Reviewerly/exHarmony huggingface dataset:
FLEDGE: Kubernetes Compatible Container Orchestration on Low-Resource Edge Devices

In recent years, containers have quickly gained popularity in the cloud, mostly thanks to their scalable, ethereal and isolated nature. Simultaneously, edge devices have become powerful enough to run containerized microservices, while remaining small and low-powered. These evolutions have triggered a wave of research into container placement strategies on clusters including edge devices, leading to concepts such as fog computing. These container placement strategies can optimize workload placement across cloud and edge clusters, but current container orchestrators are very resource intensive and are not designed to run on edge devices. This paper presents FLEDGE as a Kubernetes compatible edge container orchestrator. A number of aspects of how to achieve low-resource container orchestration are examined, for example the choice of container runtime and how to implement container networking. Finally, a number of evaluations are performed, comparing FLEDGE to K3S and Kubernetes, to show that it is a viable alternative to existing container orchestrators.
Design and Calibration of a Fast Flying-Dot Projector for Dynamic Light Transport Acquisition

The light transport captures a scene's visual complexity. Acquiring light transport for dynamic scenes is difficult, since any change in viewpoint, materials, illumination or geometry also varies the transport. One strategy to capture dynamic light transport is to use a fast “flying-dot” projector; i.e., where an impulse light-probe is quickly scanned across the scene. We have built a novel fast flying-dot projector prototype using a high speed camera and a scanning MEMS (Micro-electro-mechanical system) mirror. Our contributions are calibration strategies that enable dynamic light transport acquisition at near video rates with such a system. We develop new methods for overcoming the effects of MEMS mirror resonance. We utilize new algorithms for denoising impulse scanning at high frame rates and compare the trade-offs in visual quality between frame rate and illumination power. Finally, we show the utility of our calibrated setup by demonstrating graphics applications such as video relighting, direct/global separation, and dual videography for dynamic scenes such as fog, water, and glass. Please see our accompanying video for dynamic scene results.
Semi-supervised Classification Based Mixed Sampling for Imbalanced Data

Abstract In practical application, there are a large amount of imbalanced data containing only a small number of labeled data. In order to improve the classification performance of this kind of problem, this paper proposes a semi-supervised learning algorithm based on mixed sampling for imbalanced data classification (S2MAID), which combines semi-supervised learning, over sampling, under sampling and ensemble learning. Firstly, a kind of under sampling algorithm UD-density is provided to select samples with high information content from majority class set for semi-supervised learning. Secondly, a safe supervised-learning method is used to mark unlabeled sample and expand the labeled sample. Thirdly, a kind of over sampling algorithm SMOTE-density is provided to make the imbalanced data set become balance set. Fourthly, an ensemble technology is used to generate a strong classifier. Finally, the experiment is carried out on imbalanced data with containing only a few labeled samples, and semi-supervised learning process is simulated. The proposed S2MAID is verified and the experimental result shows that the proposed S2MAID has a better classification performance.
Non-linear mathematical model to predict the changes in underground water level and salt concentration

Abstract An urgent problem related to the process of change in underground water level and mineral salt transfer in soils is solved in the paper. The problem is described by a system of partial differential equations and the corresponding initial, internal and boundary conditions of various kinds. To derive a mathematical model of the process under consideration, a detailed review of scientific papers devoted to various aspects and software of the object of study is given. To conduct a comprehensive study of the process of filtration and change in salt regime of groundwater, mathematical models and an effective numerical algorithm are proposed taking into account external sources and evaporation. Since the process is described by a nonlinear system of partial differential equations, it is difficult to obtain an analytical solution. To solve it, a numerical algorithm based on a finite-difference scheme is developed, and an iterative scheme is used for nonlinear terms, the convergence of the iterative method is checked. In the conclusion of the paper it is shown that the developed mathematical apparatus can significantly reduce the volume of field experiments on monitoring and predicting the level of groundwater and salt concentration and minimize expensive and resource-intensive experimental work.
Improved Manipulator Obstacle Avoidance Path Planning Based on Potential Field Method

Aiming at the existing artificial potential field method, it still has the defects of easy to fall into local extremum, low success rate and unsatisfactory path when solving the problem of obstacle avoidance path planning of manipulator. An improved method for avoiding obstacle path of manipulator is proposed. First, the manipulator is subjected to invisible obstacle processing to reduce the possibility of its own collision. Second, establish dynamic virtual target points to enhance the predictive ability of the manipulator to the road ahead. Then, the artificial potential field method is used to guide the manipulator movement. When the manipulator is in a local extreme or oscillating, the extreme point jump-out function is used in real time to make the end point of the manipulator produce small displacements and change the action direction to effectively jump out of the dilemma. Finally, the manipulator is controlled to avoid all obstacles and move smoothly to form a spatial optimization path from the start point to the end point. The simulation experiment shows that the proposed method is more suitable for complex working environment and effectively improves the success rate of manipulator path planning, which provides a reference for further developing the application of manipulator in complex environment.
Studies on Search: Designing Meaningful IIR Studies on Commercial Search Engines

Abstract The purpose of this paper is (1) to show which topics are especially fruitful for researchers interested in user behavior in commercial search engines, (2) to help researchers decide which data to collect and to what extent. We classify potential areas for IIR research along two dimensions, namely the type of interaction data used (small-scale or large-scale), and whether search engine companies are likely to publish research on the topic chosen (likely or unlikely). This results in a framework consisting of five areas, which are further detailed. In the second part of the paper, we present some empirical studies showing how researchers could approach relevant topics where no results from the search engine providers themselves are published. We also show how researchers can improve the evidential value of their work by going from small-scale to at least medium-scale studies.
Learning priors for adversarial autoencoders

Abstract Most deep latent factor models choose simple priors for simplicity, tractability, or not knowing what prior to use. Recent studies show that the choice of the prior may have a profound effect on the expressiveness of the model, especially when its generative network has limited capacity. In this paper, we propose to learn a proper prior from data for adversarial autoencoders (AAEs). We introduce the notion of code generators to transform manually selected simple priors into ones that can better characterize the data distribution. Experimental results show that the proposed model can generate better image quality and learn better disentangled representations than AAEs in both supervised and unsupervised settings. Lastly, we present its ability to do cross-domain translation in a text-to-image synthesis task.
A Graph-Based Approach for Active Learning in Regression

Active learning aims to reduce labeling efforts by selectively asking humans to annotate the most important data points from an unlabeled pool and is an example of human-machine interaction. Though active learning has been extensively researched for classification and ranking problems, it is relatively understudied for regression problems. Most existing active learning for regression methods use the regression function learned at each active learning iteration to select the next informative point to query. This introduces several challenges such as handling noisy labels, parameter uncertainty and overcoming initially biased training data. Instead, we propose a feature-focused approach that formulates both sequential and batch-mode active regression as a novel bipartite graph optimization problem. We conduct experiments on both noise-free and noisy settings. Our experimental results on benchmark data sets demonstrate the effectiveness of our proposed approach.MSC codesActive learningbipartite graphsregressionapproximation algorithmnoisy data
Copy and Move Forgery Detection Using SIFT and Local Color Dissimilarity Maps

It is becoming harder and harder to believe in digital images authenticity. This is due to the increasing ease of producing forged images using retouching software more powerful than ever. On software like Photoshop, one can easily remove an element from an image with just a few clicks. Copy-move is one of the elemental forgery methods that consists in the duplication of one portion of the image. Such manipulation is very usual and dangerous in official document falsification (ID, breeder docs, bills. . . ). In this paper, we propose a novel method for the detection of Copy and Move forgery while maintaining a low false positive rate.
Rapid development of Virtual Reality based construction sequence simulations: a case study

Virtual Reality (VR) is a powerful tool for providing active and interactive learning experience for students where real-life physical conditions are not available. Visualizations of construction sequencing of components have been identified as an area where VR can enhance student learning. VR simulations allow students a flexible learning environment for them to explore without restrictions of timing physical site visits or inherent dangers of an active construction project. One of the barriers for widespread use of VR in the construction classroom is the lack of available content. This paper explores the development of a framework for rapid content development that can help students develop an understanding of the sequence and components of construction assemblies. The framework and necessary model development is discussed with initial responses from students to a pilot test of the simulations in the classroom.
ZyNet: Automating Deep Neural Network Implementation on Low-Cost Reconfigurable Edge Computing Platforms

Prevalence of internet of things (IoT) enabled applications provide a new opportunity to low-cost FPGA devices to act as edge computing neural network nodes. Although FPGA vendors provide neural network development environments, they often target high-end devices. At the same time these development platforms are not as user friendly as their software counterparts. In this work we introduce ZyNet, a Python package, which enables faster implementation of deep neural networks (DNNs) targeting low-cost hybrid FPGA platforms such as the Xilinx Zynq. Based on hardware-software co-design approach, this platform supports pre-trained or on-board trained networks with development environment very similar to the popular TensorFlow. Implementation results show that the DNNs generated by the platform achieve accuracy very close to software implementations at the same time gives throughput by an order of magnitude compared to other edge computing devices at lower energy footprint. The platform is integrated with Xilinx development tools and is distributed as open source.
Novel Approach for Teaching English Language using Emerging Information and Communication Technologies for Visual Impairment Students

Nowadays, Higher Education Institutions (HEI) need to be more inclusive from the methodological vision appropriate used in the classroom. Visual impairment students (VIS) become a challenge for teachers who look for incorporating different learning environments. This challenge increases when teaching and learning process to VIS is related to the English language; due to the absence of educational didactic resources available at the school and the lack of knowledge of appropriate teaching methodologies and strategies to develop the different language skills: listening, speaking, reading and writing that go with the educational process in the acquisition of the English language. In accordance with this problem, this research proposes a novel methodological strategy, based on the usage of meaningful learning with emerging technologies as a main guide for students with visual impairment. Thus, the road map of good practices in the use of this methodology will allow establishing a clear process to achieve students’ active, meaningful and autonomous participation, using the least time in the teaching-learning process of the English language.
Current State and Development Trends of blockchain Technology in the Financial Sector

The article analyzes the main reasons for the slow adoption of blockchain technology, in particular, in the financial sector. The authors critically analyzed the main declared properties of blockchain technologies: trust, security, decentralization, immutable data storage, lack of intermediaries, hardware protection against attacks, and openness. The aim of the study are to show that these blockchain properties are overestimated, the expectations of its adoption are inflated, and the delays in its adaptation outside of cryptocurrencies, in particular, in the financial sector, are natural. The article is based on a methodology for the qualitative and quantitative analysis of scientific publications and statistical sources on the blockchain adaptation from the perspective of the theory of diffusion of innovations, the conditions and the specifics of economic and sociological approaches for consensus-building. The study resulted in the following new systemic findings. Blockchain and distributed ledgers are not fundamentally new technologies. In general, they do not have the properties of the immutable data storage, trust, anonymity, low transaction and adoption costs. All current consensus technologies have fundamental faults. Cryptocurrency technology is original, but it was a private experimental solution to a specific ideological problem of the libertarian political agenda. Consensus does not provide trust. Delayed blockchain adoption, in particular in traditional financial institutions, is natural, since the technology does not show better results than current digital solutions, and traditional economic institutions have greater public trust. The practical implications of the findings are that they may be used by investors.
The Effectiveness of Stemming in the Stylometric Authorship Attribution in Arabic

The recent years have witnessed the development of numerous approaches to authorship attribution including statistical and linguistic methods.Stylometric authorship attribution, however, remains among the most widely used due to its accuracy and effectiveness.Nevertheless, many authorship problems remain unresolved in terms of Arabic.This can be attributed to different factors including linguistic peculiarities that are not usually considered in standard authorship systems.In the case of Arabic, the morphological features carry unique stylistic features that can be usefully used in testing authorship in controversial texts and writings.The hypothesis is that much of these morphological features are lost due to the execution of stemming.As such, this study is concerned with investigating the effectiveness of stemming in the stylometric applications to authorship attribution in Arabic.In so doing, three Arabic stemmers GOLD stemmer, Khoga stemmer, Light 10 stemmer are used.By way of illustration, a corpus of 2400 news articles written by different 97 authors is designed.To evaluate the effectiveness of stemming, the selected articles (both stemmed and unstemmed texts) are clustered using cluster analysis methods.Comparisons are made between clustering structures based on stemmed and unstemmed datasets.The results indicate that stemming has negative impacts on the accuracy of the clustering performance and thus on the reliability of stylometric authorship testing in Arabic.The peculiar stylistic features of the affixation processes in Arabic can, thus, be usefully used for improving the performance of authorship attribution applications in Arabic.It can be finally concluded that stemming is not effective in the stylometric authorship applications in Arabic.
Online bibliographic reference

What happens to the bibliographic reference when scientific publications turn from printed publications to online ones? At first glance, very little: a hyperlink to this work's online position may be added to the usual bibliographic information. However, even GOST R 7.0.5–2008 "Bibliographic reference," which was published more than 10 years ago, already provided for specifying two additional online attributes in this reference: the date when the author of the reference last accessed it, and the date when the version of the publication used by the author was posted. Over the years, much has changed in this area, and some of the old hyperlink attributes have lost their relevance. However, through the increased capacity of cross-domain solutions, now there is the technical possibility to include dynamically important to online reader information in the text of online bibliographic reference. In particular, the number of visits to work yesterday and total visits from the date of posting, the number of references to this work according to Google Scholar and/or Crossref, the date of the latest available revision of the text, etc. are in high demand by online readers. Such opportunities, which are very topical for a modern author of scientific publication, are unfortunately ignored in Russian state standard P 7.0.108-2022, which was introduced in 2022.
An Improved Deep Learning Approach based on Variant Two-State Gated Recurrent Unit and Word Embeddings for Sentiment Classification

Sentiment classification is an important but challenging task in natural language processing (NLP) and has been widely used for determining the sentiment polarity from user opinions.And word embedding technique learned from a various contexts to produce same vector representations for words with same contexts and also has been extensively used for NLP tasks.Recurrent neural networks (RNNs) are common deep learning architecture that are extensively used mechanism to address the classification issue of variable-length sentences.In this paper, we analyze to investigate variant-Gated Recurrent Unit (GRU) that includes encoder method to preprocess data and improve the impact of word embedding for sentiment classification.The real contributions of this paper contain the proposal of a novel Two-State GRU, and encoder method to develop an efficient architecture namely (E-TGRU) for sentiment classification.The empirical results demonstrated that GRU model can efficiently acquire the words employment in contexts of user's opinions provided large training data.We evaluated the performance with traditional recurrent models, GRU, LSTM and Bi-LSTM two benchmark datasets, IMDB and Amazon Products Reviews respectively.Results present that: 1) proposed approach (E-TGRU) obtained higher accuracy than three stateof-the-art recurrent approaches; 2) Word2Vec is more effective in handling as word vector in sentiment classification; 3) implementing the network, an imitation strategy shows that our proposed approach is strong for text classification.
Evaluation of Open Source Web Application Vulnerability Scanners

Nowadays, web applications are essential part of our lives. Web applications are used by people for information gathering, communication, e-commerce and variety of other activities. Since they contain valuable and sensitive information, the attacks against them have increased in order to find vulnerabilities and steal information. For this reason, it is essential to check web application vulnerabilities to ensure that it is secure. However, checking the vulnerabilities manually is a tedious and time-consuming job. Therefore, there is an exigent need for web application vulnerability scanners. In this study, we evaluate two open source web application vulnerability scanners Paros and OWASP Zed Attack Proxy (OWASP ZAP) by testing them against two vulnerable web applications buggy web application (bWAPP) and Damn Vulnerable Web Application (DVWA).
SRP‐AKAZE: an improved accelerated KAZE algorithm based on sparse random projection

The AKAZE algorithm is a typical image registration algorithm that has the advantage of high computational efficiency based on non‐linear diffusion. However, it is weaker than the scale‐invariant feature transformation (SIFT) algorithm in terms of robustness and stability. We propose a new and improved version of the AKAZE algorithm by using the SIFT descriptor based on sparse random projection (SRP). The proposed method not only retains the advantage of high efficiency of the AKAZE algorithm in feature detection but also has the stability of the SIFT descriptor. Moreover, the computational complexity due to the high dimension of the SIFT descriptor, which limits the speed of feature matching, is drastically reduced by the SRP strategy. Experiments on several benchmark image datasets demonstrate that the proposed algorithm can significantly improve the stability of the AKAZE algorithm, and the results suggest the better matching performance and robustness of the feature descriptor.
Flexible quantum tokens in spacetime

S-money [A. Kent, Proc. R. Soc. A 475, 20190170 (2019)] schemes define virtual tokens designed for networks with relativistic or other trusted signaling constraints. The tokens allow near-instant verification and guarantee unforgeability without requiring quantum state storage. We present refined two stage S-money schemes. The first stage, which may involve quantum information exchange, generates private user token data. In the second stage, which need only involve classical communications, users determine the valid presentation point, without revealing it to the issuer. This refinement allows the user to determine the presentation point anywhere in the causal past of all valid presentation points. It also allows flexible transfer of tokens among users without compromising user privacy.
A Tutorial on Using The Wambs Checklist to Avoid The Misuse of Bayesian Statistics

Chapter 3 guides readers through the steps of the When-to-Worry-and-How-to-Avoid-the-Misuse-of-Bayesian-Statistics checklist (the WAMBS checklist) in order to provide background for other chapters in this book. This chapter supplements the original WAMBS checklist with prior and posterior predictive model checking. We also compare the performance of two popular Bayesian R packages, RStan and rjags. We show why using the Hamiltonian Monte Carlo procedure, available in RStan, is more efficient in small sample sizes. All data and the annotated R code to reproduce the results are available on the Open Science Framework.