Reviewerly/exHarmony huggingface dataset:
Event Prediction Technique Based on Correlation Mapping and Fourier Decomposition

The method of predicting events based on correlation mapping and Fourier decomposition of relevant time series, which is presented in this paper, is intended for the predictive calculation of signs that an event will occur. As input data for calculating the forecast, in addition to the series that needs to be extended with forecast values, relevant series that are close in the content are used. The calculation is based on a model that calculates the correlations of the total values of the Fourier decomposition for all relevant time series with the historical series. In this case, a certain number of constant parameters of the Fourier decomposition is calculated as the result of solving the optimization problem. Based on the generated model and the values of constant parameters, further values of the initial series are calculated, that is, the predicted event is determined.
Information users as active prosumers: perspectives from social marketing and sociocultural value for academic libraries’ benchmarking processes

In order for academic libraries to carry out benchmarking processes, they need to generate a specific conceptualization and characterization of marketing to enable them to promote their services, transcending the traditional schemes that are typical of for-profit organizations and thus suggesting a definition of a new model based on social marketing as a management tool. Social marketing best practices allow a series of indicators that can be used for benchmarking processes regarding the main elements analyzed in this chapter. The chapter proposes a differentiation in treating the information user as an active prosumer instead of a passive client or beneficiary. With this, some ingrained inconvenient habits are identified in order to define and address the authentic needs, which would lead to the required improvements of information services. Such social marketing for academic libraries would consider the following four main elements: (1) redefining profitability as sociocultural value, as libraries are nonprofit service organizations; (2) enhancing the identification of the target prosumers and their needs that can be satisfied with libraries’ services; (3) identifying prosumers’ satisfaction mechanisms that provide valuable and mutual feedback for improving information products, resources, and services, while making them more efficient for the community; and (4) defining library identity as a social stakeholder, instead of centering on a corporate image, which would enhance its perception at a sociocultural level.
Applicability of federated learning for securing critical energy infrastructures

Energy grids are becoming more intelligent due to the use of a vast array of technologies, including the Internet of Things and Intelligent Systems. These critical energy infrastructures, which are essentially cyber-physical systems, are particularly vulnerable to cyber threats. Machine learning techniques have been increasingly used in security applications, and the energy domain is no exception. One approach, in particular, federated learning (FL), employs a distributed architecture and has potential in security applications, as it counters the issue of having a centralized data warehouse. The main aims of this work are to present a review of FL and its applications in security and privacy, together with a demonstration case involving the implementation of a simulated model of FL for enhancing the security of systems. This demonstration case has provided added insight into potential issues and challenges as well as mitigation strategies.
Fast forward or digital mirage—benchmarking education system responses to COVID, and what comes after?

Technology in education (edtech) has often been viewed as something of a Cinderella subject—“one day you shall go to the ball, but that day never seems to come.” This chapter explores the promise and vision of edtech espoused by its most ardent enthusiasts, and how this has collided with the realities of overhyped and underbaked technologies and conservative education systems. It goes on to consider the system shock created by the COVID-19 pandemic and how those education systems and the people involved in them ultimately responded to what was for many the most profound and far-reaching crisis they had ever experienced. The chapter includes case studies of two of these system-level responses that the author has been personally involved with, and it concludes with a discussion comparing and contrasting these system-level responses with the visions of the edtech mavens.
The “Diktat für Schlick”: Authorship Research and Computational Stylometry Revisited

Both the authorship and the dating of the so-called “Diktat für Schlick” (DFS), once attributed to Ludwig Wittgenstein and assigned by Georg Henrik von Wright to the Wittgenstein Nachlass as item 302, are debated topics in Wittgenstein and Vienna Circle research. Schulte (Waismann as Spokesman for Wittgenstein. In: McGuinness B (ed). Friedrich Waismann - causality and logical positivism. Vienna Circle Institute Yearbook 15. Springer, Dordrecht, pp 225–242, 2011) and Manninen (Waismann’s testimony of Wittgenstein’s fresh starts 1931–35. In: McGuinness B (ed). Friedrich Waismann - causality and logical positivism. Vienna Circle Institute Yearbook 15. Springer, Dordrecht, pp 243–265, 2011) hold that DFS was authored by Friedrich Waismann rather than Wittgenstein. Applying techniques from computational stylometry to the authorship question, the paper concludes that DFS is located stylometrically in the middle between Waismann’s and Wittgenstein’s writings, but slightly closer to Wittgenstein, and so Wittgenstein authorship is hence stylometrically still not unlikely. The paper concludes by presenting a number of factors that speak in favour of the view that DFS might originally indeed have been dictated by Wittgenstein. For the computational stylometry component, the paper uses the Eder et al.’s (Stylometry with R: a package for computational text analysis. R Journal 8/1:107–121. Accessed 21 Oct 2021. https://journal.r-project.org/archive/2016/RJ-2016-007/index.html , 2016) “Stylometry with R” package; the degree of similarity and dissimilarity between documents is calculated by Burrows’ Delta measure; and the results are displayed using Hierarchical Cluster Analysis and Principal Components Analysis. For the text corpus part, the paper uses texts authored by Schlick, Waismann and Wittgenstein. For the archival research part, the paper refers to materials form the Schlick Nachlass in the North Holland Archives, the Waismann Nachlass in the Bodleian Libraries, the Rose Rand Nachlass in the Pittsburgh Archives of Scientific Philosophy, the Ludwig Wittgenstein Nachlass in the Trinity College Cambridge Wren Library, and the Cornell copy of the Ludwig Wittgenstein Nachlass. The paper is a follow-up on Oakes and Pichler (Computational stylometry of Wittgenstein’s ‘Diktat für Schlick’”. In: Hareide L, Johannson C, Oakes M (eds). The many facets of corpus linguistics in Bergen: In honour of Knut Hofland. Bergen Language and Linguistics Series (BeLLS), Bergen, pp 221–240, 2013); for the current paper we have extended the Waismann text corpus with more texts written under the influence of Wittgenstein, a.o. Logik, Sprache, Philosophie (1976).
Investigation and site selection for small hydropower system

Hydropower generation requires a continuous source of water supply and an appropriate site and location where the hydropower station can be installed. Site selection, development type, and construction scale are essential aspects of the planning of small hydropower projects (SHP) project. Thus, various site investigations need to be conducted for the establishment of SHP plants which requires the collection and analysis of pertinent data. Investigations need investment in terms of time and money. Thus the extent of data to be collected/measured depend on the magnitude of the project and requirement. The advances in geomatics and availability of digital databases assist in improving its accuracy, reliability, promptness, and applicability.
A brief overview of physics-inspired metaheuristics

Metaheuristic algorithms are methods devised to efficiently solve computationally challenging optimization problems. Researchers have taken inspiration from various natural and physical processes alike to formulate metaheuristics that have successfully provided near-optimal or optimal solutions to several engineering tasks. This chapter focuses on metaheuristic algorithms modeled on nonlinear physical phenomena having a concrete optimization paradigm that have shown formidable exploration and exploitation abilities for such optimization problems. Specifically, this chapter focuses on several popular physics-based metaheuristics and describes the underlying unique physical processes associated with each of them.
Soviet Book Graphics from the Fund of the Gorky Museum in Moscow as the Artistic and Stylistic Basis of Exhibition Projects

On the eve of the 100th anniversary of the October Revolution the Gorky Museum faced the challenge of formalizing an exhibition related to this jubilee theme. Undoubtedly, the mythological theme “Gorky — Petrel of the Revolution” has long been rooted in the public consciousness, therefore, when searching for the main artistic concept of the exhibition, the main principles were formulated-based on art collections from the museum’s funds, to present materials not in the traditional historical and documentary manner, but in addition this, and in artistic-figurative.
Creativity of M. Gorky in the Interpretations of Contemporary Playwrights

Modern playwrights often turn to the works of the classics of literature and create remakes. We are interested in two pieces that were written at the turn of 20–21 centuries: farce “At The Bottom” by I. Shprits (1995) and the radio piece S. Shulyak’s “The New song about Falcon” (2000). Both works created on the basis of creativity M. Gorky’s interesting because it organically combines Russian literary tradition and techniques of the “new drama”. Playwright Igor Shprits in his play “At The Bottom” syringe complies with the features of the construction of the Gorky works, plays the situation of “theatre in theatre” and fills the bitter text with new meanings. S. Shulyak wrote a “continuation” of the story of the Snake and the Falcon — radio piece “New song of the Falcon”. The playwright remains largely faithful to the original, complements the characters-antipodes of the new features. Talented works revived interest in the classics, open new current motives and problems.
Training Dynamics Aware Neural Network Optimization with Stabilization

We investigate the process of neural network training using gradient descent-based optimizers from a dynamic system point of view. To this end, we model the iterative parameter updates as a time-discrete switched linear system and analyze its stability behavior over the course of training. Accordingly, we develop a regularization scheme to encourage stable training dynamics by penalizing divergent parameter updates. Our experiments show promising stabilization and convergence effects on regression tasks, density-based crowd counting, and generative adversarial networks (GAN). Our results indicate that stable network training minimizes the variance of performance across different parameter initializations, and increases robustness to the choice of learning rate. Particularly in the GAN setup, the stability regularization enables faster convergence and lower FID with more consistency across runs. Our source code is available at: https://github.com/fangzl123/stableTrain.git .
LSMD-Net: LiDAR-Stereo Fusion with Mixture Density Network for Depth Sensing

Depth sensing is critical to many computer vision applications but remains challenge to generate accurate dense information with single type sensor. The stereo camera sensor can provide dense depth prediction but underperforms in texture-less, repetitive and occlusion areas while the LiDAR sensor can generate accurate measurements but results in sparse map. In this paper, we advocate to fuse LiDAR and stereo camera for accurate dense depth sensing. We consider the fusion of multiple sensors as a multimodal prediction problem. We propose a novel end-to-end learning framework, dubbed as LSMD-Net to faithfully generate dense depth. The proposed method has dual-branch disparity predictor and predicts a bimodal Laplacian distribution over disparity at each pixel. This distribution has two modes which captures the information from two branches. Predictions from the branch with higher confidence is selected as the final disparity result at each specific pixel. Our fusion method can be applied for different type of LiDARs. Besides the existing dataset captured by conventional spinning LiDAR, we build a multiple sensor system with a non-repeating scanning LiDAR and a stereo camera and construct a depth prediction dataset with this system. Evaluations on both KITTI datasets and our home-made dataset demonstrate the superiority of our proposed method in terms of accuracy and computation time.
Neural Plenoptic Sampling: Learning Light-Field from Thousands of Imaginary Eyes

The Plenoptic function describes light rays observed from any given position in every viewing direction. It is often parameterized as a 5-D function $$L(x, y, z, \theta , \phi )$$ for a static scene. Capturing all the plenoptic functions in the space of interest is paramount for Image-Based Rendering (IBR) and Novel View Synthesis (NVS). It encodes a complete light-field (i.e., lumigraph) therefore allows one to freely roam in the space and view the scene from any location in any direction. However, achieving this goal by conventional light-field capture technique is expensive, requiring densely sampling the ray space using arrays of cameras or lenses. This paper proposes a much simpler solution to address this challenge by using only a small number of sparsely configured camera views as input. Specifically, we adopt a simple Multi-Layer Perceptron (MLP) network as a universal function approximator to learn the plenoptic function at every position in the space of interest. By placing virtual viewpoints (dubbed ‘imaginary eyes’) at thousands of randomly sampled locations and leveraging multi-view geometric relationship, we train the MLP to regress the plenoptic function for the space. Our network is trained on a per-scene basis, and the training time is relatively short (in the order of tens of minutes). When the model is converged, we can freely render novel images. Extensive experiments demonstrate that our method well approximates the complete plenoptic function and generates high-quality results.
Adaptive FSP: Adaptive Architecture Search with Filter Shape Pruning

Deep Convolutional Neural Networks (CNNs) have high memory footprint and computing power requirements, making their deployment in embedded devices difficult. Network pruning has received attention in reducing those requirements of CNNs. Among the pruning methods, Stripe-Wise Pruning (SWP) achieved a further network compression than conventional filter pruning methods and can obtain optimal kernel shapes of filters. However, the model pruned by SWP has filter redundancy because some filters have the same kernel shape. In this paper, we propose the Filter Shape Pruning (FSP) method, which prunes the networks using the kernel shape while maintaining the receptive fields. To obtain an architecture that satisfies the target FLOPs with the FSP method, we propose the Adaptive Architecture Search (AAS) framework. The AAS framework adaptively searches for the architecture that satisfies the target FLOPs with the layer-wise threshold. The layer-wise threshold is calculated at each iteration using the metric that reflects the filter’s influence on accuracy and FLOPs together. Comprehensive experimental results demonstrate that the FSP can achieve a higher compression ratio with an acceptable reduction in accuracy.
Meta-Det3D: Learn to Learn Few-Shot 3D Object Detection

This paper addresses the problem of few-shot indoor 3D object detection by proposing a meta-learning-based framework that only relies on a few labeled samples from novel classes for training. Our model has two major components: a 3D meta-detector and a 3D object detector. Given a query 3D point cloud and a few support samples, the 3D meta-detector is trained over different 3D detection tasks to learn task distributions for different object classes and dynamically adapt the 3D object detector to complete a specific detection task. The 3D object detector takes task-specific information as input and produces 3D object detection results for the query point cloud. Specifically, the 3D object detector first extracts object candidates and their features from the query point cloud using a point feature learning network. Then, a class-specific re-weighting module generates class-specific re-weighting vectors from the support samples to characterize the task information, one for each distinct object class. Each re-weighting vector performs channel-wise attention to the candidate features to re-calibrate the query object features, adapting them to detect objects of the same classes. Finally, the adapted features are fed into a detection head to predict classification scores and bounding boxes for novel objects in the query point cloud. Several experiments on two 3D object detection benchmark datasets demonstrate that our proposed method acquired the ability to detect 3D objects in the few-shot setting.
DecisioNet: A Binary-Tree Structured Neural Network

Deep neural networks (DNNs) and decision trees (DTs) are both state-of-the-art classifiers. DNNs perform well due to their representational learning capabilities, while DTs are computationally efficient as they perform inference along one route (root-to-leaf) that is dependent on the input data. In this paper, we present DecisioNet (DN), a binary-tree structured neural network. We propose a systematic way to convert an existing DNN into a DN to create a lightweight version of the original model. DecisioNet takes the best of both worlds - it uses neural modules to perform representational learning and utilizes its tree structure to perform only a portion of the computations. We evaluate various DN architectures, along with their corresponding baseline models on the FashionMNIST, CIFAR10, and CIFAR100 datasets. We show that the DN variants achieve similar accuracy while significantly reducing the computational cost of the original network.
WTBNeRF: Wind Turbine Blade 3D Reconstruction by Neural Radiance Fields

With the increasing popularity of wind turbines, the demand for integrity detecting of wind turbines operating in natural or extreme environments is also increasing. To better detect the state of wind turbines, rendering and 3D reconstruction of realistic wind turbine models has become a crucial task. The neural radiation field is becoming a widely used method for novel view synthesis and 3D reconstruction. In the original neural radiation field method, the scene or object is required to have a large number of features or complex textures. But for wind turbines, the surface is smooth and texture-free, which creates blur and ghosting in the results. Therefore, we propose the Wind Turbine Neural Radiance Fields (WTBNeRF), a network dedicated to wind turbine rendering and 3D reconstruction. Instead of single pixel-centered rays, we use conical truncated rays to cover individual pixel ranges in greater detail, effectively reducing aliasing and blurring in smooth, low-texture wind turbine scenes. At the same time, obtaining accurate camera poses for low-texture objects and scenes is also a challenging task. We use a pretrained camera pose estimation neural radiation field network to predict the camera pose of the wind turbines in the dataset, reducing the requirement to know the real camera parameters of the data in advance. Moreover, in the network design, we simplify the network structure, which greatly reduced the network training time. The speed is about 10 times faster than that of NeRF for our multi-scale wind turbine dataset.
Comparison of scoring systems’ mortality prediction ability in COVID-19 intensive care patients over 80 years of age

care patients over 80 years of age
Kanban in Software Development—The Role of Leadership and Metrics

Traditional software development organizations have rigid processes that can be difficult to change. One of the most critical challenges is the management processes that can ensure the customer’s desired level of quality and optimize the organization’s resources. Kanban represents one of the most well-known approaches that bring Lean principles, philosophies, and tools into software organizations. This study analyses the leadership behind the Lean methodology, based on which the Kanban software implementation method has been created, by doing a literature review focusing on lean leadership principles that can optimize the software delivery and implementation. Lean practices can be applied in almost any domain like accounting, services industry, product services, and even software development. Two basic concepts stay at the core of the Lean model that can be employed at any organization level. The first focuses on eliminating waste by optimizing processes and systems, and the second focuses on maximized customer value. Consequently, it can be affirmed that Lean is focused on maximizing customer value with a minimum amount of waste. Furthermore, a case study is presented that shows the Kanban implementation at a software development company. The practical case of the Kanban adoption is analyzed, highlighting the link between the leadership principles and the process development flow. Finally, key performance indicators are identified and instituted to guarantee the continuous improvement of the software delivery model. The software development projects driven by the Kanban project management approach reported essential improvements such as waste reduction, team communication, and collaboration enhancement, work in progress diminishment, productivity-boosting, better delivery predictability, and project visibility. Therefore, the type of leadership influences the software development projects’ performance. The findings suggest that Kanban effectively enhances the software development delivery model and team performance.