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Search results for: PROBLEM-BASED LEARNING
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Machine Learning Techniques in Concrete Mix Design
PublicationConcrete mix design is a complex and multistage process in which we try to find the best composition of ingredients to create good performing concrete. In contemporary literature, as well as in state-of-the-art corporate practice, there are some methods of concrete mix design, from which the most popular are methods derived from The Three Equation Method. One of the most important features of concrete is compressive strength, which...
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Introduction to the special issue on machine learning in acoustics
PublicationWhen we started our Call for Papers for a Special Issue on “Machine Learning in Acoustics” in the Journal of the Acoustical Society of America, our ambition was to invite papers in which machine learning was applied to all acoustics areas. They were listed, but not limited to, as follows: • Music and synthesis analysis • Music sentiment analysis • Music perception • Intelligent music recognition • Musical source separation • Singing...
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The Neural Knowledge DNA Based Smart Internet of Things
PublicationABSTRACT The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade. Smartness, however, remains a substantial challenge for IoT applications. Recent advances in networked sensor technologies, computing, and machine learning have made it possible for building new smart IoT applications. In this paper, we propose a novel approach: the Neural Knowledge DNA based Smart Internet...
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Multiple Cues-Based Robust Visual Object Tracking Method
PublicationVisual object tracking is still considered a challenging task in computer vision research society. The object of interest undergoes significant appearance changes because of illumination variation, deformation, motion blur, background clutter, and occlusion. Kernelized correlation filter- (KCF) based tracking schemes have shown good performance in recent years. The accuracy and robustness of these trackers can be further enhanced...
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Transformational Leadership and Acceptance of Mistakes as a Source of Learning: Poland-USA Cross-Country Study
PublicationThis study explores the influence of transformational leadership on internal innovativeness mediated by mistakes acceptance, including country and industry as factors to be considered and gender and risk-taking attitude as moderators. General findings, primarily based on the US samples (healthcare, construction, and IT industry), confirmed that transformational leadership and internal innovativeness are mediated by mistakes acceptance...
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The Cultures of Knowledge Organizations: Knowledge, Learning, Collaboration (KLC)
PublicationThis book focuses on seeing, understanding, and learning to shape an organization’s essential cultures. The book is grounded on a fundamental assumption that every organization has a de facto culture. These “de facto cultures” appear at first glance to be serendipitous, vague, invisible, and unmanaged. An invisible and unrecognized de facto culture can undermine business goals and strategies and lead to business failures. The authors...
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Adaptive Algorithm for Interactive Question-based Search
PublicationPopular web search engines tend to improve the relevanceof their result pages, but the search is still keyword-oriented and far from "understanding" the queries' meaning. In the article we propose an interactive question-based search algorithm that might come up helpful for identifying users' intents. We describe the algorithm implemented in a form of a questions game. The stress is put mainly on the most critical aspect of this...
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Remote learning among students with and without reading difficulties during the initial stages of the COVID-19 pandemic
PublicationThis article presents the results of a survey on yet under-researched aspects of remote learning and learning difficulties in higher education during the initial stage (March – June 2020) of the COVID-19 pandemic. A total of 2182 students from University of Warsaw in Poland completed a two-part questionnaire regarding academic achievements in the academic year 2019/2020, living conditions and stress related to learning and pandemic,...
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Enterprise Gamification - Learning as a Side Effect of Competition
PublicationGmification in companies can be used for driving desired employees behaviour that are advantageous to their development and performance improvement. This paper presents tools acquired from online social networking services and game mechanisms to encourage managers to compete by providing extended statistics and user profiles features in e-learning system.
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Noise profiling for speech enhancement employing machine learning models
PublicationThis paper aims to propose a noise profiling method that can be performed in near real-time based on machine learning (ML). To address challenges related to noise profiling effectively, we start with a critical review of the literature background. Then, we outline the experiment performed consisting of two parts. The first part concerns the noise recognition model built upon several baseline classifiers and noise signal features...
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Feature Reduction Using Similarity Measure in Object Detector Learning with Haar-like Features
PublicationThis paper presents two methods of training complexity reduction by additional selection of features to check in object detector training task by AdaBoost training algorithm. In the first method, the features with weak performance at first weak classifier building process are reduced based on a list of features sorted by minimum weighted error. In the second method the feature similarity measures are used to throw away that features...
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Big Data from Sensor Network via Internet of Things to Edge Deep Learning for Smart City
PublicationData from a physical world is sampled by sensor networks, and then streams of Big Data are sent to cloud hosts to support decision making by deep learning software. In a smart city, some tasks may be assigned to smart devices of the Internet of Things for performing edge computing. Besides, a part of workload of calculations can be transferred to the cloud hosts. This paper proposes benchmarks for division tasks between an edge...
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Improving css-KNN Classification Performance by Shifts in Training Data
PublicationThis paper presents a new approach to improve the performance of a css-k-NN classifier for categorization of text documents. The css-k-NN classifier (i.e., a threshold-based variation of a standard k-NN classifier we proposed in [1]) is a lazy-learning instance-based classifier. It does not have parameters associated with features and/or classes of objects, that would be optimized during off-line learning. In this paper we propose...
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Book Review
PublicationActing over the last three decades as an Editor and Associate Editor for a number of international journals in the general area of cybernetics and AI, as well as a Chair and Co-Chair of numerous conferences in this field, I have had the exciting opportunity to closely witness and to be actively engaged in the stimulating research area of machine learning and its important augmentation with deep learning techniques and technologies. From...
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UK travel agents’ evaluation of eLearning courses offered by destinations: an exploratory study.
PublicationThis study aims to develop an understanding of the use of e-learning courses created for travel agents by Destination Management Organizations (DMOs). It explores agents’ perceptions of such courses. The research examines the views of 304 UK-based travel agents using online survey and investigates whether age, sex, type of agency, work experience, and educational level have influence on e-learning uptake. The satisfaction of travel...
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Investigation of educational processes with affective computing methods
PublicationThis paper concerns the monitoring of educational processes with the use of new technologies for the recognition of human emotions. This paper summarizes results from three experiments, aimed at the validation of applying emotion recognition to e-learning. An analysis of the experiments’ executions provides an evaluation of the emotion elicitation methods used to monitor learners. The comparison of affect recognition algorithms...
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Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
PublicationWe present a novel deep learning model for the detection and reconstruction of dysarthric speech. We train the model with a multi-task learning technique to jointly solve dysarthria detection and speech reconstruction tasks. The model key feature is a low-dimensional latent space that is meant to encode the properties of dysarthric speech. It is commonly believed that neural networks are black boxes that solve problems but do not...
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A Concept of Automatic Film Color Grading Based on Music Recognition and Evoked Emotions
PublicationThe article presents the aspects of the final selection of the color of shots in film production based on the psychology of color. First of all, the elements of color processing, contrast, saturation or white balance in the film shots were presented and the definition of color grading was given. In the second part of the article the analysis of film music was conducted in the context of stimulating appropriate emotions while watching...
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When Neural Networks Meet Decisional DNA: A Promising New Perspective for Knowledge Representation and Sharing
PublicationABSTRACT In this article, we introduce a novel concept combining neural network technology and Decisional DNA for knowledge representation and sharing. Instead of using traditional machine learning and knowledge discovery methods, this approach explores the way of knowledge extraction through deep learning processes based on a domain’s past decisional events captured by Decisional DNA. We compare our approach with kNN (k-nearest...
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Detecting Lombard Speech Using Deep Learning Approach
PublicationRobust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the background concerning the Lombard effect. Then, assumptions of the work performed for Lombard speech detection are outlined. The framework proposed combines convolutional neural networks...
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Wisdom from Experience Paradox: Organizational Learning, Mistakes, Hierarchy and Maturity Issues
PublicationOrganizations often perceive mistakes as negligence and low-performance indicators, yet they can be a precious learning resource. However, organizations cannot learn from mistakes if they have not accepted them. This study aimed to explore how organizational hierarchy and maturity levels influence the relationship between mistakes acceptance and the ability to change. A sample composed of 380 Polish employees working in knowledge-driven...
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Optical method supported by machine learning for dynamics of C‐reactive protein concentrations changes detection in biological matrix samples
PublicationIn this article we present the novel spectroscopy method supported with machine learning for real-time detection of infectious agents in wastewater. In the case of infectious diseases, wastewater monitoring can be used to detect the presence of inflammation biomarkers, such as the proposed C-reactive protein, for monitoring inflammatory conditions and mass screening during epidemics for early detection in communities of concern,...
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TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads
PublicationTensorHive is a tool for organizing work of research and engineering teams that use servers with GPUs for machine learning workloads. In a comprehensive web interface, it supports reservation of GPUs for exclusive usage, hardware monitoring, as well as configuring, executing and queuing distributed computational jobs. Focusing on easy installation and simple configuration, the tool automatically detects the available computing...
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Concurrent Video Denoising and Deblurring for Dynamic Scenes
PublicationDynamic scene video deblurring is a challenging task due to the spatially variant blur inflicted by independently moving objects and camera shakes. Recent deep learning works bypass the ill-posedness of explicitly deriving the blur kernel by learning pixel-to-pixel mappings, which is commonly enhanced by larger region awareness. This is a difficult yet simplified scenario because noise is neglected when it is omnipresent in a wide...
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Optical method supported by machine learning for urinary tract infection detection and urosepsis risk assessment
PublicationThe study presents an optical method supported by machine learning for discriminating urinary tract infections from an infection capable of causing urosepsis. The method comprises spectra of spectroscopy measurement of artificial urine samples with bacteria from solid cultures of clinical E. coli strains. To provide a reliable classification of results assistance of 27 algorithms was tested. We proved that is possible to obtain...
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Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France
PublicationCurrent Earth observation systems generate massive amounts of satellite image time series to keep track of geographical areas over time to monitor and identify environmental and climate change. Efficiently analyzing such data remains an unresolved issue in remote sensing. In classifying land cover, utilizing SITS rather than one image might benefit differentiating across classes because of their varied temporal patterns. The aim...
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THE ROLE OF INFERENCE IN MOBILE MEDICAL APPLICATION DESIGN
PublicationIn the early 21st century, artificial intelligence began to be used to process medical information. However, before this happened, predictive models used in healthcare could only consider a limited number of variables, and only in properly structured and organised medical data. Today, advanced tools based on machine learning techniques - which, using artificial neural networks, can explore extremely complex relationships - and...
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COLLABORATIVE LEARNING ENVIRONMENT FOR ENGINEERING EDUCATION (COLED)
PublicationCollaborative Learning Environment for Engineering Education is a European project implemented under the Erasmus + program, The main goal of 5 partners from 4 different European countries – Bulgaria, Poland, Portugal and Romania is to develop an innovative collaborative training approach, encompassing curricula related to the introduction of enterprise automation. Project activities are carried out in the period from Dctober 2018...
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Analysis of Denoising Autoencoder Properties Through Misspelling Correction Task
PublicationThe paper analyzes some properties of denoising autoencoders using the problem of misspellings correction as an exemplary task. We evaluate the capacity of the network in its classical feed-forward form. We also propose a modification to the output layer of the net, which we called multi-softmax. Experiments show that the model trained with this output layer outperforms traditional network both in learning time and accuracy. We...
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Uczenie maszynowe do samoorganizacji systemów rozproszonych w zastosowaniach gospodarczych
PublicationW pracy omówiono uczenie maszynowe do samoorganizacji systemów rozproszonych w zastosowaniach gospodarczych ze szczególnym uwzględnieniem sieci neuronowych do predykcji finansowych oraz szacowania ratingu przedsiębiorstw. Oprócz sieci neuronowych, istotną rolę w przygotowaniu i testowaniu informatycznych systemów finansowych może pełnić programowanie genetyczne. Z tego powodu omówiono uczenie maszynowe w aplikacjach konstruowanych...
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Organizational Wisdom: The Impact of Organizational Learning on the Absorptive Capacity of an Enterprise
PublicationPurpose: In this article, we analyze the concept of organizational wisdom, indicating its key elements and verifieng the relationships between them. Design/Methodology/Approach: The study was conducted at Vive Textile Recycling Sp. z o.o in Poland. Empirical data was collected from 138 managers using the PAPI technique. Structural equation modelling (SEM) was performed to test the research hypotheses. Additionally, the significance...
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MP3vec: A Reusable Machine-Constructed Feature Representation for Protein Sequences
Publication—Machine Learning (ML) methods have been used with varying degrees of success on protein prediction tasks, with two inherent limitations. First, prediction performance often depends upon the features extracted from the proteins. Second, experimental data may be insufficient to construct reliable ML models. Here we introduce MP3vec, a transferable representation for protein sequences that is designed to be used specifically for sequence-to-sequence...
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Internet photogrammetry as a tool for e-learning
PublicationAlong with Internet development, there were interactive applications which allow for remote sensing and photogrammetric analysis. An example of an application that can provide Earth images and make it possible to measure distances in these images is Google Earth. The authors, who have experience from 2001-2015 argue that it is possible and it is important to create more advanced photogrammetric network applications. In this there...
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Double Bias of Mistakes: Essence, Consequences, and Measurement Method
PublicationThere is no learning without mistakes. However, there is a clash between‘positive attitudes and beliefs’regarding learning processes and the ‘negative attitudes and beliefs’towardthese being accompanied bymistakes. Thisclash exposesa cognitive bias towardmistakesthat might block personal and organizational learning. This study presents an advanced measurement method to assess thebias of mistakes. The essence of it is the...
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Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing
PublicationCirculating tumor cells (CTCs) are tumor cells that separate from the solid tumor and enter the bloodstream, which can cause metastasis. Detection and enumeration of CTCs show promising potential as a predictor for prognosis in cancer patients. Furthermore, single-cells sequencing is a technique that provides genetic information from individual cells and allows to classify them precisely and reliably. Sequencing data typically...
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Social learning and knowledge flows in cluster initiatives, In: Sanz S.C., Blanco F.P., Urzelai B. (Eds). Human and Relational Resources (pp. 44-45). the 4th International Conference on Clusters and Industrial Districts CLUSTERING, University of Valencia, Spain, May 23–24 (ISBN: 978-84-09-11926-4).
PublicationPurpose – The purpose of the paper is to explore how learning manifests and knowledge flows in cluster initiatives (CIs) due to interactions undertaken by their members. The paper addresses the research question of how social learning occurs and knowledge flows in CIs. Design/methodology/approach – The qualitative study of four cluster initiatives helped to identify various symptoms of social learning and knowledge flows in...
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E-learning versus traditional learning - Polish case
PublicationE-learning jest współczesnym fenomenem, który pozwala na dostęp do kształcenia i treści edukacyjnych, niezależnie od czasu i miejsca, dla każdego użytkownika. E-learnig tworzy ogromne możliwości dla uczelni akademickich, organizacji, instytucji komercyjnych i szkoleniowych, dostarczając na żądanie kształcenia i szkoleń w wirtualnym środowisku. Student może stworzyć własny plan kształcenia, dostosowując go do swojej pracy i sytuacji...
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Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
PublicationIn this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KDbased methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Our analysis reveals that this issue originates from substantial representation shifts in the teacher...
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The maximum edge-disjoint paths problem in complete graphs
PublicationRozważono problem ścieżek krawędziowo rozłącznych w grafach pełnych. Zaproponowano wielomianowe algorytmy: 3.75-przybliżony (off-line) oraz 6.47-przybliżony (on-line), poprawiając tym samym wyniki wcześniej znane z literatury [P. Carmi, T. Erlebach, Y. Okamoto, Greedy edge-disjoint paths in complete graphs, in: Proc. 29th Workshop on Graph Theoretic Concepts in Computer Science, in: LNCS, vol. 2880, 2003, pp. 143-155]. Ponadto...
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imPlatelet classifier: image‐converted RNA biomarker profiles enable blood‐based cancer diagnostics
PublicationLiquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor-educated platelets. Here, we developed the imPlatelet classifier, which converts RNA-sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia...
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Experience-Based Cognition for Driving Behavioral Fingerprint Extraction
PublicationABSTRACT With the rapid progress of information technologies, cars have been made increasingly intelligent. This allows cars to act as cognitive agents, i.e., to acquire knowledge and understanding of the driving habits and behavioral characteristics of drivers (i.e., driving behavioral fingerprint) through experience. Such knowledge can be then reused to facilitate the interaction between a car and its driver, and to develop better and...
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A novel hybrid adaptive framework for support vector machine-based reliability analysis: A comparative study
PublicationThis study presents an innovative hybrid Adaptive Support Vector Machine - Monte Carlo Simulation (ASVM-MCS) framework for reliability analysis in complex engineering structures. These structures often involve highly nonlinear implicit functions, making traditional gradient-based first or second order reliability algorithms and Monte Carlo Simulation (MCS) time-consuming. The application of surrogate models has proven effective...
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Study on Strategy in University Laboratory Class Teaching
PublicationLaboratory teaching is a critical way to ensure the effective input of techniques in engineering learning. Laboratory teaching not only contributes to improving course quality but also helps enrich comprehensive engineering application ability. However, there are some typical problems in current university laboratory teaching, such as rigid and isolated course design, outdated contents and materials, and not encouraging innovation...
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Robust and Efficient Machine Learning Algorithms for Visual Recognition
PublicationIn visual recognition, the task is to identify and localize all objects of interest in the input image. With the ubiquitous presence of visual data in modern days, the role of object recognition algorithms is becoming more significant than ever and ranges from autonomous driving to computer-aided diagnosis in medicine. Current models for visual recognition are dominated by models based on Convolutional Neural Networks (CNNs), which...
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JamesBot - an intelligent agent playing StarCraft II
PublicationThe most popular method for optimizing a certain strategy based on a reward is Reinforcement Learning (RL). Lately, a big challenge for this technique are computer games such as StarCraft II which is a real-time strategy game, created by Blizzard. The main idea of this game is to fight between agents and control objects on the battlefield in order to defeat the enemy. This work concerns creating an autonomous bot using reinforced...
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Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters
PublicationThis paper focuses on convolution neural network quantization problem. The quantization has a distinct stage of data conversion from floating-point into integer-point numbers. In general, the process of quantization is associated with the reduction of the matrix dimension via limited precision of the numbers. However, the training and inference stages of deep learning neural network are limited by the space of the memory and a...
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The chemistry, properties and performance of flame-retardant rubber composites: Collecting, analyzing, categorizing, machine learning modeling, and visualizing
PublicationRubbers combine the flexibility with mechanical strength, supporting myriad applications, but suffer from inherent flammability. Formulation and production of flame-retardant rubber composites (FRRCs) have intensively been practiced over years, but not comprehensively reviewed. This necessity has outlined collecting, analyzing, screening, classifying, and interpreting the literature with the aim of classifying the FRRCs. We quantified...
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Evaluating the risk of endometriosis based on patients’ self-assessment questionnaires
PublicationBackground Endometriosis is a condition that significantly affects the quality of life of about 10 % of reproductive-aged women. It is characterized by the presence of tissue similar to the uterine lining (endometrium) outside the uterus, which can lead lead scarring, adhesions, pain, and fertility issues. While numerous factors associated with endometriosis are documented, a wide range of symptoms may still be undiscovered. Methods In...
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Strategic Flexibility as a Mediator in Relationship between Managerial Decisions and Organizational Learning: Ambidexterity Perspective
PublicationPurpose: The purpose of the article is to determine strategic flexibility in the relationship between managerial decisions and organizational learning. The analyses are conducted in the ambidexterity convection. Design/Methodology/Approach: The study was conducted at a textile company. The company is a leader in the textile recycling industry in Poland. Empirical data were collected using the PAPI technique. The survey questionnaire...
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Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various Machine learning techniques
PublicationMachine Learning (ML) method is widely used in engineering applications such as fracture mechanics. In this study, twenty different ML algorithms were employed and compared for the prediction of the fracture toughness and fracture load in modes I, II, and mixed-mode (I-II) of various materials, including fibre-reinforced concrete, cement mortar, sandstone, white travertine, marble, and granite. A set of 401 specimens of “Brazilian...