Reviewerly/exHarmony huggingface dataset:
Unsupervised Representation Learning with Task-Agnostic Feature Masking for Robust End-to-End Speech Recognition

Unsupervised learning-based approaches for training speech vector representations (SVR) have recently been widely applied. While pretrained SVR models excel in relatively clean automatic speech recognition (ASR) tasks, such as those recorded in laboratory environments, they are still insufficient for practical applications with various types of noise, intonation, and dialects. To cope with this problem, we present a novel unsupervised SVR learning method for practical end-to-end ASR models. Our approach involves designing a speech feature masking method to stabilize SVR model learning and improve the performance of the ASR model in a downstream task. By introducing a noise masking strategy into diverse combinations of the time and frequency regions of the spectrogram, the SVR model becomes a robust representation extractor for the ASR model in practical scenarios. In pretraining experiments, we train the SVR model using approximately 18,000 h of Korean speech datasets that included diverse speakers and were recorded in environments with various amounts of noise. The weights of the pretrained SVR extractor are then frozen, and the extracted speech representations are used for ASR model training in a downstream task. The experimental results show that the ASR model using our proposed SVR extractor significantly outperforms conventional methods.
Blockchain Enabled Credible Energy Trading at the Edge of the Internet of Things

In order to promote the value circulation of energy resources and improve energy efficiency, credible energy sharing between Internet of Things Devices (IoTDs) came into being. However, sometimes IoTDs do not obtain the required energy in the required time period, resulting in less active participation in energy sharing. To address these challenges, this paper first proposes a credible energy transaction model based on the distributed ledger blockchain at the Edge of the Internet of Things, where the Edge Cloud Server (ECS) can collect a large number of surplus energy resources of IoTDs in a secure and credible energy sharing environment and share them with other IoTDs in urgent need of charging. Meanwhile, in order to attract IoTDs to participate in energy sharing for a long time and meet the energy demand of ECS to the maximum extent, a smart contract-based Expected Social Welfare Maximized double auction incentive mechanism of Single ECS to Multi-IoTDs (ESWM-StM) is proposed to enable dynamic and adaptive energy sharing from multiple IoTDs to a single ECS. In addition, this paper compares the proposed algorithm with the benchmark method in terms of energy-sharing cost and long-term utility. The simulation results show that the proposed incentive mechanism can enable IoTDs to provide more surplus energy per unit cost to meet the energy demand of ECSs, and can sustainably attract more energy trading participants to enhance the expected social welfare in the long term.
A Cloud-Based Software Defect Prediction System Using Data and Decision-Level Machine Learning Fusion

This research contributes an intelligent cloud-based software defect prediction system using data and decision-level machine learning fusion techniques. The proposed system detects the defective modules using a two-step prediction method. In the first step, the prediction is performed using three supervised machine learning techniques, including naïve Bayes, artificial neural network, and decision tree. These classification techniques are iteratively tuned until the maximum accuracy is achieved. In the second step, the final prediction is performed by fusing the accuracy of the used classifiers with a fuzzy logic-based system. The proposed fuzzy logic technique integrates the predictive accuracy of the used classifiers using eight if–then fuzzy rules in order to achieve a higher performance. In the study, to implement the proposed fusion-based defect prediction system, five datasets were fused, which were collected from the NASA repository, including CM1, MW1, PC1, PC3, and PC4. It was observed that the proposed intelligent system achieved a 91.05% accuracy for the fused dataset and outperformed other defect prediction techniques, including base classifiers and state-of-the-art ensemble techniques.
Stochastic Configuration Based Fuzzy Inference System with Interpretable Fuzzy Rules and Intelligence Search Process

In this paper, a stochastic configuration based fuzzy inference system with interpretable fuzzy rules (SCFS-IFRs) is proposed to improve the interpretability and performance of the fuzzy inference system and determine autonomously an appropriate model structure. The proposed SCFS-IFR first accomplishes a fuzzy system through interpretable linguistic fuzzy rules (ILFRs), which endows the system with clear semantic interpretability. Meanwhile, using an incremental learning method based on stochastic configuration, the appropriate architecture of the system is determined by incremental generation of ILFRs under a supervision mechanism. In addition, the particle swarm optimization (PSO) algorithm, an intelligence search technique, is used in the incremental learning process of ILFRs to obtain better random parameters and improve approximation accuracy. The performance of SCFS-IFRs is verified by regression and classification benchmark datasets. Regression experiments show that the proposed SCFS-IFRs perform best on 10 of the 20 data sets, statistically significantly outperforming the other eight state-of-the-art algorithms. Classification experiments show that, compared with the other six fuzzy classifiers, SCFS-IFRs achieve higher classification accuracy and better interpretation with fewer rules.
Gender tagging of named entities using retrieval‐assisted multi‐context aggregation: An unsupervised approach

Abstract Inferring the gender of named entities present in a text has several practical applications in information sciences. Existing approaches toward name gender identification rely exclusively on using the gender distributions from labeled data. In the absence of such labeled data, these methods fail. In this article, we propose a two‐stage model that is able to infer the gender of names present in text without requiring explicit name‐gender labels. We use coreference resolution as the backbone for our proposed model. To aid coreference resolution where the existing contextual information does not suffice, we use a retrieval‐assisted context aggregation framework. We demonstrate that state‐of‐the‐art name gender inference is possible without supervision. Our proposed method matches or outperforms several supervised approaches and commercially used methods on five English language datasets from different domains.
ICT in the Classroom – Didactical Challenges for Practitioners and Researchers

Digital competence is considered to be a crucial aspect of education, and the discourse around digital technologies has been full of promises of more and better learning. It has also been quite general and not taking into account subject specific differences in what digital competence may be across disciplines and grades. In this chapter, I will discuss why access to technology it not enough to digitalize education, and what kind of knowledge specific educational research we need to address digitalization in the classroom. I will draw on empirical data from two different projects to shed light on these questions: the large scale video study Linking Instruction and Student Achievement (LISA) and a national survey to parents in Norway about what characterized teaching when schools went 100% digital overnight in March 2020, due to the global outbreak of COVID-19. I will point to important implications of these studies for the field of didactical research and underscore the need for more studies looking systematically into what digital competence means within specific didactical contexts.
UBayFS: An R Package for User Guided Feature Selection

Feature selection, also known as variable selection in statistics, is the process of selecting important variables (features) from a list of variables in a dataset.When training predictive models, the intention behind removing the least informative features from a dataset beforehand is (a) to reduce the computational burden and mathematical limitations associated with the curse of dimensionality, and (b) to increase interpretability of the model by allowing the user to obtain insights into the relevant input variables.In particular, feature selection speeds up the training process of machine learning models, especially when the dataset is high-dimensional.
A comprehensive account of the burden of persuasion in abstract argumentation

Abstract In this paper, we provide a formal framework for modeling the burden of persuasion in legal reasoning. The framework is based on abstract argumentation, a frequently studied method of non-monotonic reasoning, and can be applied to different argumentation semantics; it supports burdens of persuasion with arbitrary many levels, and allows for the placement of a burden of persuasion on any subset of an argumentation framework’s arguments. Our framework can be considered an extension of related works that raise questions on how burdens of persuasion should be handled in some conflict scenarios that can be modeled with abstract argumentation. An open source software implementation of the introduced formal notions is available as an extension of an argumentation reasoning library. A theoretical analysis shows that our approach can be generalized to a novel method for the preference-based selection of extensions from argumentation frameworks.
Outlier robust extreme learning machine: Predicting river water temperature in the absence of air temperature

Modeling river water temperature using air temperature is broadly discussed in the literature, and up to now, all proposed models were based on establishing a direct relationship between water and air temperatures variables at different timescale. The need for a stronger link between these two variables was strongly emphasized, and the number of published work has substantially increased during the last few years, and it was demonstrated that the proposed models were robust, accurate and worked with unusually high level of precision, presumably due to the greater effect of air temperature on water temperature. In the absence of air temperature, the developed models do not therefore seem to be possible to use. In the present investigation, we demonstrate that it is now possible to predict river water temperature without the need of air temperature, and using only another weather variable which is the barometric pressure. We apply and compare four nontuned machines learning namely, (i) the original extreme learning machines, (ii) outlier robust extreme learning machine, (iii) regularized extreme learning machines, and (iv) weighted regularized extreme learning machines. The proposed models were developed using large data set collected from four stations in the Columbia River, United States. We demonstrate here that the inclusion of the periodicity represented by the component of the Gregorian calendar, that is, year number, month, day, and the hour of day, will substantially help in improving the models accuracies, and a high level of correlation was found between in situ measured data and predicted data, reaching an average values of correlation coefficient (R) and Nash-Sutcliffe (NSE) efficiency of 0.990 and 0.980, respectively, while the root mean square error and mean absolute error were below 0.650°C and 0.500°C, respectively. With the excellent results obtained, we are showing that this new modeling approach is being confirmed and consolidated.
Bat algorithm optimized extreme learning machine: A new modeling strategy for predicting river water turbidity at the United States

Turbidity (TU) is one of the most important water quality variables and despite its great importance, the need to increase the number of monitoring stations is becoming a major issue for many regions of the world. In the absence of direct in situ measurement, alternative methods based on the different modeling approaches can be useful tools for predicting river TU. In this research, a novel modeling strategy for predicting river water turbidity using only measured river discharge (Q) has been proposed. Time series of river TU and Q measured at four United States Geological Survey (USGS) gauging stations located in a different region of the United States of America are employed for calibrating and testing several machine learning models. Specifically, four models were proposed and compared: the hybrid bat algorithm optimized extreme learning machine (Bat-ELM), the standalone feedforward artificial neural network (FFNN), the off-line dynamic evolving neural-fuzzy inference system called DENFIS_F, and the on-line dynamic evolving neural fuzzy inference system called DENFIS_O. The models were developed using the Q as input variables combined with the three components of the Gregorian calendar, i.e., the year, month, and day numbers. The models were evaluated using well-known performance evaluation metrics, i.e., coefficient of correlation (R), Nash-Sutcliffe efficiency (NSE), MAE, and RMSE. In general, the Bat-ELM performed best, more accurate than all other models, exhibiting the high R and NSE indexes with values ranging from 0.898 to 0.971 and from 0.806 to 0.936, respectively. It was also found that the inclusion of the periodicity contributed significantly to the improvement of the models performances. Furthermore, obtained results in the present study were encouraging and can be used a basis for future investigation related to river water turbidity modeling and forecasting.
A novel directional sampling-based path planning algorithm for ambient intelligence navigation scheme in autonomous mobile robots

Path planning algorithms determine the performance of the ambient intelligence navigation schemes in autonomous mobile robots. Sampling-based path planning algorithms are widely employed in autonomous mobile robot applications. RRT*, or Optimal Rapidly Exploring Random Trees, is a very effective sampling-based path planning algorithm. However, the RRT* solution converges slowly. This study proposes a directional random sampling-based RRT* path planning algorithm known as DR-RRT* to address the slow convergence issue. The novelty of the proposed method is that it reduces the search space by combining directional non-uniform sampling with uniform sampling. It employs a random selection approach to combine the non-uniform directional sampling method with uniform sampling. The proposed path planning algorithm is validated in three different environments with a map size of 384*384, and its performance is compared to two existing algorithms: RRT* and Informed RRT*. Validation is carried out utilizing a TurtleBot3 robot with the Gazebo Simulator and the Robotics Operating System (ROS) Melodic. The proposed DR-RRT* path planning algorithm is better than both RRT* and Informed RRT* in four performance measures: the number of nodes visited, the length of the path, the amount of time it takes, and the rate at which the path converges. The proposed DR-RRT* global path planning algorithm achieves a success rate of 100% in all three environments, and it is suited for use in all kinds of environments.
Open-Source Application for Mri and Ct Registration Using Homography Transformation

Medical imaging is substantial in diagnosing and treatment of various types of diseases, however, a single modality may not be sufficient. Therefore, gathering more than one imaging modality to provide additional information is needed. Thus, an open-source application for the registration of the two medical imaging modalities using a homography transformation was implemented. Using homography, the mapping of the points of the magnetic resonance image to the corresponding points of the computer tomography image was performed. The application enables the radiologist to finely adjust the two modalities by moving the homography points and performing isometric transformations on the slices. The usage of the developed open-source application was evaluated by one experienced radiologist, and calculating the mutual information (MI) on the data of the axial plane from three pediatric patients (8.33±4.19 years), collected from the University Children's Hospital, Belgrade, Serbia. The radiologist adjusted the registered images until a satisfactory level of alignment was achieved, and the increase of the initial MI∼0.65 ranging from 0.17 to 0.23 was achieved. The developed software, based on the homography transformations combined with feedback in the form of MI, a simple graphical interface, and an open-source implementation enable its easy use in everyday clinical practice.
Light field representation

Capture is the first step in light field application: Whether in plenoptic cameras or in camera arrays, neither the geometry nor the coloring is perfect. Consequently, light field pre-processing pipelines do include De-Bayering: Typical sensors record raw-luminance in Bayer patterns, i.e., single pixels represent one of the three primary colors, and consequently for full color images interpolation techniques need to be applied. Color correction: Majorly in arrays, but to a smaller extent also in plenoptic cameras the coloring is not identical for all subaperture views. Color-matching techniques have to be applied to harmonize the colors and to enable the assignment of rays to objects by means of their color. De-vignetting: Majorly in plenoptic cameras, but to a smaller extent also in arrays lenses are not adequately effective in avoiding vignetting effects. Consequently, the brightness of rays needs to be harmonized. Rectification: To establish the correct relationship between any captured sensor pixel and the respective ray in space, camera positions need to be known perfectly, and lens distortions need to be corrected. The processing step is called rectification, since in many applications (μ-lenses on the MLA in plenoptic cameras, horizontal and vertical alignment in planar camera arrays) the underlying position ideally reflects an equidistantly sampled rectangle. After rectification, all (virtual) extrinsics and intrinsics are known. After the pre-processing rays can be unambiguously identified by four parameters, plus the known geometry of the capturing device. How these four parameters are defined and what advantages/disadvantages of different representations are will be introduced in this chapter.
Fundamental optimization methods for machine learning

Optimization is one of the core pillars of machine learning, and it has attracted much attention of researchers. Nearly all machine-learning algorithms exploits optimization techniques for parameters to choose optimal solution of objective function before learning the models. Optimization problems are usually solved by numerical or analytical methods. For a given objective function, optimization method tries to find out the global optimal solution. Several issues and challenges for optimization methods occur with the increase of the complexity of the machine-learning models and growth of immense data. In this chapter, the authors discuss traditional optimization methods and their advanced versions, which is widely used in machine learning. Predominantly, the modern prevalent fundamental optimization methods from the perspective of gradient information including the first-order methods, high-order methods, and derivative-free optimization algorithms are introduced. The chapter also discuss some issues and challenges in the field of deep neural networks and various machine-learning optimization methods.
Omnidirectional video saliency

When exploring the visual world, humans (and other species) are faced with more information than they are able to process. To overcome this, selective visual attention allows them to gaze rapidly towards objects of interest in the visual environment. This function of the visual system is of high importance and has received a lot of attention from the scientific community. Being able to understand how selective attention works find applications in various fields, such as design, advertising, perceptual coding and streaming, etc., which makes it a highly regarded research topic. In recent years, with the advent of omnidirectional images and videos (ODI, ODV) new challenges are raised compared to traditional 2D visual content. Indeed, with such type of OD content, users are not restricted to explore what is shown to them in a static viewport, but are now able to freely explore the entire visual world around them by moving their head and even in some cases, by moving their entire body. Therefore, in this chapter, the work on the analysis of user behavior as well as the effort done towards modeling selective visual attention and visual exploration in OD images and video will be introduced. This chapter will provide information on the different approaches that have been taken, and key challenges that have been raised compared to traditional 2D contents, allowing the reader to grasp the key work that has been done on the understanding and modeling of the exploration of OD contents.
Streaming and user behavior in omnidirectional videos

Omnidirectional videos (ODVs) have gone beyond the passive paradigm of traditional video, offering higher degrees of immersion and interaction. The revolutionary novelty of this technology is the possibility for users to interact with the surrounding environment, and to feel a sense of engagement and presence in a virtual space. Users are clearly the main driving force of immersive applications, and, consequentially, the services need to be properly tailored to them. In this context, this chapter highlights the importance of the new role of users in ODV streaming applications, and thus the need for understanding their behavior while navigating within ODVs. A comprehensive overview of the research efforts aimed at advancing ODV streaming systems is also presented. In particular, the state-of-the-art solutions under examination in this chapter are distinguished in terms of system-centric and user-centric streaming approaches: the former approach comes from a quite straightforward extension of well-established solutions for the 2D video pipeline, whereas the latter one takes the benefit of understanding users' behavior and enable more personalized ODV streaming.
Social virtual reality (VR) applications and user experiences

Virtual reality (VR) is the experience in a simulated interactive virtual space. It provides synthetic sensory feedback to users' actions that can physically and mentally immerse the users. Social VR is one type of VR system that allows multiple users to join a collaborative virtual environment and communicate with each other, usually by means of visual and audio cues. The virtual space can be a computer-generated 3D scene or a 360° scene captured by an omnidirectional camera. Each user is represented as a computer-generated avatar or using a virtual representation based on live capture. Social VR has shown to be a promising solution to enrich remote communication experiences in the face of increasing pressures to reduce travel and work remotely. In this chapter, we will showcase the design, implementation, and real-world deployment of a series of social VR applications, which are developed for immersive interactive communication and collaboration for multiple domains (e.g., healthcare, cultural heritage). Moreover, we discuss the experimental protocols for evaluating user experiences in this new medium.