Naturalistic stimuli, including movie, music, and speech, have been increasingly applied in the research of neuroimaging. Relative to a resting-state or single-task state, naturalistic stimuli can evoke more intense brain activities and have been proved to possess higher test–retest reliability, suggesting greater potential to study adaptive human brain function. In the current research, naturalistic functional magnetic resonance imaging (N-fMRI) has been a powerful tool to record brain states under naturalistic stimuli, and many efforts have been devoted to study the high-level (...) semantic features from spatial or temporal representations via N-fMRI. However, integrating both spatial and temporal characteristics of brain activities for better interpreting the patterns under naturalistic stimuli is still underexplored. In this work, a novel hybrid learning framework that comprehensively investigates both the spatial (via Predictive Model) and the temporal [via convolutional neural network (CNN) model] characteristics of the brain is proposed. Specifically, to focus on certain relevant regions from the whole brain, regions of significance (ROS), which contain common spatial activation characteristics across individuals, are selected via the Predictive Model. Further, voxels of significance (VOS), whose signals contain significant temporal characteristics under naturalistic stimuli, are interpreted via one-dimensional CNN (1D-CNN) model. In this article, our proposed framework is applied onto the N-fMRI data during naturalistic classical/pop/speech audios stimuli. The promising performance is achieved via the Predictive Model to differentiate the different audio categories. Especially for distinguishing the classic and speech audios, the accuracy of classification is up to 92%. Moreover, spatial ROS and VOS are effectively obtained. Besides, temporal characteristics of the high-level semantic features are investigated on the frequency domain via convolution kernels of 1D-CNN model, and we effectively bridge the “semantic gap” between high-level semantic features of N-fMRI and low-level acoustic features of naturalistic audios in the frequency domain. Our results provide novel insights on characterizing spatiotemporal patterns of brain activities via N-fMRI and effectively explore the high-level semantic features under naturalistic stimuli, which will further benefit the understanding of the brain working mechanism and the advance of naturalistic stimuli clinical application. (shrink)

_role, especially in learning, and through devising hybrid neural network models that (in a qualitative manner) approxi-_ _mate characteristics of human consciousness. In doing so, the paper examines explicit and implicit learning in a variety_ _of psychological experiments and delineates the conscious/unconscious distinction in terms of the two types of learning_ _and their respective products. The distinctions are captured in a two-level action-based model C_larion_. Some funda-_ _mental theoretical issues are also clari?ed with the help of the (...) model. Comparisons with existing models of conscious-_. (shrink)

Accurate and reliable prediction of Perfobond Rib Shear Strength Connector is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression, artificial neural network, and M5 tree model. In (...) the second scenario, a comparable AI model hybridized with genetic algorithm as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient and lowest value root mean square error. However, the other comparable models such as GA-M5Tree, GA-ANN, and GA-SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model. (shrink)

In the field of cybernetic systems and more specifically in robotics, one of the fundamental objectives is the detection of anomalies in order to minimize loss of time. Following this idea, this paper proposes the implementation of a Hybrid Intelligent System in four steps to impute the missing values, by combining clustering and regression techniques, followed by balancing and classification tasks. This system applies regression models to each one of the clusters built on the instances of data set. Subsequently, (...) a variety of balancing techniques are applied to improve the classifier’s ability to discern whether it is in an error or a normal state. These techniques support to obtain better classification ratios in which a robot is close to error and allow us to bring the behavior back to a normal state. The experimentation is performed using a modern and public data set, which has been extracted from a component-based robotic system, in which different anomalies are induced by software in their components. (shrink)

ABSTRACT: This article addresses two main concerns: first, the relation between the truth/falsehood and purity/impurity criteria as applied to pleasure, and, second, the status of our pleasures of learning. In addressing the first, I argue that Plato keeps the truth/falsehood and purity/impurity criteria distinct in his assessment of pleasures and thus leaves room for the possibility of hybrid pleasures in the form of true impure pleasures and false pure pleasures. In addressing the second issue, I show that Plato's (...) view is perfectly able to accommodate an understanding of our pleasures of learning as pure pleasures even if such pleasures are preceded by awareness of a lack (ignorance).RÉSUMÉ: Cet article aborde deux concepts principaux: premièrement, la relation entre les critères de vérité/fausseté et de pureté/impureté appliqués au plaisir, et deuxièmement, le statut de notre plaisir d'apprendre. En abordant le premier concept, j'argumente que Platon tient distincts les critères de vérité/fausseté et de'pureté Iimpureté dans son évaluation du plaisir, laissant ouverte la possibilité de plaisirs hybrides sous forme de vrais plaisirs impurs et faux plaisirs purs. En abordant le deuxième concept, je montre que le point de vue de Platon est parfaitement capable d'accommoder la compréhension de nos plaisirs d'apprendre comme plaisirs purs si de tels plaisirs sont précédés par la conscience d'un manque (ignorance). (shrink)

top-down approach (that is, turning declarative knowledge into procedural knowledge), we adopt a bottom-up approach toward lowlevel skill learning, where procedural knowledge develops rst and declarative knowledge develops from it. Clarionwhich follows this approach is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line learning. We compare the model with human data in a mine eld navigation task. A match between the model and human data is observed in several comparisons.

To deal with reactive sequential decision tasks we present a learning model which is a hybrid connectionist model consisting of both localist and distributed representations based on the two level approach proposed in..

For efficient energy distribution, microgrids provide significant assistance to main grids and act as a bridge between the power generation and consumption. Renewable energy generation resources, particularly photovoltaics, are considered as a clean source of energy but are highly complex, volatile, and intermittent in nature making their forecasting challenging. Thus, a reliable, optimized, and a robust forecasting method deployed at MG objectifies these challenges by providing accurate renewable energy production forecasting and establishing a precise power generation and consumption matching at (...) MG. Furthermore, it ensures effective planning, operation, and acquisition from the main grid in the case of superior or inferior amounts of energy, respectively. Therefore, in this work, we develop an end-to-end hybrid network for automatic PV power forecasting, comprising three basic steps. Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. These features are then fed to fully connected layers for optimal PV power forecasting. In the third step, the proposed model is evaluated on publicly available PV power generation datasets, where its performance reveals lower error rates when compared to state-of-the-art methods. (shrink)

With the expansion of the digital business line, the network flow behind the digital power grid is also exploding. To prevent network congestion, this article proposes a novel network flow forecasting model, which is composed of variational mode decomposition, GRU-xgboost block, and a forecasting adjustment block, to grasp the changing patterns and trends of network flow in advance, and to formulate reasonable and effective flow management strategies and meet the requirements of users for network service quality. The network flow series (...) in power grid enterprise always contain complex patterns and outliers, and VMD is applied to adaptively process complex net flow time series into several subseries with simpler patterns. A GRU-xgboost block is designed to reconstruct the features of historical series. Then, xgboost model is applied to generate predictions for all decomposed subsignals. For the final predictions, we design a forecasting adjustment block to further remove the influence of random noise. Finally, the empirical results show the superior performance of the proposed model on network flow forecasting task. (shrink)

We present a theoretical account of implicit and explicit learning in terms of ACT-R, an integrated architecture of human cognition as a computational supplement to Dienes & Perner's conceptual analysis of knowledge. Explicit learning is explained in ACT-R by the acquisition of new symbolic knowledge, whereas implicit learning amounts to statistically adjusting subsymbolic quantities associated with that knowledge. We discuss the common foundation of a set of models that are able to explain data gathered in several signature (...) paradigms of implicit learning. (shrink)

In recent times, the role of the regional economy changed significantly under certain conditions of globalization and structural adjustment. The process of changing must be crucial to analyse regional economy and develop the planning of regional economy. Developing economies depend often on industries and country policies. Modern studies tend to participate in important factors in this field such as energy intensity, labour skills, local industries, resources, and local expertise. Furthermore, in this study, to start developing the regional economy and make (...) the revolution in this field to connect it with new technology, we train the deep learning algorithm of gathering factors to manage them perfectly and make a good prediction for the future economy. Hybrid sequence to sequence algorithms of deep learning fed with previous information from past years and run the system to compare the predicted result data with current information to evaluate the method to be certified for the coming years. (shrink)

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network including the neural network, the fuzzy logic, the wavelet transform, and the genetic algorithm was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm (...) and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network, the wavelet neural network, and the neural network. The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river. (shrink)

Brain monitoring combined with automatic analysis of EEGs provides a clinical decision support tool that can reduce time to diagnosis and assist clinicians in real-time monitoring applications (e.g., neurological intensive care units). Clinicians have indicated that a sensitivity of 95% with specificity below 5% was the minimum requirement for clinical acceptance. In this study, a high-performance automated EEG analysis system based on principles of machine learning and big data is proposed. This hybrid architecture integrates hidden Markov models (HMMs) (...) for sequential decoding of EEG events with deep learning-based postprocessing that incorporates temporal and spatial context. These algorithms are trained and evaluated using the Temple University Hospital EEG, which is the largest publicly available corpus of clinical EEG recordings in the world. This system automatically processes EEG records and classifies three patterns of clinical interest in brain activity that might be useful in diagnosing brain disorders: (1) spike and/or sharp waves, (2) generalized periodic epileptiform discharges, (3) periodic lateralized epileptiform discharges. It also classifies three patterns used to model the background EEG activity: (1) eye movement, (2) artifacts and (3) background. Our approach delivers a sensitivity above 90% while maintaining a specificity below 5%. We also demonstrate that this system delivers a low false alarm rate, which is critical for any spike detection application. (shrink)

This article discusses authentic inquiry-based learning in a hybrid graduate course, Teaching Hidden History, taught in 2015 and 2016. Students in this course created online history learnin...

During the two days of the workshop, various presentations and discussions brought to light many new ideas, controv ersies, and syntheses. The fo cus was on learning and architecture s that feature hybrid representations and supp ort hybrid learning. It was a general consensus among the workshop participants that hybrid connectionist-symb olic mo dels constitute a promising aven ue toward developing more robust, more p owerful, and more versatile architecture s b oth for cognitive mo (...) deling and for intelligen t systems. The need for such mo dels has b een slowly but steadily growing over the past 5 years. Some new, imp ortant approaches have b een prop osed and develop ed, some of which were presented at the workshop. worthwhil e further pursuing In sum. (shrink)

Due to the inherent chaotic and fractal dynamics in the price series of Bitcoin, this paper proposes a two-stage Bitcoin price prediction model by combining the advantage of variational mode decomposition and technical analysis. VMD eliminates the noise signals and stochastic volatility in the price data by decomposing the data into variational mode functions, while technical analysis uses statistical trends obtained from past trading activity and price changes to construct technical indicators. The support vector regression accepts input from a (...) class='Hi'>hybrid of technical indicators and reconstructed variational mode functions. The model is trained, validated, and tested in a period characterized by unprecedented economic turmoil due to the COVID-19 pandemic, allowing the evaluation of the model in the presence of the pandemic. The constructed hybrid model outperforms the single SVR model that uses only TI and rVMF as features. The ability to predict a minute intraday Bitcoin price has a huge propensity to reduce investors’ exposure to risk and provides better assurances of annualized returns. (shrink)

Sentence boundary detection (SBD) represents an important first step in natural language processing since accurately identifying sentence boundaries significantly impacts downstream applications. Nevertheless, detecting sentence boundaries within legal texts poses a unique and challenging problem due to their distinct structural and linguistic features. Our approach utilizes deep learning models to leverage delimiter and surrounding context information as input, enabling precise detection of sentence boundaries in English legal texts. We evaluate various deep learning models, including domain-specific transformer models like (...) LegalBERT and CaseLawBERT. To assess the efficacy of our deep learning models, we compare them with a state-of-the-art domain-specific statistical conditional random field (CRF) model. After considering model size, F1-score, and inference time, we identify the Convolutional Neural Network Model (CNN) as the top-performing deep learning model. To further enhance performance, we integrate the features of the CNN model into the subsequent CRF model, creating a hybrid architecture that combines the strengths of both models. Our experiments demonstrate that the hybrid model outperforms the baseline model, achieving a 4% improvement in the F1-score. Additional experiments showcase the superiority of the hybrid model over SBD open-source libraries when confronted with an out-of-domain test set. These findings underscore the importance of efficient SBD in legal texts and emphasize the advantages of employing deep learning models and hybrid architectures to achieve optimal performance. (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)

Chinese kindergartens’ over 110 years of adaptation of foreign models is a vivid example of how globalization comes into direct contact with Chinese culture and creates cultural hybridities. Learning Stories as a narrative assessment tool to children’s development from New Zealand, has swept China with the endorsement from the professional organizations and local authorities, especially attracting many followers in Beijing. Based on a two-year participatory action research in Beijing, the article examines Learning Stories as policy borrowing, redesigned as (...) an innovative reflection tool for professional learning community which results from processes of mutual influence between the global and the local. The article highlights the importance of culture and context in considering professional development and the ‘fluid’ and dynamic nature of ‘glocalization’ working towards integration of globalization and localization. Furthermore, borrowing or learning foreign policies in preprimary education can be... (shrink)

With the mobility and ease of connection, wireless sensor networks have played a significant role in communication over the last few years, making them a significant data carrier across networks. Additional security, lower latency, and dependable standards and communication capability are required for future-generation systems such as millimeter-wave LANs, broadband wireless access schemes, and 5G/6G networks, among other things. Effectual congestion control is regarded as of the essential aspects of 5G/6G technology. It permits operators to run many network illustrations on (...) a single organization while maintaining higher service quality. A sophisticated decision-making system for arriving network traffic is necessary to confirm load balancing, limit network slice letdown, and supply alternative slices in slice letdown or congestion. Because of the massive amount of data being generated, artificial intelligence and machine learning play a vital role in reconfiguring and improving a 5G/6G wireless network. In this research work, a hybrid deep learning method is being applied to forecast optimal congestion improvement in the wireless sensors of 5G/6G IoT networks. This proposed model is applied to a training dataset to govern the congestion in a 5G/6G network. The proposed approach provided promising results, with 0.933 accuracy, and 0.067 miss rate. (shrink)

The vital data about the electrical activities of the brain are carried by the electroencephalography signals. The recordings of the electrical activity of brain neurons in a rhythmic and spontaneous manner from the scalp surface are measured by EEG. One of the most important aspects in the field of neuroscience and neural engineering is EEG signal analysis, as it aids significantly in dealing with the commercial applications as well. To uncover the highly useful information for neural classification activities, EEG studies (...) incorporated with machine learning provide good results. In this study, a Fusion Hybrid Model with Singular Value Decomposition Based Estimation of Robust Parameters is proposed for efficient feature extraction of the biosignals and to understand the essential information it has for analyzing the brain functionality. The essential features in terms of parameter components are extracted using the developed hybrid model, and a specialized hybrid swarm technique called Hybrid Differential Particle Artificial Bee algorithm is proposed for feature selection. To make the EEG more practical and to be used in a plethora of applications, the robust classification of these signals is necessary thereby relying less on the trained professionals. Therefore, the classification is done initially using the proposed Zero Inflated Poisson Mixture Regression Model and then it is also classified with a deep learning methodology, and the results are compared with other standard machine learning techniques. This proposed flow of methodology is validated on a few standard Biosignal datasets, and finally, a good classification accuracy of 98.79% is obtained for epileptic dataset and 98.35% is obtained for schizophrenia dataset. (shrink)

Drawing on a global sample of microfinance institutions (MFIs), this paper offers insights into the trade-off versus synergy debate of adopting multiple institutional goals in hybrid organisations. Additionally, it unravels which organisation- and country-specific determinants associate with top joint performance using machine-learning techniques. We find that the synergy versus trade-off debate is not dichotomous. Rather, MFIs can be strong both socially and financially but not while charging low interest rates. In our sample, 17% of MFIs serve a low-income (...) clientele in need with a diverse range of services while remaining financially sustainable and ask a close-to-average interest rate. These organisations are larger and more mature as well as financially prudent in that they minimize both financial and credit risk. Moreover, they are located in countries where their services can create larger benefits regarding their underlying goals. (shrink)

With the increasing degrees A.I.s and machines are applied across different social contexts, the need for implementing ethics in A.I.s is pressing. In this paper, we argue for a pluralist hybrid model for the implementation of moral A.I.s. We first survey current approaches to moral A.I.s and their inherent limitations. Then we propose the pluralist hybrid approach and show how these limitations of moral A.I.s can be partly alleviated by the pluralist hybrid approach. The core ethical decision-making (...) capacity of an A.I. based on the pluralist hybrid approach consists of two systems. The first is a deterministic algorithm system that incorporates different moral rules for making explicit moral decisions. The second is a machine learning system that calculates the value of the variables required by the application of moral principles. The pluralist hybrid system is better than the existing proposals as (i) it better addresses the moral disagreement problem of the top-down approach by including different moral principles, while (ii) it reduces the opacity of ethical decision-making compared with a bottom-up system by implementing explicit moral principles for moral decision making. (shrink)

Due to the huge amount of data being generating from different sources, the analyzing and extracting of useful information from these data becomes a very complex task. The difficulty of dealing with big data optimization problems comes from many factors such as the high number of features, and the existing of lost data. The feature selection process becomes an important step in many data mining and machine learning algorithms to reduce the dimensionality of the optimization problems and increase the (...) performance of the classification or clustering algorithms. In this paper, a set of hybrid and efficient genetic algorithms are proposed to solve feature selection problem, when the handled data has a large feature size. The proposed algorithms use a new gene-weighted mechanism that can adaptively classify the features into strong relative features, weak or redundant features, and unstable features during the evolution of the algorithm. Based on this classification, the proposed algorithm gives the strong features high priority and the weak features less priority when generating new candidate solutions. In the same time, the proposed algorithm tries to more concentrate on unstable features that sometimes appear and sometimes disappear from the best solutions of the population. The performance of proposed algorithms is investigated by using different datasets and feature selection algorithms. The results show that our proposed algorithms can outperform the other feature selection algorithms and effectively enhance the classification performance over the tested datasets. (shrink)

Until now, the mass spread of fake news and its negative consequences have implied mainly textual content towards a loss of citizens' trust in institutions. Recently, a new type of machine learning framework has arisen, Generative Adversarial Networks (GANs) – a class of deep neural network models capable of creating multimedia content (photos, videos, audio) that simulate accurate content with extreme precision. While there are several areas of worthwhile application of GANs – e.g., in the field of audio-visual production, (...) human-computer interactions, satire, and artistic creativity – their deceptive uses, at least as currently foreseeable, are just as numerous and worrying. The main concern is linked to the so-called “deepfakes”, fake images or videos that simulate real events with extreme precision. When trained on a human face, GANs can make the face assume hyper-realistic movements, expressions and (verbal and non-verbal) communication abilities. This technology poses an urgent threat to the governance of democratic processes concerning the production of public opinions and political discourses, with significant potential for reality-altering and disinformation. After a short introduction of their current technical state-of-the-art, in this paper we want to enquire the GANs` socio-technical system alongside different and intertwined philosophical accounts. Firstly, we will argue about the conditions that make perceived a GANs-generated content as trustworthy, arguing also about the general effects GANs might have on the perceived trustworthiness of individuals. Thereafter, we will discuss about the inadequacy to approach GANs only as perception-altering technology. Against this backdrop, we will propose a theoretical turn that considers the human-machine relationships of trustworthiness as elements of a broader hybrid socio-technical systems. This turn come up with political repercussions that we will discuss in the last part of the paper. (shrink)

Two main schemes explain how a system adapts to its environment. Evolutionary models are grounded on three usual processes (variation, transmission, selection) acting at the population level. Learning models are concerned with the endogenous search for a better performance at the individual level. The first ones were initially favored by biology and the second well illustrated by game theory. The article examines first how game theory went to evolution and how biology later considered learning. It shows some examples (...) of a hybrid use of models of each type. It finally proposes a common framework for both types of models. (shrink)

The impact of Online Social Networks on human lives is foreseen to be very large with unprecedented amount of data and users. OSN users share their ideas, photos, daily life events, feelings and news. Since OSNs’ security and privacy challenges are more potential than ever before, it is necessary to enhance the protection and filtering approaches of OSNs contents. This paper explores OSNs’ threats and challenges, and categorize them into: account-based, URL-based and content-based threats. In addition, we analyze the existing (...) protection methods and highlight their limitations and weaknesses. Based on that, we propose a comprehensive, user-level, proactive and real-time OSNs’ protection system, called Hybrid Real-time Social Networks Protector. HRSP has three components; a user-level security protocol and two classification models. The protocol defines a structure for OSN’s cryptographic services, including encryption, access control and users’ authentication. The classification models employ machine learning, black lists, white lists and users’ feedback, in order to classify URLs into: Benign, Risk and Inappropriate classes, and contents into: Benign, Hate speech and Inappropriate classes. We constructed two data sets of 150,000 URLs and 22,000 tweets to build and test the two classification models. Results show an overall accuracy of 93.2% for the URL model and 84.4% for the content model, while the protocol implementation produces compatible size and time overhead. The components of HRSP are integrated and have compatible design with OSN platforms. (shrink)

This paper presents a novel learning model Clarion , which is a hybrid model based on the two-level approach proposed in Sun (1995). The model integrates neural, reinforcement, and symbolic learning methods to perform on-line, bottom-up learning (i.e., learning that goes from neural to symbolic representations). The model utilizes both procedural and declarative knowledge (in neural and symbolic representations respectively), tapping into the synergy of the two types of processes. It was applied to deal with (...) sequential decision tasks. Experiments and analyses in various ways are reported that shed light on the advantages of the model. (shrink)

This article maintains that a new wave in the development of the productive forces of society triggered by the revolution in information and communication technologies is taking place. Production carried out by single organizations is increasingly replaced by forms of production that are based on close long-term collaboration between specialized firms. This transition reflects the increasing importance of research and development as well as collective learning in business competition. New information and communication technologies enable new forms of distributed and (...) collaborative knowledge creation and learning. The article explores an emerging new form of innovation-oriented inter-firm collaboration called co-configuration and the new kind of dualistic agency it seems to be calling for. In this form of collaboration the traditional boundary between producer and provider as well as the boundaries between product development, sales and maintenance within the provider organization become blurred. The article presents a case of the development of co-configuration work in the provision of optimization software for pulp production. The case shows some of the contradictions involved in this new form of collaboration and the development of a new kind of object-oriented collaborative agency mediated through a real-time information and communication technological platform and uniting two processes of continuous development. (shrink)

We seem to learn and use concepts in a variety of heterogenous “formats”, including exemplars, prototypes, and theories. Different strategies have been proposed to account for this diversity. Hybridists consider instances in different formats to be instances of a single concept. Pluralists think that each instance in a different format is a different concept. Eliminativists deny that the different instances in different formats pertain to a scientifically fruitful kind and recommend eliminating the notion of a “concept” entirely. In recent years, (...) hybridism has received the most attention and support. However, we are still lacking a cognitive-computational model for concept representation and processing that would underpin hybridism. The aim of this paper is to advance the understanding of concepts by grounding hybridism in a neuroscientific model within the Predictive Processing framework. In the suggested view, the different formats are not distinct parts of a concept but arise from different ways of processing a functionally unified representational structure. (shrink)

Clustering is one of the most fundamental and essential data analysis tasks with broad applications. It has been studied in various research fields: data mining, machine learning, pattern recognition and in engineering, economics and biomedical data analysis. Headache is not a disease that typically shortens one’s life, but it can be a serious social as well as a health problem. Approximately 27 billion euros per year are lost through reduced work productivity in the European community. This paper is focused (...) on a new strategy based on a hybrid model for combining fuzzy partition method and maximum likelihood estimation clustering algorithm for diagnosing primary headache disorder. The proposed hybrid system is tested on two data sets for diagnosing headache disorder collected from Clinical Centre of Vojvodina in Serbia. (shrink)

Research on human causal learning has largely focused on strength learning, or on computational-level theories; there are few formal algorithmic models of how people learn causal structure from covariations. We introduce a model that learns causal structure in a local manner via prediction-error learning. This local learning is then integrated dynamically into a unified representation of causal structure. The model uses computationally plausible approximations of rational learning, and so represents a hybrid between the associationist (...) and rational paradigms in causal learning research. We conclude by showing that the model provides a good fit to data from a previous experiment. (shrink)

Depression has become one of the most common mental illnesses, and the widespread use of social media provides new ideas for detecting various mental illnesses. The purpose of this study is to use machine learning technology to detect users of depressive patients based on user-shared content and posting behaviors in social media. At present, the existing research mostly uses a single detection method, and the unbalanced class distribution often leads to a low recognition rate. In addition, a large number (...) of irrelevant or redundant features in high-dimensional data sets interfere with the accuracy of recognition. To solve this problem, this paper proposes a hybrid feature selection and stacking ensemble strategy for depression user detection. First, recursive elimination method and extremely randomized trees method are used to calculate feature importance and mutual information value, calculate feature weight vector, and select the optimal feature subset according to the feature weight. Second, naive bayes, k-nearest neighbor, regularized logistic regression and support vector machine are used as base learners, and a simple logistic regression algorithm is used as a combination strategy to build a stacking model. Experimental results show that compared with other machine learning algorithms, the proposed hybrid method, which integrates feature selection and ensemble, has a higher accuracy of 90.27% in identifying online patients. We believe this study will help develop new methods to identify depressed people in social networks, providing guidance for future research. (shrink)

O'Brien et al. reported that readers generated elaborative inferences only when a text contained characteristics that made it easy to predict the specific inference that a reader would draw, and virtually eliminated the possibility of the inference being discon-firmed. Garrod et al., however, offered two qualifications to these conclusions. First, the two text characteristics manipulated may have produced different types of elaborative inferencing: a biasing context results in a passive form of elaborative inferencing, involving setting up a context of interpretation, (...) whereas the presence of a demand sentence invites the reader to actively predict a subsequent expression. Secondly, clear evidence for either type of inference will be apparent only with truly anaphoric materials. This work describes how a passive form of elaborative inferencing, reported by Garrod et al, may be implemented in a connectionist manner. We take the connectionist model proposed by Shastri and Ajjanagadde in order to represent a text in the form of a network. Next we analyze and discuss how an attentional focus could operate with the proposed reasoner system in order to deal with inference control and anaphora resolution, during text understanding. Our own suggestion is that the system proposed by Shastri and Ajjanagadde could be extended by incorporating focus in order to apply it to some open problems related to text processing. However, this extension presents implementational problems due to its local character. Distributed models seem to be in a better position to deal with the shift of the attentional states, given that these systems can learn. Particularly, the model proposed by Sun is close to our aims, and so we will discuss its implications in order to design a cognitive architecture for text comprehension. (shrink)

O'Brien et al. reported that readers generated elaborative inferences only when a text contained characteristics that made it easy to predict the specific inference that a reader would draw, and virtually eliminated the possibility of the inference being discon-firmed. Garrod et al., however, offered two qualifications to these conclusions. First, the two text characteristics manipulated may have produced different types of elaborative inferencing: a biasing context results in a passive form of elaborative inferencing, involving setting up a context of interpretation, (...) whereas the presence of a demand sentence invites the reader to actively predict a subsequent expression. Secondly, clear evidence for either type of inference will be apparent only with truly anaphoric materials. This work describes how a passive form of elaborative inferencing, reported by Garrod et al, may be implemented in a connectionist manner. We take the connectionist model proposed by Shastri and Ajjanagadde in order to represent a text in the form of a network. Next we analyze and discuss how an attentional focus could operate with the proposed reasoner system in order to deal with inference control and anaphora resolution, during text understanding. Our own suggestion is that the system proposed by Shastri and Ajjanagadde could be extended by incorporating focus in order to apply it to some open problems related to text processing. However, this extension presents implementational problems due to its local character. Distributed models seem to be in a better position to deal with the shift of the attentional states, given that these systems can learn. Particularly, the model proposed by Sun is close to our aims, and so we will discuss its implications in order to design a cognitive architecture for text comprehension. (shrink)

Advances in machine learning and natural language processing are revolutionizing the way we live, work, and think. As for any science, they are based on assumptions about what the world is, and how humans interact with it. In this paper, I discuss what is potentially one of these assumptions: structuralism, which states that all cultures share a hidden structure. I illustrate this assumption with political footprints: a machine-learning technique using pre-trained word vectors for political discourse analysis. I introduce (...) some of the benefits and limitations of structuralism when applied to machine learning, and the risks of exploiting a technology before establishing the validity of all its hypotheses. I consider how machine-learning techniques could evolve towards hybrid structuralism or post-structuralism, and how deeply these developments would impact cultural studies. (shrink)

This article outlines the context in which General National Vocational Qualifications (GNVQs) have been developed with particular reference to the independent learning dimension of their principles and practice. It provides an overview of the problems associated with the GNVQ approach from the literature and from a study by the authors of twelve post-16 institutions in the process of implementing Advanced GNVQ programmes. It analyses the dimensions of independent learning and argues that GNVQs provide a hybrid learner experience (...) in which autonomy in the organisation of the individual learning process is mediated by a heavily prescribed GNVQ framework. The article concludes by locating this paradox in four central dilemmas characterising post-16 curricular policy in the UK. (shrink)

Syntactic priming in language production is the increased likelihood of using a recently encountered syntactic structure. In this paper, we examine two theories of why speakers can be primed: error‐driven learning accounts (Bock, Dell, Chang, & Onishi, 2007; Chang, Dell, & Bock, 2006) and activation‐based accounts (Pickering & Branigan, 1999; Reitter, Keller, & Moore, 2011). Both theories predict that speakers should be primed by the syntactic choices of others, but only activation‐based accounts predict that speakers should be able to (...) prime themselves. Here we test whether speakers can be primed by their own productions in three behavioral experiments and find evidence of structural persistence following both comprehension and speakers’ own productions. We also find that comprehension‐based priming effects are larger for rarer syntactic structures than for more common ones, which is most consistent with error‐driven accounts. Because neither error‐driven accounts nor activation‐based accounts fully explain the data, we propose a hybrid model. (shrink)

Product differentiation has emerged as a central dynamic in contemporary agrofood systems. Departure from the mode of standardization emblematic of agrofood modernization raises questions about future technical trajectories and the ways in which learning will be sustained. This article examines two innovation trajectories: the rapid coupling of biotechnologies and information technologies to yield products differentiated by constituent components—a model based on a cognitive logic of decomposition/ recomposition—and the proliferation of product networks that mobilize distinctive, localized resources to create complete (...) identities—a model based on a cognitive logic of identity. The article analyzes the information structures—institutional mechanisms that support information exchange and learning—in each of these opposed development paradigms. We find that knowledge creation under each of the logics occurs through mechanisms not recognized within the respective paradigms. On this basis, we derive institutional hybridity as a fundamental resource in systems of innovation. (shrink)

Advances in machine learning and natural language processing are revolutionizing the way we live, work, and think. As for any science, they are based on assumptions about what the world is, and how humans interact with it. In this paper, I discuss what is potentially one of these assumptions: structuralism, which states that all cultures share a hidden structure. I illustrate this assumption with political footprints: a machine-learning technique using pre-trained word vectors for political discourse analysis. I introduce (...) some of the benefits and limitations of structuralism when applied to machine learning, and the risks of exploiting a technology before establishing the validity of all its hypotheses. I consider how machine-learning techniques could evolve towards hybrid structuralism or post-structuralism, and how deeply these developments would impact cultural studies. (shrink)

This paper introduces a hybrid model that unifies connectionist, symbolic, and reinforcement learning into an integrated architecture for bottom-up skill learning in reactive sequential decision tasks. The model is designed for an agent to learn continuously from on-going experience in the world, without the use of preconceived concepts and knowledge. Both procedural skills and high-level knowledge are acquired through an agent’s experience interacting with the world. Computational experiments with the model in two domains are reported.