Reviewerly/exHarmony huggingface dataset:
Microservice Performance in Container- and Function-as-a-Service Architectures

Function-as-a-Service (FaaS) is a new cloud-based computing model that promises a more cost-efficient deployment of microservices with respect to other cloud paradigms, like Container-as-a-Service (CaaS). However, requests served under a FaaS approach often experience a cold start condition, that occurs when the execution of an inactive function occurs for the first time and a container environment has to be set up afresh. In such cases, performance deteriorates and response times increase. This paper proposes an analysis of the performance of the Function-as-a-Service model for two single offered microservices. Specifically, we carry out a performance evaluation of the Function-as-a-Service model, implemented through OpenWhisk, using as a baseline for comparison the Container-as-a-Service approach, implemented with Docker. Our analysis focuses on metrics related to the response time and to the usage of main server resources such as CPU and memory. For the performance comparison, we exploited two different microservices based on face recognition and image conversion, respectively, in order to evaluate the performance over popular and modern kinds of services included in artificial intelligence and multimedia applications.
A 4.6-8.3 TOPS/W 1.2-4.9 TOPS CNN-based Computational Imaging Processor with Overlapped Stripe Inference Achieving 4K Ultra-HD 30fps

In this paper, we present an energy-efficient acceler-ator chip which supports high-quality CNN-based computational imaging applications at 4K UItra-UD 30fps. To address the huge requirement of DRAM bandwidth and computing energy, an overlapped stripe inference flow and a structure-sparse <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CONV}3\mathrm{x}3$</tex> engine are proposed respectively. The former reduces DRAM bandwidth to 0.81-1.74 GB/s when supporting high-quality CNN inference with 16 to 29 layers at 4K UItra-UD 30fps. The latter reduces computing complexity by 40% without noticeable quality degradation, e.g. 0.02-0.03 dB of PSNR drop. More specifically, it uses only 4.9 intrinsic TOPS of computing capability at 200 MHz to approach the quality of dense models which demand up to 8.2 TOPS. In addition, a coarse-grained reconfigurable datapath is designed to support diverse Applications including super-resolution, denoising, and style transfer with high hardware efficiency. Fabricated in 40nm CMOS, this chip achieves 4.6-8.3 TOP/W of energy efficiency for high-quality computational imaging applications. We also implement an FPGA-aided system to demonstrate real-time processing for the diverse applications supported by the fabricated chip.
Pyramidal Image Compression Based on Machine Learning

We develop a machine learning-based image compression method. We propose the general structure of the method based on detection of uninformative image areas and subsequent pyramidal compression of highly informative areas. The use of image inpainting algorithms during decompression allows us not to compress uninformative areas and not to put the areas in the archive. We use adversarial neural networks for the image inpainting. The irredundant pyramidal representation of the image allows us to compress the layers of this pyramid sequentially. Therefore, we use the structural features of each layer of the pyramid for machine learning. Super-resolution neural network algorithms allow us to improve the efficiency of interpolation of all image pyramid layers. Approximation of image spectral bands allows us to use interband correlations and generalize the proposed compression method to hyperspectral images. We analyze machine learning algorithms and select a specific algorithm for each stage of the proposed compression method. We perform computational experiments to study the effectiveness of individual stages of the proposed compression method in real images. We experimentally prove that the selected machine learning algorithms are effective within the image compression method.
Digital Certificate Authentication with Three-Level Cryptography (SHA-256, DSA, 3DES)

The rapid development of technology, makes it easier for everyone to exchange information and knowledge. Exchange information via the internet is threatened with security. Security issues, especially the issue of the confidentiality of information content and its authenticity, are vital things that must protect. Peculiarly for agencies that often hold activities that provide certificates in digital form to participants. Digital certificates are digital files conventionally used as proof of participation or a sign of appreciation owned by someone. We need a security technology for certificates as a source of information known as cryptography. This study aims to validate and authenticate digital certificates with digital signatures using SHA-256, DSA, and 3DES. The use of the SHA-256 hash function is in line with the DSA method and the implementation of 3DES which uses 2 private keys so that the security of digital certificate files can be increased. The pixel changes that appear in the MSE calculation have the lowest value of 7.4510 and the highest value of 165.0561 when the file is manipulated, it answers the security of the proposed method is maintained because the only valid file is the original file.
Question-Driven Multiple Attention(DQMA) Model for Visual Question Answer

Visual Question and Answer (VQA) refers to a typical multimodal problem in the fields of computer vision and natural language processing, which aims to give an open-ended question about an image that can be answered accurately. The currently existing visual question answer models inevitably introduce redundant and inaccurate visual information when exploring the rich interaction between complex image targets and texts, and they also fail to focus effectively on the targets in the scene. To address this problem, the Question-Driven Multiple Attention Model (QDMA) is proposed. Firstly, Faster R-CNN and LSTM are used to extract visual features of images and textual features of questions. Then we design a question-driven attention network to obtain question regions of interest in images so that the model can accurately target relevant targets in complex scenes. To establish intensive interaction between the image region of interest and the question word, the co-attentive network consisting of self-attentive and guided-attentive units is introduced. Finally, the correct answer is obtained by inputting question features and image features into an answer prediction module consisting of two-layer Multi-Layer Perceptron. On the VQA2.0 dataset, the suggested method is empirically compared with other methods. The results reveal that the model outperforms other methods, demonstrating the usefulness of the framework.
Parametric Quality Models for Multiscreen Video Systems

We propose simple parametric models for predicting visual quality scores on different devices in multiscreen systems. As input parameters, the proposed models take the distortion measure for the encoded video and parameters of viewing setup: the resolution of projected video, size of the display, and viewing distance. We derive models for the following distortion measures: PSNR, SSIM, VIF, and VMAF. We validate the proposed models using datasets corresponding to three different reproduction environments: standard TV sets, UltraHD TV sets, and mobiles. The obtained results confirm the improved accuracy of the prediction of MOS scores by the proposed techniques. The paper also includes introductory material explaining the usefulness of parametric quality for the analysis and optimizations of multiscreen video systems.
Design and Implementation of the Application for the Irrigation System

The aim of this paper is to create an application that will serve for remote control and automation of an intelligent irrigation system. This paper presents several front-end and back-end technologies, including programming languages, frameworks, databases and other technologies that can be used in application development. The paper offers the comparison of these technologies and their selection for the implementation of presented application. In the case study, the implementation of the application is situated: the creation of the project, the installation of libraries and necessary dependencies, the development and testing of the application.
Three-dimensional surface displacement tracking for in-situ experiments: An alternative to digital image correlation (DIC)

We develop an approach for tracking the three-dimensional (3D) surface displacements of a material undergoing in situ mechanical testing inside a scanning electron microscope. Specifically, we estimate the out-of-plane intrusions/extrusions and in-plane motion of surface points from multiple views of the sample at the end of the experiment. Then, using reverse optical flow, propagate these displacements backwards in time using interim single view images. These measurements can be extended to map the 3D surface strain tensors. This approach offers an alternative to the commonly used digital image correlation (DIC) technique, which relies on tracking a speckle pattern applied to the material surface. DIC based on single views only produces in-plane two-dimensional (2D) measurements, whereas our approach enables reconstruction of the 3D surface morphology and is completely non-invasive (requires no pattern being applied to the material surface).
Lithology mapping with satellite images, fieldwork-based spectral data, and machine learning algorithms: The case study of Beiras Group (Central Portugal)

Geological remote sensing has been an invaluable means to obtain data to perform geological mapping objectively and with high accuracy. However, there is a significant gap in geological cartography information at 1:50 000 scale throughout the territory in mainland Portugal. The lack of geological mapping is reflected in the geological resources and land management information. This investigation’s main objective was to assess the viability of using remote sensing, machine learning and geochemical techniques as proof of concept for the Portuguese context using the Beiras Group (mainland Portugal) as a case study area of 341 km2. Multispectral analysis was carried out in two Landsat-8 images of 2015, one in the winter and the other in the summer. Hyperspectral data were obtained using a 400–1000 nm spectroradiometer applied to 23 rock samples collected in two field campaigns – the first in January and the second in April 2019. Spectral differences were found distinguishing the two main lithological units, where the granites (Granito de Coentral and Granito de Vila Nova) had an increasing wavelength spectra shape throughout the whole VNIR measurements. Geochemical data was carried out using X-ray Fluorescence, where the average quantity of major elements such as Na2O [2.15 %] and CaO [0.41 %] was higher in granites than metasediments: 0.38 % and 0.11 %, respectively. The J48 machine learning algorithm was performed using as input Landsat-8 reflectance data which showed a high success rate in both confusion matrixes (83,72 % and 94,08 %).
AdaCC: cumulative cost-sensitive boosting for imbalanced classification

Abstract Class imbalance poses a major challenge for machine learning as most supervised learning models might exhibit bias towards the majority class and under-perform in the minority class. Cost-sensitive learning tackles this problem by treating the classes differently, formulated typically via a user-defined fixed misclassification cost matrix provided as input to the learner. Such parameter tuning is a challenging task that requires domain knowledge and moreover, wrong adjustments might lead to overall predictive performance deterioration. In this work, we propose a novel cost-sensitive boosting approach for imbalanced data that dynamically adjusts the misclassification costs over the boosting rounds in response to model’s performance instead of using a fixed misclassification cost matrix. Our method, called AdaCC, is parameter-free as it relies on the cumulative behavior of the boosting model in order to adjust the misclassification costs for the next boosting round and comes with theoretical guarantees regarding the training error. Experiments on 27 real-world datasets from different domains with high class imbalance demonstrate the superiority of our method over 12 state-of-the-art cost-sensitive boosting approaches exhibiting consistent improvements in different measures, for instance, in the range of [0.3–28.56%] for AUC, [3.4–21.4%] for balanced accuracy, [4.8–45%] for gmean and [7.4–85.5%] for recall.
Controlling Problem within a Class of Two-Level Positive Maps

This paper aims to define the set of unital positive maps on M2(C) by means of quantum Lotka–Volterra operators which are quantum analogues of the classical Lotka–Volterra operators. Furthermore, a quantum control problem within the class of quantum Lotka–Volterra operators are studied. The proposed approach will lead to the understanding of the behavior of the classical Lotka–Volterra systems within a quantum framework.
The Structure of Local Rings with Singleton Basis and Their Enumeration

A local ring is an associative ring with unique maximal ideal. We associate with each Artinian local ring with singleton basis four invariants (positive integers) p,n,s,t. The purpose of this article is to describe the structure of such rings and classify them (up to isomorphism) with the same invariants. Every local ring with singleton basis can be constructed over its coefficient subring by a certain polynomial called the associated polynomial. These polynomials play significant role in the enumeration.
The Key Technology of Smart Energy System and Its Disciplinary Teaching Reform Measures

Based on the rapid development of new energy technology, modern information technology, intelligent management technology and different countries’ strategic demand and deployment in the field of energy, the construction of intelligent energy systems is booming with the combination of new energy technology and Internet technology. The Energy Internet is the representative product of intelligent energy systems at the present stage. Its advantages are the effective promotion of energy saving, consumption reduction and optimization of deployment, thus improving the energy system. However, the large-scale construction of the Energy Internet requires a large number of professionals. In order to meet the needs of Energy Internet construction, the talent training mode of higher education is facing new challenges. To cultivate talents in Energy Internet construction, an effective measure is to reform the teaching system based on the current electrical engineering major in universities. This paper investigates the development and construction of the Energy Internet and the current situation of the electrical engineering discipline and puts forward teaching reform measures to transform the traditional electrical engineering discipline into an Energy Internet engineering discipline, considering course structure design, examination form, teacher allocation and teaching mode. This is important for promoting the large-scale construction of the Energy Internet and improving the competitiveness of graduates in the electrical engineering field.
DA-Transfer: A Transfer Method for Malicious Network Traffic Classification with Small Sample Problem

Deep learning is successful in providing adequate classification results in the field of traffic classification due to its ability to characterize features. However, malicious traffic captures insufficient data and identity tags, which makes it difficult to reach the data volume required to drive deep learning. The problem of classifying small-sample malicious traffic has gradually become a research hotspot. This paper proposes a small-sample malicious traffic classification method based on deep transfer learning. The proposed DA-Transfer method significantly improves the accuracy and efficiency of the small-sample malicious traffic classification model by integrating both data and model transfer adaptive modules. The data adaptation module promotes the consistency of the distribution between the source and target datasets, which improves the classification performance by adaptive training of the prior model. In addition, the model transfer adaptive module recommends the transfer network structure parameters, which effectively improves the network training efficiency. Experiments show that the average classification accuracy of the DA-Transfer method reaches 93.01% on a small-sample dataset with less than 200 packets per class. The training efficiency of the DA-Transfer model is improved by 20.02% compared to traditional transfer methods.
Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems

An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the algorithm autonomously learns the control strategy in real time from when the system starts to operate. Even with limited deep RL input variables, such as global horizontal irradiance, time, tilt angle, and power, the proposed AI control successfully learns and achieves a 4.0–9.2% higher electrical-energy yield in high-albedo cases (0.5 and 0.8) as compared to traditional sun-tracking control; however, the energy yield of AI control is slightly lower in low-albedo cases (0.2). AI control also demonstrates a superior performance when there are seasonal changes in albedo. Moreover, AI control is robust against long-term system degradation by manipulating the database used for reward setting.
Epistemological challenges in the study of alternating constructions

In this article we identify and discuss a number of epistemological challenges in the study of alternating constructions in natural languages.After proposing a working definition for the concept of 'alternating constructions', we address the following three specific issues: (i) the need to distinguish between qualitatively different types of alternating constructions, (ii) the need to distinguish between rules of grammar and regularities of language use in the study of alternating constructions, and (iii) the various challenges related to assessing correlation and causation in the study of alternating constructions.On the basis of these three challenges, we take stock of current research on alternating constructions and provide a number of suggestions for future research.
Resisting the criminalisation of rap

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DIVRS: Data integrity verification based on ring signature in cloud storage

Nowadays, cloud service is becoming an indispensable part of people's lives and a growing number of users store data on servers. Although this way can reduce storage and maintenance costs, data owners lose the control and possession of the data, which will result in some additional security issues, such as data corruption. It means that cloud server providers may delete or tamper with the infrequently used data to economize on storage space. Therefore, it is essential to provide an audit service to verify integrity of data stored on the cloud. Most of the existing audit schemes are based on the difficult problems of large integer decomposition and discrete logarithms. With the development of quantum computers, these schemes will face the security threat, because quantum computers can easily solve these difficult problems. In short, it is essential to design a data audit scheme that is resistant to quantum attacks. Therefore, we design a novel data audit scheme to achieve post-quantum security, in which the ring signature based on the problem of learning with errors is employed. In addition, two data possession games are designed to prove the security of the DIVRS. Experiment results show that the communication and computing costs are acceptable, and the time spent in each phase of the DIVRS is shorter than that of the comparison schemes.
A Study on the Analysis of Related Keywords on the Perception of Untact Coding Education in the Post-COVID Era Using Big Data Analysis

The novel infectious disease COVID-19, which started in December 2019, has plunged the world into a pandemic era. Accordingly, countries around the world are concerned about the spread of COVID-19, and have promoted large-scale gatherings and education through immigration control and social distancing. In particular, from March 15, 2020, elementary, secondary, high school, and university have conducted an unprecedented online opening of school in Korea's school education operation, and distance education classes have been conducted starting with the third year of middle and high schools across the country. In this process, students, faculty, and parents were faced with unexpected non-face-to-face distance education, and experienced confusion, tension, and uncertainty about the future society. Due to the spread of COVID-19, the online school curriculum has exposed the limitations and problems of the existing face-to-face school education system. In particular, coding education aimed at understanding and using software can interest students through face-to-face computer practice education. In addition, face-to-face education is important for non-IT majors to realize the importance of SW education and improve their convergence thinking skills. In this study, big data (SNS, Naver blog, youtube, google search keyword frequency, etc.) collected online to analyze the recognition and evaluation of untact (real-time online or pre-recorded video lecture) coding education was used as a text mining technique. Analysis was performed. ‘Coding education’, the keyword of this study, was selected as an analysis keyword, and data was collected through portals/SNSs of Google, Naver, Daum, and YouTube. In addition, after refining/morpheme analysis based on the collected list, word frequency, TF-IDF, N-gram, and topic modeling were analyzed through text mining, and matrix analysis, matrix chart, and sentiment analysis were performed. The results of this study will be used as basic data for college students’ coding education.