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Federated learning (FL) has experienced a boom in recent years, which is jointly promoted by the prosperity of machine learning and Artificial Intelligence along with emerging privacy issues. In the FL paradigm, a central server and local end devices maintain the same model by exchanging model updates instead of raw data, with which the privacy of data stored on end devices is not directly revealed. In this way, the privacy violation caused by the growing collection of sensitive data can be mitigated. However, the performance of FL with a central server is reaching a bottleneck, while new threats are emerging simultaneously. There are various reasons, among which the most significant ones are centralized processing, data falsification, and lack of incentives. To accelerate the proliferation of FL, blockchain-enabled FL has attracted substantial attention from both academia and industry. A considerable number of novel solutions are devised to meet the emerging demands of diverse scenarios. Blockchain-enabled FL provides both theories and techniques to improve the performance of FL from various perspectives. In this survey, we will comprehensively summarize and evaluate existing variants of blockchain-enabled FL, identify the emerging challenges, and propose potentially promising research directions in this under-explored domain. [ABSTRACT FROM AUTHOR]

This paper describes a narrative-based interactive learning environment which aims to elucidate reasoning using interactive scenarios that may be used in training novices in decision-making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a narrative model that is guided partially by inference and contextual information contained in the particular knowledge representation used, the generic/actual argument model of structured reasoning. The approach is described with examples in the area of critical care nursing training. A study of the effectiveness of this approach on learning outcomes was conducted with final year nursing students and provides evidence of improved learning outcomes. (Contains 4 tables and 7 figures.)

Narrative and story telling has a long history of use in structuring, organising and communicating human experience. This paper describes a narrative based interactive intelligent learning environment which aims to elucidate practical reasoning using interactive emergent narratives that can be used in training novices in decision making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a narrative model that is guided partially by inference and contextual information contained in the particular knowledge representation used, the Generic/Actual argument model of structured reasoning. The approach is described with examples in the area of critical care nursing training and positive learning outcomes are reported. (Contains 3 tables and 7 figures.)

The Internet of Things (IoT)-enabled electrocardiogram (ECG) monitoring system is an evolution in healthcare, and it is used to monitor heart conditions during daily living conditions. However, the ECG signals from the wearable device are often contaminated with noise, such as power line interference, baseline wander, and muscle artefact. The presence of these noises increases the communication cost of IoT-enabled devices by transmitting unusable noisy signals as well as affecting the clinical decision-making at the application level. A real-time ECG noise detection system at the IoT-enabled gateway can reduce this communication cost and improve the overall quality of the transmitted signal. In this article, we propose a tunable ECG noise localization system to detect noisy ECG segments with a selected percentage of noise-free ECG levels. The proposed system will hold the following: 1) deployable ECG noise detector in the low-computational devices, such as smart phone; 2) reduce the data dropping rate at the network layer; 3) reduce the transmission cost; and 4) improve the reliability of the automated analysis of ECG at the IoT-enabled gateway. We evaluated the performance of the proposed ECG noise detector using publicly available and real-time ECG datasets from wearable ECG sensors. We also measured the data drop rate and compared it with the state-of-the-art algorithms. The impact of reduced data dropping rate also justifies using two commonly used R-peak detection algorithms. The proposed system reduces the data dropping rate by 21.09% on average over public and real-time datasets. Similarly, it increases the number of R-Peak detection by 15.33% on average compared with the existing binary noise classification system by saving partial noisy ECG segments. Thus, the proposed noise detection system improves the reliability of the automated ECG analysis. In summary, the study results support the necessity of a noise detection system for wearable ECG monitoring in daily living conditions to improve the data acquisition efficiency. In addition, a noise localizer can be used for dropping a noisy portion of the signal or filtering the only noisy segment of the ECG signal without blindly imposing a filter on both noise-free and noisy ECG signals. [ABSTRACT FROM AUTHOR]

This paper attempts to overcome the tendency of the expectation--maximization (EM) algorithm to locate a local rather than global maximum when applied to estimate the hidden Markov model (HMM) parameters in speech signal modeling. We propose a hybrid algorithm for estimation of the HMM in automatic speech recognition (ASR) using a constraint-based evolutionary algorithm (EA) and EM, the CEL-EM. The novelty of our hybrid algorithm (CEL-EM) is that it is applicable for estimation of the constraint-based models with many constraints and large numbers of parameters (which use EM) like HMM. Two constraint-based versions of the CEL-EM with different fusion strategies have been proposed using a constraint-based EA and the EM for better estimation of HMM in ASR. The first one uses a traditional constraint-handling mechanism of EA. The other version transforms a constrained optimization problem into an unconstrained problem using Lagrange multipliers. Fusion strategies for the CEL-EM use a staged-fusion approach where EM has been plugged with the EA periodically after the execution of EA for a specific period of time to maintain the global sampling capabilities of EA in the hybrid algorithm. A variable initialization approach (VIA) has been proposed using a variable segmentation to provide a better initialization for EA in the CEL-EM. Experimental results on the TIMIT speech corpus show that CEL-EM obtains higher recognition accuracies than the traditional EM algorithm as well as a top-standard EM (VIA-EM, constructed by applying the VIA to EM). Index Terms--Constraint-based evolutionary algorithm (EA), expectation maximization (EM), fusion strategies, hidden Markov model (HMM), hybrid algorithm, Lagrange multiplier (LM), signal modeling and classification, speech recognition.

Understanding an unknown facial emotion that emerges in the future underpins significant impacts in various domains. Knowing the fact that emotional states grow in vocabulary, new emotional states need to be adapted while the existing knowledge of known emotional states is preserved. While human beings spontaneously perform this task, the challenge is, how to devise a deep learning technique that can effectively recognize an unknown emotion category in the future. Although the deep convolutional neural network has shown excellent emotion recognition performances in the past, it is conventionally a predefined multi-way classifier showing little resilience towards adding a new emotion class. Considering the aforementioned challenge, in this paper, we propose a generic deep convolutional neural network-based architecture that constantly absorbs the upcoming emotion categories and recognizes them effectively. We further propose an indicator loss, which is associated with the distillation mechanism that preserves the existing knowledge. In order to demonstrate the feasibility of our proposed method, we evaluated our model using benchmark emotion datasets. The results confirm that the proposed approach is superior in recognizing unknown emotional states compared to continual learning benchmarks. Further, our proposed method demonstrates higher accuracy, compared to the transfer learning baselines. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2004.06.014 Byline: Adil M. Bagirov, John Yearwood Keywords: Nonsmooth optimization; Cluster analysis; Minimum sum-of-squares clustering Abstract: The minimum sum-of-squares clustering problem is formulated as a problem of nonsmooth, nonconvex optimization, and an algorithm for solving the former problem based on nonsmooth optimization techniques is developed. The issue of applying this algorithm to large data sets is discussed. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithm. Author Affiliation: Centre for Informatics and Applied Optimization, School of Information Technology and Mathematical Sciences, University of Ballarat, P.O. Box 663, Vic. 3353, Australia Article History: Received 25 November 2002; Accepted 17 June 2004

Multivariate monitoring of industrial or clinical procedures often involves more than three correlated quality characteristics and the status of the process is judged using a sample of size one. Majority of existing control charts for monitoring process variability for individual observations are capable of monitoring up to three characteristics. One of the hurdles in designing optimal control charts for large dimension data is the enormous computing resources and time that is required by simulation algorithm to estimate the charts parameters. This paper proposes a novel algorithm based on Parallelised Monte Carlo simulation to improve the ability of the Multivariate Exponentially Weighted Mean Squared Deviation and Multivariate Exponentially Weighted Moving Variance charts to monitor process variability for high dimensions in a computationally efficient way. Different techniques have been deployed to reduce computing space and execution time. The optimal control limits (L) to detect small, medium and large shifts in the covariance matrix of up to 15 characteristics are provided. Furthermore, utilising the large number of optimalLvalues generated by the algorithm enabled authors to develop exponential decay functions to predictLvalues. This eliminates the need for further execution of the parallelised Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

Abstract: In this study we investigated a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. [...]

Summary: With the development of internet technologies, social media and mobile devices, short texts have become an increasingly popular medium among users to communicate with friends, search information and review products. Measuring the similarity between short texts is a fundamental task due to its importance in many applications, such as text retrieval, topic discovery, and event detection. However, short texts generally comprise sparse, noisy, and ambiguous information. Hence, effectively measuring the distance between short texts is a challenging task. In this paper, we exploit the advantageous corpus‐wide word co‐occurrence information into document‐level feature enrichment to mitigate the challenges caused by the sparseness of short texts for distance measurement. We propose a novel context‐aware weighted Biterm method for short text Distance Measurement (BDM). In BDM, we extract biterms (ie, word pairs) from a short text corpus and exploit a biterm topic model to determine the global weights of biterms in the corpus. We then determine the local importance of a biterm in different contexts (ie, short texts) based on the corpus‐level biterm weight. The distance between two short texts is computed using the context‐aware weighted biterms. Experimental results on three real‐world datasets demonstrate better accuracy and effectiveness of the proposed BDM. [ABSTRACT FROM AUTHOR]

Clinical Study and automatic diagnosis of electrocardiogram (ECG) data always remain a challenge in diagnosing cardiovascular activities. The analysis of ECG data relies on various factors like morphological features, classification techniques, methods or models used to diagnose and its performance improvement. Another crucial factor in the methodology is how to train the model for each patient. Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy. This paper proposes an adaptive approach to identify performance improvement in building a training model that analyze global training methodology against an individual training methodology and identifying a gap between them. We provide our investigation and comparative study on these methods and model with standard classification techniques with basic morphological features and Heart Rate Variability (HRV) that may aid real time application. This approach helps in analyzing and evaluating the performance of different techniques and can suggests adoption of a best model identification with efficient technique and efficient attribute set for real-time systems. [ABSTRACT FROM AUTHOR]