Visuomotor adaptation to novel environments can occur via non-physical means, such as observation. Observation does not appear to activate the same implicit learning processes as physical practice, rather it appears to be more strategic in nature. However, there is evidence that interspersing observational practice with physical practice can benefit performance and memory consolidation either through the combined benefits of separate processes or through a change in processes activated during observation trials. To test these ideas, we asked people to practice aiming (...) to targets with visually rotated cursor feedback or engage in a combined practice schedule comprising physical practice and observation of projected videos showing successful aiming. Ninety-three participants were randomly assigned to one of five groups: massed physical practice, distributed physical practice, or one of 3 types of combined practice: alternating blocks, or all observation before or after blocked physical practice. Participants received 100 practice trials. All groups improved in adaptation trials and showed savings across the 24-h retention interval relative to initial practice. There was some forgetting for all groups, but the magnitudes were larger for physical practice groups. The Act and Obs_During groups were most accurate in retention and did not differ, suggesting that observation can serve as a replacement for physical practice if supplied intermittently and offers advantages above just resting. However, after-effects associated with combined practice were smaller than those for physical practice control groups, suggesting that beneficial learning effects as a result of observation were not due to activation of implicit learning processes. Reaction time, variable error, and post-test rotation drawings supported this conclusion that adaptation for observation groups was promoted by explicit/strategic processes. (shrink)

Adaptive training adjusts a training task with the goal of improving learning outcomes. Adaptive training has been shown to improve human performance in attention, working memory capacity, and motor control tasks. Additionally, correlations have been observed between neural EEG spectral features and the performance of some cognitive tasks. This relationship suggests some EEG features may be useful in adaptive training regimens. Here, we anticipated that adding a neural measure into a behavioral-based adaptive training system would improve (...) human performance on a subsequent transfer task. We designed, developed, and conducted a between-subjects study of 44 participants comparing three training regimens: Single Item Fixed Difficulty, Behaviorally Adaptive Training, and Combined Adaptive Training using both behavioral and EEG measures. Results showed a statistically significant transfer task performance advantage of the CAT-based system relative to SIFD and BAT systems of 6 and 9 percentage points, respectively. Our research shows a promising pathway for designing closed-loop BCI systems based on both users' behavioral performance and neural signals for augmenting human performance. (shrink)

It has been suggested that hierarchically structured symbols, a remarkable feature of human language, are produced via the operation of recursive combination. Recursive combination is frequently observed in human behavior, not only in language but also in action sequences, mind-reading, technology, et cetera.; in contrast, it is rarely observed in animals. Why is it that only humans use this operation? What is the adaptability of recursive combination? We aim (1) to identify the environmental feature(s) in which recursive (...) combination is effective for survival and reproduction, and that has facilitated the evolution of this ability, and (2) to demonstrate the possible evolutionary processes of recursive combination. To achieve this, we constructed an evolutionary simulation of agents that generated products using recursive combination and used the results to explore the types of fitness functions (that reflect the kinds of adaptive environments) that give rise to this ability. We identified two types of adaptability of the recursive combination: (1) diversifiability of production and (2) diversifiability of products. Through the former, recursive combination promotes robustness against failure of production caused by inaccurate manipulations or irreversible changes. In an environment in which diversified products are preferable, sharing a portion of the production process for these products entails producing multiple products in which recursive combination plays a key role. We suppose that recursive combination works as a driving force of material culture. Finally, we discuss the possible evolutionary scenarios of recursive combination that is later generalized to encompass many aspects of human cognition, including human language. (shrink)

Music's efficacy as a credible signal and/or as a tool for social bonding piggybacks on a diverse set of biological and cognitive processes, implying different proximate mechanisms. It is likely this multiplicity of mechanisms that explains why it is so difficult to account for music's putative biological role, as well as its possible origins, by proposing a single adaptive function.

Multi-Task Learning tries to improve the learning process of different tasks by solving them simultaneously. A popular Multi-Task Learning formulation for SVM is to combine common and task-specific parts. Other approaches rely on using a Graph Laplacian regularizer. Here we propose a combination of these two approaches that can be applied to L1, L2 and LS-SVMs. We also propose an algorithm to iteratively learn the graph adjacency matrix used in the Laplacian regularization. We test our proposal with synthetic and (...) real problems, both in regression and classification settings. When the task structure is present, we show that our model is able to detect it, which leads to better results, and we also show it to be competitive even when this structure is not present. (shrink)

A robust adaptive fuzzy nonlinear controller based on dynamic surface and integral sliding mode control strategy is proposed to realize trajectory tracking for a class of quadrotor UAVs. In this study, the composite factors including parametric uncertainties and external disturbances are added to controller design, which make it more realistic. The quadrotor model is divided into two subsystems of attitude and position that make the control design become feasible. The main contributions of the proposed ADSISMC strategy are as follows: (...) The combination of dynamic surface and integral sliding mode makes the system always in sliding stage by finding the appropriate initial position compared with the common sliding mode, and the complexity of explosion in backstepping method is eliminated. By introducing the fuzzy system, the unknown functions and uncertainties can be approximated which significantly improves the robustness and the tracking performance. The switching control strategy is utilized to compensate for the errors between estimated and ideal inputs; the tracking performance of the whole system has been significantly improved. The simulation results show the effectiveness of the proposed control method. (shrink)

Long before the origins of agriculture human ancestors had expanded across the globe into an immense variety of environments, from Australian deserts to Siberian tundra. Survival in these environments did not principally depend on genetic adaptations, but instead on evolved learning strategies that permitted the assembly of locally adaptive behavioral repertoires. To develop hypotheses about these learning strategies, we have modeled the evolution of learning strategies to assess what conditions and constraints favor which kinds of strategies. To build on (...) prior work, we focus on clarifying how spatial variability, temporal variability, and the number of cultural traits influence the evolution of four types of strategies: (1) individual learning, (2) unbiased social learning, (3) payoff-biased social learning, and (4) conformist transmission. Using a combination of analytic and simulation methods, we show that spatial—but not temporal—variation strongly favors the emergence of conformist transmission. This effect intensifies when migration rates are relatively high and individual learning is costly. We also show that increasing the number of cultural traits above two favors the evolution of conformist transmission, which suggests that the assumption of only two traits in many models has been conservative. We close by discussing how (1) spatial variability represents only one way of introducing the low-level, nonadaptive phenotypic trait variation that so favors conformist transmission, the other obvious way being learning errors, and (2) our findings apply to the evolution of conformist transmission in social interactions. Throughout we emphasize how our models generate empirical predictions suitable for laboratory testing. (shrink)

This article discusses the proof theory, semantics and meta-theory of a class of adaptive logics, called hierarchic adaptive logics. Their specific characteristics are illustrated throughout the article with the use of one exemplary logic HKx, an explicans for reasoning with prioritized belief bases. A generic proof theory for these systems is defined, together with a less complex proof theory for a subclass of them. Soundness and a restricted form of completeness are established with respect to a non-redundant semantics. (...) It is shown that all hierarchic adaptive logics are reflexive, have the strong reassurance property and that a subclass of them is a fixed point for a broad class of premise sets. Finally, they are compared to a different yet related class of adaptive logics. (shrink)

There is a growing need to implement anticipatory climate change adaptation measures, particularly in vulnerable sectors, such as in agriculture. However, setting goals to adapt is wrought with several challenges. This paper discusses two sets of challenges to goals of anticipatory adaptation, of empirical and normative character. The first set of challenges concern issues such as the extent to which the climate will change, the local impacts of such changes, and available adaptive responses. In the second set of uncertainties (...) are issues such as the distribution of burdens to enhance adaptive capacities in vulnerable agents with a legitimate claim to such resources, and what anticipatory adaptation ideally should result in. While previous discussions have been limited to either discuss the first or second set of uncertainties, this paper suggests that both dimensions should be considered when setting goals in social planning with long time frames. A taxonomy will be suggested that combines both dimensions. Furthermore, strategies for managing situations in which there are either empirical, or normative, uncertainties will be proposed which could be used in social decision-making with long planning time-frames in which goals must be set. (shrink)

Cultures of Uneven and Combined Development seeks to explore and develop Leon Trotsky's concept of uneven and combined development. In particular, it aims to adapt the political and historical analysis which originated in Trotsky's Russia for use within the contemporary field of world literature. As such, it draws together the work of scholars from both the field of international relations and the field of literature and the arts. This collection will therefore be of particular interest to anyone who is interested (...) in new ways of understanding world literary texts, or interested in new ways of applying Trotsky's revolutionary politics to the contemporary world order. (shrink)

This paper develops an adaptive fixed-time trajectory tracking controller of an underactuated hovercraft with a prescribed performance in the presence of model uncertainties and unknown time-varying environment disturbances. It is the first time that the proposed method is applied to the motion control of the hovercraft. To begin with, based on the hovercraft's four degrees of freedom model, the virtual control laws are designed using an error transforming function and the fixed-time stability theory to guarantee that the position tracking (...) errors are constrained within the prescribed convergence rates and minimum overshoot. In addition, by combining the Lyapunov direct method and the adaptive radial basis function neural network, the actual control laws are designed to ensure that the velocity tracking errors converge to a small region containing zero while handling model uncertainties and external disturbances effectively. Finally, all tracking errors of the closed-loop system are uniformly ultimately bounded and fixed-time convergent. Results from a comparative simulation study verify the effectiveness and advantage of the proposed method. (shrink)

This book presents adaptive logics as an intuitive and powerful framework for modeling defeasible reasoning. It examines various contexts in which defeasible reasoning is useful and offers a compact introduction into adaptive logics. The author first familiarizes readers with defeasible reasoning, the adaptive logics framework, combinations of adaptive logics, and a range of useful meta-theoretic properties. He then offers a systematic study of adaptive logics based on various applications. The book presents formal models for defeasible (...) reasoning stemming from different contexts, such as default reasoning, argumentation, and normative reasoning. It highlights various meta-theoretic advantages of adaptive logics over other logics or logical frameworks that model defeasible reasoning. In this way the book substantiates the status of adaptive logics as a generic formal framework for defeasible reasoning. (shrink)

The two authors of this paper have diametrically opposed views of the prevalence and strength of adaptation in nature. Hendry believes that adaptation can be seen almost everywhere and that evidence for it is overwhelming and ubiquitous. Gonzalez believes that adaptation is uncommon and that evidence for it is ambiguous at best. Neither author is certifiable to the knowledge of the other, leaving each to wonder where the other has his head buried. Extensive argument has revealed that each author thinks (...) his own view is amply supported by both theory and empirical evidence. Further reflection has revealed that the differences in opinion may start with the different disciplines in which we work: evolutionary ecology for Hendry and community ecology for Gonzalez. In the present paper, we each present devastating evidence supporting our own position and thus refuting that of the other. We then identify the critical differences that led to such opposing views. We close by combining our two perspectives into a common framework based on the adaptive landscape, and thereby suggest means by which to assess the prevalence and strength of adaptation. (shrink)

This paper introduces the concept of Adaptive Rooms, which are virtual environments able to dynamically adapt to users’ needs, including ‘physical’ and cognitive workflow requirements, number of users, differing cognitive abilities and skills. Adaptive rooms are collections of virtual objects, many of them self-transforming objects, housed in an architecturally active room with information spaces and tools. An ontology of objects used in adap- tive rooms is presented. Virtual entities are classified as passive, reactive, ac- tive, and information entities, (...) and their sub-categories. Only active objects can be self-transforming. Adaptive Rooms are meant to combine the insights of ubiquitous computing -- that computerization should be everywhere, transpar- ently incorporated -- with the insights of augmented reality -- that everyday ob- jects can be digitally enhanced to carry more information about their use. To display the special potential of adaptive rooms, concrete examples are given to show how the demands of cognitive workflow can be reduced. (shrink)

This paper introduces the concept of Adaptive Rooms, which are virtual environments able to dynamically adapt to users’ needs, including ‘physical’ and cognitive workflow requirements, number of users, differing cognitive abilities and skills. Adaptive rooms are collections of virtual objects, many of them self-transforming objects, housed in an architecturally active room with information spaces and tools. An ontology of objects used in adap- tive rooms is presented. Virtual entities are classified as passive, reactive, ac- tive, and information entities, (...) and their sub-categories. Only active objects can be self-transforming. Adaptive Rooms are meant to combine the insights of ubiquitous computing -- that computerization should be everywhere, transpar- ently incorporated -- with the insights of augmented reality -- that everyday ob- jects can be digitally enhanced to carry more information about their use. To display the special potential of adaptive rooms, concrete examples are given to show how the demands of cognitive workflow can be reduced. (shrink)

Work in online learning traditionally considered induction-friendly (e.g. stochastic with a fixed distribution) and induction-hostile (adversarial) settings separately. While algorithms like Exp3 that have been developed for the adversarial setting are applicable to the stochastic setting as well, the guarantees that can be obtained are usually worse than those that are available for algorithms that are specifically designed for stochastic settings. Only recently, there is an increasing interest in algorithms that give (near-)optimal guarantees with respect to the underlying setting, even (...) in case its nature is unknown to the learner. In this paper, we review various online learning algorithms that are able to adapt to the hardness of the underlying problem setting. While our focus lies on the application of adaptive algorithms as meta-inductive methods that combine given base methods, concerning theoretical properties we are also interested in guarantees that go beyond a comparison to the best fixed base learner. (shrink)

Man is a biosocial entity, so, in the study of his adaptive peculiarities two directions, that is, biologic and social, can be determined. Within the biological framework it is possible to combine evolutionary, genetic, medical-biological and ecological investigations. Recently, the problem of man’s adaptation to profound changes taking place in the environment, under the impact of man’s activity, becomes of growing importance. The second direction of the man adaptation research may be called social or socio-cultural. In the course of (...) social adaptation man acts as an adaptively-adapting entity, inasmuch as, unlike animals, he does not only adapt himself to the environment, but also transforms it in course of his activity, sometimes creating a new environment. The complex study of human being makes necessary a synthesis of natural-scientific and socialhumanitariansides of the man’s adaptation problem. The analysis of the above mentioned directions, of social and biological man’s adaptations research, enables us tocome to the conclusion that no one direction, taking separately, can resolve the problem of man’s adaptation, which has a complicated pattern with many aspects. The man’s adaptation problem emerged within the framework of biology, and during a long time it was of evolutionary-biological character. However, in the course of the development of science it has become an interdisciplinary issue. It may be conceived profoundly and with all aspects only by means of an interdisciplinary synthetic analysis. (shrink)

Transportation cyber-physical systems are constrained by spatiality and real-time because of their high level of heterogeneity. Therefore, applications like traffic control generally manage moving objects in a single-machine multithreaded manner, whereas suffering from frequent locking operations. To address this problem and improve the throughput of moving object databases, we propose a GPU-accelerated indexing method, based on a grid data structure, combined with quad-trees. We count object movements and decide whether a particular node should be split or be merged on the (...) GPU. In this case, bottlenecked nodes can be translated to quad-tree without interfering with the CPU. Hence, waiting time of other threads caused by locking operations raised by object data updating can be reduced. The method is simple while more adaptive to scenarios where the distribution of moving objects is skewed. It also avoids shortcomings of existing methods with performance bottleneck on the hot area or spending plenty of calculation resources on structure balancing. Experiments suggest that our method shows higher throughput and lower response time than the existing indexing methods. The advantage is even more significant under the skewed distribution of moving objects. (shrink)

This paper firstly designs a five-dimensional model of learners’ characteristics and a three-dimensional model of English reading resources’ characteristics in a fragmented learning environment through literature research. At the same time, to make the learning resources meet the characteristics of fragmented learning time and space, the English Level 4 reading resources are reasonably designed and segmented to adapt to the needs of learners’ mobile fragmented learning. Then, combined with machine learning algorithms, an adaptive recommendation model of learning resources in (...) English fragmented reading is constructed. The algorithm-based adaptive recommendation algorithm for English fragmented reading resources is designed. Based on the generated decision trees, the expression rules are parsed to achieve adaptive pushing of resources. The results of this study show that adaptive recommendation of learning resources in English fragmented reading can help teachers to develop future resource recommendation strategies through effective data collection to adaptively push resources that are close to learners’ individual needs. The use of mobile by English learners to learn to read in a fragmented learning context enables targeted training in weak areas of English reading, thus enhancing different aspects of learners’ reading skills. (shrink)

Earthquake is a kind of natural disaster, which will have a great impact on the building structure. In the vibration control field of building structures, the timeliness of system stability is extremely important. In traditional control methods, the timeliness is not paid enough attention for systems with uncertain seismic waves. For setting this problem, fuzzy adaptive compensation control for uncertain building structural systems by sliding-mode technology is proposed. It is combined with fuzzy adaptive control and sliding-mode control to (...) ensure that the system can be stable with satisfied timeliness. Also, saturation function is used to ensure the feasible physical implementation of the control system. Compared with the traditional LQR control, the simulation results showed that the proposed method can make the system reach a stable state with rapid convergence performance and has a feasible physical implementation. (shrink)

In this paper, we propose an adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis and prediction through the study and analysis of sports videos. The features with more discriminative power are selected from the set of positive and negative templates using a feature selection mechanism, and a sparse discriminative model is constructed by combining a confidence value metric strategy. The sparse generative model is constructed by combining L1 regularization and subspace representation, which retains sufficient representational (...) power while dealing with outliers. To overcome the shortcomings of the traditional multiplicative fusion mechanism, this paper proposes an adaptive selection mechanism based on Euclidean distance, which aims to detect deteriorating models in time during the dynamic tracking process and adopt corresponding strategies to construct more reasonable likelihood functions. Based on the Bayesian citation framework, the adaptive selection mechanism is used to combine the sparse discriminative model and the sparse generative model. Also, different online updating strategies are adopted for the template set and Principal Component Analysis subspace to alleviate the drift problem while ensuring that the algorithm can adapt to the changes of target appearance in the dynamic tracking environment. Through quantitative and qualitative evaluation of the experimental results, it is verified that the algorithm proposed in this paper has stronger robustness compared with other classical algorithms. Our proposed visual object tracking algorithm not only outperforms existing visual object tracking algorithms in terms of accuracy, success rate, accuracy, and robustness but also achieve the performance required for real-time tracking in terms of execution speed on the central processing unit. This paper provides an in-depth analysis and discussion of the adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis. Using a variety of county-level algorithms for analysis and multiple solutions to improve the accuracy of the results, we obtain a more efficient and accurate algorithm. (shrink)

Virtual reality provides an immersive 360-degree viewing experience and has been widely used in many areas. However, the transmission of panoramic video usually places a large demand on bandwidth; thus, it is difficult to ensure a reliable quality of experience under a limited bandwidth. In this paper, we propose a field-of-view prediction methodology based on limited FoV feedback that can fuse the heat map and FoV information to generate a user view. The former is obtained through saliency detection, while the (...) latter is extracted from some user perspectives randomly, and it contains the FoV information of all users. Then, we design a QoE-driven panoramic video streaming system with a client/server architecture, in which the server performs rate adaptation based on the bandwidth and the predicted FoV. We then formulate it as a nonlinear integer programming problem and propose an optimal algorithm that combines the Karush–Kuhn–Tucker conditions with the branch-and-bound method to solve this problem. Finally, we evaluate our system in a simulation environment, and the results show that the system performs better than the baseline. (shrink)

Inverse probability weighting (IPW) is a general tool in survey sampling and causal inference, used in both Horvitz–Thompson estimators, which normalize by the sample size, and Hájek/self-normalized estimators, which normalize by the sum of the inverse probability weights. In this work, we study a family of IPW estimators, first proposed by Trotter and Tukey in the context of Monte Carlo problems, that are normalized by an affine combination of the sample size and a sum of inverse weights. We show (...) how selecting an estimator from this family in a data-dependent way to minimize asymptotic variance leads to an iterative procedure that converges to an estimator with connections to regression control methods. We refer to such estimators as adaptively normalized estimators. For mean estimation in survey sampling, the adaptively normalized estimator has asymptotic variance that is never worse than the Horvitz–Thompson and Hájek estimators. Going further, we show that adaptive normalization can be used to propose improvements of the augmented IPW (AIPW) estimator, average treatment effect (ATE) estimators, and policy learning objectives. Appealingly, these proposals preserve both the asymptotic efficiency of AIPW and the regret bounds for policy learning with IPW objectives, and deliver consistent finite sample improvements in simulations for all three of mean estimation, ATE estimation, and policy learning. (shrink)

Most research on transmission biases in cultural evolution has treated different biases as distinct strategies. Here I present a model that combines both frequency dependent bias (including conformist bias) and payoff bias in a single decision-making calculus and show that such an integrated learning strategy may be superior to relying on either bias alone. Natural selection may operate on humans’ relative dependence on frequency and payoff information, but both are likely to contribute to the spread of variants with high payoffs. (...) Importantly, the magnitude of conformist bias affects the evolutionary dynamics, and I show that an intermediate level of conformity may be most adaptive and may spontaneously evolve as it resists the invasion of low-payoff variants yet enables the fixation of high-payoff variants in the population. (shrink)

An integrated hog futures price forecasting model based on whale optimization algorithm, LightGBM, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise is proposed to overcome the limitations of a single machine learning model with low prediction accuracy and insufficient model stability. The simulation process begins with a grey correlation analysis of the hog futures price index system in order to identify influencing factors; after that, the WOA-LightGBM model is developed, and the WOA algorithm is used to optimize the (...) LightGBM model parameters; and, finally, the residual sequence is decomposed and corrected by using the CEEMDAN method to build a combined WOA-LightGBM-CEEMDAN model. Furthermore, it is used for comparison experiments to check the validity of the model by using data from CSI 300 stock index futures. Based on all experimental results, the proposed combined model shows the highest prediction accuracy, surpassing the comparative model. The model proposed in this study is accurate enough to meet the forecasting accuracy requirements and provides an effective method for forecasting future prices. (shrink)

With the rapid expansion of Internet technology, this research aims to explore the teaching strategies of ceramic art for contemporary students. Based on deep learning, an automatic question answering system is established, new teaching strategies are analyzed, and the Internet is combined with the automatic QA system to help students solve problems encountered in the process of learning. Firstly, the related theories of DL and personalized learning are analyzed. Among DL-related theories, Back Propagation Neural Network, Convolutional Neural Network, Long Short-Term (...) Memory, and Gated Recurrent Unit are compared to implement a single model and a mixed model. Secondly, the collected student questions are selected and processed, and experimental parameters in different models are set for comparative experiments. Experiments reveal that the average accuracy and Mean Reciprocal Rank of traditional retrieval methods can only reach about 0.5. In the basic neural network, the average accuracy of LSTM and GRU structural models is about 0.81, which can achieve better results. Finally, the accuracy of the hybrid model can reach about 0.82, and the accuracy and MRR of the Bidirectional Gated Recurrent Unit Network-Attention model are 0.87 and 0.89, respectively, achieving the best results. The established DL model meets the requirements of the online automatic QA system, improves the teaching system, and helps students better understand and solve problems in the ceramic art courses. (shrink)

Currently much debate surrounds the significance of cross-cultural variation in mother-infant attachment. Is only one form of attachment “healthy,” or are different types of attachment adaptations to local socioecological conditions? Juvenile mortality rates have been promoted as important features of local environments that shape attachment, which in turn affects later reproductive strategies. To this we add fertility. Fertility changes the environment of a child by influencing the number of potential caregivers and competitors for care, and the cultural ethos regarding the (...) rights of children. Different combinations of fertility and mortality will likely give rise to different attachment forms, and only under one regime (low fertility and mortality) do we expect exclusivity in attachment. (shrink)

Flipped learning could improve the learning effectiveness of students. However, some studies have pointed out the limitations related to flipped classrooms because the content of the flipped course does not vary according to the needs of the students. On the other hand, adaptive teaching, which customizes the learning mode according to the individual needs of students, can make up for some of the shortcomings of flipped teaching. This study combines adaptive teaching with flipped teaching and applies it to (...) face-to-face classroom activities. The purpose of this research is to explore whether the implementation of flipping and adaptive learning in a computer programming course can improve the learning effectiveness of students. The experimental subjects of this study are the sophomore students in the Department of Information Management. The flipped classroom with adaptive instruction has been realized in the limited course time. This study uses questionnaires to collect pre- and post-test data on the “learning motivation” of students. The learning effectiveness was evaluated based on the students' previous programming course and the semester scores of this course. Research results show that the post-test “learning motivation” has improved overall compared with the pre-test, and the learning effect is significant. The results of this research not only prove the effectiveness of modern teaching theories in programming courses but also lay the foundation for future teaching design. (shrink)

Relational database management system is the most popular database system. It is important to maintain data security from information leakage and data corruption. RDBMS can be attacked by an outsider or an insider. It is difficult to detect an insider attack because its patterns are constantly changing and evolving. In this paper, we propose an adaptive database intrusion detection system that can be resistant to potential insider misuse using evolutionary reinforcement learning, which combines reinforcement learning and evolutionary learning. The (...) model consists of two neural networks, an evaluation network and an action network. The action network detects the intrusion, and the evaluation network provides feedback to the detection of the action network. Evolutionary learning is effective for dynamic patterns and atypical patterns, and reinforcement learning enables online learning. Experimental results show that the performance for detecting abnormal queries improves as the proposed model learns the intrusion adaptively using Transaction Processing performance Council-E scenario-based virtual query data. The proposed method achieves the highest performance at 94.86%, and we demonstrate the usefulness of the proposed method by performing 5-fold cross-validation. (shrink)

A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function neural network approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of (...) the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme. (shrink)

Whereas Brian Skyrms in his chapter views rationality and evolution as alternative ways to derive decision behaviour, indirect evolution allows us to combine the two approaches. By focussing on Skyrms’ examples it will be illustrated how optimal decisions for given rules of interaction can influence the future rules of interaction. Here evolution does not determine behaviour directly, but only indirectly via the rules. We, furthermore, report on an experiment, related to Skyrms’ examples, revealing effects of deliberation in the sense of (...) forward-looking considerations and of path dependence in the sense of adapting to past success. (shrink)

This essay provides a sketch of religion as a set of biologically and culturally evolved adaptations that enable human groups to function as adaptive units. Recent developments in evolutionary biology make such a group-level interpretation of religion more plausible than in the past. A brief survey of relevant concepts is followed by a relatively detailed interpretation of Calvinism as a religious system in which explicit behavioral prescriptions, beliefs about God and his relationship with people, and numerous social control mechanisms (...) combined to change the city of Geneva from a collection of warring factions to a unified population. (shrink)

Stroke is a severe health issue, and motor recovery after stroke remains an important challenge in the rehabilitation field. Neurofeedback, as part of a brain–computer interface, is a technique for modulating brain activity using on-line feedback that has proved to be useful in motor rehabilitation for the chronic stroke population in addition to traditional therapies. Nevertheless, its use and applications in the field still leave unresolved questions. The brain pathophysiological mechanisms after stroke remain partly unknown, and the possibilities for intervention (...) on these mechanisms to promote cerebral plasticity are limited in clinical practice. In NFB motor rehabilitation, the aim is to adapt the therapy to the patient’s clinical context using brain imaging, considering the time after stroke, the localization of brain lesions, and their clinical impact, while taking into account currently used biomarkers and technical limitations. These modern techniques also allow a better understanding of the physiopathology and neuroplasticity of the brain after stroke. We conducted a narrative literature review of studies using NFB for post-stroke motor rehabilitation. The main goal was to decompose all the elements that can be modified in NFB therapies, which can lead to their adaptation according to the patient’s context and according to the current technological limits. Adaptation and individualization of care could derive from this analysis to better meet the patients’ needs. We focused on and highlighted the various clinical and technological components considering the most recent experiments. The second goal was to propose general recommendations and enhance the limits and perspectives to improve our general knowledge in the field and allow clinical applications. We highlighted the multidisciplinary approach of this work by combining engineering abilities and medical experience. Engineering development is essential for the available technological tools and aims to increase neuroscience knowledge in the NFB topic. This technological development was born out of the real clinical need to provide complementary therapeutic solutions to a public health problem, considering the actual clinical context of the post-stroke patient and the practical limits resulting from it. (shrink)

Human diversity cannot be denied. In our everyday social interactions, we constantly experience the fact that each individual is a unique combination of characteristics with specific cultural norms, roles, personality, and mood. Efficient social interaction thus requires an adaptation of communication behaviors to each specific interlocutor that one encounters. This is especially true for non-verbal communication that is more unconscious and automatic than verbal communication. Consequently, non-verbal communication needs to be understood as a dynamic and adaptive process in (...) the theoretical modeling and study of social interactions. This perspective paper presents relevance, challenges, and future directions for the study of non-verbal adaptation in social interactions. It proposes that non-verbal adaptability is more pertinently studied as adaptation to interlocutor's inner characteristics than to interlocutor's behaviors per se, because behaviors are communication messages that individuals interpret in order to understand their interlocutors. The affiliation and control dimensions of the Interpersonal Circumplex Model are proposed as a framework to measure both the interlocutors' inner characteristics and the individuals' non-verbal responses. These measures can then be compared across different interactions to assess an actual change in behavior tailored to different interlocutors. These recommendations are proposed in the hope of generating more research on the topic of non-verbal adaptability. Indeed, after having gathered the evidence on average effects of non-verbal behaviors, the field can go further than a “one size fits all” approach, by investigating the predictors, moderators, and outcomes of non-verbal adaptation to the interlocutors' inner characteristics. (shrink)

This paper investigates the antidisturbance formation control problem for a class of cluster aerospace unmanned systems suffering from multisource high-dynamic uncertainties. Firstly, to estimate and compensate the uncertainties existing in CAUS coordinate dynamics, an adaptive antidisturbance formation control law, which is combined by a robust adaptive control law and the second order disturbance observer, has been designed. Secondly, aiming at the adverse influences caused by the nonlinear time-varying nonlinearities existing in the formation flight dynamics, the radial basis function (...) neural network is introduced. Furthermore, considering the rapidly varying characteristics of the aforementioned formation flight nonlinearities, a novel board RBFNN has been constructed and utilized to improve the approximation and compensation performance. In virtue of the fusing of the B-RBFNN and the second-order disturbance observer-based adaptive formation control law, the rapid response rate and the higher control accuracy of the formation control system can be achieved. As a result, a novel B-RBFNN-based intelligence adaptive antidisturbance formation control algorithm has been established for CAUS trajectory coordination and formation flight. Numerical simulation results are proposed to illustrate the effectiveness and advantages of the proposed B-RBFNN-based intelligent adaptive formation control method for the CAUS. (shrink)

Image restoration is a research hotspot in computer vision and computer graphics. It uses the effective information in the image to fill in the information of the designated damaged area. This has high application value in environmental design, film and television special effects production, old photo restoration, and removal of text or obstacles in images. In traditional sparse representation image restoration algorithms, the size of dictionary atoms is often fixed. When repairing the texture area, the dictionary atom will be too (...) large to cause blurring. When repairing a smooth area, the dictionary atom is too small to cause the extension of the area, which affects the image repair effect. In this paper, the structural sparsity of the block to be repaired is used to adjust the repair priority. By analyzing the structure information of the repair block located in different regions such as texture, edge, and smoothing, the size of the dictionary atom is adaptively determined. This paper proposes a color image restoration method that adaptively determines the size of dictionary atoms and discusses a model based on the partial differential equation restoration method. Through simulation experiments combined with subjective and objective standards, the repair results are evaluated and analyzed. The simulation results show that the algorithm can effectively overcome the shortcomings of blurred details and region extension in fixed dictionary restoration, and the restoration effect has been significantly improved. Compared with the results of several other classic algorithms, it shows the effectiveness of the algorithm in this paper. (shrink)

In the research of motion video, the existing target detection methods are susceptible to changes in the motion video scene and cannot accurately detect the motion state of the target. Moving target detection technology is an important branch of computer vision technology. Its function is to implement real-time monitoring, real-time video capture, and detection of objects in the target area and store information that users are interested in as an important basis for exercise. This article focuses on how to efficiently (...) perform motion detection on real-time video. By introducing the mathematical model of image processing, the traditional motion detection algorithm is improved and the improved motion detection algorithm is implemented in the system. This article combines the advantages of the widely used frame difference method, target detection algorithm, and background difference method and introduces the moving object detection method combining these two algorithms. When using Gaussian mixture model for modeling, improve the parts with differences, and keep the unmatched Gaussian distribution so that the modeling effect is similar to the actual background; the binary image is obtained through the difference between frames and the threshold, and the motion change domain is extracted through mathematical morphological filtering, and finally, the moving target is detected. The experiment proved the following: when there are more motion states, the recall rate is slightly better than that of the VIBE algorithm. It decreased about 0.05 or so, but the relative accuracy rate increased by about 0.12, and the increase ratio is significantly higher than the decrease ratio. Departments need to adopt effective target extraction methods. In order to improve the accuracy of moving target detection, this paper studies the method of background model establishment and target extraction and proposes its own improvement. (shrink)

A novel data-driven model-free adaptive control approach is first proposed by combining the advantages of model-free adaptive control and data-driven optimal iterative learning control, and then its stability and convergence analysis is given to prove algorithm stability and asymptotical convergence of tracking error. Besides, the parameters of presented approach are adaptively adjusted with fuzzy logic to determine the occupied proportions of MFAC and DDOILC according to their different control performances in different control stages. Lastly, the proposed fuzzy DDMFAC (...) approach is applied to the control of particle quality in drug development phase of spray fluidized-bed granulation process, and its control effect is compared with MFAC and DDOILC and their fuzzy forms, in which the parameters of MFAC and DDOILC are adaptively adjusted with fuzzy logic. The effectiveness of the presented FDDMFAC approach is verified by a series of simulations. (shrink)

All species continuously evolve to adapt to changing environments. The genetic variation that fosters such adaptation is caused by a plethora of mechanisms, including meiotic recombination that generates novel allelic combinations in the progeny of two parental lineages. However, a considerable number of eukaryotic species, including many fungi, do not have an apparent sexual cycle and are consequently thought to be limited in their evolutionary potential. As such organisms are expected to have reduced capability to eliminate deleterious mutations, they are (...) often considered as evolutionary dead ends. However, inspired by recent reports we argue that such organisms can be as persistent as organisms with conventional sexual cycles through the use of other mechanisms, such as genomic rearrangements, to foster adaptation. (shrink)

The system B+ is the minimal positive relevant logic. B+ is trivially extended to B+T on adding a greatest truth (Church constant) T. If we leave ∨ out of the formation apparatus, we get the fragment B∧T. It is known that the set of ALL B∧T theories provides a good model for the combinators CL at Level-I, which is the theory level. Restoring ∨ to get back B+T was not previously fruitful at Level-I, because the set of all B+T theories (...) is NOT a model of CL. It was to be expected from semantic completeness arguments for relevant logics that basic combinator laws would hold when restricted to PRIME B+T theories. Overcoming some previous difficulties, we show that this is the case, at Level I. But this does not form a model for CL. This paper also looks for corresponding results at Level-II, where we deal with sets of theories that we call propositions. We adapt work by Ghilezan to note that at Level-II also there is a model of CL in B∧T propositions. However, the corresponding result for B+T propositions extends smoothly to Level-II only in part. Specifically, only some of the basic combinator laws are proved here. We accordingly leave some work for the reader. (shrink)

This article is a case study that describes the natural and human history of the gaze heuristic. The gaze heuristic is an interception heuristic that utilizes a single input repeatedly as a task is performed. Its architecture, advantages, and limitations are described in detail. A history of the gaze heuristic is then presented. In natural history, the gaze heuristic is the only known technique used by predators to intercept prey. In human history the gaze heuristic was discovered accidentally by Royal (...) Air Force fighter command just prior to World War II. As it was never discovered by the Luftwaffe, the technique conferred a decisive advantage upon the RAF throughout the war. After the end of the war in America, German technology was combined with the British heuristic to create the Sidewinder AIM9 missile, the most successful autonomous weapon ever built. There are no plans to withdraw it or replace its guiding gaze heuristic. The case study demonstrates that the gaze heuristic is a specific heuristic type that takes a single best input at the best time. Its use is an adaptively rational response to specific, rapidly evolving decision environments that has allowed those animals/humans/machines who use it to survive, prosper, and multiply relative to those who do not. (shrink)

While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that can be used outside-of-the-lab by end-users, e.g., severely motor-impaired ones. Therefore, we propose and evaluate the design of a multi-class Mental Task (MT)-based BCI for longitudinal training (20 sessions over 3 months) of a tetraplegic user for the CYBATHLON BCI series 2019. In (...) this BCI championship, tetraplegic pilots are mentally driving a virtual car in a racing video game. We aimed at combining a progressive user MT-BCI training with a newly designed machine learning pipeline based on adaptive Riemannian classifiers shown to be promising for real-life applications. We followed a two step training process: the first 11 sessions served to train the user to control a 2-class MT-BCI by performing either two cognitive tasks (REST and MENTAL SUBTRACTION) or two motor-imagery tasks (LEFT-HAND and RIGHT-HAND). The second training step (9 remaining sessions) applied an adaptive, session-independent Riemannian classifier that combined all 4 MT classes used before. Moreover, as our Riemannian classifier was incrementally updated in an unsupervised way it would capture both within and between-session non-stationarity. Experimental evidences confirm the effectiveness of this approach. Namely, the classification accuracy improved by about 30% at the end of the training compared to initial sessions. We also studied the neural correlates of this performance improvement. Using a newly proposed BCI user learning metric, we could show our user learned to improve his BCI control by producing EEG signals matching increasingly more the BCI classifier training data distribution, rather than by improving his EEG class discrimination. However, the resulting improvement was effective only on synchronous (cue-based) BCI and it did not translate into improved CYBATHLON BCI game performances. For the sake of overcoming this in the future, we unveil possible reasons for these limited gaming performances and identify a number of promising future research directions. Importantly, we also report on the evolution of the user's neurophysiological patterns and user experience throughout the BCI training and competition. (shrink)

In a series of important papers published roughly twenty years ago, Professor Robert Cooter developed a comprehensive economic theory of moral norms. He explained the value of those norms, described the process by which norms are adopted, and offered a set of predictions regarding the circumstances under which an individual will choose to adopt a particular moral norm. This brief Article applies behavioral law and economics and hedonic psychology to expand upon Professor Cooter’s path-breaking theory. In particular, understanding welfare in (...) hedonic terms — rather than preference-satisfaction terms — suggests a multitude of further situations in which individuals will justifiably seek to internalize moral norms. The hedonic approach to welfare then further suggests an enhanced role for the government to play in encouraging the adoption of welfare-enhancing norms. Cooter’s theory, combined with modern understandings of welfare and human behavior, thus offers powerful predictive and prescriptive possibilities. (shrink)

A flourishing literature assesses how sustainable business models create and capture value in socio-ecological systems. Nevertheless, we still know relatively little about how the organization of sustainable business models—of which cross-sector partnerships represent a core and distinctive mechanism—can support socio-ecological resilience. We address this knowledge gap by taking a complex adaptive systems (CAS) perspective. We develop a framework that identifies the key strategic, institutional, and learning elements of partnerships that sustainable business models rely on to support socio-ecological resilience. With (...) our analytical framework, we underpin the importance of assessing sustainable business initiatives in terms of their impact on resilience at the level of socio-ecological systems, not just of organizations. Therefore, we reveal how cross-sector partnerships provide the organizational support for sustainable business models to support socio-ecological resilience. By combining the key features of CAS and the key elements of partnerships, we provide insight into the formidable task of designing cross-sector partnerships so that they support socio-ecological resilience and avoid unintended consequences. (shrink)