Reviewerly/exHarmony huggingface dataset:
Network architecture and ROA protection of government mail domains: A case study

Email is a crucial technology used in daily interactions of citizens, enterprises and organizations with their respective governments. In this work we are concerned with the country-wide network architecture of mail domains of public administrations. We analyze a dataset of government mail domains in Italy, Germany, the United Kingdom and the United States of America in order to investigate the opportunities for a network attacker to violate security properties of email communication, including availability, in large portions of a country. Issues of this kind are particularly relevant in times of high international tension and in which every country should treat its networks as a potential target for other countries. We define a framework for describing the opportunities for a network attacker in the resolution of mail domain names, resolution of mail server names, access to a mail server. Based on this framework, we investigate in detail a number of issues related to redundancy and distribution of dependencies among networks and autonomous systems. We also analyze the usage in the access to mail domains of Route Origin Authorization (ROA), an important defensive technology for detecting attacks at the IP routing level. Our analysis allows gaining important insights into the actual network architecture of such an important piece of critical infrastructure as government mail domains.
Algorithm of adaptive Fourier decomposition in H2(ℂ+)

In this paper, we propose an applicable algorithm of the so-called adaptive Fourier decomposition in [Formula: see text] the Hardy [Formula: see text] space on the upper half-plane [Formula: see text], which provides a new method for decomposing real-valued signals and analytic signals on the real line. As a by-product, a new approach for computing the Hilbert transform on the real line is also given. Numerical experiments are used to demonstrate the performance of the proposed algorithm.
Collaboration in Publication: Can We Practice What We Preach?

Abstract As editors it is our intent to continually improve the quality of both published articles for our readers and reviews received by our authors here at Transportation Journal. With that purpose in mind, this article seeks to draw parallels between the editorial review process and supply chain collaboration, helping us practice what we teach. Importantly, the review process is intended to co-create value among authors and reviewers under the guidance of the editors. While Transportation Journal already has a core group of scholars who excel in both roles as authors and reviewers, our goal is for this editorial to serve as a guide for our extended network of contributors to adopt an enhanced, collaborative approach. In doing so, we believe that the intended results will ultimately lead us toward production process that is at an even higher quality to generate even greater value for the community.
Harnessing Immersive Technologies for Innovation in Teaching and Learnings

Immersive technologies, due to their transformative potential, do indeed attract the attention of educational researchers. Educators in various fields are beginning to take advantage of VR technologies, and as a result, a growing number of them seek to integrate the technology into their specific areas of expertise. It has been widely documented and addressed extensively that immersive technologies will contribute to learning and teaching, particularly with the rapid rise of augmented reality, virtual reality, and mixed Reality hardware and applications over recent years. As part of the World Economic Forum's latest survey on the Future of Jobs, which was conducted in December 2019, 58% of the businesses surveyed indicated that they are close to adopting immersive technologies by 2022. It was predicted in a report by Goldman Sachs (2018) that 15 million people could benefit from virtual reality and related technologies by the year 2025. In the future, conversational platforms, which range from virtual personal assistants to chatbots, may incorporate expanded sensory channels through which the platform may be able to detect emotions based on facial expressions. As a result, they will become more conversational in interactions. It is the anticipated growth and possibilities of immersive technologies and media in teaching and learning that generate great optimism and excitement for technologists and educational researchers, but it is clear that their attention is insufficiently directed to the instructional design and learning outcomes that result from integrating immersive technologies in such teaching and learning. The educators and learning scientists concerned about the use of these new technologies within educational systems are more concerned with the driving factors that are not pedagogical in nature, but rather technological in origin. It is for this reason that this chapter focuses on a limited but growing body of literature which explores the science of immersive learning beyond simply improving student engagement in the classroom. According to the literature, there are a number of learning affordances that can be identified, including immersion, interactivity, presence and student agency. As important as this chapter's pedagogical framework, instructional design and learning outcomes are, it is intended to facilitate educators’ consideration and adoption of immersive technologies in their content and context. In the context of prevailing instructional design models which are complex and more appropriate for research, this chapter proposes a revised model that puts a greater emphasis on learning than on technologies.
Concluding Thoughts

This chapter covers the holistic insights and future directions of teaching and learning towards twenty-first century and beyond. In this chapter, the “gaps” in terms of where universities and higher education institutions should emphasize would be discussed and suggestions advocated. The implications and ripple effects are highlighted, and possible recommendations proposed with supporting discussions.
Listening to the Anthropocene: A Queda do Céu

Abstract This article discusses the algorithmic design and implementation of A Queda do Céu, a sound installation and kinetic sculpture related to the Soundlapse project. In it we provide an overview of the project and go on to describe the main computational challenges related to the installation, which included a variety of real-time processing, interpolation, and mapping algorithms. We contextualize the work in relation to regional ecological and political debates, as well as the global climate crisis. In doing so, we echo other sound and field-recording artists in proposing that artworks have an important function as experimental arenas in which new technological applications can be probed and where new modes of listening can be investigated, reconfigured, and exercised. In closing, we lay out an overview of the current challenges being tackled by the Soundlapse project, specifically the development of a refined version of the sonic time-lapse method that incorporates machine learning routines and user-defined spatialization capabilities.
Transferable and differentiable discrete network embedding for multi-domains with hierarchical knowledge distillation

Discrete network embedding aims to learn compact and low-dimensional discrete representations for network (graph) nodes by preserving semantical and structural information of networks. In practice, there are numerous domains for network data, but expensive semantical labels may only be available for some of them. This demands those embedding algorithms to be equipped with transferability, i.e., unsupervisedly adapting to other domains. However, the prevailing conventional discrete network embedding methods force on a single domain. This work bridges this gap by developing a transferable discrete network embedding method. We focus on solving two problems: knowledge transfer from a labeled domain to a new unlabeled domain and non-differentiable discretization in the discrete embedding. Specifically, for the former, we propose a hierarchical knowledge distillation strategy to mitigate the knowledge gap between the domains. At the same time, for the latter, we novelly treat the discretization as a classification problem instead of the conventional regression problem. Finally, we conduct a wide range of experiments to evaluate our method on a suite of tasks, including link prediction, node classification, and neighbor recommendation. Our evaluation results demonstrate that our model performs better than the conventional discrete embedding methods over all the tasks.
High-speed implementation of rainbow table method on heterogeneous multi-device architecture

Heterogeneous multi-device architecture has been widely used to solve computationally intensive problems. This paper proposes a high-speed implementation of the rainbow table method based on perfect table in a CPU and GPU based computing system. Firstly, we introduce a memory optimization method to reduce the size of the rainbow table. Then, we show how to parallelize each phase of the rainbow table method using as many devices as possible to achieve optimal performance. We conduct a series of comparative experiments with different cryptographic hash functions on a heterogeneous architecture powered by an Intel Xeon 8176M CPU and two NVIDIA Tesla V100 GPUs. Compared with the state-of-the-art third-party tools, Cryptohaze and RainbowCrack, our implementation can reduce the size of the rainbow table by at least 57.1%, and we can achieve significant speedup: 2.03× and 131.3× speedup in the precomputation phase (table generation); 1.97× and 1.44× speedup in the online phase (searching the pre-image of a hash value), respectively.
Multi-Context Mining-Based Graph Neural Network for Predicting Emerging Health Risks

Patients with similar diseases are able to have similar treatments, care, symptoms, and causes. Based on these relations, it is possible to predict latent risks. Therefore, this study proposes Graph Neural Network-based Multi-Context mining for predicting emerging health risks. The proposed method collects and pre-processes chronic disease patients’ disease information, behavioral pattern information, and mental health information. After that, it performs context mining. This is a multivariate regression analysis for predicting multiple dependent variables, it extracts a regression model and generates a feature map. Then, the initial graph is created by defining the number of clusters as nodes and constructing edges through correlation. By expanding the graph according to the results of context mining, it is possible to predict that a user has a similar chronic disorder and similar symptoms through users’ connection relations. For performance evaluation, the validity of the regression analysis of context mining used in the proposed method, and the suitability of the clustering technique are evaluated.
Optimal Multi-Bit Toffoli Gate Synthesis

Multi-bit Toffoli gates form an essential quantum gate class for quantum algorithms. For the scalable implementation of a quantum algorithm, a multi-bit Toffoli gate should be efficiently decomposed into elementary single- or multi-qubit quantum gates, such as CNOT, T, and Hadarmard. We propose an engineering method for the practical synthesis of multi-bit Toffoli gates. Two optimization models and their closed-form solutions are presented for optimal decomposition of the multi-bit Toffoli gate. These models are based on linearized multi-objective integer programming with parameters such as the targeting ancillae number, ancillae states, and basis gates. This method supports the systematic handling of quantum circuit constraints, such as the total number of available qubits and limit on circuit depth, drawn from various quantum hardware specifications. Our approach shows promise in a noisy intermediate-scale quantum environment, providing a quick and optimal method to synthesize multi-bit Toffoli gates with characteristics such as diverse and unpredictable quantum hardware specifications.
Ensemble Classification With Noisy Real-Valued Base Functions

In data-intensive applications, it is advantageous to perform partial processing close to the data, and communicate intermediate results to a central processor, instead of the data itself. When the communication or computation medium is noisy, the resulting degradation in computation quality at the central processor must be mitigated. We study this problem for the setup of binary classification performed by an ensemble of base functions communicating real-valued confidence levels. We propose a noise-mitigation solution that optimizes the transmission gains and aggregation coefficients of the base functions. Toward that, we formulate a post-training gradient-based optimization algorithm that minimizes the error probability given the training dataset and the noise parameters. We further derive lower and upper bounds on the optimized error probability, and show empirical results that demonstrate the enhanced performance achieved by our approach on real data.
GDSSR: Toward Real-World Ultra-High-Resolution Image Super-Resolution

Although single image super-resolution (SR) has achieved great success, super-resolving the real-world Ultra-High-Resolution (UHR) image remains a challenging issue. Confronted with UHR images, most existing SR methods resort to patch-splitting so that the interconnections among the cropped patches are not attracted reasonable attention during the training and inference procedure. Rather than considering global image degradation levels and types, previous methods only focus on local degradation and unavoidably lead to inter-patch inconsistency, like blocking artifacts in the UHR image. To address this issue, we propose a real-world super-resolution framework to integrate the restoration of different patches through a Global Degradation Supervision Super-Resolution (GDSSR) method. Specifically, a lightweight Global Degradation Extractor is used for extracting global degradation features, which can facilitate restoring better local patches independently and enforce inter-patch consistency. Additionally, a joint training method of local and global patches is proposed to exercise global supervision during the training process, which enhances the degradation estimation and restores more natural results. Experiments show that our GDSSR method achieves superior restoration performance on real-world and UHR image SR datasets.
Reference Datasets for Analysis of Traditional Japanese and German Martial Arts

The study of Japanese fencing and German Longsword Mastercuts based on exact motion measurements in specialist labs is summarized in this work. Based on a streamlined measuring technique, the need for a more thorough study has been suggested that might apply to the observation and evaluation of movement during training in a real-world setting. The requisite data sets and domain knowledge must be available to create motion analysis methods of human sword combat. Such information helps compare several algorithms and techniques, and develop and test new computational methods. In 2020, we created one of the world’s first reference databases of fencing actions, which included five master long sword strikes with kinetic, kinematic, and video modalities. We were able to assess these movements and suggest potential study directions thanks to the created methods and algorithms. This paper proposes to extend the presented registration technologies for long swords and swordsmanship to similar combat, such as Japanese Kendo sword fencing.
A Comparison of Covariate Shift Detection Methods on Medical Datasets

The performance of machine learning models is known to deteriorate on datasets drawn from a different distribution from the one used in model building. In a supervised setting, this deterioration can be assessed by a decrease in the evaluation metrics of the models. When no gold standard information is available, and thus evaluation metrics cannot be determined, one may directly address the problem of detecting whether two datasets differ in distribution. Methods for assessing the difference of distribution from their samples are known as covariate shift detection algorithms. We investigate the ability of the maximum mean discrepancy method, of univariate tests, and of a domain classifier trained to distinguish two datasets, to detect covariate shift in two datasets: one collected for predicting stroke, and one collected for predicting acute myocardial infarction. For this, we artificially perturb parts of the datasets, and check how well these modified datasets can be distinguished from the remaining portions of the original datasets. We observe that univariate tests compare favorably with the other two methods, that changes can be detected more easily in large datasets, that smaller changes are more difficult to detect than larger changes, and that dimensionality reduction is detrimental to detecting covariate shift.
Lower Limbs Gesture Recognition Approach to Control a Medical Treatment Bed

Human machine interaction is showing increasing importance in various areas. In this context a gesture control using machine learning algorithms for a contactless control of a therapy table has been identified as interesting application. Predefined lower limb gestures are performed by an operator, classified by a pocket worn tag, and the results are transferred wirelessly to a remote controller. Two algorithms were compared using a k-nearest neighbor (KNN) and a convolutional neural network (CNN), which are responsible for the classification of the gestures. By using the KNN an accuracy in the range of 75%–82% was achieved. Compared to KNN, CNN achieves 89.1% by applying the categorical classifier and 93.7% by applying the binary classifier. Simplification of work and convenience in using the therapy table can be achieved by high accuracy and fast response of the control system.
Shapley Value Based Variable Interaction Networks for Data Stream Analysis

Due to the growing use of machine learning models in many critical domains, ambitions to make the models and their predictions explainable have increased recently significantly as new research interest. In this paper, we present an extension to the machine learning based data mining technique of variable interaction networks, to improve their structural stability, which enables more meaningful analysis. To verify the feasibility of our approach and it’s capability to provide human-interpretable insights, we discuss the results of experiments with a set of challenging benchmark instances, as well as with real-world data from energy network monitoring.
Deep Learning Data Association Applied to Multi-object Tracking Systems

In this work, a robust and reliable multi-object tracking (MOT) system for autonomous vehicles is presented. In crowded urban road scenes accurate data association between tracked objects and incoming new detections is crucial. To achieve that, a combination of deep learning techniques and a square-root unscented Kalman filter is used. The system follows a tracking-by-detection paradigm and the new deep learning architecture presented is based on Siamese and convolutional LSTM networks. The effectiveness of the proposed system has been tested using the Argoverse dataset.
Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization

Fast Function Extraction (FFX) is a deterministic algorithm for solving symbolic regression problems. We improve the accuracy of FFX by adding parameters to the arguments of nonlinear functions. Instead of only optimizing linear parameters, we optimize these additional nonlinear parameters with separable nonlinear least squared optimization using a variable projection algorithm. Both FFX and our new algorithm is applied on the PennML benchmark suite. We show that the proposed extensions of FFX leads to higher accuracy while providing models of similar length and with only a small increase in runtime on the given data. Our results are compared to a large set of regression methods that were already published for the given benchmark suite.
Dynamic Fitness Landscape Analysis

Dynamic optimization problems pose a big challenge for classic optimization algorithms. They could simply be viewed as a series of related optimization problems. In particular the aspect of time-linkage has not been well studied yet. In this work we are analyzing an artificial problem based on real-world data to elucidate the potential of fitness landscape analysis methods to discover problem difficulty and follow along the changes of dynamic problems and how these changes can be measures and might be exploited by enabling algorithm introspection.
Automatic Book Page Flipper for Disabled People

Reading is an important aspect of human life. Even in today’s digital world, people prefer a physical book for reading instead of using digital content. But what about those people who are paralyzed, or disable in any manner due to which they are unable to turn the pages of a book and are dependent on others for their all needs, page-turning is an essential auxiliary process of reading. People with disabilities and those who have restricted upper-body extremity function may benefit from an automatic page turner. A page-turner can provide handy book support and page-turning features to avid readers. In this research two applications have been used for operation, that’s why it is hybrid research. This research can be operated by any one of two applications either by push button or head-based movement which makes this invention unique. It is based on Arduino and consists of five servomotors which enable the movement of page in both directions and help the user in turning the page easily so that user can read a book. The goal of this research is to discover a new innovative type of automatic book page-turner that will assist such impaired persons in completing the movements of turning pages to fulfill their desire to read and learn.
Decentralized Data Privacy Protection and Cloud Auditing Security Management

Decentralized data protection is becoming more important as more firms employ Cloud resources. Securing, protecting, and processing user data are primary issues that Cloud Computing faces. This study’s primary goal is to detect and address attack protection in Cloud. Our objectives are to comprehend the present state of cloud computing security concerns and methods, identify future cloud computing security concerns to offer countermeasures for potential. Preventative measures to employing cloud services include data security and privacy concerns. The challenge is to create cloud platforms for integrating decentralized information systems to ensure the user’s privacy. For management units of distributed systems, the requirements analysis yielded a model for organizational levels of cloud systems. By bypassing the single-point-of-failure of TPA, our suggested approach may prevent service disruption and privacy-preserving concerns. The extensive security analysis proves that our suggested solution meets all the needed security criteria. We also implement our suggested method with PSO-Homomorphic Algorithm (PSO-HA) and evaluate its efficiency in real-world situations, demonstrating its efficacy, high efficiency, and portability. Our results show that our methodology for simultaneously-integrated recovery can be implemented in polynomial time and is thriving.