Reviewerly/exHarmony huggingface dataset:
$$\mathsf {SceneChecker}$$: Boosting Scenario Verification Using Symmetry Abstractions

Abstract We present $$\mathsf {SceneChecker}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>SceneChecker</mml:mi> </mml:math> , a tool for verifying scenarios involving vehicles executing complex plans in large cluttered workspaces. $$\mathsf {SceneChecker}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>SceneChecker</mml:mi> </mml:math> converts the scenario verification problem to a standard hybrid system verification problem, and solves it effectively by exploiting structural properties in the plan and the vehicle dynamics. $$\mathsf {SceneChecker}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>SceneChecker</mml:mi> </mml:math> uses symmetry abstractions, a novel refinement algorithm, and importantly, is built to boost the performance of any existing reachability analysis tool as a plug-in subroutine. We evaluated $$\mathsf {SceneChecker}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>SceneChecker</mml:mi> </mml:math> on several scenarios involving ground and aerial vehicles with nonlinear dynamics and neural network controllers, employing different kinds of symmetries, using different reachability subroutines, and following plans with hundreds of waypoints in complex workspaces. Compared to two leading tools, DryVR and Flow*, $$\mathsf {SceneChecker}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>SceneChecker</mml:mi> </mml:math> shows 14 $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> average speedup in verification time, even while using those very tools as reachability subroutines.
Retrospective IP Address Geolocation for Geography-Aware Internet Services

The paper deals with the locations of IP addresses that were used in the past. This retrospective geolocation suffers from continuous changes in the Internet space and a limited availability of past IP location databases. I analyse the retrospective geolocation of IPv4 and IPv6 addresses over five years. An approach is also introduced to handle missing past IP geolocation databases. The results show that it is safe to retrospectively locate IP addresses by a couple of years, but there are differences between IPv4 and IPv6. The described parametric model of location lifetime allows us to estimate the time when the address location changed in the past. The retrospective geolocation of IP addresses has a broad range of applications, including social studies, system analyses, and security investigations. Two longitudinal use cases with the applied results are discussed. The first deals with geotargeted online content. The second deals with identity theft prevention in e-commerce.
A TensorFlow implementation of Local Binary Patterns Transform

Feature extraction layers like Local Binary Patterns (LBP) transform can be very useful for improving the accuracy of machine learning and deep learning models depending on the problem type. Direct implementations of such layers in Python may result in long running times, and training a computer vision model may be delayed significantly. For this purpose, TensorFlow framework enables developing accelerated custom operations based on the existing operations which already have support for accelerated hardware such as multicore CPU and GPU. In this study, LBP transform which is used for feature extraction in various applications, was implemented based on TensorFlow operations. The evaluations were done using both standard Python operations and TensorFlow library for performance comparisons. The experiments were realized using images in various dimensions and various batch sizes. Numerical results show that algorithm based on TensorFlow operations provides good acceleration rates over Python runs. The implementation of LBP can be used for the accelerated computing for various feature extraction purposes including machine learning as well as in deep learning applications.
Noise in digital and digital-analog quantum computation

Abstract Quantum computing uses quantum resources provided by the underlying quantum nature of matter to enhance classical computation. However, the current Noisy Intermediate-Scale Quantum (NISQ) era in quantum computing is characterized by the use of quantum processors comprising from a few tens to, at most, a few hundreds of physical qubits without implementing quantum error correction techniques. This limits the scalability in the implementation of quantum algorithms. Digital-analog quantum computing (DAQC) has been proposed as a more resilient alternative quantum computing paradigm to outperform digital quantum computation within the NISQ era framework. It arises from adding the flexibility provided by fast single-qubit gates to the robustness of analog quantum simulations. Here, we perform a careful comparison between the digital and digital-analog paradigms under the presence of noise sources. The comparison is illustrated by comparing the performance of the quantum Fourier transform and quantum phase estimation algorithms under a wide range of single- and two-qubit noise sources. Indeed, we obtain that when the different noise channels usually present in superconducting quantum processors are considered, the fidelity of these algorithms for the digital-analog paradigm outperforms the one obtained for the digital approach. Additionally, this difference grows when the size of the processor scales up, making DAQC a sensible alternative paradigm in the NISQ era. Finally, we show how to adaptthe DAQC paradigm to quantum error mitigation techniques for canceling different noise sources, including the bang error.
hub at SemEval-2021 Task 1: Fusion of Sentence and Word Frequency to Predict Lexical Complexity

In this paper, we propose a method of fusing sentence information and word frequency information for the SemEval 2021 Task 1-Lexical Complexity Prediction (LCP) shared task.In our system, the sentence information comes from the RoBERTa model, and the word frequency information comes from the Tf-Idf algorithm.Use Inception block as a shared layer to learn sentence and word frequency information.We described the implementation of our best system and discussed our methods and experiments in the task.The shared task is divided into two subtasks.The goal of the two subtasks is to predict the complexity of a predetermined word.The evaluation index of the task is the Pearson correlation coefficient.Our best performance system has Pearson correlation coefficients of 0.7434 and 0.8000 in the single-token subtask test set and the multi-token subtask test set, respectively.
Blender: A Traffic-Aware Container Placement for Containerized Data Centers

Instantiated containers of an application are distributed across multiple Physical Machines (PMs) to achieve high parallel performance. Container placement plays a vital role in network traffic and the performance of containerized data centers. Existing container placement techniques do not consider the container traffic pattern, which is inadequate. To resolve this conflict, we investigate network traffic between containers and observe that it exhibits a Zipf-like distribution. We propose a novel container placement approach - Blender - by leveraging the Zipf-like distribution. Based on network traffic correlation, Blender employs RefineAlg and SplitAlg to divide containers of applications into blocks, and place these blocks across virtual machines. Blender exhibits two salient features: (i) it minimizes inter-block traffic by arranging the containers that communicate frequently in the same block. (ii) it achieves good load balancing by combining blocks according to the resource types they require and distributing them across multiple PMs. We compare Blender against two state-of-the-art methods SBP and CA-WFD. The experimental results show that Blender significantly reduces communication traffic. In particular, for the same number of PMs, Blender reduces the traffic of SBP and CA-WFD by 22% and 32%, respectively. Furthermore, with Blender in place, the physical resources of hosting PMs are well balanced and utilized.
Filtration of histogram evaluation of probability density based on fuzzy data accessibility to a grouping interval

The paper proposes a histogram estimate of the probability density based on fuzzy data belonging to a grouping interval. A methodology for constructing a histogram estimate using a histogram smoothing filter is presented. The technique of constructing such a filter is described. The main filter parameter is established – the coefficient of the statistical relationship between the amount of data falling into the grouping interval for a single inclusion function and when approaching to use the membership function. The use of an iterative procedure for a histogram filter allows for a greater “smoothness” of the histogram. The simulation results show the effectiveness of using a histogram filter for different data volumes. At the same time, the choice of the number of grouping intervals for the “correct” recognition of probability density becomes not critical. The histogram filter is a simple tool that can easily be built into any algorithm for constructing histogram estimates.
Using blockchain technology to extend the vendor managed inventory for sustainability

Nowadays, Information Technology (IT) is changing the way traditional enterprise management concepts work. One of the most dominant IT achievements is the Blockchain Technology. This technology enables the distributed collaboration of stakeholders for their interactions while fulfilling the security and consensus rules among them. This paper has focused on the application of Blockchain technology to enhance one of traditional inventory management models. The Vendor Managed Inventory (VMI) has been considered one of the most efficient mechanisms for vendor inventory planning by the suppliers. While VMI has brought competitive advantages for many industries, however its centralized mechanism limits the collaboration of a pool of suppliers and vendors simultaneously. This paper has studied the recent research for VMI application in industries and also has investigated the applications of Blockchain technology for decentralized collaboration of stakeholders. Focusing on sustainability issue for total supply chain consisting suppliers and vendors, it has proposed a Blockchain based VMI conceptual model. The different capabilities of this model for enabling the collaboration of stakeholders while maintaining the competitive advantages and sustainability issues have been discussed.
Shallow2Deep: Restraining Neural Networks Opacity Through Neural Architecture Search

Recently, the Deep Learning (DL) research community has focused on developing efficient and highly performing Neural Networks (NN). Meanwhile, the eXplainable AI (XAI) research community has focused on making Machine Learning (ML) and Deep Learning methods interpretable and transparent, seeking explainability. This work is a preliminary study on the applicability of Neural Architecture Search (NAS) (a sub-field of DL looking for automatic design of NN structures) in XAI. We propose Shallow2Deep, an evolutionary NAS algorithm that exploits local variability to restrain opacity of DL-systems through NN architectures simplification. Shallow2Deep effectively reduces NN complexity – therefore their opacity – while reaching state-of-the-art performances. Unlike its competitors, Shallow2Deep promotes variability of localised structures in NN, helping to reduce NN opacity. The proposed work analyses the role of local variability in NN architectures design, presenting experimental results that show how this feature is actually desirable.
Environmental and digital competencies in the education of engineering specialists

The article discusses the acquisition of environmental and related digital competencies in the training of engineering specialists of different levels. Provides data on current trends in education. The approaches to basic, universal and general technical competencies, and their relationship with digital competencies are considered. When working with expert opinions, the necessary, in the opinion of the expert community, environmental and digital competencies that are relevant in modern conditions are identified. Based on these results, changes were proposed to the work program for the discipline related to ensuring environmental safety on the one hand and digital competencies on the other. Based on a review of modern approaches, a survey of experts and work with new generation work programs, conclusions were drawn about the advisability of continuing research in this direction, as well as the implementation of digital competencies in the field of environmental education.
Comparative Study of Proof of Work (PoW) and Delegated Proof of Stake (DPoS) Blockchain Consensus Algorithm

As opposed to the current system, that is controlled by a central authority, blockchain was invented by Satoshi Nakamoto in 2008 as a distributed ledger system. A major development in the technology came with the introduction of virtual cryptocurrencies where transactions were recorded and verified by miners or validators at various nodes in different locations. Since these transaction records are available to everyone, this solved the current problem of excessive double spending. In a decentralized system like blockchain, a reliable mechanism was necessary to verify the integrity of all transactions. This led to the introduction of a variety of consensus mechanisms. This paper presents the results of a comparative study between Proof of Work and Delegated Proof of Stake, both of which have been widely used in Blockchain projects recently. Based on the above, this study will primarily discuss a brief explanation of the workings of both algorithms, comparisons between them, and various factors impacting these algorithms from a crypto currency standpoint.
Bibliologos – the vaccine from ignorance and nonsense

The neologism “Bibliologos” is literally translated from Greek as a “Book mind”. Real areas of bibliologos existence are education, science, management. Bibliologos can be considered from three points of view: first, genetic (or rather epistemological, theoretical and cognitive) measurement, revealing the initial substances and philosophical categories that are inherent to both the National Logos in general and its varieties – Bibliologos; secondly, the socio-functional dimension, considering the productive forces, material tools and bibliosphere technologies; thirdly, system-structural measurement, studying signs, symbols and documents that form the system of bibliologos. Hence, there are three private definitions: genetic, functional and system-structural. The generalization of private definitions is the general definition: Bibliologos-Σ is a collective mind of a civilized society in the form of a productive force providing creation, preservation, use of book culture. The idea of the Bibliologos in all interpretations will undoubtedly find use in such areas of modern mental labor as education, science, management for to educate a new type of person. It is obvious. Prospects of the synthesis of book culture and digital culture are problematic. It should be assumed that in the future digital sphere for processing the Big Data arrays, Big Logos will be required as the universal vaccine from ignorance and nonsense.
A Novel Decision Mechanism for Image Edge Detection

Edge detection plays an important role in image processing and computer vision tasks such as image matching and image segmentation. In this paper, we argue that edges in image should always be detected even under different image rotation transformations or noise conditions. Then a novel edge detection method is presented for improving the robustness of image edge detection. Firstly, we perform the following operations on the input image to obtain a set of images: random destruction operation, random rotation operation, and randomly addition of Gaussian noise with zero mean and random variance. Secondly, multi-directional Gabor filters with multiple scales are used as a tool to smooth the set of images and obtain candidate edges from the set of images. Thirdly, a novel edge decision mechanism is designed with a 0.95 confidence interval for selecting true edges from the candidate edges. Finally, two edge detection evaluation criteria (i.e., the aggregate test receiver-operating-characteristic and the Pratt's figure of merit) are utilized to evaluate the proposed edge detection method against five state-of-the-art methods. The experimental results show that our proposed method outperforms all the other tested methods.
Effectiveness of Insect Encyclopedia E-Book With Mind Mapping Strategy to Train Students' Creative Thinking Skills

As technology advances, learning materials in learning are expected to be able to apply technological developments and practice the skills needed in the 21st century, namely communication and creative thinking. E-book, which was developed with a mind mapping strategy, is equipped with various features that can support student creativity. This study aims to produce an insect encyclopedia e-book with a mind mapping strategy to practice creative thinking skills for class X Senior High School which is declared effective. The effectiveness of the insect encyclopedia e-book in terms of learning outcomes, mind mapping skills and student responses. Creative thinking skill can be used to solve the problems and develop the idea. The method used is a modified research and development (R&amp;D) method. The data collection technique was through validation, observation, questionnaires and tests. The data analysis technique was carried out quantitatively. Based on the results of the study, the average score of student learning outcomes was 82.60% and the N gain value was 0.50 in the medium category. The average score of students' mind mapping skills in the high category. And reviewed based on the skills of making mind mapping with good categories and based on student responses positively.
Change the World - How Hard Can that Be? On the Computational Complexity of Fixing Planning Models

Incorporating humans into AI planning is an important feature of flexible planning technology. Such human integration allows to incorporate previously unknown constraints, and is also an integral part of automated modeling assistance. As a foundation for integrating user requests, we study the computational complexity of determining the existence of changes to an existing model, such that the resulting model allows for specific user-provided solutions. We are provided with a planning problem modeled either in the classical (non-hierarchical) or hierarchical task network (HTN) planning formalism, as well as with a supposed-to-be solution plan, which is actually not a solution for the current model. Considering changing decomposition methods as well as preconditions and effects of actions, we show that most change requests are NP-complete though some turn out to be tractable.
Analysis of net trade, FDI and GDP growth using cointegration, VECM, Granger causality and a regression approach: A case study of Sub Saharan African region

Net trade and Foreign Direct Investment (FDI) are both important factors of economic growth and development in Sub Saharan African (SSA) region.Many different studies have shown the impact of trade and FDI in economy growth in SSA countries.However, few is known about the effect in SSA region in overall.To use these factors in policy making and development planning, the following questions need to be addressed.As both factors impact the economy growth, can net trade (NT) historical performance be used to forecast FDI?Is there any short-run or long-run relationship effect among GDP growth, FDI and net trade?This paper has implemented the cointegration methodology to analyze the impact of net trade and FDI on GDP growth, used the Vector Error Correction Model (VECM) for long-run and short-run relationship effect, an Ordinary Least Square (OLS) regression to determine the significant impact of FDI and net trade on GDP growth and finally the granger causality test.This study shows a positive long-run relationship effect of FDI on GDP.The positive short-run effect of both FDI and net trade is also detected on GDP growth.The regression result shows that net trade has a significant impact on GDP growth and finally from the Granger causality test, the study shows that net trade Grangercause FDI.
High Speed Data Exchange Algorithm in Telemedicine with Wavelet based on 4D Medical Image Compression

Existing Medical imaging techniques such as fMRI, positron emission tomography (PET), dynamic 3D ultrasound and dynamic computerized tomography yield large amounts of four-dimensional sets. 4D medical data sets are the series of volumetric images netted in time, large in size and demand a great of assets for storage and transmission. Here, in this paper, we present a method wherein 3D image is taken and Discrete Wavelet Transform(DWT) and Dual-Tree Complex Wavelet Transform(DTCWT) techniques are applied separately on it and the image is split into sub-bands. The encoding and decoding are done using 3D-SPIHT, at different bit per pixels(bpp). The reconstructed image is synthesized using Inverse DWT technique. The quality of the compressed image has been evaluated using some factors such as Mean Square Error(MSE) and Peak-Signal to Noise Ratio (PSNR).
Use of a Local Local Oscillator for the Satellite-to-Earth Channel

Continuous variable quantum key distribution (CV-QKD) offers information-theoretic secure key sharing between two parties. The sharing of a phase reference frame is an essential requirement for coherent detection in CV-QKD. Due to the potential attacks related to transmitting the local oscillator (LO) alongside quantum signals, there has been a focus on using local LOs (LLOs) to establish a shared phase reference. In this work, we develop a new noise model of a current state-of-the-art LLO scheme in the context of the satellite-to-Earth channel. In doing this, we encapsulate detailed phase-screen calculations that determine the coherent efficiency - a critical parameter in free-space CV-QKD that characterizes the wavefront aberrations caused by atmospheric turbulence. Using our new noise model we then determine the CV-QKD key rates for the satellite-to-Earth channel, secure under general attacks in the finite-size regime of the LLO scheme. Our results are of practical importance for next-generation quantum-enabled satellites that utilize multi-photon technology as opposed to single-photon technology.