Reviewerly/exHarmony huggingface dataset:
Learning in the presence of concept recurrence in data stream clustering

Abstract In the case of real-world data streams, the underlying data distribution will not be static; it is subject to variation over time, which is known as the primary reason for concept drift. Concept drift poses severe problems to the accuracy of a model in online learning scenarios. The recurring concept is a particular case of concept drift where the concepts already seen in the past reappear as the stream evolves. This problem is not yet studied in the context of stream clustering. This paper proposes a novel algorithm for identifying the recurring concepts in data stream clustering. During concept recurrence, the most matching model is retrieved from the repository and reused. The algorithm has minimum memory requirements and works online with the stream. Some of the concepts and definitions, already familiar in concept recurrence studies of stream classification have been redefined for clustering. The experiments conducted on real and synthetic data streams reveal that the proposed algorithm has the potential to identify recurring concepts.
BAT—Block Analytics Tool Integrated with Blockchain Based IoT Platform

The Internet of Things (IoT) is the novel paradigm of connectivity and the driving force behind state-of-the-art applications and services. However, the exponential growth of the number of IoT devices and services, their distributed nature, and scarcity of resources has increased the number of security and privacy concerns ranging from the risks of unauthorized data alterations to the potential discrimination enabled by data analytics over sensitive information. Thus, a blockchain based IoT-platform is introduced to address these issues. Built upon the tamper-proof architecture, the proposed access management mechanisms ensure the authenticity and integrity of data. Moreover, a novel approach called Block Analytics Tool (BAT), integrated with the platform is proposed to analyze and make predictions on data stored on the blockchain. BAT enables the data-analysis applications to be developed using the data stored in the platform in an optimized manner acting as an interface to off-chain processing. A pharmaceutical supply chain is used as the use case scenario to show the functionality of the proposed platform. Furthermore, a model to forecast the demand of the pharmaceutical drugs is investigated using a real-world data set to demonstrate the functionality of BAT. Finally, the performance of BAT integrated with the platform is evaluated.
Smartphone Naïve Bayes Human Activity Recognition Using Personalized Datasets

Recognizing human activity in real time with a limited dataset is possible on a resource-constrained device. However, most classification algorithms such as Support Vector Machines, C4.5, and K Nearest Neighbor require a large dataset to accurately predict human activities. In this paper, we present a novel real-time human activity recognition model based on Gaussian Naïve Bayes (GNB) algorithm using a personalized JavaScript object notation dataset extracted from the publicly available Physical Activity Monitoring for Aging People dataset and University of Southern California Human Activity dataset. With the proposed method, the personalized JSON training dataset is extracted and compressed into a 12×8 multi-dimensional array of the time-domain features extracted using a signal magnitude vector and tilt angles from tri-axial accelerometer sensor data. The algorithm is implemented on the Android platform using the Cordova cross-platform framework with HTML5 and JavaScript. Leave-one-activity-out cross validation is implemented as a testTrainer() function, the results of which are presented using a confusion matrix. The testTrainer() function leaves category K as the testing subset and the remaining K-1 as the training dataset to validate the proposed GNB algorithm. The proposed model is inexpensive in terms of memory and computational power owing to the use of a compressed small training dataset. Each K category was repeated five times and the algorithm consistently produced the same result for each test. The result of the simulation using the tilted angle features shows overall precision, recall, F-measure, and accuracy rates of 90%, 99.6%, 94.18%, and 89.51% respectively, in comparison to rates of 36.9%, 75%, 42%, and 36.9% when the signal magnitude vector features were used. The results of the simulations confirmed and proved that when using the tilt angle dataset, the GNB algorithm is superior to Support Vector Machines, C4.5, and K Nearest Neighbor algorithms.
Implementing Open Science policies into library processes – case study of the University of Eastern Finland library

This is a case study about the creation of open science services in the University of Eastern Finland. The library has overseen the open science services that have been actively implemented from 2010 onwards due to the development of the digitalisation of science and open science policies. A survey was conducted to determine how the UEF’s academic faculty use the services provided as well as their attitudes towards opening their own research findings in this manner. The researchers seem to be most interested in issues that influence their daily work, i.e. data management plans and opening their publications. It seems that the culture of openness is still at the development stage within UEF. The innovators, i.e. active research groups and researchers, are already practicing and encouraging openness, but the majority of the academic staff seems to be either unaware of open science or unwilling to implement it, due to the fact that incentives and career advancements still support the traditional way of conducting research.
On finite mixture modeling and model-based clustering of directed weighted multilayer networks

A novel approach relying on the notion of mixture models is proposed for modeling and clustering directed weighted networks. The developed methodology can be used in a variety of settings including multilayer networks. Computational issues associated with the developed procedure are effectively addressed by the use of MCMC techniques. The utility of the methodology is illustrated on a set of experiments as well as applications to real-life data containing export trade amounts for European countries.
Offline Arabic Handwriting Recognition Using Deep Learning: Comparative Study

By virtue of advances in machine learning, handwriting recognition is considered as one of the main research topics in this field. Many studies have been proposed to improve this recognition of handwritten texts for different languages such as Latin and Chinese. Yet, the processing of Arabic texts remains a particularly distinctive problem due to the complicated nature of the Arabic script compared to other scripts. In this work, we display a study and an evaluation of relevant articles recently published in conferences and indexed journals. The core of the problem is to relatively find out an efficient method capable of recognizing the handwritten text by any user via digital devices. In this article, we study the various works interested in the recognition of handwritten Arabic script implemented by deep learning. We thouroughly discuss different classification approaches like CNN, RNN and DBN. The pros and cons of each approach will be presented, as well as their different results.
Asynchronous Stochastic Approximations With Asymptotically Biased Errors and Deep Multiagent Learning

Asynchronous stochastic approximations (SAs) are an important class of model-free algorithms, tools, and techniques that are popular in multiagent and distributed control scenarios. To counter Bellman's curse of dimensionality, such algorithms are coupled with function approximations. Although the learning/control problem becomes more tractable, function approximations affect stability and convergence. In this article, we present verifiable sufficient conditions for stability and convergence of asynchronous SAs with biased approximation errors. The theory developed herein is used to analyze policy gradient methods and noisy value iteration schemes. Specifically, we analyze the asynchronous approximate counterparts of the policy gradient (A2PG) and value iteration (A2VI) schemes. It is shown that the stability of these algorithms is unaffected by biased approximation errors, provided that they are asymptotically bounded. With respect to convergence (of A2VI and A2PG), a relationship between the limiting set and the approximation errors is established. Finally, experimental results that support the theory are presented.
Aggregation of Order-2 Fuzzy Sets

In this article, we are concerned with a problem of aggregation of order-2 information granules, and fuzzy sets, in particular. When processing order-1 fuzzy sets, the structural information about the space over which fuzzy sets are defined is not taken into account at all. In contrast, the aggregation of order-2 fuzzy sets requires a careful attention that needs to be paid both to the closeness determined in the space of membership degrees and the collection of information granules over which such fuzzy sets are defined. We formulate an original optimization problem that simultaneously involves considerations of distances in the membership space (space of membership grades) and some measure of resemblance formed in the space of relationships of reference information granules. The gradient-based learning scheme is constructed. Some illustrative examples are included.
Algorithm to quantify nuclear features and confidence intervals for classification of oral neoplasia from high-resolution optical images

Purpose:In vivo optical imaging technologies like high-resolution microendoscopy (HRME) can image nuclei of the oral epithelium. In principle, automated algorithms can then calculate nuclear features to distinguish neoplastic from benign tissue. However, images frequently contain regions without visible nuclei, due to biological and technical factors, decreasing the data available to and accuracy of image analysis algorithms. Approach: We developed the nuclear density-confidence interval (ND-CI) algorithm to determine if an HRME image contains sufficient nuclei for classification, or if a better image is required. The algorithm uses a convolutional neural network to exclude image regions without visible nuclei. Then the remaining regions are used to estimate a confidence interval (CI) for the number of abnormal nuclei per mm2, a feature used by a previously developed algorithm (called the ND algorithm), to classify images as benign or neoplastic. The range of the CI determines whether the ND-CI algorithm can classify an image with confidence, and if so, the predicted category. The ND and ND-CI algorithm were compared by calculating their positive predictive value (PPV) and negative predictive value (NPV) on 82 oral biopsies with histopathologically confirmed diagnoses. Results: After excluding the images that could not be classified with confidence, the ND-CI algorithm had higher PPV (65% versus 59%) and NPV (78% versus 75%) than the ND algorithm. Conclusions: The ND-CI algorithm could improve the real-time classification of HRME images of the oral epithelium by informing the user if an improved image is required for diagnosis.
An Explainable Recommendation Method Based on Multi-timeslice Graph Embedding

Deep neural networks (DNN) can be used to model users' behavior sequences and predict their interest based on the historical behavior. However, current DNN-based recommendation methods lack explainability, making them difficult to guarantee the credibility of the recommendation results. In this paper, a Multi-Timeslice Graph Embedding (MTGE) model is proposed. First, it can effectively obtain the embedded representations of user behavior (or items) on a single timeslice. Second, the dynamic evolution of user preferences can be analyzed through integrating the embedded representations on multi-timeslices. Then, an explainable recommendation algorithm based on MTGE is proposed, which can effectively improve the accuracy of recommendation and support the model-level explainability. The feasibility and effectiveness of the key technologies proposed in the paper are verified through experiments.
Fit to Run: Personalised Recommendations for Marathon Training

Training for the marathon is a complex problem. In order to run an optimal time, runners must find the right workload for their current abilities and identify the correct balance between the hard work and rest throughout their training programmes. We propose a recommender system that will help guide runners through the weeks leading up to the marathon. Using a large sample of marathon training data (8730 runners), we generate user profiles that capture both a runner’s current fitness and training levels, and leverage this information to generate tailored recommendations for future weeks of training. We investigate patterns of successful runners to determine how best to schedule recommendations and training to allow for improvement in fitness levels alongside adequate rest.
A Novel Post-Processing Method Based on a Weighted Composite Filter for Enhancing Semantic Segmentation Results

Image semantic segmentation is one of the key problems in computer vision. Despite the enormous advances in applications, almost all the image semantic segmentation algorithms fail to achieve satisfactory segmentation results due to lack of sensitivity to details, or difficulty in evaluating the global similarity of pixels, or both. Posting-processing enhancement methods, as the outstandingly crucial means to ameliorate the above-mentioned inherent flaws of algorithms, are almost based on conditional random fields (CRFs). Inspired by CRFs, this paper proposes a novel post-processing enhancement framework with theoretical simplicity from the perspective of filtering, and a new weighted composite filter (WCF) is designed to enhance the segmentation masks in a unified framework. First, by adjusting the weight ratio, the WCF is decomposed into a local part and a global part. Secondly, a guided image filter is designed as the local filter, which can restore boundary information to present necessary details. Moreover, a minimum spanning tree (MST)-based filter is designed as the global filter to provide a natural measure of global pixel similarity for image matching. Thirdly, a unified post-processing enhancement framework, including selection and normalization, WCF and argmax, is designed. Finally, the effectiveness and superiority of the proposed method for enhancement, as well as its range of applications, are verified through experiments.
Physical Layer Security for Multiple-Input Multiple-Output Systems by Alternating Orthogonal Space-Time Block Codes

Algorithms are provided to build a large set of unique complex-valued orthogonal space-time block codes (STBCs) from a known or standard STBC. A physical layer security (PLS) scheme is proposed to take advantage of this set by alternating the STBC in use over a multiple-input single-output (MISO) or multiple-input multiple-output (MIMO) communications link between base station (BS) and user equipment (UE). A practical procedure is proposed and demonstrated to build individual STBCs from the set without use of a lookup table. The sufficient statistic is given and proven to allow for maximal ratio combining (MRC) by the intended receiver for all STBCs in the set. An algorithm is offered for the UE to update the MRC matrix in use as the STBC alternates. Definitions are provided for cryptograms, key residues (KRs), key residue classes (KRCs), and message and cryptogram residue classes pertaining to STBC PLS schemes. These definitions are used to present analysis of the information-theoretic security of the proposed PLS scheme to include message and key equivocation. Theoretical expected bit error rate (BER) for a passive eavesdropper is proven and plotted along with Monte Carlo simulations for confirmation. Discussion of different attack models is provided. Cost and attack complexity are compared between the proposed scheme and two related techniques from the literature.
Named Entity Recognition in Aircraft Design Field Based on Deep Learning

Aircraft design is a kind of knowledge-intensive work involving multi-disciplinary integration, which needs the support of a large amount of knowledge on aircraft design field (ADF). At the same time, a large number of technical documents about AD also accumulate rich aircraft design knowledge. If this knowledge can be extracted, it can be used to guide the intelligent design and maintenance of aircraft. In this paper, we conduct our research for the named entity recognition, which is an important step of knowledge graph construction in ADF. For the problem of knowledge dispersion in ADF and lacking of training dataset, we design a platform for data acquisition and processing, and corpus annotation by crowdsourcing. And a novel deep neural network model, named AR+BiLSTM+CRF, which combines attention mechanism, Ranger optimizer, bidirectional LSTM, and CRF, is proposed for named entity recognition in ADF. The experimental results show that AR+BiLSTM+CRF model has excellent performance for named entity recognition in ADF.
BERT-Based Sentiment Analysis Using Distillation

In this paper, we present our experiments with BERT (Bidirectional Encoder Representations from Transformers) models in the task of sentiment analysis, which aims to predict the sentiment polarity for the given text. We trained an ensemble of BERT models from a large self-collected movie reviews dataset and distilled the knowledge into a single production model. Moreover, we proposed an improved BERT’s pooling layer architecture, which outperforms standard classification layer while enables per-token sentiment predictions. We demonstrate our improvements on a publicly available dataset with Czech movie reviews.
Mobile Sensor-Based Fall Detection Framework

Fall is a major concern among elderly population. Accidental fall if unattended for long time, may lead to severe injuries and disability. Prompt detection of fall is an important research problem, particularly in the homecare settings for elderly citizens, where not enough service providers are available to monitor their health and welfare daily. There are some available fall detection approaches, however, they are either expensive or the accuracy of fall detection is not satisfactory. In this paper, we develop a low cost fall detection framework using Android phone's built in sensors with the goal of detecting fall and notifying to emergency responders. We generate a dataset and train 3 popular machine learning algorithms to detect fall events: Logistic Regression, Naïve Bayes and Neural Network. Our study shows the performance comparison of the learning algorithms. The evaluation results show that our approach can successfully detect fall and neural network-based technique can perform better than other learning techniques.
Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming

Abstract The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming.
UAV security situation awareness method based on semantic analysis

Battlefield unmanned aerial vehicles (UAVs) usually suffer from complex, flexible and invisible electromagnetic interference, and as a result, they are easy to get out of control from the ground base. To address this problem, the method of the UAV electromagnetic jamming security situation awareness based on semantic analysis is proposed in this paper. The data source of semantic analysis is collected based on the subtle changes of the UAV state parameters during the electromagnetic interference process. Abnormal behavior detection is realized by a tracing comparison method. Subsequently, the fuzzy logic reasoning is adopted to realize the semantic analysis of the link jamming and intrusion situation. Finally, the semantic evaluation of the link situation is developed. Simulation results show that the proposed method can realize the evaluation of the security situation according to the limited and isomerism state parameters, which can, therefore, improve the active defense capacity of battlefield UAVs.
Forecasting Nonlinear Nonstationary Processes in Machine Learning Task

The article discusses the features of the solving the forecasting problems using machine learning techniques. The issues of accounting and correctly processing non-linear non-stationary processes in the problems of modeling and forecasting time series in various areas are considered. The analysis of stages and methods for solving machine learning problems. As an example, consider the problem of predicting currency pairs based on historical data. A comparative analysis of the normalization methods in data clustering is given. For the six currency pairs a short-term forecast is proposed.
Procedural Generation of Interactive Stories using Language Models

In this paper we introduce an architecture, an implementation and an evaluation of a system for the automatic creation of interactive stories for games. Our goal is to algorithmically create a branched story for the entire game; in each game run a different variant is generated. The architecture uses natural language processing (NLP) to generate meaningful stories. For NLP we use a statistical language model based on a neural network (Generative Pretrained Transformer, GPT-2). The basic architecture generates stories with too many characters which tend to get incoherent for longer texts, so we add a component restricting the number of persons and improving the consistency. The system is initialized with a hand-written game introduction that defines the main characters and the inventory; it also sets the goals for the game. From that text the remainder of the game story is generated algorithmically. We have fully implemented our system, and we report initial, encouraging experimental results.
Modular framework for implementation and analysis of recursive filters with considered and neglected parameters

This paper develops novel covariance and square-root factor formulations of a consider-neglect Kalman filter for navigation applications. The proposed filter partitions system parameters into three distinct categories: those to be estimated by the filter, those whose contribution to the system are considered without being explicitly estimated, and those with sufficiently low effect on the system such that their contribution can be neglected altogether. Discussion on appropriate selection of parameters to be considered and neglected is provided with specific attention given to descent-to-landing navigation. Monte Carlo simulations and analysis are performed to assess the performance of the developed square-root consider-neglect filter in a descent-to-landing navigation scenario.