Reviewerly/exHarmony huggingface dataset:
Top Vulnerabilities in Cloud Computing

Cloud computing is rising quickly and without uncertainty it is the new generation of technology where people will utilize whenever and wherever. In this web-based world, cloud computing is rising higher by giving everything to the necessary assets, applications, programming, equipment, registering capacity to figuring foundations, business procedure to control joint effort. Today clouds with virtualization are changing IT. Separated by their prominences it has a few concerns which are turning out to be complications for its more extensive selection. Being a relatively newer technology, cloud computing comes with its own array of security vulnerabilities and new perspectives for attacks to happen. Major cloud providers already have infrastructure with tools and services in place designed to improve security in the cloud and the overall performance. There are a few vulnerabilities that have arisen in the previous decade that have managed to slip by unnoticed. These have caused a great deal of damage to cloud infrastructure and its overall reputation. This paper focuses on how these specific vulnerabilities affect different cloud computing environments and how to resolve them.
Bi Histogram Equalization Based Image Enhancement with Bicubic Interpolation

In image processing, enhancement histogram equalization is the widely used technique for contrast enhancement. However, this technique tends to change the brightness of the image. Here, the contrast and resolution of image were enhanced using the proposed Bi Histogram Equalization Based Image Enhancement with Bicubic Interpolation (BHBI) technique. Bi Histogram for contrast enhancement and bicubic interpolation for resolution enhancement has taken. Bi Histogram Equalization separates the input image's histogram into two based on input mean before equalizing them independently. Bicubic Interpolation can generate bigger or high-resolution image from one or more low resolution or smaller images. The performance of the BHBI method can be compared for some typical image’s cameraman lens that are applied to some existing enhancement methods like Adaptive Gamma Correction with Weighting Distribution (AGCWD), Adaptive Scale Adjustment Design of Unsharp Masking Filters (ASAUMF), Averaging Histogram Equalization (AVGHEQ), and Median-Mean Based Sub-Image-Clipped Histogram Equalization (MMSICHE). The performance of these existing techniques can be evaluated subjectively in terms of person illustration observation and measurably using Discrete Entropy (DE), Image Quality Index (IQI), Normalized Correlation Coefficient (NCC), Contrast Improvement Index (CII), and Absolute Mean Brightness Error (AMBE). The results obtained from the BHBI technique shows better when compared with respect to the various existing techniques.
Analysis of cold chain issue changes in the news

본 연구는 최근 3년간의 콜드체인 이슈변화를 살펴보기 위해 언론보도를 중심으로 2019년부터 2021년까지 콜드체인분야의 핵심키워드와 뉴스간 관계도를 분석하였다. 분석방법은 뉴스 빅데이터 분석서비스인 빅카인즈를 활용하여 2019년부터 2021년까지 중앙지 10개사, 경제지 8개사, 지역종합지 29개사, 방송 5개사를 중심으로 자료를 수집하여 뉴스 건 수 변화, 핵심키워드 빈도분석, 워드클라우드, 뉴스관계도 분석을 수행하였다. 분석 결과를 정리하면 다음과 같다. 첫째, 최근 10년간 콜드체인 뉴스 건 수는 꾸준히 증가하였으며 특히 코로나19 발생 이후인 2020년부터 2021년까지 급속한 증가가 나타났다. 둘째, 최근 3년간의 핵심키워드 변화를 제시하였으며 연도별로 워드클라우드 분석을 통해 시각화하였다. 셋째, 연도별로 콜드체인 뉴스의 인물, 장소, 기관, 키워드와 이슈관계도를 시각화하여 제시하였다. 본 연구는 최근 3년간의 콜드체인 관련 언론보도를 중심으로 핵심키워드 분석을 통해 콜드체인의 변화를 제시하고 시각화한 첫 연구이다. 연구 결과를 기반으로 콜드체인분야의 세부적 사례연구나 국가별 사업 확장전략 등 다양한 연구적 접근이 요구된다.Purpose : From 2019 to 2021, the study analyzed the relationship between keywords and news in the cold chain industry, concentrating on media reports to examine the last three years of cold chain issue developments. Research design, data, methodology : Using BIGKinds, a news big data analysis service, I collected data from 10 major news magazines, 8 economic news magazines, 29 regional magazines, and 5 broadcasting companies from 2019 to 2021. Following that, I did frequency analysis, word clouds, and news relationship analysis. Results : The following is a summary of the findings: First, from 2020 to 2021, the number of cold chain news incidents has progressively increased over the last decade, particularly after COVID-19. Second, I presented key keyword changes over the last three years, which I depicted using a WordCloud analysis by year. Third, cold chain news information, locations, institutions, keywords, and issue relationships were visualized by year. Conclusions : This is the first study to use core keyword analysis to present and depict developments in cold chains, focusing on the last three years of cold chain-related media reports. Various research methodologies, such as extensive case studies in the cold chain industry and strategies for developing the business by country, are necessary based on the study s findings.
Envelopes and waves: safe multivehicle collision avoidance for horizontal non-deterministic turns

We present an approach to analyzing the safety of asynchronous, independent, non-deterministic, turn-to-bearing horizontal maneuvers for two vehicles. Future turn rates, final bearings, and continuously varying ground speeds throughout the encounter are unknown but restricted to known ranges. We develop a library of formal proofs about turning kinematics, and apply the library to create a formally verified timing computation. Additionally, we create a technique that evaluates future collision possibilities that is based on waves of position possibilities and relies on the timing computation. The result either determines that the encounter will be collision-free, or computes a safe overapproximation for when and where collisions may occur.
ICT Pandemic Time Adoption and Immersive Technologies

It was not until recently that we could imagine immersive technology's popularity would be raised and adaptability would reshape almost all businesses in the tourism landscape. Immersive technology has been a transition phenomenon from a traditional marketing strategy to a postmodern approach that encourages, motivates, and satisfies the potential consumers towards a particular tourism product or service. On the other hand, the tourism industry has been the hardest hit and most suffering sector due to the declaration of emergency measures such as travel bans which caused catastrophic consequences in the industry during the coronavirus pandemic. Hence, this chapter intends to present comprehensive reviews about the impact of COVID-19 on the tourism industry through conceptualizing and contextualizing the remarkable aspects of innovation, digitalization, and digital transformation using immersive technologies.
Quantum Computing by Coherent Cooling

Interesting problems in quantum computation take the form of finding low-energy states of (pseudo)spin systems with engineered Hamiltonians that encode the problem data. Motivated by the practical possibility of producing very low-temperature spin systems, we propose and exemplify the possibility to compute by coupling the computational spins to a quantum coherent bath that serves as a heat sink. The quantum tunneling effect provides additional cooling channels to accelerate the cooling process. We demonstrate both analytically and numerically that this strategy can achieve a quantum advantage in the unstructured search problem.
Cases of Misunderstanding

The article reviews cases of unsuccessful implicatures and possible reasons for misunderstanding the speaker’s meaning. The focus is on explaining misunderstanding with the graded salience hypothesis. Under review are examples of cases where the conventional meaning and intended meaning differ thus resulting in a misunderstanding. The graded salience hypothesis offers an explanation of how we understand expressions based on personal preference priority, so the main argument made is that in cases of misunderstanding the speaker and the listener prioritize different meanings attributed to the same expression due to differences in knowledge, personal background, familiarity with the expression in particular usage, etc. There are also cases of scalar implicatures where the inference meaning is not always the same. In such cases, the speaker’s meaning could be misunderstood if the listener considers a different meaning of the scalar expression as more salient than the one the speaker wishes to convey.
Determining an author's impact with f-, c- and mh-indices

The existing indices like h-index, i10-index, g-index and author impact factor (AIF) do not take into account of the order of an author in the list of authors in an article. In this article we have proposed f-index to show the number of articles with the concerned author as the first author, c-index to show the number of articles with the concerned author as the corresponding author and mh-index as the modified form of h-index. The modified h-index mh incorporates only 50% credit of articles in which the concerned author is not the first author.
Location Privacy Issues in Location-Based Services

The growing privacy concerns for location information across location-based services raise the need for advanced confidentiality and access control mechanisms to secure sensitive user information from unauthorized access or disclosure. Such privacy management strategies safeguard identifiable location information, preventing the service provider from accessing such data. This paper examines this location privacy issue by reviewing existing research papers on the problem and proposing a security solution that resolves the limitations of these strategies. This approach enhances location privacy across multiple platforms, leading to a reliable location-based service environment.
Water Sector Supply Chain Issues and Lessons

Journal AWWAVolume 114, Issue 4 p. 72-75 Water Quality Matters Water Sector Supply Chain Issues and Lessons Daniel Nix, Daniel NixSearch for more papers by this authorEnoch Nicholson, Corresponding Author Enoch Nicholson brent.alspach@arcadis.com Search for more papers by this author Daniel Nix, Daniel NixSearch for more papers by this authorEnoch Nicholson, Corresponding Author Enoch Nicholson brent.alspach@arcadis.com Search for more papers by this author First published: 03 May 2022 https://doi.org/10.1002/awwa.1906Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume114, Issue4May 2022Pages 72-75 RelatedInformation
In the evolving digital environment: is cataloging lagging behind?

Purpose The purpose of this paper is to suggest that cataloguing departments and agencies could benefit from Fourth Industrial Revolution and advanced technologies. Design/methodology/approach A desk research based on literature review extracted from different information sources. Literature was interpreted based on the search of the researcher on key concepts of the topic. Findings Literature indicates new trends in cataloguing and how technologies could be incorporated into cataloguing. Practical implications This study can inform policies on artificial intelligence adoption in cataloguing. Originality/value The 4IR and artificial intelligence does not often occur in cataloguing literature. Linking 4IR and advanced technologies, artificial intelligence and robotic science in cataloguing could inform policies and practice in cataloguing.
Quantum arithmetic operations for HSI based quantum color image

Arithmetic operations on images are important primary transformations that can be applied to many complicated image operations. We mainly study the intensity-based arithmetic operations on the QIRHSI quantum image representation model to facilitate the development of quantum image processing. The quantum circuits and unitary matrices for four arithmetic operations including reverse, complement, addition, and subtraction are proposed. Finally, the complexity analysis of quantum networks is illustrated to show the effectiveness of the proposed arithmetic operations.
Wi-Fi-Based Proximity Social Distancing Alert to Fight Against COVID-19

The Pandemic COVID-19 outbreak is only due to the transmission of the virus. This virus transmission is mainly possible due to coughing, sneezing, or droplets. Hence the WHO issued the guideline as to the use of masks and maintain social distancing. Social distancing is used to break the chain of transmission. The social distancing parameter is decided as a 1.5-meter minimum in case of having a mask. So that after sneezing, coughing the droplets may translate to less than a 1.5-meter area only. Due to this, the transmission chain of the COVID-19 will be going to shrink. The one more major issue faced by the Government is to trace the person who was in contact with the COVID positive. There is no such technical solution found. Due to this, the chain of transmission is not possible to break. By considering this factor, the proposed technique will be suggesting Wi-Fi-based proximity estimators, which will continuously monitor the Wi-Fi-enabled devices. Wifi-enabled devices such as smartphones will specify the position of another person whose Wi-Fi is turned on. If that distance is more than 2 meters, it will not alert. But if the distance is less than 2 meters, then it will go to alert. Also, storing the details of the person will be in proximity less than 1.5 meters. 226This proposed technique not only maintains the social distance but also useful to provide the contact tracing.
Anomaly or Possible Risk Factor? Simple-To-Use Tests

Asset pricing theory predicts high expected returns are a compensation for risk. However, high expected returns might also represent anomalies due to frictions or behavioral biases. We propose two complementary tests to assess whether risk can explain differences in expected returns, provide general-equilibrium foundations, and study their properties in simulations. The tests account for any risk disliked by riskaverse individuals, including high-order moments and tail risks. The tests do not rely on the validity of a factor model or other parametric statistical models. Empirically, we find risk cannot explain a large majority of differences in expected returns of characteristic-sorted portfolios.
Seismic data acquisition including survey design and factors affecting seismic acquisition

Geophysicists collect seismic data in the same way as earthquake seismologists. Elastic waves propagate through the Earth as a result of earthquakes. The ground motions caused by elastic wave propagation are then recorded by seismologists using a seismograph. A controlled seismic source of energy, but one that is much lower than an earthquake, is used in seismic exploration to generate elastic waves that propagate through the subsurface. Multiple receivers at the surface detect and record reflected seismic energy.
A Sparse-Dense HOG Window Sampling Technique for Fast Pedestrian Detection in Aerial Images

Pedestrian detection from Unmanned Aerial Vehicle (UAV) has been an important part of surveillance systems. A Two-stage (Sparse-Dense) sliding window technique has been proposed to increase the speed of pedestrian detection using HOG-SVM classifier. Standard techniques follow a sliding window approach with a fixed sliding strides over a multi-resolution image pyramid for detection. The presented technique breaks down the detection task into sparse sampling and dense sampling stages where the first one is region proposal step and second stage scans only the proposed regions for objects. Sparse sampling stage is working as weak classifier whereas the dense sampling stage works as strong classifier for an image patch. Average pedestrian detection speed using the proposed technique gave improvement from 1.95 fps to 15.36 fps for input images of dimension [640, 360] on a system with 3.2 GHz CPU. UAV123 [1] dataset has been chosen to train the classifier. For detection, Average Center Prediction Error has been taken to quantify detection performance with increased speed.
On Higher-Order Reachability Games Vs May Reachability

We consider the reachability problem for higher-order functional programs and study the relationship between reachability games (i.e., the reachability problem for programs with angelic and demonic nondeterminism) and may-reachability (i.e., the reachability problem for programs with only angelic nondeterminism). We show that reachability games for order- $$n$$ programs can be reduced to may-reachability problems for order-( $$n+1$$ ) programs, and vice versa. We formalize the reductions by using higher-order fixpoint logic and prove their correctness. We also discuss applications to higher-order program verification.
Top-Down Driver Head Orientation Detection Method Based on Monocular Camera and Virtual Scene Generation

Head posture assessment plays a very important role in the fields of driver attention detection, driving behavior analysis, and driving habit analysis. The mainstream head posture assessment methods use cameras to capture all the driver’s front face videos, this approach has a natural drawback that it requires a lot of facially sensitive data which is contrary to the increasing awareness of privacy protection. Considering privacy protection, we innovatively apply a monocular camera to acquire an image of the driver’s head with top-down shooting view and develop a corresponding TOP OF HEAD ONLY (TOHO) algorithm to estimate the head orientation. Subsequently, the virtual scene generation technique is leveraged to generate diverse model data. The generated data is afterwards used to validate the effectiveness of the proposed TOHO algorithm. The experimental results show that the error of the head attention angle calculated by the proposed algorithm is within 15 $$^{\circ }$$ compared to the labeled data.
Modeling of Explainable Artificial Intelligence with Correlation-Based Feature Selection Approach for Biomedical Data Analysis

In recent days, biomedical data analysis became a challenging problem due to the massive increase in the quantity of healthcare data. The emerging explainable artificial intelligence (XAI) tools can be applied for the effective examination of biomedical data and perform classification process. Besides, the high dimensionality of the medical data requires proper selection of features to reduce the complexity level. This paper presents an explainable artificial intelligence with correlation-based feature selection for biomedical data analysis (XAICFS-BDA) technique. The XAICFS-BDA technique aims to choose optimal features and then classifies data for biomedical decision making. In addition, the XAICFS-BDA technique performs the optimal selection of features using the correlation-based feature selection (CFS) technique. Besides, the classification of biomedical data is carried out by the use of fuzzy k-nearest neighbor classifier (FKNN), and the parameter tuning of this model is performed utilizing black widow optimization (BWO) approach. The experimental result analysis of the XAICFS-BDA technique is carried out using distinct benchmark biomedical dataset. Extensive comparative analysis pointed out the better performance of the XAICFS-BDA technique over the recent techniques.
RESEARCH OF STUDENTS’ VALUE ORIENTATIONS AT VOLOGDA STATE UNIVERSITY

The article focuses on the problem of students' value orientations. The authors reveal the concepts of “values”, “value orientations”, highlight the factors that contribute to the formation of modern students’ values. The paper presents the results of an empirical study on the students’ value orientations within various educational program specializations at Vologda State University. Based on the results of the study, the authors gave recommendations to the teaching staff and practical psychologists on the provision of socio-psychological and psychological-pedagogical counseling for students.
PROBLEMS OF STUDENT TRAINING IN PANDEMIC CONDITIONS IN THE COUNTRIES OF THE EUROPEAN UNION AND IN RUSSIA

Abstract. The closure of educational institutions and the transfer of training to online format in the context of a pandemic has become very relevant both for the EU countries and for Russia. Using the example of the work of the Northern State Medical University with foreign students studying in Russia, we can analyze the situation in the educational process in the context of the global crisis. The administration of the university and teachers are looking for new forms of work to organize the training of foreign students, developing and implementing educational programs in a digital format. The COVID-19 epidemic has affected the work of teachers and students, highlighted the challenges of distance education related to the Internet, access to digital devices, willingness to work and study remotely. Teachers and students noted the difficulties associated with online learning. Only high-quality distance learning can provide effective search, analysis, processing and transmission of the necessary information using information technologies, including when solving professional problems.
<b>GANBLR++</b>: Incorporating Capacity to Generate Numeric Attributes and Leveraging Unrestricted Bayesian Networks

Generative Adversarial Networks (GAN) models have led to a major breakthrough in data generation of various sorts. Over the years, we have seen several applications of GAN-based learning for tabular data generation as well. Very recently, GAN-based learning by incorporating Bayesian Networks (BN) as generator and discriminator – GANBLR, has shown to lead to state-of-the-art (SOTA) results for tabular data generation. Despite the impressive performance, GANBLR has an inherent weakness that it can only generate data with categorical attributes. Additionally, the model is trained and tested only with a restricted Bayesian Network. In this work, we have proposed an extension over GANBLR framework – GANBLR++, that has the capacity to generate numeric attributes, by leveraging Dirichlet Mixture Model. We also leverage unrestricted BN in GANBLR framework, and discuss how the use of unrestricted BN can lead to better quality data, as well as more interpretable model. We evaluate the effectiveness of GANBLR++ on wide range of datasets by demonstrating that it generates data of better quality as compared to existing SOTA models for tabular (numeric and categorical) data generation such as CTGAN, MedGAN and TableGAN.
Permutation-Invariant Representation of Neural Networks with Neuron Embeddings

Neural networks are traditionally represented in terms of their weights. A key property of this representation is that there are multiple representations of a network which can be obtained by permuting the order of the neurons. These representations are generally not compatible between networks, making recombination a challenge for two arbitrary neural networks - an issue known as the “permutation problem” in neuroevolution. This paper proposes an indirect encoding in which a neural network is represented in terms of interactions between neurons rather than explicit weights, and which works for both fully connected and convolutional networks. In addition to reducing the number of free parameters, this encoding is agnostic to the ordering of neurons, bypassing a key problem for direct weight-based representation. This allows us to transplant individual neurons and layers into another network without accounting for the specific ordering of neurons. We show through experiments on the MNIST and CIFAR-10 datasets that this method is capable of representing networks which achieve comparable performance to direct weight representation, and that combining networks this way preserves a larger degree of performance than through direct weight transfer.