. While (...) there is much research on fair machine learning in general, there is a need to investigate fair machine learning techniques as they pertain to the predictive policing. Therefore, we evaluate the existing publications in the field of fairness in machine learning and predictive policing to arrive at a set of standards for fair predictive policing. We also review the evaluations of ML applications in the area of criminal justice and potential techniques to improve these technologies going forward. We urge that the growing literature on fairness in ML be brought into conversation with the legal and social science concerns being raised about predictive policing. Lastly, in any area, including predictive policing, the pros and cons of the technology need to be evaluated holistically to determine whether and how the technology should be used in policing. (shrink)

Idea generation, the process of creating and developing candidate solutions that when implemented can solve ill-defined and complex problems, plays a pivotal role in creativity and innovation. The algorithms that underlie classical evolutionary, cognitive, and process models of idea generation, however, appear too inefficient to effectively help solve the ill-defined and complex problems for which one would engage in idea generation. To address this, these classical models have recently been redesigned as forward models, drawing heavily on the “predictive mind” (...) literature. These pose that more efficiency can be achieved by making predictions based on heuristics, previous experiences, and domain knowledge about what material to use to generate ideas with, and evaluate these subsequently generated ideas based on whether they indeed match the initial prediction. When a discrepancy occurs between prediction and evaluation, new predictions are made, and thus shaping what actions, and how these actions, are undertaken. Although promising, forward models of idea generation remain theoretical and thus no empirical evidence exists about whether such predictions and evaluations indeed form part of the idea generation process. To take a first empirical look at this, a mixed-methods study was conducted by analyzing people’s self-reports for the reasons of the actions that they take during an idea generation task. The results showed that predictions and evaluations are pervasive in the idea generation process. Specifically, switching between concept selection and conceptual combination and idea generation, as well as repeating idea generation based on earlier selected conceptual combination, and possibly (but to a lesser extent) concept selection and the repetition thereof, are likely to be driven by predictions and evaluations. Moreover, the frequencies of the predictions and evaluations that drive these actions influenced the amount of ideas generated, amount of concepts used, and within-concept fluency (the ratio of the amount of ideas generated per concept used). Therefore, the contribution of this paper is the first empirical evidence that indicates that the idea generation process is driven by both predictions and evaluations. (shrink)

Legal decision-support systems have the potential to improve access to justice, administrative efficiency, and judicial consistency, but broad adoption of such systems is contingent on development of technologies with low knowledge-engineering, validation, and maintenance costs. This paper describes two approaches to an important form of legal decision support—explainable outcome prediction—that obviate both annotation of an entire decision corpus and manual processing of new cases. The first approach, which uses an attention network for prediction and attention weights to highlight salient (...) case text, was shown to be capable of predicting decisions, but attention-weight-based text highlighting did not demonstrably improve human decision speed or accuracy in an evaluation with 61 human subjects. The second approach, termed semi-supervised case annotation for legal explanations, exploits structural and semantic regularities in case corpora to identify textual patterns that have both predictable relationships to case decisions and explanatory value. (shrink)

A model predictive control method based on recursive backpropagation neural network and genetic algorithm is proposed for a class of nonlinear systems with time delays and uncertainties. In the offline modeling stage, a multistep-ahead predictor with GA-RBP neural network is designed, where GA-BP neural network is used as a one-step prediction model and GA is employed to train the initial weights and bias of the BP neural network. The incorporation of GA into RBP can reduce the possibility of the (...) BP neural network falling into a local optimum instead of reaching global optimization. In the online optimizing stage, a multistep-ahead GA-RBP neural network predictor and an improved gradient descent method are proposed to efficiently solve the online optimization problem of nonlinear MPC by minimizing a modified quadratic criterion. The designed MPC strategy can avoid information loss while linearizing the controlled system and computing the Hessian matrix and its inverse matrix. Experimental results show that the proposed approach can reduce the computational burden and improve the performance of MPC for the solution of nonlinear controlled systems. (shrink)

There is a growing body of literature investigating the impact of retraining and motivation on employee work efficiency. However, little seems to be understood about the effects of employee placement on the commitment of teachers to their jobs. To the best of the researcher's awareness, the partial and composite impact of staff placement, retraining, and motivation on the three aspects of job commitment (affective, continuance and normative) among secondary educators have scarcely been examined. This research was intended to fill (...) this vacuum by using a predictive path modelling approach to analyse the association between these endogenous and exogenous variables. A random sample of 500 secondary school principals was surveyed using two forms of questionnaires. Collected data were analysed and formulated hypotheses tested using Path and multiple linear regression analyses, with the aid of Amos and SPSS packages. Findings indicated that staff placement and motivation were highly predictive of instructors' commitment (at the affective and continuance dimension), but not at the normative dimension; Employee dedication in the three dimensions was not predicted by personnel retraining, but it did result in employee attrition; workers retraining only increased teachers' work commitment when it was augmented with placement and motivation. The combined effect of staff placement, retraining, and motivation was statistically significant in two dimensions of teachers’ job commitment (affective and continuance), but not on the normative dimension. Based on these findings, policy and theoretical ramifications for effective instructional management, assessment, classroom practice, and future study are discussed. (shrink)

This paper describes a new approach to natural language processing which results in a very robust and efficient system. The approach taken is to integrate the parser with the rest of the system. This enables the parser to benefit from predictions that the rest of the system makes in the course of its processing. These predictions can be invaluable as guides to the parser in such difficult problem areas as resolving referents and selecting meanings of ambiguous words. A program, called (...) FRUMP for Fast Reading Understanding and Memory Program, employs this approach to parsing. FRUMP skims articles rather than reading them for detail. The program works on the relatively unconstrained domain of news articles. It routinely understands stories it has never before seen. The program's success is largely due to its radically different approach to parsing. (shrink)

Machine stability and energy efficiency have become major issues in the manufacturing industry, primarily during the COVID-19 pandemic where fluctuations in supply and demand were common. As a result, Predictive Maintenance (PdM) has become more desirable, since predicting failures ahead of time allows to avoid downtime and improves stability and energy efficiency in machines. One type of machine failure stands out due to its impact, machine overstrain, which can occur when machines are used beyond their tolerable limit. (...) From the current literature, there are little to no relevant works that focus on machine overstrain failure detection or prediction. Accordingly, the purpose of this paper is to implement and compare four Machine Learning (ML) algorithms for PdM applied to machine overstrain failures: Artificial Neural Network (ANN), Gradient Boosting, Random Forest and Support Vector Machine (SVM). Moreover, it proposes a training methodology for imbalanced data and the automatic optimization of hyperparameters, which aims to improve performance in the ML models. To evaluate the performance of the ML models, a synthetic dataset that simulates industrial machine data is used. The obtained results show the robustness of the proposed methodology, with the ANN and SVM models achieving a perfect recall score, with 98.95% and 98.85% in accuracy, respectively. (shrink)

In an increasingly volatile geopolitical and environmental landscape, global supply chains are under constant threat from a myriad of disruptions, ranging from political conflicts and trade restrictions to natural disasters and port congestion. This paper investigates the integration of predictive Artificial Intelligence (AI) models into international logistics systems as a proactive mechanism to anticipate, evaluate, and mitigate such disruptions. Utilizing a multidisciplinary approach, this study synthesizes advancements in machine learning, network optimization, and real‑time data analytics to demonstrate how AI (...) is revolutionizing supply chain risk management. Through comparative analysis and empirical examples, it is shown that predictive AI not only enhances operational resilience but also offers economic advantages by minimizing downtime and rerouting logistics in near‑real‑time. The paper presents a framework for implementing AI‑driven predictive systems within global logistics operations, emphasizing ethical considerations, data governance, and international policy harmonization. Ultimately, this research highlights AI’s transformative potential in shaping a more adaptive, secure, and efficient global trade network. (shrink)

Why do we seek out and enjoy uncertain success in playing games? Game designers and researchers suggest that games whose challenges match player skills afford engaging experiences of achievement, competence, or effectance—ofdoing well. Yet, current models struggle to explain why such balanced challenges best afford these experiences and do not straightforwardly account for the appeal of high- and low-challenge game genres like Idle and Soulslike games. In this article, we show that Predictive Processing (PP) provides a coherent formal cognitive (...) framework which can explain the fun in tackling game challenges with uncertain success as the dynamic process of reducing uncertainty surprisingly efficiently. In gameplay as elsewhere, people enjoydoing better than expected, which can track learning progress. In different forms, balanced, Idle, and Soulslike games alike afford regular accelerations of uncertainty reduction. We argue that this model also aligns with a popular practitioner model, Raph Koster’sTheory of Fun for Game Design, and can unify currently differentially modelled gameplay motives around competence and curiosity. (shrink)

Connected vehicle fleets have formed significant component of industrial internet of things scenarios as part of Industry 4.0 worldwide. The number of vehicles in these fleets has grown at a steady pace. The vehicles monitoring with machine learning algorithms has significantly improved maintenance activities. Predictive maintenance potential has increased where machines are controlled through networked smart devices. Here, benefits are accrued considering uptimes optimization. This has resulted in reduction of associated time and labor costs. It has also provided significant (...) increase in cost benefit ratios. Considering vehicle fault trends in this research predictive maintenance problem is addressed through hybrid deep learning-based ensemble method (HDLEM). The ensemble framework which acts as predictive analytics engine comprises of three deep learning algorithms viz modified cox proportional hazard deep learning (MCoxPHDL), modified deep learning embedded semi supervised learning (MDLeSSL) and merged LSTM (MLSTM) networks. Both sensor as well as historical maintenance data are collected and prepared using benchmarking methods for HDLEM training and testing. Here, times between failures (TBF) modeling and prediction on multi-source data are successfully achieved. The results obtained are compared with stated deep learning models. This ensemble framework offers great potential towards achieving more profitable, efficient and sustainable vehicle fleet management solutions. This helps better telematics data implementation which ensures preventative management towards desired solution. The ensemble method's superiority is highlighted through several experimental results. (shrink)

Zipf famously stated that, if natural language lexicons are structured for efficient communication, the words that are used the most frequently should require the least effort. This observation explains the famous finding that the most frequent words in a language tend to be short. A related prediction is that, even within words of the same length, the most frequent word forms should be the ones that are easiest to produce and understand. Using orthographics as a proxy for phonetics, we test (...) this hypothesis using corpora of 96 languages from Wikipedia. We find that, across a variety of languages and language families and controlling for length, the most frequent forms in a language tend to be more orthographically well‐formed and have more orthographic neighbors than less frequent forms. We interpret this result as evidence that lexicons are structured by language usage pressures to facilitate efficient communication. (shrink)

What makes understanding an important cognitive state? And what does having the concept of understanding do for us? This paper offers a unifying account of understanding by jointly reverse-engineering the function of both the state and the concept. We argue that we care about understanding because it grounds and predicts robust competence: the stable ability to succeed across novel scenarios. Our concept of understanding evolved as an efficient proxy to track this elusive property, allowing us to identify who to trust (...) and learn from. This highlights the sociality of understanding and how it shapes what kinds of understanding we are apt to form. We then argue that understanding is the result of convergent pressures on social agents to predict the world using models that are not only accurate, but also compressed enough to be stored, demonstrated, and transmitted. This allows us to integrate a number of ostensibly competing accounts of understanding. Finally, we show how the forces at the root of human understanding elucidate debates over AI understanding. (shrink)

Technology corporations and the emerging digital health market are exerting increasing influence over the public healthcare agendas forming around the application of mobile medical devices. By promising quick and cost-effective technological solutions to complex healthcare problems, they are attracting the interest of funders, researchers, and policymakers. They are also shaping the public facing discourse, advancing an overwhelmingly positive narrative predicting the benefits of wearable medical devices to include personalised medicine, improved efficiency and quality of care, the empowering of under-resourced (...) communities, and delivery of health services previously unavailable to the citizens of developing countries. Typically techno-optimist in their description, the key barriers to this impending inflection point in healthcare are identified as technical issues such as short battery life and a lack of data protection. However, this tech innovation narrative is consistent with problematic ethical, social, and political assumptions that have practical and normative effects, and risk, one, undermining the real clinical potential of wearable devices and, two, designing social inequality and injustice into our mobile health interventions in global healthcare. I argue that the foundational assumptions dealing with the just distribution of healthcare ‘goods’, the individual as part of society, and the political framing of future healthcare policy devalue equity and despite what they promise cannot meet the distinctive needs of individuals and groups that do not conform to a standardised concept of care-receiver. (shrink)

When the first r lower extreme order statistics of a sample of large size n, 1 < r < s < n, are observed, asymptotic predictive intervals of the future extreme order statistic with a rank s are constructed. The only assumption that we adopt is that the first failure time is attracted to the Weibull distribution. In addition, we suggest an efficient point estimator of its shape parameter and then a confidence interval is constructed for it. Moreover, new (...) interesting asymptotic properties of the distributions that belong to the minimum domain of attraction of the Weibull distribution are revealed. Furthermore, extensive simulation studies are conducted to demonstrate the efficiency of the proposed methods. Two real datasets are analyzed to illustrate and corroborate the obtained results. The main results concern large samples. (shrink)

When the first r lower extreme order statistics of a sample of large size n, 1 < r < s < n, are observed, asymptotic predictive intervals of the future extreme order statistic with a rank s are constructed. The only assumption that we adopt is that the first failure time is attracted to the Weibull distribution. In addition, we suggest an efficient point estimator of its shape parameter and then a confidence interval is constructed for it. Moreover, new (...) interesting asymptotic properties of the distributions that belong to the minimum domain of attraction of the Weibull distribution are revealed. Furthermore, extensive simulation studies are conducted to demonstrate the efficiency of the proposed methods. Two real datasets are analyzed to illustrate and corroborate the obtained results. The main results concern large samples. (shrink)

The crime is difficult to predict; it is random and possibly can occur anywhere at any time, which is a challenging issue for any society. The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. The model analyzes the top ten crimes to make predictions about different categories, which account for 97% of the incidents. These two significant crime classes, that is, violent and nonviolent, are (...) created by merging multiple smaller classes of crimes. Exploratory data analysis is performed to identify the patterns and understand the trends of crimes using a crime dataset. The accuracies of Naive Bayes, Random Forest, and Gradient Boosting Decision Tree techniques are 65.82%, 63.43%, and 98.5%, respectively, and the proposed model is further evaluated for precision and recall matrices. The results show that the Gradient Boosting Decision Tree prediction model is better than the other two techniques for predicting crime, based on historical data from a city. The analysis and prediction model can help the security agencies utilize the resources efficiently, anticipate the crime at a specific time, and serve society well. (shrink)

With the rapid development of Internet of Things technology, RFID technology has been widely used in various fields. In order to optimize the RFID system hardware deployment strategy and improve the deployment efficiency, the prediction of the RFID system identification rate has become a new challenge. In this paper, a neighborhood rough set and random forest combination model is proposed to predict the identification rate of an RFID system. Firstly, the initial influencing factors of the RFID system identification rate (...) are reduced using neighborhood rough set theory combined with the principle of heuristic attribute reduction of neighborhood weighted dependency, thus obtaining a kernel factor subset. Secondly, a random forest prediction model is established based on the kernel factor subset, and a confusion matrix is established using out-of-bag data to evaluate the prediction results. The test is conducted under the constructed RFID experimental environment, whose results showed that the model can predict the identification rate of the RFID system in a fast and efficient way, and the classification accuracy can reach 90.5%. It can effectively guide the hardware deployment and communication parameter protocol setting of the system and improve the system performance. Compared with BP neural network and other prediction models, NRS-RF has shorter prediction time and faster calculation speed. Finally, the validity of the proposed model was verified by the RFID intelligent archives management platform. (shrink)

PurposeThe purpose of this study was to introduce an orthogonal experimental design to improve the efficiency of building and optimizing models for freezing of gait prediction.MethodsA random forest model was developed to predict FOG by using acceleration signals and angular velocity signals to recognize possible precursor signs of FOG. An OED was introduced to optimize the feature extraction parameters.ResultsThe main effects and interaction among the feature extraction hyperparameters were analyzed. The false-positive rate, hit rate, and mean prediction time were (...) 27%, 68%, and 2.99 s, respectively.ConclusionThe OED was an effective method for analyzing the main effects and interactions among the feature extraction parameters. It was also beneficial for optimizing the feature extraction parameters of the FOG prediction model. (shrink)

Seepage analysis is always a concern in dam safety and stability research. The prediction and analysis of seepage pressure monitoring data is an effective way to ensure the safety and stability of dam seepage. With the timeliness of a change in a monitoring value and lag due to external influences, a RS-LSTM model written in Python is developed in this paper which combines rough set theory and the long- and short-term memory network model. The model proposed calculates the prediction score (...) of the seepage pressure of a dam experiencing multiple effects by preordering factor importance values to eliminate the interference of redundant factors. A case study shows that the water level, rainfall, temperature, and duration are all factors that affect the seepage pressure, and their importance values decrease successively. Thus, the seepage pressure of a dam can be predicted with a determination coefficient R2 of 0.96. Compared with the recurrent neural network model and BP neural network model, the training time of the RS-LSTM model proposed is 6.37 s, and the operation efficiency is 41% and 59% higher than that of the RNN and BP models, respectively. The mean relative error is also 3.00%, which is 50% lower than that of the RNN model and 31% lower than that of the BP model. Based on these results, this model has the advantages of fast computation speed and high accuracy in prediction. (shrink)

The irreversibility in time, the multicausality on lines, and the uncertainty of feedbacks make economic systems and the predictions of economic chaotic time series possess the characteristics of high dimensionalities, multiconstraints, and complex nonlinearities. Based on genetic algorithm and fuzzy rules, the chaotic genetics combined with fuzzy decision-making can use simple, fast, and flexible means to complete the goals of automation and intelligence that are difficult to traditional predicting algorithms. Moreover, the new combined method’s ergodicity can perform nonrepetitive searches in (...) a global scope, hence improving the algorithm’s accuracy and efficiency. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of the prediction of economic chaotic time series, elaborated the development background, current status, and future challenges of the combined algorithm of chaotic genetics with fuzzy decision, introduced the basic principles of chaotic genetic algorithm and fuzzy decision algorithm, constructed a prediction model for economic chaotic time series, performed parameter synchronization optimization and moderate function construction, analyzed the prediction processes of economic chaotic time series, conducted phase space reconstruction and correlation dimension calculation, and finally carried out a simulation experiment with its result analysis. The study results show that the algorithm of chaotic genetics combined with fuzzy decision-making can dynamically adjust chaotic mutation operators and summarize fussy expert experiences. The phase space of its reconstructed chaotic attractor has high-precision predictability and can find orderly processes from changeable economic results, which in turn can be used to analyze and predict the complex economic chaotic time series. The study results of this paper provide a reference for further research on predictive analysis of economic chaotic time series based on chaotic genetics combined with fuzzy decision algorithm. (shrink)