Reviewerly/exHarmony huggingface dataset:
Simulating Random Walks in Random Streams

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Simulating Random Walks in Random StreamsJohn Kallaugher, Michael Kapralov, and Eric PriceJohn Kallaugher, Michael Kapralov, and Eric Pricepp.3091 - 3126Chapter DOI:https://doi.org/10.1137/1.9781611977073.120PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract The random order graph streaming model has received significant attention recently, with problems such as matching size estimation, component counting, and the evaluation of bounded degree constant query testable properties shown to admit surprisingly space efficient algorithms. The main result of this paper is a space efficient single pass random order streaming algorithm for simulating nearly independent random walks that start at uniformly random vertices. We show that the distribution of k-step walks from b vertices chosen uniformly at random can be approximated up to error ∊ per walk using words of space with a single pass over a randomly ordered stream of edges, solving an open problem of Peng and Sohler [SODA '18]. Applications of our result include the estimation of the average return probability of the k-step walk (the trace of the kth power of the random walk matrix) as well as the estimation of PageRank. We complement our algorithm with a strong impossibility result for directed graphs. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
A new digital currency system

Aim: The aim of this paper is to describe the construction of a new system for digital currency governed by the central bank, Central Bank Digital Currency (CBDC). Although the system uses cryptography, it is a new alternative for crypto currency like the Bitcoin. Today there is a global discussion about the process of money creation by the commercial banks and the need for CBDC available for a broad public. There is almost no literature how such a system could be constructed. In this paper we fill this gap. The system we describe, uses modern cryptography that guarantees privacy on the one hand but that allows for traceability on the other hand. Also we consider the possibilities for new fintech initiatives and the new role of commercial banks.&#x0D; Research methods: The research method can be classified as design research since we present a high-level model of the system as a proof-of concept. So it proofs that such a system is feasible in principle. It is expected that the paper contributes to the discussion on CBDC systems.&#x0D; Conclusions: It is shown that it is indeed possible to design a CBDC system that is far more efficient than the well-known crypto currency systems. But the system uses one distributed system for transaction processing governed by the Central Bank or a trusted third party. This might be seen as a drawback but the system is performing only very elementary transactions that are easy to verify.&#x0D; Originality: The approach is new. Although existing cryptography techniques are used, the system as such is a completely new alternative for CBDC.&#x0D; Implications: The paper shows that a CBDC system is relatively easy to construct and so this paper could play a role in the transition to such a system in reality.
A Learning Framework for Intelligent Selection of Software Verification Algorithms

Software verification is a key technique to ensure the correctness of software. Although numerous verification algorithms and tools have been developed in the past decades, it is still a great challenge for engineers to accurately and quickly choose the appropriate verification techniques for the software at hand. In this work, we propose a general learning framework for the intelligent selection of software verification algorithms, and instantiate the framework with two state-of-the-art learning algorithms: Broad learning (BL) and deep learning (DL). The experimental evaluation shows that the training efficiency of the BL-based model is much higher than the DL-based models and the support vector machine (SVM)-based models, while the prediction accuracy of the DLbased model is much higher than other models.
VPN Network Traffic Classification Using Entropy Estimation and Time-Related Features

The classification of Internet traffic is gaining prominence for the past few years due to the widening of the current Internet network and web-based applications. Many different approaches are being practiced based on the numerous studies conducted so far. The newer methodology can become imperative for service providers to offer a better quality of service to the users. Deep packet inspection is one such methodology used in the past for traffic characterization where network traffic is subsequently classified into different classes, but their accuracy has been declined due to the drastic changes in the Internet traffic, particularly the increase in encrypted traffic. Virtual private networks (VPNs) have become a preferred choice among the users for remote access communication over other public Internet or IP-based networks. In this paper, our proposed model is a combined approach of entropy estimation and machine learning algorithms, especially deep learning for the network traffic classification. We have used the time-related features to classify VPN or non-VPN network traffic and characterize encrypted traffic into different categories and application identification. We used random forest, KNN, and ANN to test the accuracy of our model. Our results show a good accuracy rate and performance and thus proved our proposed model with entropy as an additional feature, achieving accuracy levels above 90% for all the three algorithms to characterize VPN or non-VPN traffic and application identification.
Dynamic Equilibrium Module for Action Recognition

Temporal variations, such as sudden motion, acceleration and occlusions, occur frequently in real-world videos and force video-modeling networks to account for them. However. often they are not beneficial for recognizing actions at coarse granularity and thus may impede spatio-temporal learning. Prior solutions to this problem usually introduce multiple network branches to process input frames at different sampling rates or employ special components to explore inter-frame relations, which are computationally expensive. In this paper we propose a simple and flexible Dynamic Equilibrium Module (DEM) for video modeling through adaptive Eulerian motion manipulation. The proposed module can be directly inserted into 3D and (2+1)D backbone networks to effectively reduce the impact of temporal variations on video modeling and learn spatio-temporal representations with higher robustness. We demonstrate performance gains due to the use of DEM in R3D and R(2+1)D models on Kinetics-400, UCF-101, and HMDB-51 datasets.
Engaging Technologies Evolve

Recently, colleagues on LinkedIn have been asking why I am reposting articles regarding blockchain, emergent crypto coins, non‐fungible tokens, and the metaverse. Readers will learn in this column that these trending platforms are not just ancillary trends, but rather key transformative technologies that will influence today's utility industry towards a climate conscious future.
El uso de podcast como instrumento de evaluación sobre el aprendizaje en la enseñanza de las ciencias

This research addresses the use of podcasts as an innovative teaching tool to reflect on and evaluate the teaching-learning process in the training of trainers, giving students a voice.The main objective is twofold: on the one hand, to contribute to the development of competences for the 21st century and, on the other, to analyse student learning through the content of the podcasts, checking whether the objectives of the subject (Didactics of Experimental Sciences) have been achieved.The methodology used is qualitative based on the qualitative content analysis technique.The analysis of the results shows how all the objectives of the subject have been reflected in the students' learning reflections, with special emphasis on the topics related to promoting a positive attitude towards the process of teaching science; understanding and encouraging reflection and critical thinking; the design of teaching materials and evaluation.Consequently, it is concluded that the use of the podcast as a teaching tool in this context promotes the development of competences for the 21st century and provides a glimpse of the learning obtained by the students.
Implementation of the Digital COVID-19 Vaccination Passport based on Blockchain Protecting Privacy

The paper discusses proposals for implementing the COVID-19 digital Vaccination Passport based on Blockchain that protects privacy. Since the end of the last year, after the commencement of vaccination against COVID-19, there has been an intense discussion on the form of introducing such a tool and the consequences of its implementation. This discussion is taking place in many European countries. One element of this discussion was the safety and anonymity of the massively verified data of persons on vaccinations in various areas of society functioning. These issues are being resolved by the proposed digital Vaccination Passport system. This system uses two major methods: Blockchain and hash functions, which allow you to maintain security, privacy, and anonymity at the same time. To improve the intuitiveness and simplicity of the system operation, the QR code technology was proposed in the passport verification process. The system has been implemented and tested in the Amazon AWS cloud computing environment. A reference architecture based on Blockchain for the AWS environment was proposed, dedicated to large and demanding application solutions. In addition, the cloud environment offers access to many tools that were used in the system’s implementation, significantly increasing the security of the entire solution.
An Automated Solution For Securing Confidential Documents in a BYOD Environment

BYOD or Bring Your Own Device is a set of policies that allow employees of an organization to use their own devices for official work purposes. BYOD is an immensely popular concept in the present day due to the many advantages it provides. However, the implementation of BYOD policies entail diverse problems and as a result, the confidentiality of documents can be breached. Furthermore, employees without security awareness and training are highly vulnerable to endpoint attacks, network attacks, and zero-day attacks that lead to a breach of confidentiality, integrity, and availability (CIA). In this context, this paper proposes a comprehensive solution; ‘BYODENCE’, for the detection and prevention of unauthorized access to organizational documents. BYODENCE is an efficient BYOD solution which can produce competitive results in terms of accuracy and speed.
Appearance generation for colored spun yarn fabric based on conditional image‐to‐image translation

Abstract The repeated trial‐weaving and proofing process are traditionally conducted to exhibit the ever‐changing appearance of the colored spun yarn fabric knitted by different colored spun yarns. In this paper, a novel method of appearance generation for colored spun yarn fabric was proposed based on conditional image‐to‐image translation. The generated image is required to have the same color and the fabric style simultaneously as the color card and the assigned style. The common pix2pix model was modified by adding the category label to the image channel to involve the style constraint. To improve the generation performance, the U‐Net architecture was replaced by the residual block architecture. Four commonly used style fabrics and different color cards were adopted to build the image pairs for experiments. The visual inspection and category similarity were used as the evaluation metrics. Experimental results reveal that the proposed method can transfer different color cards to the designated style fabrics based on the constraints, being effective and superior for image generation of colored spun yarns. The proposed scheme can provide references for the designer by presenting the image perception, saving labor, and material resources in the proofing process.
Congestion-Aware Bayesian Loss for Crowd Counting

Deep learning-based crowd density estimation can greatly improve the accuracy of crowd counting.Though a Bayesian loss method resolves the two problems of the need of a hand-crafted ground truth (GT) density and noisy annotations, counting accurately in high-congested scenes remains a challenging issue.In a crowd scene, people's appearances change according to the scale of each individual (i.e., the person-scale).Also, the lower the sparsity of a local region (i.e., the crowd-sparsity), the more difficult it is to estimate the crowd density.In this paper, we propose a novel congestion-aware Bayesian loss method that considers the person-scale and crowd-sparsity.We estimate the person-scale based on scene geometry, and we then estimate the crowd-sparsity using the estimated person-scale.The estimated person-scale and crowd-sparsity are utilized in the novel congestion-aware Bayesian loss method to improve the supervising representation of the point annotations.We verified the effectiveness of each proposed component through several ablation experiments, and in the various experiments on public datasets, our proposed method achieved state-of-the-art performance.
Methodologies for Synthesizing and Analyzing Dynamic Dataflow Programs in Heterogeneous Systems for Edge Computing

The possibility of using the increasing computing power available in cloud infrastructures requires the development of new approaches for application software development and optimization. Emerging edge computing paradigms offer the possibility of reducing bandwidth needs and of optimizing latency, features particularly relevant for Big Data applications, by bringing computation closer to the user and to the data generation processes. However, edge computing approaches pose several challenges in terms of how to be able to efficiently take advantage of a distributed network of heterogeneous processing nodes. This paper deals with this problem by extending a dynamic dataflow software development framework and related design flow tools to support heterogeneous platforms. The paper describes the methodology steps for the synthesis of application software executing on heterogeneous CPU/GPU co-processing nodes. The steps do include the optimization of the communication between heterogeneous processing elements, a technique for the efficient mapping and parallelization of computation on independent GPU partitions, and the introduction of dynamic programming approach for leveraging the SIMD nature of GPU computing. To complete the methodology of seamless porting of dataflow software and partition on CPU or GPU computing nodes, an automated methodology for exploring the configuration space and to identify high performance working points is developed.
Quantum Hybrid Plasmonic Nanocircuits for Versatile Polarized Photon Generation

Abstract Hybrid plasmonic nanocircuits developed insofar enable efficient coupling and routing of single photons generated by embedded quantum emitters (QEs), providing at the same time little or no control over the polarization of out‐coupled single photons. Here, with numerical simulations a QE‐driven hybrid plasmonic nanocircuit for versatile polarized photon generation by using ultracompact polarization out‐couplers in multi‐branched dielectric‐loaded plasmonic waveguides is proposed and demonstrated. Upon excitation of a QE, which is embedded at the center of a star‐shaped six‐branched hybrid plasmonic waveguide and characterized with a normal to the surface radiative transition dipole, plasmonic waveguide modes are efficiently (≈85%) excited and routed by the six‐branched waveguide toward propitiously designed out‐couplers. By exploiting two types of subwavelength polarization out‐couplers producing linearly and circularly polarized free‐space radiation, spatially separated single‐photon generation channels characterized by four different linear and two circular polarizations are created. The demonstrated hybrid plasmonic nanocircuit addresses specifically the scenario where different quantum chips communicate via polarization‐compatible channels, thereby opening new design possibilities for energy‐efficient and ultracompact quantum photonic nanocircuits in nanophotonic quantum technologies.
(Retracted) Estimation of human age by features of face and eyes based on multilevel feature convolutional neural network

Age estimation can be effectively used in security, human–computer interaction, entertainment, and audio-visual fields. However, current face-based age recognition algorithms are not highly accurate due to factors, such as face makeup and plastic surgery. Among the various feature points of a human face, the eyes are the most difficult part to be modified. And with the changes of age, the lens, cornea, and vitreous of eyes will also change accordingly. Therefore, we believe that paying attention to the local feature of the eye can improve the accuracy of age estimation to a certain extent. The multilevel feature convolutional neural network (MLFCNN) is proposed, which values eyes features and combines them with face features to jointly estimate human age. MLFCNN performs two rounds of estimation based on extracted features. First, the age range of the sample is estimated as the age group, and then on this basis, the fine age of the sample is further estimated. Field tests show that the mean absolute error of MLFCNN is 2.87, which is lower than other network models tested. When the MLFCNN estimated age did not differ more than 4 years from the actual age of the sample, the network predictions were considered correct (i.e., the tolerable age error was 4). The age estimation accuracy of MLFCNN under this condition was 91.14%. And 98.32% accuracy can be reached when the tolerable age error is 6. Ablation experiments verify that the fusion of ocular features and facial features can improve the performance of the age estimation network. In addition, it can still maintain a good performance under small training dataset.
Comparison of Current Convolutional Neural Network Architectures for Classification of Damaged and Undamaged Cars

Deep learning algorithms, one of the most popular topics of recent years, are frequently encountered in many areas. One of these areas is traffic. Many applications such as vehicle identification and damage detection using images obtained by various imaging methods in traffic are successfully carried out by deep learning algorithms. In this study, damaged and undamaged car images are detected using current convolutional neural network architectures. In this study, the publicly published data set named damaged car dataset (DCD)-2 was used. This data set includes 80 normal and 80 accident vehicle images. The low number of images in the preliminary analyzes made it cause us to increase the data (image augmentation). With the increase in data, the number of images was increased to 400 damaged vehicles and 400 solid vehicles. VGG-16, VGG-19, Inception V3, NasNetLarge, DenseNet201 were applied from the transfer learning algorithm to the data incremented dataset. At the end of the application, the classification accuracy was 84.11% with VGG-16, 81.96% with VGG-19, 94.11% with Inceptionv3, 98.39% with NasnetLarge and 97.14% with Densenet201.
Deciding a Fragment of $$(\alpha , \beta )$$-Privacy

We show how to automate fragments of the logical framework \((\alpha , \beta )\)-privacy which provides an alternative to bisimilarity-based and trace-based definitions of privacy goals for security protocols. We consider the so-called message-analysis problem, which is at the core of \((\alpha , \beta )\)-privacy: given a set of concrete messages and their structure, which models can the intruder rule out? While in general this problem is undecidable, we give a decision procedure for a standard class of algebraic theories.
Speed-up of the Matrix Computation on the Ridge Regression

Artificial intelligence has emerged as the core of the 4th industrial revolution, and large amounts of data processing, such as big data technology and rapid data analysis, are inevitable.The most fundamental and universal data interpretation technique is an analysis of information through regression, which is also the basis of machine learning.Ridge regression is a technique of regression that decreases sensitivity to unique or outlier information.The time-consuming calculation portion of the matrix computation, however, basically includes the introduction of an inverse matrix.As the size of the matrix expands, the matrix solution method becomes a major challenge.In this paper, a new algorithm is introduced to enhance the speed of ridge regression estimator calculation through series expansion and computation recycle without adopting an inverse matrix in the calculation process or other factorization methods.In addition, the performances of the proposed algorithm and the existing algorithm were compared according to the matrix size.Overall, excellent speed-up of the proposed algorithm with good accuracy was demonstrated.
Status of the cartographic model

Abstract The author presents the proposal for a map as a model based on the current concepts in the philosophy of science. It is the attempt to define a map within the general theory of the model – in its ontological, semantical, and epistemological aspect. Treating a map as a model of reality boils down to specifying several characteristics determining its character. The article primarily aims at broadening the discussion on what a map is and what defines it as a model of reality. A new definition has been proposed in effect of the deliberations based on the analysis of models’ typology in the sphere of philosophy.
DeepMemory: Model-based Memorization Analysis of Deep Neural Language Models

The neural network model is having a significant impact on many real-world applications. Unfortunately, the increasing popularity and complexity of these models also amplifies their security and privacy challenges, with privacy leakage from training data being one of the most prominent issues. In this context, prior studies proposed to analyze the abstraction behavior of neural network models, e.g., RNN, to understand their robustness. However, the existing research rarely addresses privacy breaches caused by memorization in neural language models. To fill this gap, we propose a novel approach, DeepMemory, that analyzes memorization behavior for a neural language model. We first construct a memorization-analysis-oriented model, taking both training data and a neural language model as input. We then build a semantic first-order Markov model to bind the constructed memorization-analysis-oriented model to the training data to analyze memorization distribution. Finally, we apply our approach to address data leakage issues associated with memorization and to assist in dememorization. We evaluate our approach on one of the most popular neural language models, the LSTM-based language model, with three public datasets, namely, WikiText-103, WMT2017, and IWSLT2016. We find that sentences in the studied datasets with low perplexity are more likely to be memorized. Our approach achieves an average AUC of 0.73 in automatically identifying data leakage issues during assessment. We also show that with the assistance of DeepMemory, data breaches due to memorization of neural language models can be successfully mitigated by mutating training data without reducing the performance of neural language models.