The development and use of nanotechnology in the food industry have grown steadily. While visions for nanofood suggest that the applications will improve quality and safety, they are also controversial for several reasons including potential health risks coupled with difficulty in assessing low-dosage nanoparticle risks as well as values-based objections. In recent years, debate over nanofoods has sparked inquiry into factors that predict public attitudes and purchase intentions. Such studies have investigated the roles of demographics and sociographics, value predispositions toward (...) science and technology, preferences for natural products, trust in regulatory agencies, scientific knowledge, and media attention. This study assesses the role of each of these factors in shaping public attitudes toward nanofood and improves the predictive models by evaluating concepts from protection motivation theory. We find that incorporating threat and coping appraisals provides the best predictive models of public attitudes and intention to purchase nanofood products. (shrink)

In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in hospitals. The evaluation (...) of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (shrink)

Many philosophers claim that the neurocomputational framework of predictive processing entails a globally inferentialist and representationalist view of cognition. Here, I contend that this is not correct. I argue that, given the theoretical commitments these philosophers endorse, no structure within predictive processing systems can be rightfully identified as a representational vehicle. To do so, I first examine some of the theoretical commitments these philosophers share, and show that these commitments provide a set of necessary conditions the satisfaction of (...) which allows us to identify representational vehicles. Having done so, I introduce a predictive processing system capable of active inference, in the form of a simple robotic “brain”. I examine it thoroughly, and show that, given the necessary conditions highlighted above, none of its components qualifies as a representational vehicle. I then consider and allay some worries my claim could raise. I consider whether the anti-representationalist verdict thus obtained could be generalized, and provide some reasons favoring a positive answer. I further consider whether my arguments here could be blocked by allowing the same representational vehicle to possess multiple contents, and whether my arguments entail some extreme form of revisionism, answering in the negative in both cases. A quick conclusion follows. (shrink)

Apoptosis proteins play an essential role in regulating a balance between cell proliferation and death. The successful prediction of subcellular localization of apoptosis proteins directly from primary sequence is much benefited to understand programmed cell death and drug discovery. In this paper, by use of Chou’s pseudo amino acid composition , a total of 317 apoptosis proteins are predicted by support vector machine . The jackknife cross-validation is applied to test predictive capability of proposed method. The predictive (...) results show that overall prediction accuracy is 91.1% which is higher than previous methods. Furthermore, another dataset containing 98 apoptosis proteins is examined by proposed method. The overall predicted successful rate is 92.9%. (shrink)

There is a trend toward the use of predictive systems in communications networks. At the systems and network management level predictive capabilities are focused on anticipating network faults and performance degradation. Simultaneously, mobile communication networks are being developed with predictive location and tracking mechanisms. The interactions and synergies between these systems present a new set of problems. A new predictive network management framework is developed and examined. The interaction between a predictive mobile network and the (...) proposed network management system is discussed. The Rapidly Deployable Radio Network is used as a specific example to illustrate these interactions. (shrink)

Recently, research capability has received an overwhelming and remarkable interest among academics and practitioners. This is timely since the Department of Education had institutionalized research and encouraged teachers to engage in it to support evidence-based practice, decision-making, policy, and program development. On these premises, a study was carried out to assess the research capability of public teachers in Malaybalay City, determine its correlates and determinants. It utilized descriptive, correlational, and explanatory designs. It administered survey questionnaires to 92 participants. (...) Data were analyzed using descriptive and inferential statistics. The results revealed that teachers were slightly capable of conducting research and having neutral attitudes toward it, motivated to write research, had a high level of difficulties in research processes, and moderately capable of action planning. They also show evidence of potential in mentoring. Further, mentoring and action planning skills, motivation to write research, attitudes toward research, and the number of studies completed by teachers were the correlates of their research capability at different magnitudes of the relationship. Notably, the research capability of teachers had a low, negative but significant relationship with their age and accumulated years of service. Thus, this capability deteriorates as they age and accumulate years of service. The motivation to write research, the number of studies completed, and age were the determinants of research capability. It was concluded that research capability can be determined and predicted by how motivated teachers are to write research, how productive they are in research, and how young they are when they engage in this rigorous endeavor. The study identified and recommended topics for continuing professional development. (shrink)

Recently, research capability has received an overwhelming and remarkable interest among academics and practitioners. This is timely since the Department of Education had institutionalized research and encouraged teachers to engage in it to support evidence-based practice, decision-making, policy, and program development. On these premises, a study was carried out to assess the research capability of public teachers in Malaybalay City, determine its correlates and determinants. It utilized descriptive, correlational, and explanatory designs. It administered survey questionnaires to 92 participants. (...) Data were analyzed using descriptive and inferential statistics. The results revealed that teachers were slightly capable of conducting research and having neutral attitudes toward it, motivated to write research, had a high level of difficulties in research processes, and moderately capable of action planning. They also show evidence of potential in mentoring. Further, mentoring and action planning skills, motivation to write research, attitudes toward research, and the number of studies completed by teachers were the correlates of their research capability at different magnitudes of the relationship. Notably, the research capability of teachers had a low, negative but significant relationship with their age and accumulated years of service. Thus, this capability deteriorates as they age and accumulate years of service. The motivation to write research, the number of studies completed, and age were the determinants of research capability. It was concluded that research capability can be determined and predicted by how motivated teachers are to write research, how productive they are in research, and how young they are when they engage in this rigorous endeavor. The study identified and recommended topics for continuing professional development. (shrink)

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system that relies on generative models to predict the structure of sensory information. Such a view resonates with a body of work in machine learning that has explored the problem-solving capabilities of hierarchically-organized, multi-layer (i.e., deep) neural networks, many of which acquire and deploy generative models of their training data. The present chapter explores the extent to which the ostensible convergence on a common neurocomputational (...) architecture (centred on predictive processing schemes, hierarchical organization, and generative models) might provide inroads into the problem of digital immortality. In contrast to approaches that seek to recapitulate the connectomic microstructure of the human brain, the present chapter advocates an approach that is rooted in the use of machine learning algorithms. The claim is that a future form of deep learning system could be used to acquire generative models of a given individual or (alternatively) the sensory data that is processed by the brain of a given individual during the course of their biological life. The differences between these two forms of digital immortality are explored, as are some of the options for digital resurrection. (shrink)

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system that relies on generative models to predict the structure of sensory information. Such a view resonates with a body of work in machine learning that has explored the problem-solving capabilities of hierarchically-organized, multi-layer neural networks, many of which acquire and deploy generative models of their training data. The present chapter explores the extent to which the ostensible convergence on a common neurocomputational architecture might (...) provide inroads into the problem of digital immortality. In contrast to approaches that seek to recapitulate the physical structure of the human brain, the present chapter advocates an approach that is rooted in the use of machine learning algorithms. The claim is that a future form of deep learning system could be used to acquire generative models of a given individual or the sensory data that is processed by the brain of a given individual during the course of their biological life. The differences between these two forms of digital immortality are explored, as are some of the options for digital resurrection. (shrink)

Recent conjectures regarding the nature and mechanism of consciousness are extended to include the contribution of the cerebellum. The role of this brain structure appears to be a rather sophisticated form of prediction, as exemplified by certain dynamical capabilities of the visual system, and by the difficulty of self-administered tickling. The pars intermedia of the cerebellum is perceived as a direct feedback device, functioning in parallel to the primary neuronal circuit involved in consciousness; this leads to the suggestion that it (...) serves as a tutor for the putative master node, the latter governing the collective movements of all the body's muscles. The cerebellar hemispheres are believed to act as an internal feedback device, and to be more directly coupled to the master node; they are seen as serving plenisentient unconsciousness. The possible importance of muscular spindles for the generation of qualia is discussed, as are the significance of the frontal lobes, the basal ganglia and the limbic system for the content of consciousness. Finally, the stages through which infant consciousness gradually acquires its sophistication are tentatively identified. (shrink)

This article is a comparative study between predictive processing (PP, or predictive coding) and cognitive dissonance (CD) theory. The theory of CD, one of the most influential and extensively studied theories in social psychology, is shown to be highly compatible with recent developments in PP. This is particularly evident in the notion that both theories deal with strategies to reduce perceived error signals. However, reasons exist to update the theory of CD to one of “predictive dissonance.” First, (...) the hierarchical PP framework can be helpful in understanding varying nested levels of CD. If dissonance arises from a cascade of downstream and lateral predictions and consequent prediction errors, dissonance can exist at a multitude of scales, all the way up from sensory perception to higher order cognitions. This helps understand the previously problematic dichotomy between “dissonant cognitive relations” and “dissonant psychological states,” which are part of the same perception-action process while still hierarchically distinct. Second, since PP is action-oriented, it can be read to support recent action-based models of CD. Third, PP can potentially help us understand the recently speculated evolutionary origins of CD. Here, the argument is that responses to CD can instill meta-learning which serves to prevent the overfitting of generative models to ephemeral local conditions. This can increase action-oriented ecological rationality and enhanced capabilities to interact with a rich landscape of affordances. The downside is that in today’s world where social institutions such as science a priori separate noise from signal, some reactions to predictive dissonance might propagate ecologically unsound (underfitted, confirmation-biased) mental models such as climate denialism. (shrink)

In this paper I propose minimal criteria for a successful theory of the mechanisms of motivation, and argue that extant philosophical accounts fail to meet them. Further, I argue that a predictive processing framework gives us the theoretical power to meet these criteria, and thus ought to be preferred over existing theories. The argument proceeds as follows—motivational mental states are generally understood as mental states with the power to initiate, guide, and control action, though few existing theories of motivation (...) explicitly detail how they are meant to explain these functions. I survey two contemporary theories of motivational mental states, due to Wayne Wu and Bence Nanay, and argue that they fail to satisfactorily explain one or more of these functions. Nevertheless, I argue that together, they are capable of giving a strong account of the control function, which competing theories ought to preserve. I then go on to argue that what I call the ‘predictive theory’ of motivational mental states, which makes use of the notion of active inference, is able to explain all three of the key functions and preserves the central insights of Wu and Nanay on control. It thus represents a significant step forward in the contemporary debate. (shrink)

The predictable rhythmic structure is important to most ecologically relevant sounds for humans, such as is found in the rhythm of speech or music. This study addressed the question of how rhythmic predictions are maintained in the auditory system when there are multiple perceptual interpretations occurring simultaneously and emanating from the same sound source. We recorded the electroencephalogram (EEG) while presenting participants with a tone sequence that had two different tone feature patterns, one based on the sequential rhythmic variation in (...) tone duration and the other on sequential rhythmic variation in tone intensity. Participants were presented with the same sound sequences and were instructed to listen for the intensity pattern (ignore fluctuations in duration) and press a response key to detected pattern deviants (attend intensity pattern task); to listen to the duration pattern (ignore fluctuations in intensity) and make a button press to duration pattern deviants (attend duration pattern task), and to watch a movie and ignore the sounds presented to their ears (attend visual task). Both intensity and duration patterns occurred predictably 85% of the time, thus the key question involved evaluating how the brain treated the irrelevant feature patterns (standards and deviants) while performing an auditory or visual task. We expected that task-based feature patterns would have a more robust brain response to attended standards and deviants than the unattended feature patterns. Instead, we found that the neural entrainment to the rhythm of the standard attended patterns had similar power to the standard of the unattended feature patterns. In addition, the infrequent pattern deviants elicited the event-related brain potential called the mismatch negativity component (MMN). The MMN elicited by task-based feature pattern deviants had a similar amplitude to MMNs elicited by unattended pattern deviants that were unattended because they were not the target pattern or because the participant ignored the sounds and watched a movie. Thus, these results demonstrate that the brain tracks multiple predictions about the complexities in sound streams and can automatically track and detect deviations with respect to these predictions. This capability would be useful for switching attention rapidly among multiple objects in a busy auditory scene. (shrink)

The application of recycled aggregate as a sustainable material in construction projects is considered a promising approach to decrease the carbon footprint of concrete structures. Prediction of compressive strength of environmentally friendly concrete containing recycled aggregate is important for understanding sustainable structures’ concrete behaviour. In this research, the capability of the deep learning neural network approach is examined on the simulation of CS of EF concrete. The developed approach is compared to the well-known artificial intelligence approaches named multivariate adaptive (...) regression spline, extreme learning machines, and random forests. The dataset was divided into three scenarios 70%-30%, 80%-20%, and 90%-10% for training/testing to explore the impact of data division percentage on the capacity of the developed AI model. Extreme gradient boosting was integrated with the developed AI models to select the influencing variables on the CS prediction. Several statistical measures and graphical methods were generated to evaluate the efficiency of the presented models. In this regard, the results confirmed that the DLNN model attained the highest value of prediction performance with minimal root mean squared error. The study revealed that the highest prediction performance could be attained by increasing the number of variables in the prediction problem and using 90%-10% data division. The results demonstrated the robustness of the DLNN model over the other AI models in handling the complex behaviour of concrete. Due to the high accuracy of the DLNN model, the developed method can be used as a practical approach for future use of CS prediction of EF concrete. (shrink)

Amartya Sen’s capability approach has become increasingly popular in development studies. This paper identifies controllability and operationalisability as two key stumbling blocks which prevent the capability approach from being used even more widely in development practice. It discusses the origins and application of the Choice Framework, a conceptual tool designed to help operationalise the approach. The framework can be used to deconstruct embedded ideologies and analyse the appropriateness of development goals, to map development as a systemic process, and (...) to plan interventions which can result in increased freedom of choice for people. Three examples of the application of the Choice Framework in the field of information and communication for development (ICT4D) are given. The three technologies which are examined, telecentres (Infocentros), Chilecompra and Fair Tracing, can be placed at different places of a determinism continuum, some reducing the spectrum of choices a user has. The paper argues that while frameworks such as the Choice Framework can be developed further to increase the operationalisability of the capability approach, it is up to development funders to accept the fact that people’s choices are never fully predictable and thus Sen’s ‘development as freedom’ will inevitably be a dynamic and open-ended process. (shrink)

In everyday interactions, humans naturally exhibit behavioral cues, such as gaze and head movements, that signal their intentions while interpreting the behavioral cues of others to predict their intentions. Such intention prediction enables each partner to adapt their behaviors to the intent of others, serving a critical role in joint action where parties work together to achieve a common goal. Among behavioral cues, eye gaze is particularly important in understanding a person's attention and intention. In this work, we seek to (...) quantify how gaze patterns may indicate a person's intention. Our investigation was contextualized in a dyadic sandwich-making scenario in which a “worker” prepared a sandwich by adding ingredients requested by a “customer.” In this context, we investigated the extent to which the customers' gaze cues serve as predictors of which ingredients they intend to request. Predictive features were derived to represent characteristics of the customers' gaze patterns. We developed a support vector machine-based (SVM-based) model that achieved 76% accuracy in predicting the customers' intended requests based solely on gaze features. Moreover, the predictor made correct predictions approximately 1.8 s before the spoken request from the customer. We further analyzed several episodes of interactions from our data to develop a deeper understanding of the scenarios where our predictor succeeded and failed in making correct predictions. These analyses revealed additional gaze patterns that may be leveraged to improve intention prediction. This work highlights gaze cues as a significant resource for understanding human intentions and informs the design of real-time recognizers of user intention for intelligent systems, such as assistive robots and ubiquitous devices, that may enable more complex capabilities and improved user experience. (shrink)

With the progress of social production, the competition for talents among enterprises is fierce, and the market often lacks capable leaders, which leads to the lack of management of enterprise employees and cannot bring more economic benefits to enterprises. Traditional leaders make subordinate employees work actively and achieve the common goal of the enterprise by exerting their own leadership characteristics and observing their subordinates, but they cannot take care of the psychological state of each employee, resulting in the employee's work (...) efficiency is not very high. In recent years, charismatic leadership has become an important economic leader in the new era, and the theoretical spirit of charismatic leadership can well guide employees to work actively. Artificial intelligence affective computing can well identify the psychological state of the subject, and the combination of artificial intelligence affective computing and charismatic leadership can achieve effective management of employees through the predictive analysis of employees' psychological state. This paper compares the psychological state prediction analysis of employees' work attitudes between charismatic leaders based on artificial intelligence affective computing and traditional leaders through experiments. The results show that: charismatic leaders based on artificial intelligence affective computing predictive analytics can improve sensitivity to employee needs, resulting in an 8.2% increase in employee trust in leadership, a 4.4% increase in employee commitment to achieving organizational goals, and a 19.3% increase in employee job satisfaction. The psychological state prediction analysis of charismatic leaders based on artificial intelligence affective computing on employees' work attitudes can improve the work efficiency of employees and the economic benefits of enterprises. (shrink)

Despite the popularity of the employment interview in the employee selection literature and organizational talent selection process, few have examined the comments interviewers give after each interview. This study investigated the predictability of the match between interviewer post-interview notes and radar charts from job analysis on the candidate’s later career performance using text mining techniques and data from one of the largest internet-based technology companies in China. A large sample of 7,650 interview candidates who passed the interviews and joined the (...) company was obtained to show that the number of job-related capabilities interviewers mentioned in their notes was positively related to candidate’s job performance, the number of promotions, and negatively related to turnover. Moreover, the dimensions of the radar chart from job analysis covered in the interview moderated the predictability of interview post-interview notes. Our results indicated that a smaller number of radar chart dimensions by which interviewers assessed the candidates in the interview positively moderated candidates’ promotion for product development jobs, and negatively moderated turnover for technical jobs. The implications of these results for structured interview research in both theory and practice are discussed. (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)

During the lengthy and complex process of human evolution our ancestors had to adapt to extremely testing situations in which survival depended on making rapid choices that subjected muscles and the body as a whole to extreme tension. In order to seize a prey traveling at speeds that could reach 36 km per hour Homo sapiens had just thousandths of a second in which to anticipate the right moment and position himself before the prey arrived. He also had to prepare (...) the appropriate gesture, tensing his muscles and overcoming the resistance determined by body weight. While we are no longer faced with an environment that is anything so threatening, our brain continues to use these mechanisms day in day out to save time and energy, enabling us to avoid situations of danger, sense in advance the intentions of an interlocutor, and more besides. In this article we set out to show that our brain is not only a reactive mechanism, capable of reacting quickly to the stimuli that arrive from the external environment, but is above all a pro-active mechanism that allows us to make hypotheses, anticipate the consequences of actions, and formulate expectations: in short, to wrong foot an adversary. (shrink)

Supported by a qualitative study of triple bottom line firms—those that simultaneously prioritize economic, social, and environmental objectives—we investigated the market logic and practices of TBL firms to better understand how they fulfill their mission and achieve their goals. We explored if and how TBL firms may differ in their approach to stakeholders and the management of their resources, including dynamic capabilities. We employed a research design that emphasizes the iterative comparison of narrative data within themselves and with scholarly literature (...) [i.e., resource-based view ] to develop new theoretical insights. Because the RBV is commonly used to theorize how firms achieve competitive advantage, we explored whether TBL firms achieve competitive advantage differently from what RBV theory would predict. Our data suggest that how a firm defines value has a significant influence on the capabilities it creates and how it treats its resources. We find that TBL firms redefine value to not only focus on the end product or service but also to include the systemic cost of delivering goods. As a result, TBL firms differ from prevailing scholarly thought in RBV. They strive to have resources that are sustainable and therefore imitable, commonly found, and substitutable. Moreover, they are not only transparent in their processes but also collaborate with others in the value chain and in their sector. In doing so, they deliberately create new markets from which other firms can benefit. Rather than focusing on competitive advantage, they focus on collaborative advantage. (shrink)

We argue that prediction success maximization is a basic objective of cognition and cortex, that it is compatible with but distinct from prediction error minimization, that neither objective requires subtractive coding, that there is clear neurobiological evidence for the amplification of predicted signals, and that we are unconvinced by evidence proposed in support of subtractive coding. We outline recent discoveries showing that pyramidal cells on which our cognitive capabilities depend usually transmit information about input to their basal dendrites and amplify (...) that transmission when input to their distal apical dendrites provides a context that agrees with the feedforward basal input in that both are depolarizing, i.e., both are excitatory rather than inhibitory. Though these intracellular discoveries require a level of technical expertise that is beyond the current abilities of most neuroscience labs, they are not controversial and acclaimed as groundbreaking. We note that this cellular cooperative context-sensitivity greatly enhances the cognitive capabilities of the mammalian neocortex, and that much remains to be discovered concerning its evolution, development, and pathology. (shrink)

The global progress empowers the development of new business and expansion of existing business. The availability of sufficient accounting professional are necessary to manage and document the business activities. However, youth are less inclined to purse accounting as profession to keep the progress of global and local economic development. The current study aimed to explore the formation of the intention to pursue Certified Professional Accountancy Qualification with factor of capabilities, career opportunities, job security with respect to the extended theory of (...) planned behavior, i.e., attitude, subjective norms and perceived behavioral control. The study adopted a cross-sectional design and collected quantitative data from a total of 339 accounting students from Malaysia using an online survey. The finding revealed that capabilities and career opportunities influenced the students’ intention to pursue CPAQ. Meanwhile, perceived behavioral control had significantly affect the students’ decision to pursue CPAQ, which is in line with the TPB. The study concentrated on the importance of these factors in influencing the students’ intention and decision to pursue CPAQ. The study offered vital implications for accounting educators and educational institutions to promote the accounting profession as choice and students engage in pursuing CPAQ. The Malaysian government should encourage and support accounting students financially for pursuing CPAQ by providing job security and enhancing subjective norms that may enable these students to complete the required professional qualifications. The study’s limitations and future research opportunities are documented at the end of this article. (shrink)

The prediction of legal judgments is one of the most recognized fields in Natural Language Processing, Artificial Intelligence, and Law combined. By legal prediction, we mean intelligent systems capable of predicting specific judicial characteristics such as the judicial outcome, the judicial class, and the prediction of a particular case. In this study, we used an artificial intelligence classifier to predict the decisions of Brazilian courts. To this end, we developed a text crawler to extract data from official Brazilian electronic legal (...) systems, consisting of two datasets of cases of second-degree murder and active corruption. We applied various classifiers, such as Support Vector Machines, Neural Networks, and others, to predict judicial outcomes by analyzing text features from the dataset. Our research demonstrated that Regression Trees, Gated Recurring Units, and Hierarchical Attention Networks tended to have higher metrics across our datasets. As the final goal, we searched the weights of one of the algorithms, Hierarchical Attention Networks, to find samples of the words that might be used to acquit or convict defendants based on their relevance to the algorithm. (shrink)

Criteria of empirical significance are supposed to state conditions under which reference to an unobservable object or property is “empirically meaningful.” The intended kind of empirical meaningfulness should be necessary for admissibility into the selective contexts of scientific inquiry. I defend Justus’s recent argument that the reasons generally given for rejecting the project of defining a significance criterion are unpersuasive. However, as I show, this project remains wedded to an overly narrow conception of its subject matter. Even the most cutting (...) edge significance criteria identify empirical significance with predictive power, and thereby rule out vocabulary with legitimate scientific functions. In a nutshell, the problem is that there are terms that reduce the computational burden of extracting predictions from theory, and that may therefore be scientifically useful, but that do not add to the theory’s observational consequences, and so are ruled scientifically inadmissibility by existing significance criteria. I spell out this objection by specifying shortcut terms that are ruled inadmissible by Creath’s and Schurz’s criteria. Having objected in this way to extant criteria, and to the equation of empirical significance with predictive power in general, I discuss an approach to defining empirical significance that is capable of avoiding my objection and, more ambitiously, that may break the cycle of “punctures and patches” that has plagued the project from the beginning. I gloss Goldfarb and Ricketts’s idea of “case-by-case” delineations of empirically significant terms as the provision of special rather than general explications of the informal concept of empirical significance. (shrink)

Often, the behavior of animals can be better explained and predicted, it seems, if we ascribe the capacity to have beliefs, intentions, and concepts to them. Whether we really can do so, however, is a debated issue. Particularly, Donald Davidson maintains that there is no basis in fact for ascribing propositional attitudes or concepts to animals. I will consider his and rival views, such as Colin Allen's three-part approach, for determining whether animals possess concepts. To avoid pure theoretical debate, however, (...) I will test these criteria using characteristic examples from ethology that depict a broad range of animal behavior. This will allow us to detect a series of gradations in animals' capacities, in the course of which we can think over what would count for or against an attribution of concepts and propositional attitudes to them in each single case. Self-conceit is our natural hereditary disease. Of all creatures man is the most wretched and fragile, and at once the most supercilious. ... It is by this conceit that man arrogates to himself ... divine properties, that he segregates himself from the mass of other creatures and raises himself above them .. (shrink)

As a brand-new marketing method, network marketing has gradually become one of the main ways and means for enterprises to improve profitability and competitiveness with its unique advantages. Using these marketing data to build a model can dig out useful information that the business is concerned about, and the company can then formulate marketing strategies based on this information. Sales forecasting is to speculate on the future based on historical sales. It is a tool for companies to determine production volume (...) and ensure the balance of product supply and sales. It can help companies make correct business decisions to maximize profits. The neural network can approximate the nonlinear function with arbitrary precision, and the time series prediction model based on the neural network can well reflect the nonlinear development trend of information. Based on the analysis of the shortcomings of the traditional BP network, this paper uses a genetic algorithm with good global search capabilities to improve the neural network. The thought and theory of optimizing the initial weight and threshold of the neural network of the GA algorithm are discussed in detail. While expounding the forecasting method, it uses specific examples to analyze the performance and characteristics of the GA-BP network in the enterprise network marketing forecasting. The results show that the GA-BP neural network is higher than the traditional BP neural network in terms of prediction accuracy and adaptability. (shrink)

Criteria of empirical significance are supposed to state conditions under which reference to an unobservable object or property is “empirically meaningful”. The intended kind of empirical meaningfulness should be necessary for admissibility into the selective contexts of scientific inquiry. I defend Justus’s recent argument that the reasons generally given for rejecting the project of defining a significance criterion are unpersuasive. However, as I show, this project remains wedded to an overly narrow conception of its subject matter. Even the most cutting (...) edge significance criteria identify empirical significance with predictive power, and thereby rule out vocabulary with legitimate scientific functions. In a nutshell, the problem is that there are terms that reduce the computational burden of extracting predictions from theory, and that may therefore be scientifically useful, but that do not produce any additional predictions, and so are ruled scientifically inadmissibility by existing significance criteria. I spell out this objection by specifying terms of this kind that are ruled inadmissible by Creath’s and Schurz’s criteria. Having objected in this way to extant criteria, and to the equation of empirical significance with predictive power in general, I discuss an approach to defining empirical significance that is capable of avoiding my objection and, more ambitiously, that may break the cycle of “punctures and patches” that has plagued the project from the beginning: I gloss Goldfarb and Ricketts’s idea of “case-by-case” delineations of empirically significant terms as the provision of special rather than general explications of the informal concept of empirical significance. (shrink)

Le présent article examine une objection contre le conséquentialisme provenant de ce que l’on peut appeler « le problème de prédiction ». Le conséquentialisme nous demande de choisir l’action qui apporte la meilleure conséquence. Or, il n’est pas toujours facile, ou même possible, de prédire le futur et de savoir quelles seront exactement les conséquences de nos actions. Il n’est pas raisonnable d’attendre l’action juste de l’agent moral si le critère de justice requiert de celui-ci une prédiction qu’il n’est pas (...) en position de faire avec précision. Cette objection est relativement moins souvent discutée. Elle présente tout de même un défi important au conséquentialisme car ce qui est suggéré c’est que le conséquentialisme nierait le principe du « devoir faire implique pouvoir faire ». Cet article propose une version du conséquentialisme qui évite ce problème. Ce qui est offert, cependant, n’est pas un argument pour le conséquentialisme, mais plutôt un argument dont le but est de montrer que le conséquentialisme est capable de répondre à ce problème spécifique. (shrink)

In their contribution ‘Ethics of the algorithmic prediction of goal of care preferences’1 Ferrario et al elaborate a from theory to practice contribution concerning the realisation of artificial intelligence (AI)-based patient preference prediction (PPP) systems. Such systems are intended to help find the treatment that the patient would have chosen in clinical situations—especially in the intensive care or emergency units—where the patient is no longer capable of making that decision herself. The authors identify several challenges that complicate their effective development, (...) application and evaluation—and offer solutions to them. One of these issues is the question of who should ultimately use said systems. While it is undisputed that clinicians should use these AI systems for their decision-making process, there is an ongoing debate about whether next of kin should use them as well. The authors advocate that ‘’access should be provided to both clinicians and loved ones with due explanations and as desired’. We will disagree with this assessment and explain in our commentary why it is important that surrogates provide their own assessments with as little external (AI) influence as possible. Why do next of kin actually participate in the process of preference finding and treatment decision-making? A key reason is that clinicians usually …. (shrink)

The integration of cellular networks and vehicular networks is complex and heterogeneous. Synchronization among vehicles in heterogeneous vehicular clusters plays an important role in effective data sharing and the stability of the cluster. This synchronization depends on the smooth exchange of information between vehicles and remote servers over the Internet. The remote servers predict road traffic patterns by adopting deep learning methods to help drivers on the roads. At the same time, local data processing at the vehicular cluster level may (...) increase the capabilities of remote servers. However, global positioning system signal interruption, especially in the urban environment, plays a big part in the detritions of synchronization among the vehicles that lead to the instability of the cluster. Instability of connections is a major hurdle in developing cost-effective solutions for deriving assistance and route planning applications. To solve this problem, a self-localization scheme within the vehicular cluster is proposed. The proposed self-localization scheme handles GPS signal interruption to the vehicle within the cluster. A unique clustering criterion and a synchronization mechanism for sharing traffic information system data among multiple vehicles are developed. The developed scheme is simulated and compared with existing known approaches. The results show the better performance of our proposed scheme over others. (shrink)

Purpose – The purpose of this paper is to investigate the relationship between cognitive styles of managers working in tourism companies and dynamic capabilities of these companies. Design – The research relies on a quantitative questionnaire. Methodology – To answer the research question, the bivariate (Pearson) correlation was applied. A number of 268 answers from people working in tourism were received. Findings – We found a positive correlation between different dimensions of dynamic capabilities of tourism companies. These capabilities are influenced (...) by managers’ cognitive characteristics. The organizational culture plays a mediating role in the latter relationship. Implications for theory – The paper offers an alternative understanding of dynamic capabilities in tourism and hospitality; the paper also opens new paths for academic research on the impact of cognitive characteristics of managers on the dynamic capabilities of tourism companies. Implications for practitioners – Making accurate psychological portrait of the candidate can predict his/her behavior in certain situation, such as response towards environmental change using dynamic capabilities and when making the necessary changes to the organizational culture. Originality – This study proposes model of influence of a manager’s cognitive style on dynamic capabilities, whereby organizational culture moderates this relationship. (shrink)

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure which is physically related to rainfall on land and thus able to predict unseen rainfall (...) events. Daily rainfall of east coast of Peninsular Malaysia was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine, Bayesian regularized neural networks, Bayesian additive regression trees, extreme gradient boosting, and hybrid neural fuzzy inference system were used considering the complex relationship of rainfall with sea level pressure. Principle components of SLP domain correlated with daily rainfall were used as predictors. The results revealed that the efficacy of AI models is predicting daily rainfall one day before. The relative performance of the models revealed the higher performance of BRNN with normalized root mean square error of 0.678 compared with HNFIS, BART, xgBoost, and ELM. Visual inspection of predicted rainfall during model validation using density-scatter plot and other novel ways of visual comparison revealed the ability of BRNN to predict daily rainfall one day before reliably. (shrink)

Flow prediction in a vegetated channel has been extensively studied in the past few decades. A number of equations that essentially differ from each other in derivation and form have been developed. Because the process is extremely complex, getting the deterministic or analytical form of the process phenomena is too difficult. Hybrid neural network model is particularly useful in modeling processes where an adequate knowledge of the physics is limited. This hybrid model is presented here as a complementary tool to (...) model channel flow–vegetation interactions in submerged vegetation conditions. The hybrid model is used to overcome the local minima limitations of a feed-forward neural network. The prediction capability of model has been found to be better than past empirical predictors. The model developed herein showed significantly better results in several model performance criteria compared with empirical models. (shrink)

Chaos-related obstructions to predictability have been used to challenge accounts of theory validation based on the agreement between theoretical predictions and experimental data. These challenges are incomplete in two respects: they do not show that chaotic regimes are unpredictable in principle and, as a result, that there is something conceptually wrong with idealized expectations of correct predictions from acceptable theories, and they do not explore whether chaos-induced predictive failures of deterministic models can be remedied by stochastic modeling. In this (...) paper we appeal to an asymptotic analysis of state space trajectories and their numerical approximations to show that chaotic regimes are deterministically unpredictable even with unbounded resources. Additionally, we explain why stochastic models of chaotic systems, while predictively successful in some cases, are in general predictively as limited as deterministic ones. We conclude by suggesting that the way in which scientists deal with such principled obstructions to predictability calls for a more comprehensive approach to theory validation, on which experimental testing is augmented by a multifaceted mathematical analysis of theoretical models, capable of identifying chaos-related predictive failures as due to principled limitations which the world itself imposes on any less-than-omniscient epistemic access to some natural systems. (shrink)

A fuzzy predictive fault-tolerant control scheme is proposed for a wide class of discrete-time nonlinear systems with uncertainties, interval time-varying delays, and partial actuator failures as well as unknown disturbances, in which the main opinions focus on the relevant theory of FPFTC based on Takagi-Sugeno fuzzy model description of these systems. The T-S fuzzy model represents the discrete-time nonlinear system in the form of the discrete uncertain time-varying delay state space, which is firstly constructed by a set of local (...) linear models and the nonlinear membership functions. The novel improved state space model can be further obtained by extending the output tracking error to the constructed model. Then the fuzzy predictive fault-tolerant control law based on this extended model is designed, which can increase more control degrees of freedom. Utilizing Lyapunov-Krasovskill theory, less conservative delay-range-dependent stable conditions in terms of linear matrix inequality constraints are given to ensure the asymptotically robust stability of closed-loop system. Meanwhile, the optimized cost function and H-infinity performance index are introduced to the stable conditions to guarantee the robust performance and antidisturbance capability. The simulation results on the temperature control of a strong nonlinear continuous stirred tank reactor show that the proposed control scheme is feasible and effective. (shrink)

Predicting the defect-prone modules when the previous defect labels of modules are limited is a challenging problem encountered in the software industry. Supervised classification approaches cannot build high-performance prediction models with few defect data, leading to the need for new methods, techniques, and tools. One solution is to combine labeled data points with unlabeled data points during learning phase. Semi-supervised classification methods use not only labeled data points but also unlabeled ones to improve the generalization capability. In this study, (...) we evaluated four semi-supervised classification methods for semi-supervised defect prediction. Low-density separation, support vector machine, expectation-maximization, and class mass normalization methods have been investigated on NASA data sets, which are CM1, KC1, KC2, and PC1. Experimental results showed that SVM and LDS algorithms outperform CMN and EM-SEMI algorithms. In addition, LDS algorithm performs much better than SVM when the data set is large. In this study, the LDS-based prediction approach is suggested for software defect prediction when there are limited fault data. (shrink)

Research on children’s ability to attribute false mental states to others has focused exclusively on false beliefs. We developed a novel paradigm that focuses instead on another type of false mental state: false perceptions. From approximately 4 years of age, children begin to recognize that their perception of an illusory object can be at odds with its true properties. Our question was whether they also recognize that another individual viewing the object will similarly experience a false perception. We tested 33 (...) preschool children with a task in which distorting lenses caused a small object to appear large and a large object to appear small. To succeed, children needed to recognize that a naive agent would falsely perceive the relative size of the objects and to correctly anticipate the agent’s actions on that basis. Children performed significantly better than chance in our false perception test, and there was a developmental progression in performance from 4 to 5 years of age similar to that seen in standard false belief tests. Our findings demonstrate that preschool children are capable of understanding that other individuals will be perceptually misled by illusory objects and that these false perceptions will influence their actions in predictable ways. (shrink)

Air temperature prediction can play a significant role in studies related to climate change, radiation and heat flux estimation, and weather forecasting. This study applied and compared the outcomes of three advanced fuzzy inference models, i.e., dynamic evolving neural-fuzzy inference system, hybrid neural-fuzzy inference system, and adaptive neurofuzzy inference system for AT prediction. Modelling was done for three stations in North Dakota, USA, i.e., Robinson, Ada, and Hillsboro. The results reveal that FIS type models are well suited when handling highly (...) variable data, such as AT, which shows a high positive correlation with average daily dew point, total solar radiation, and negative correlation with average wind speed. At the Robinson station, DENFIS performed the best with a coefficient of determination of 0.96 and a modified index of agreement of 0.92, followed by ANFIS with R2 of 0.94 and md of 0.89, and HyFIS with R2 of 0.90 and md of 0.84. A similar result was observed for the other two stations, i.e., Ada and Hillsboro stations where DENFIS performed the best with R2: 0.953/0.960, md: 0.903/0.912, then ANFIS with R2: 0.943/0.942, md: 0.888/0.890, and HyFIS with R2: 0.908/0.905, md: 0.845/0.821, respectively. It can be concluded that all three models are capable of predicting AT with high efficiency by only using DP, TSR, and WS as input variables. This makes the application of these models more reliable for a meteorological variable with the need for the least number of input variables. The study can be valuable for the areas where the climatological and seasonal variations are studied and will allow providing excellent prediction results with the least error margin and without a huge expenditure. (shrink)

Student performance and its evaluation remain a serious challenge for education systems. Frequently, the recording and processing of students’ scores in a specific curriculum have several flaws for various reasons. In this context, the absence of data from some of the student scores undermines the efficiency of any future analysis carried out in order to reach conclusions. When this is the case, missing data imputation algorithms are needed. These algorithms are capable of substituting, with a high level of accuracy, the (...) missing data for predicted values. This research presents the hybridization of an algorithm previously proposed by the authors called adaptive assignation algorithm, with a well-known technique called multivariate imputation by chained equations. The results show how the suggested methodology outperforms both algorithms. (shrink)

Employing a constructionist framework of emotion, this study examines whether parental language during emotion belief discussions predicts parents’ self-reported beliefs about emotion and child emotion regulation (ER). 102 parents of children ages 8 through 12 participated in focus groups about emotion beliefs, and nine months later, completed questionnaires on their emotion beliefs and child ER. Focus group content was analyzed for positive and negative emotion talk, cognitive process talk, and an established linguistic marker of psychological distancing. Parents’ positive emotion talk (...) and parental linguistic distancing when discussing their child’s (but not their own) emotion experiences positively predicted beliefs about children’s emotional capabilities. Finally, negative emotion talk negatively predicted parental beliefs about children’s capacity to control their own emotions and the value of anger expression as well as child ER. Current findings contribute to our understanding of how parental communication patterns about emotions may influence emotion beliefs and child emotion development. (shrink)

In international relations theory, there is a long history of Richardson-like modeling of the evolution of military capability. Usually, such models are deterministic and predictive and do not allow for the representation of the transition from competitive peace to shooting war. More recently, models have been developed which attempt to represent the evolution of relationship between nations. The relationship between nations, varying from friendship to hostility, is taken to be synonymous with the intent of nations towards each other, (...) varying from good will to malice. Generally, these relationship models do not include capability though common sense would indicate that capability and mutual intent should profoundly influence each other. A model is presented here which combines these two fundamental attributes of international relations and attempts to represent the outbreak of war in the world system by the onset of deterministic chaos in the extended model. (shrink)

With the emergence of numerous link prediction methods, how to accurately evaluate them and select the appropriate one has become a key problem that cannot be ignored. Since AUC was first used for link prediction evaluation in 2008, it is arguably the most preferred metric because it well balances the role of wins and the role of draws. However, in many cases, AUC does not show enough discrimination when evaluating link prediction methods, especially those based on local similarity. Hence, we (...) propose a new metric, called W-index, which considers only the effect of wins rather than draws. Our extensive experiments on various networks show that the W-index makes the accuracy scores of link prediction methods more distinguishable, and it can not only widen the local gap of these methods but also enlarge their global distance. We further show the reliability of the W-index by ranking change analysis and correlation analysis. In particular, some community-based approaches, which have been deemed effective, do not show any advantages after our reevaluation. Our results suggest that the W-index is a promising metric for link prediction evaluation, capable of offering convincing discrimination. (shrink)

All football teams that compete within the F. A. Premier League possess an academy, whose objective is to produce more and better home-grown players that are capable of playing professionally. These young players spend a large amount of time with their coach, but little is known about player’s perception of the coach-athlete relationship within F.A. Premier League Academies. The objectives of this study were to examine whether perceptions of the coach-athlete relationship changed over six months and if the coach-athlete relationship (...) predicted self-reported goal achievement among F. A. Premier League academy players. This study included cross-sectional (n = 104) and longitudinal (n = 52) assessments, in which academy soccer players completed a measure of the coach-athlete relationship and goal achievement across either one or two time periods. The cross-sectional data were subjected to bivariate correlations, whereas the longitudinal data were analyzed using multiple regressions. Perceptions of the coach-athlete relationship remained stable over time. The coach-athlete relationship predicted the achievement of mastery goals six months later. Enhancing the quality of the coach-athlete relationship among elite adolescent athletes appears to be a suitable way of maximizing mastery achievement goals, particularly among developmental athletes who participate in team sports. (shrink)

Octopuses are highly intelligent animals with vertebrate-like cognitive and behavioural repertoires. Despite these similarities, vertebrate-based models of cognition and behaviour cannot always be successfully applied to octopuses, due to the structural and functional characteristics that have evolved in their nervous system in response to the unique challenges posed by octopus morphology. For instance, the octopus brain does not support a _somatotopic_ or point-for-point spatial map of the body—an important feature of vertebrate nervous systems. Thus, while octopuses are capable of motor (...) tasks whose vertebrate counterparts require detailed interoceptive monitoring, these movements may not be explainable using motor control frameworks premised on internal spatial representation. One such motor task is the extension of a single arm. The ability of octopuses to select and use a single arm without the guidance of a somatotopic map has been regarded as a motor control puzzle. In an attempt at a solution, this paper develops a predictive processing account of single-arm extension in octopuses. (shrink)

Mindfulness training is often promoted as a method to train cognitive functions and has shown such effects in previous studies. However, many conventional mindfulness exercises for beginners require cognitive effort, which may be prohibitive for some, particularly for people who have more pronounced cognitive problems to begin with. An alternative mindfulness-based approach, called restoration skills training, draws on a restorative natural practice setting to help regulate attention effortlessly and promote meditative states during exercises. Previous research has shown that a 5-week (...) ReST course requires less effort and is attended by higher compliance with practice recommendations than a conventional mindfulness course, without compromising long-term outcomes. Here, we compare ReST and a formally matched conventional mindfulness course regarding the role that initial individual differences in cognitive functioning play in determining practice compliance and long-term improvements in dispositional mindfulness and cognitive functioning. In line with expectations, ReST participants who had more pronounced cognitive problems to begin with practiced more during the course, which in turn explained much of their improvement in dispositional mindfulness and cognitive functioning. In contrast, initial cognitive functioning did not explain practice and improvement in the conventional mindfulness course. The results provide further support for the potential utility of ReST as a low-effort method for enhancing cognitive functioning among people who would struggle with the demands of conventional mindfulness training. With careful integration of mindfulness practices with a restorative natural setting, these people can develop mindfulness and self-regulation capabilities without relying on effortful training. (shrink)

The current problems of rural people and communities expose the weakness of the social sciences in agriculture, both as to epistemological limitations and as to the environment in which they operate. That weakness involves not ony an inability to make reasonably accurate predictions, but also to explain scientifically the nature of the current situation. This conclusion is reached after an examination of the context of the current problems of rural America, an evaluation of the epistemological capabilities of the relevant social (...) sciences, and an appraisal of the environment in which the social sciences within agriculture exist. An over-allocation of resources to the economics of commercial agriculture and an under-allocation to the other social sciences and the economics of rural development is suggested as a serious problem. Significant reallocation of resources will be required if the traditional agricultural establishment is to remain relevant to rural people and their needs. (shrink)

It is a popular view thatpractical deliberation excludes foreknowledge of one's choice. Wolfgang Spohn and Isaac Levi have argued that not even a purely probabilistic self-predictionis available to thedeliberator, if one takes subjective probabilities to be conceptually linked to betting rates. It makes no sense to have a betting rate for an option, for one's willingness to bet on the option depends on the net gain from the bet, in combination with the option's antecedent utility, rather than on the offered (...) odds. And even apart from this consideration, assigning probabilities to the options among which one is choosing is futile since such probabilities could be of no possible use in choice. The paper subjects these arguments to critical examination and suggests that, appearances notwithstanding, practical deliberation need not crowd outself-prediction. (shrink)

The first systematic collaboration between cognitive scientists and sports psychologists considers the mind–body relationship from the perspective of athletic skill and sports practice. This landmark work is the first systematic collaboration between cognitive scientists and sports psychologists that considers the mind–body relationship from the perspective of athletic skill and sports practice. With twenty-six chapters by leading researchers, the book connects and integrates findings from fields that range from philosophy of mind to sociology of sports. The chapters show not only that (...) sports can tell scientists how the human mind works but also that the scientific study of the human mind can help athletes succeed. Sports psychology research has always focused on the themes, notions, and models of embodied cognition; embodied cognition, in turn, has found striking confirmation of its theoretical claims in the psychological accounts of sports performance and athletic skill. Athletic skill is a legitimate form of intelligence, involving cognitive faculties no less sophisticated and complex than those required by mathematical problem solving. After presenting the key concepts necessary for applying embodied cognition to sports psychology, the book discusses skill disruption ; sensorimotor skill acquisition and how training correlates to the development of cognitive faculties; the intersubjective and social dimension of sports skills, seen in team sports; sports practice in cultural and societal contexts; the notion of “affordance” and its significance for ecological psychology and embodied cognition theory; and the mind's predictive capabilities, which enable anticipation, creativity, improvisation, and imagination in sports performance. Contributors Ana Maria Abreu, Kenneth Aggerholm, Salvatore Maria Aglioti, Jesús Ilundáin-Agurruza, Duarte Araújo, Jürgen Beckmann, Kath Bicknell, Geoffrey P. Bingham, Jens E. Birch, Gunnar Breivik, Noel E. Brick, Massimiliano L. Cappuccio, Thomas H. Carr, Alberto Cei, Anthony Chemero, Wayne Christensen, Lincoln J. Colling, Cassie Comley, Keith Davids, Matt Dicks, Caren Diehl, Karl Erickson, Anna Esposito, Pedro Tiago Esteves, Mirko Farina, Giolo Fele, Denis Francesconi, Shaun Gallagher, Gowrishankar Ganesh, Raúl Sánchez-García, Rob Gray, Denise M. Hill, Daniel D. Hutto, Tsuyoshi Ikegami, Geir Jordet, Adam Kiefer, Michael Kirchhoff, Kevin Krein, Kenneth Liberman, Tadhg E. MacIntyre, Nelson Mauro Maldonato, David L. Mann, Richard S. W. Masters, Patrick McGivern, Doris McIlwain, Michele Merritt, Christopher Mesagno, Vegard Fusche Moe, Barbara Gail Montero, Aidan P. Moran, David Moreau, Hiroki Nakamoto, Alberto Oliverio, David Papineau, Gert-Jan Pepping, Miriam Reiner, Ian Renshaw, Michael A. Riley, Zuzanna Rucinska, Lawrence Shapiro, Paula Silva, Shannon Spaulding, John Sutton, Phillip D. Tomporowski, John Toner, Andrew D. Wilson, Audrey Yap, Qin Zhu, Christopher Madan. (shrink)