eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss)

The notion that learning can be enhanced when a teaching approach matches a learner’s learning style has been widely accepted in classroom settings since the latter represents a predictor of student’s attitude and preferences. As such, the traditional approach of ‘one-size-fits-all’ as may be applied to teaching delivery in Educational Hypermedia Systems (EHSs) has to be changed with an approach that responds to users’ needs by exploiting their individual differences. However, establishing and implementing reliable approaches for matching the teaching delivery and modalities to learning styles still represents an innovation challenge which has to be tackled. In this paper, seventy six studies are objectively analysed for several goals. In order to reveal the value of integrating learning styles in EHSs, different perspectives in this context are discussed. Identifying the most effective learning style models as incorporated within AEHSs. Investigating the effectiveness of different approaches for modelling students’ individual learning traits is another goal of this study. Thus, the paper highlights a number of theoretical and technical issues of LS-BAEHSs to serve as a comprehensive guidance for researchers who interest in this area.

Adaptive routing in active networks

New conceptual ideas on network architectures have been proposed in the recent past. Current store-andforward routers are replaced by active intermediate systems, which are able to perform computations on transient packets, in a way that results very helpful for developing and deploying new protocols in a short time. This paper introduces a new routing algorithm, based on a congestion metric, and inspired by the behavior of ants in nature. The use of the Active Networks paradigm associated with a cooperative learning environment produces a robust, decentralized algorithm capable of adapting quickly to changing conditions.

Conceptualisation of an application of adaptive synthetic socioeconomic agents for intelligent network control

The deployment of Quality of Service (QoS) techniques involves careful analysis of area including: those business requirements; corporate strategy; and technical implementation process, which can lead to conflict or contradiction between those goals of various user groups involved in that policy definition. In addition long-term change management provides a challenge as these implementations typically require a high-skill set and experience level, which expose organisations to effects such as “hyperthymestria” [1] and “The Seven Sins of Memory”, defined by Schacter and discussed further within this paper. It is proposed that, given the information embedded within the packets of IP traffic, an opportunity exists to augment the traffic management with a machine-learning agent-based mechanism. This paper describes the process by which current policies are defined and that research required to support the development of an application which enables adaptive intelligent Quality of Service controls to augment or replace those policy-based mechanisms currently in use.

Self-organising neural networks for adaptive control

Self-organizing neural networks have been implemented in a wide range of application areas such as speech processing, image processing, optimization and robotics. Recent variations to the basic model proposed by the authors enable it to order state space using a subset of the input vector and to apply a local adaptation procedure that does not rely on a predefined test duration limit. Both these variations have been incorporated into a new feature map architecture that forms an integral part of an Hybrid Learning System (HLS) based on a genetic-based classifier system. Problems are represented within HLS as objects characterized by environmental features. Objects controlled by the system have preset targets set against a subset of their features. The system's objective is to achieve these targets by evolving a behavioural repertoire that efficiently explores and exploits the problem environment. Feature maps encode two types of knowledge within HLS — long-term memory traces of useful regularities within the environment and the classifier performance data calibrated against an object's feature states and targets. Self-organization of these networks constitutes non-genetic-based (experience-driven) learning within HLS. This paper presents a description of the HLS architecture and an analysis of the modified feature map implementing associative memory. Initial results are presented that demonstrate the behaviour of the system on a simple control task.

Adaptive alarm processor for fault diagnosis on power transmission networks

The authors describe a learning classifier system (LCS) which employs genetic algorithms (GA) for adaptive online diagnosis of power transmission network faults. The system monitors switchgear indications produced by a transmission network, reporting fault diagnoses on any patterns indicative of faulted components. The system evaluates the accuracy of diagnoses via a fault simulator developed by National Grid Co. and adapts to reflect the current network topology by use of genetic algorithms.

Adaptive modelling with tunable RBF network using multi-innovation RLS algorithm assisted by swarm intelligence

In this paper, we propose a new on-line learning algorithm for the non-linear system identification: the swarm intelligence aided multi-innovation recursive least squares (SI-MRLS) algorithm. The SI-MRLS algorithm applies the particle swarm optimization (PSO) to construct a flexible radial basis function (RBF) model so that both the model structure and output weights can be adapted. By replacing an insignificant RBF node with a new one based on the increment of error variance criterion at every iteration, the model remains at a limited size. The multi-innovation RLS algorithm is used to update the RBF output weights which are known to have better accuracy than the classic RLS. The proposed method can produces a parsimonious model with good performance. Simulation result are also shown to verify the SI-MRLS algorithm.

Adaptive recursive control of a soil testing load frame

Recursive Learning Control (RLC) has the potential to significantly reduce the tracking error in many repetitive trajectory applications. This paper presents an application of RLC to a soil testing load frame where non-adaptive techniques struggle with the highly nonlinear nature of soil. The main purpose of the controller is to apply a sinusoidal force reference trajectory on a soil sample with a high degree of accuracy and repeatability. The controller uses a feedforward control structure, recursive least squares adaptation algorithm and RLC to compensate for periodic errors. Tracking error is reduced and stability is maintained across various soil sample responses.

Adaptive behavior by single-product and multiproduct price setting firms in experimental markets

We focus on the learning dynamics in multiproduct price-setting markets, where firms use past strategies and performance to adapt to the corresponding equilibrium.

Differential brain activity during emotional versus nonemotional reversal learning

The ability to change an established stimulus–behavior association based on feedback is critical for adaptive social behaviors. This ability has been examined in reversal learning tasks, where participants first learn a stimulus–response association (e.g., select a particular object to get a reward) and then need to alter their response when reinforcement contingencies change. Although substantial evidence demonstrates that the OFC is a critical region for reversal learning, previous studies have not distinguished reversal learning for emotional associations from neutral associations. The current study examined whether OFC plays similar roles in emotional versus neutral reversal learning. The OFC showed greater activity during reversals of stimulus–outcome associations for negative outcomes than for neutral outcomes. Similar OFC activity was also observed during reversals involving positive outcomes. Furthermore, OFC activity is more inversely correlated with amygdala activity during negative reversals than during neutral reversals. Overall, our results indicate that the OFC is more activated by emotional than neutral reversal learning and that OFC's interactions with the amygdala are greater for negative than neutral reversal learning.

The impacts of learning styles, learning patterns and demographic factors on learners’ performance in a blended learning environment

Many studies have widely accepted the assumption that learning processes can be promoted when teaching styles and learning styles are well matched. In this study, the synergy between learning styles, learning patterns, and gender as a selected demographic feature and learners’ performance were quantitatively investigated in a blended learning setting. This environment adopts a traditional teaching approach of ‘one-sizefits-all’ without considering individual user’s preferences and attitudes. Hence, evidence can be provided about the value of taking such factors into account in Adaptive Educational Hypermedia Systems (AEHSs). Felder and Soloman’s Index of Learning Styles (ILS) was used to identify the learning styles of 59 undergraduate students at the University of Babylon. Five hypotheses were investigated in the experiment. Our findings show that there is no statistical significance in some of the assessed factors. However, processing dimension, the total number of hits on course website and gender indicated a statistical significance on learners’ performance. This finding needs more investigation in order to identify the effective factors on students’ achievement to be considered in Adaptive Educational Hypermedia Systems (AEHSs).

Adaptive communication: languages with more non-native speakers tend to have fewer word forms

Explaining the diversity of languages across the world is one of the central aims of typological, historical, and evolutionary linguistics. We consider the effect of language contact-the number of non-native speakers a language has-on the way languages change and evolve. By analysing hundreds of languages within and across language families, regions, and text types, we show that languages with greater levels of contact typically employ fewer word forms to encode the same information content (a property we refer to as lexical diversity). Based on three types of statistical analyses, we demonstrate that this variance can in part be explained by the impact of non-native speakers on information encoding strategies. Finally, we argue that languages are information encoding systems shaped by the varying needs of their speakers. Language evolution and change should be modeled as the co-evolution of multiple intertwined adaptive systems: On one hand, the structure of human societies and human learning capabilities, and on the other, the structure of language.

Adaptive governance: livelihood innovation for climate resilience in Uganda

Adaptive governance is the use of novel approaches within policy to support experimentation and learning. Social learning reflects the engagement of interdependent stakeholders within this learning. Much attention has focused on these concepts as a solution for resilience in governing institutions in an uncertain climate; resilience representing the ability of a system to absorb shock and to retain its function and form through reorganisation. However, there are still many questions to how these concepts enable resilience, particularly in vulnerable, developing contexts. A case study from Uganda presents how these concepts promote resilient livelihood outcomes among rural subsistence farmers within a decentralised governing framework. This approach has the potential to highlight the dynamics and characteristics of a governance system which may manage change. The paper draws from the enabling characteristics of adaptive governance, including lower scale dynamics of bonding and bridging ties and strong leadership. Central to these processes were learning platforms promoting knowledge transfer leading to improved self-efficacy, innovation and livelihood skills. However even though aspects of adaptive governance were identified as contributing to resilience in livelihoods, some barriers were identified. Reflexivity and multi-stakeholder collaboration were evident in governing institutions; however, limited self-organisation and vertical communication demonstrated few opportunities for shifts in governance, which was severely challenged by inequity, politicisation and elite capture. The paper concludes by outlining implications for climate adaptation policy through promoting the importance of mainstreaming adaptation alongside existing policy trajectories; highlighting the significance of collaborative spaces for stakeholders and the tackling of inequality and corruption.

Cross-sector surveys assessing perceptions of key stakeholders towards barriers, concerns and facilitators to the appropriate use of adaptive designs in confirmatory trials

Background Appropriately conducted adaptive designs (ADs) offer many potential advantages over conventional trials. They make better use of accruing data, potentially saving time, trial participants, and limited resources compared to conventional, fixed sample size designs. However, one can argue that ADs are not implemented as often as they should be, particularly in publicly funded confirmatory trials. This study explored barriers, concerns, and potential facilitators to the appropriate use of ADs in confirmatory trials among key stakeholders. Methods We conducted three cross-sectional, online parallel surveys between November 2014 and January 2015. The surveys were based upon findings drawn from in-depth interviews of key research stakeholders, predominantly in the UK, and targeted Clinical Trials Units (CTUs), public funders, and private sector organisations. Response rates were as follows: 30(55 %) UK CTUs, 17(68 %) private sector, and 86(41 %) public funders. A Rating Scale Model was used to rank barriers and concerns in order of perceived importance for prioritisation. Results Top-ranked barriers included the lack of bridge funding accessible to UK CTUs to support the design of ADs, limited practical implementation knowledge, preference for traditional mainstream designs, difficulties in marketing ADs to key stakeholders, time constraints to support ADs relative to competing priorities, lack of applied training, and insufficient access to case studies of undertaken ADs to facilitate practical learning and successful implementation. Associated practical complexities and inadequate data management infrastructure to support ADs were reported as more pronounced in the private sector. For funders of public research, the inadequate description of the rationale, scope, and decision-making criteria to guide the planned AD in grant proposals by researchers were all viewed as major obstacles. Conclusions There are still persistent and important perceptions of individual and organisational obstacles hampering the use of ADs in confirmatory trials research. Stakeholder perceptions about barriers are largely consistent across sectors, with a few exceptions that reflect differences in organisations’ funding structures, experiences and characterisation of study interventions. Most barriers appear connected to a lack of practical implementation knowledge and applied training, and limited access to case studies to facilitate practical learning. Keywords: Adaptive designs; flexible designs; barriers; surveys; confirmatory trials; Phase 3; clinical trials; early stopping; interim analyses

A fast adaptive tunable RBF network for nonstationary systems

This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.