Reviewerly/exHarmony huggingface dataset:
Core Container Security Frameworks

With the introduction of Docker and Kubernetes, the container environment, along with DevOps, is evolving rapidly. The importance of container security has been highlighted by the discovery of new security vulnerabilities in container environments such as DIRTY COW (CVE-2016-5195) and RunC Container Escape (CVE-2019- 5736). Further, in February 2018, the system that used to be serviced in Amazon's AWS environment and that mined the cryptocurrency after Tesla's Kubernetes environment was hacked has become a significant issue. Despite these security threats, however, research into the container security framework is still in its early stages. Thus, the authors intend to analyze the existing container security framework to find deficiencies and to suggest the actual applicable container security framework by supplementing the items that need to be newly added.To this end, the security framework and solutions of three (3) Korean banks and one (1) credit card company collected between January and March of 2020 were analyzed to derive forty-four (44) solutions in five (5) areas to be migrated from the traditional security framework, while eight (8) criteria for analysis were prepared through an attack vector analysis of the container environment. Based on the above, finally, 20 solutions in 6 areas were derived and the core container security framework was presented.
Applying Your Skills to Cybersecurity

This chapter encourages you to join the field of cybersecurity to leverage your existing skills and everything that you have learned in this book about the root causes of data breaches and how to fight them. The cybersecurity field is one of the highest growth fields and most deeply needed professions with hundreds of thousands of open positions in the United States and millions of open positions worldwide.
Exploring the relationship between the faithfulness and entanglement of two qubits

A conceptually simple and experimentally prevalent class of entanglement witnesses, known as fidelity witnesses, detect entanglement via a state's fidelity with a pure reference state. While existence proofs guarantee that a suitable witness can be constructed for every entangled state, such assurances do not apply to fidelity witnesses. Recent results have found that entangled states that cannot be detected by a fidelity witness, known as unfaithful states, are exceedingly common among bipartite states. In this paper, we show that even among two-qubit states, the simplest of all entangled states, unfaithful states can be created through a suitable application of decoherence and filtering to a Bell state. We also show that the faithfulness is not monotonic to entanglement, as measured by the concurrence. Finally, we experimentally verify our predictions using polarization-entangled photons and specifically demonstrate a situation where an unfaithful state is brought to faithfulness at the expense of further reducing the entanglement of the state.
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT

We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing. Character representations can easily be added in a sequence-to-sequence model in either one encoder or as a fully separate encoder, with improvements that are robust to different language models, languages and data sets. For English, these improvements are larger than adding individual sources of linguistic information or adding non-contextual embeddings. A new method of analysis based on semantic tags demonstrates that the character-level representations improve performance across a subset of selected semantic phenomena.
Computing Preimages of Deep Neural Networks with Applications to Safety

To apply an algorithm in a sensitive domain it is important to understand the set of input values that result in specific decisions. Deep neural networks suffer from an inherent instability that makes this difficult: different outputs can arise from very similar inputs. We present a method to check that the decisions of a deep neural network are as intended by constructing the exact, analytical preimage of its predictions. Preimages generalize verification in the sense that they can be used to verify a wide class of properties, and answer much richer questions besides. We examine the functioning and failures of neural networks used in robotics, including an aircraft collision avoidance system, related to sequential decision making and extrapolation. Our method iterates backwards through the layers of piecewise linear deep neural networks. Uniquely, we compute \emph{all} intermediate values that correspond to a prediction, propagating this calculation through layers using analytical formulae for layer preimages.
Analysis of the usage of the android-based scantling calculation application for wooden ship construction for survey activity based on BKI vol vii 2013 rules

Abstract In survey activities at wooden shipyards, surveyors often find scantling calculations not comply with the regulations. Because the use of technology has not yet developed, based on traditional technology development so that the weakness occurs in the design and construction. The survey system for scantling calculation is yet manually, surveyors write actual results and carry documents in paper form which is less efficient. Designing media in the form of android application for calculating wooden ship scantling based on BKI Volume VII 2013 rules concerning ‘Rules for Small Vessel Up to 24 M’ is needed. The initial step of a design application is creating database structure that normalization BKI table become a system, then, making a mock up for display. After that, create functional as back end system that is processing database and front end that is screen display, and the last step is creating system for reporting result. This application accessed by input the actual data of wood type, mechanical properties and main dimension on each form, then the application will display the results of BKI calculations. The surveyor fills actual data of scantling in column and gives mark if the results comply and will gives a note if not complied then insert a photo to prove. The PELRA ship with length overall (LOA) of 17.65 meters used as the research object. The questionnaire is applied to know response of user about the application. The results, a total percentage is 75.6% to average total score is 37.8 which shows that the application very helpful to assist survey activities. The conclusion, the use of android application for scantling calculation of wooden ship construction is needed because more practical and can accessed anywhere compared to manual survey systems, more efficient in terms of time, and more safety for data storage of report.
Using Web Crawling and Word Cloud Methods to Investigate Issues Arising from Expanded Implementation of Vehicle and Garage Joint Registration Policy

제주특별자치도는 2007년 전국에서 최초로 차고지증명제를 도입하여 시행하고있다. 2017년 1월부터 적용 대상이 대형차량에서 중형차량까지 확대 되었고, 2019년 7월부터 적용 지역이 제주시 동지역에서 제주 전 지역으로 확대되었다. 본 논문은 차고지증명제 전 지역 확대 시행과 관련한 우려를 언론기사와 댓글을 중심으로 살펴보고자 한다. 이를 위해 웹 크롤링 기법을 적용하여 차고지증명제와 관련이 있는 언론기사 및 댓글을 추출하고, 언급된 단어를 워드 클라우드를 통해 시각적, 정량적으로 분석하였다. 언론기사 분석결과 ‘차량’, ‘주차장’, ‘확대’ 등 차고지증명제 확대 시행에 대한 정보 전달과 관련한 단어들이 언급됨에 반해, 댓글에서는 ‘차고지’, ‘주차장’이 ‘불편’ 등 주차공간 부족에 대한 우려와 같은 부정적인 반응, ‘장애인’, ‘버스’, ‘단속’ 등 제도의 사각지대 혹은 보완방안과 관련한 단어들이 많이 언급되었다. 이와 같은 우려 사항과 보완방안들은 차고지증명제의 정책 수용성을 확대하고 2022년 예정된 차고지증명제 전 차종 전면시행을 선제적으로 준비하기 위해 적극적 활용할 필요가 있다.In 2007, Jeju Special Self-governing Province implemented vehicle and garage joint registration policy (levying garage option on car buyers or garage identification system), requiring a garage or parking space to register for auto ownership. This policy has been applied to a full-size car, while it has expanded to a mid-size car since 2017. Recently, the entire localities became target areas for this policy while only urbanized area of Jeju-city has been in effective over the past 12 years. Due to the early geographic expansion, which is originally scheduled to year 2022, various issues and concerns arise. This paper investigates issues on expansion of the vehicle and garage joint registration policy, using Web Crawling and Word Cloud methods. Web Crawling archives online news and its comments relevant to the vehicle and garage joint registration policy while Word Cloud visualizes keywords frequently presented in the news and comments. Results show that a group of words found in online news, such as ‘Vehicle’, ‘acquisition’, ‘garage’, ‘expansion’, providing information about expansion of vehicle and garage joint registration policy. On the other hand, in comments, concerns on insufficient number of garage or parking space, negative responses (‘problem’, inconvenience’, etc.), and finally, mid-term and/or long-term improvement (‘handicapped person’, rental car’, ‘public transportation’, ‘enforcement’, etc.) are largely found. Implications on policy acceptance and improvement are discussed.
The Shape of Cloud Security in The WFH ERA

Abstract It's been a year since the first lockdown came into effect in the UK and brought with it the shift to working remotely, writes Danny Phillips, EMEA VP, Sales Engineering at Zscaler.
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification

Adversarial attack is aimed at fooling the target classifier with imperceptible perturbation. Adversarial examples, which are carefully crafted with a malicious purpose, can lead to erroneous predictions, resulting in catastrophic accidents. To mitigate the effects of adversarial attacks, we propose a novel purification model called CAP-GAN. CAP-GAN takes account of the idea of pixel-level and feature-level consistency to achieve reasonable purification under cycle-consistent learning. Specifically, we utilize the guided attention module and knowledge distillation to convey meaningful information to the purification model. Once a model is fully trained, inputs would be projected into the purification model and transformed into clean-like images. We vary the capacity of the adversary to argue the robustness against various types of attack strategies. On the CIFAR-10 dataset, CAP-GAN outperforms other pre-processing based defenses under both black-box and white-box settings.
LayoutTransformer: Relation-Aware Scene Layout Generation

In the areas of machine learning and computer vision, text-to-image synthesis aims at producing image outputs given the input text. In particular, the task of layout generation requires one to describe the spatial information for each object component, with the ability to model their relationships. In this paper, we present a LayoutTransformer Network (LT-Net), which is a generative model for text-conditioned layout generation. By extracting semantics-aware yet object discriminative contextual features from the input, we utilize Gaussian mixture models to describe the layouts for each object with relation consistency enforced. Finally, a co-attention mechanism across textual and visual features is deployed to produce the final output. In our experiments, we conduct extensive experiments on both MS-COCO and Visual Genome (VG) datasets, and confirm the effectiveness and superiority of our LT-Net over recent text-to-image and layout generation models.
Learning to Correct Reconstructions from Multiple Views

This paper is about reducing the cost of building good large-scale 3D reconstructions post-hoc. We render 2D views of an existing reconstruction and train a convolutional neural network (CNN) that refines inverse-depth to match a higher-quality reconstruction. Since the views that we correct are rendered from the same reconstruction, they share the same geometry, so overlapping views complement each other. We take advantage of that in two ways. Firstly, we impose a loss during training which guides predictions on neighbouring views to have the same geometry and has been shown to improve performance. Secondly, in contrast to previous work, which corrects each view independently, we also make predictions on sets of neighbouring views jointly. This is achieved by warping feature maps between views and thus bypassing memory-intensive 3D computation. We make the observation that features in the feature maps are viewpoint-dependent, and propose a method for transforming features with dynamic filters generated by a multi-layer perceptron from the relative poses between views. In our experiments we show that this last step is necessary for successfully fusing feature maps between views.
Comparative Study of Different Haemoglobin Estimation Methods

Introduction: Anaemia is one of the most common haematological findings encountered in clinicalpractice. Diagnosis of anaemia is mainly done by haemoglobinestimation. Among many methodsavailable for hemoglobin estimation, for outpatient department we need fast and cheap method, forindoor patients we need more accurate methods for treatment direction and monitoring of the patient.Methods: 500 EDTA blood samples were obtained randomly from various indoor and outdoor patientdepartments of a tertiary care hospital in Ahmedabad, India and their hemoglobin was measured bySahli’sHaemoglobinometer, Cyanmethemoglobin Method &7-part haematology analyzer: SiemensADVIA2120i.Results:Repeatability standard deviationsof Sahli’s method, Drabkin’s method and cell counterrespectively were 0.61 g/dl, 0.38 g/dland 0.15 g/dl. When comparing Sahli’s method with Drabkin’smethod and cell counter, we found p value of 0.05.Conclusion: Sahli’s method although cheap and easy, is inaccurate and has subjective bias. So it can beused for screening purpose, but not for diagnosis and follow up of anaemia,Haemoglobin measurementby Drabkin’s method is very cost effective and it is as efficient as cell counter. It is especially usefulin fund deprived areas and where only haemoglobin value is required.Cell counter although highlyaccurate and versatile, requires good equipment, quality control, laboratory setup and trained personnel.So it should be preferably used when complete blood count is required.
Machine learning text classification model with NLP approach

This article describes the relevance of the word processing task that is written in human language by the methods of Machine Learning and NLP approach, that can be used on Python programming language. It also portrays the concept of Machine Learning, its main varieties and the most popular Pythonpackages and libraries for working with text data using Machine Learning methods. The concept of NLP and the most popular python packages are also presented in the article. The machine learning classification model algorithm based on the text processing is introduced in the article. It shows how to use classification machine learning and NLP methods in practice.
THE EFFECT OF GROUP INVESTIGATION METHOD ON STUDENTS’ ABILITY IN WRITING PROCEDURE TEXT

This study is an experimental design. It was intended to prove that there is a significance effect of using group investigation method on students’ ability in writing procedure text. The population of this study was the eighth grade student of SMP Negeri 2 Lintongnihuta in Academic year 2019/2020 which consisted of six parallel classes. The total number of population was 240 students. Two classes were selected as the sample by using random sampling technique. The total of the sample was 60 students. The samples were divided into two groups namely experimental and control group. The experimental group was taught by using group investigation method while the control group was taught by conventional teaching. Writing test was conducted as instrument for collecting data. The topic of the test was how to operate a computer.The result of the data  analysis showed  that t-calculated was 4.874 at the level of significance p = 0.05 while t-table is 2.00 at the level. It was found that t-calculated was higher than t-table (4.874 > 2.00). Thus, the alternative hypothesis (Ha) is accepted and it means that there is a significance effect of using group investigation method on students’ ability in writing procedure text.
Survey of cryptography algorithms for Sub-Saharan countries

With the increase of Internet of Things (IoT) and threats on Information Systems (IS) resources, it is urgent for Africa countries to stop being spectator on cyber security and begin to play a more active role. This paper aimed at helping Africa country governments and  organizations to understand security challenges that they may face, and propose solutions. The paper described security investment factors, proposed a survey of IoT Worldwide and in Africa, and details as well a survey of cryptography algorithms (symmetric and  asymmetric). Indeed, information security, in particular cryptography, seems alien to Africa IT professionals. This lack stems from a deep ignorance about this wonderful field of cryptography and its importance in the system. The authors believe that this trend must be reversed as soon as possible, as Africa strives opening up to the world. Henceforth, this paper presented a comprehensive introductory survey on cryptographic algorithms especially tailored for Africans.Key Words: Internet, Things, (IoT), Security, costs, cryptography, algorithms, Ciphers, Encryption, Decryption, methods.
Multiplexed photon number measurement

The evolution of quantum systems under measurement is a central aspect of quantum mechanics. When a two level system -- a qubit -- is used as a probe of a larger system, it naturally leads to answering a single yes-no question about the system state followed by its corresponding quantum collapse. Here, we report an experiment where a single superconducting qubit is counter-intuitively able to answer not a single but nine yes-no questions about the number of photons in a microwave resonator at the same time. The key ingredients are twofold. First, we exploit the fact that observing the color of a qubit carries additional information to the conventional readout of its state. The qubit-system interaction is hence designed so that the qubit color encodes the number of photons in the resonator. Secondly, we multiplex the qubit color observation by recording how the qubit reflects a frequency comb. Interestingly the amount of extracted information reaches a maximum at a finite drive amplitude of the comb. We evidence it by direct Wigner tomography of the quantum state of the resonator. Our experiment unleashes the full potential of quantum meters by bringing the measurement process in the frequency domain.
BCS Impact Report

Abstract As the third national lockdown throws the digital divide into sharper focus, BCS continues to speak boldly on issues from home-schooling to our analysis of the loss of police database records. Claire Penketh reports.
NATURE AND CRITIQUE OF THE GAMIFICATION PHENOMENON

Digital games are a significant fragment of lives of many people of almost every age category. Fashionability and popularity of these media products have launched an extensive discussion in the society of possible application of this concept in education, as, according to many theoreticians, the currently used teaching methods are inefficient, outdated, and lower the motivation of individuals to continue learning. Digital games might present one of the adequate solutions to this problem. Their partial components are being applied in the process of education through gamification. In practice, this phenomenon creatively uses elements and mechanics of digital games in order to naturally encourage interest, activity, and motivation of individuals during the educational process. Any element of a digital game can thus be a potential impulse for changing the character of education, whereas in the current situation regarding the spreading coronavirus, this form of education is more than desirable, despite its many pitfalls. The primary objective of the submitted paper is to present the nature and possible risks closely connected to the discussed phenomenon. In the presented theoretical reflection, we thus work with relevant publications by N. Esposito, M. J. P. Wolf, N. Vrabec, V. Kacinova, D. Buckingham, S. Deterding, S. Sanduskyova, and other authors that address definitions of a (digital) game, media education, and gamification itself. Our goal is to present the basic and topical terminological axis of the analysed issue on the one hand and, on the other hand, to point out the critical aspects related to this instrument of modern education, which might appear to be solely positive at first sight. To achieve the set ambition, we will apply logical-conceptual methods in the text.
El podcast como herramienta de retroalimentación en la evaluación de actividades virtuales / The podcast as a feedback tool in evaluating virtual activities

Evaluation and feedback are closely related processes that make up an important part of the binomial teaching learning. In virtual learning environments, the realization of this task become essential and translates into a huge challenge for educators. This proposal was conceived as a response to the limitations presented by teachers at a private university in Lima, Peru, when accompanying students in achieving significant learning in virtual courses. Along the same lines, it is an attempt to make the use of ICT as a functional alternative to achieve ambitious learning achievements. The objective of this article is to review the effect of the use of the podcast in the learning process of first cycle students of transversal subject of Language. In turn, it exposes the results of the implementation of the use of podcast as a feedback tool in the evaluation of virtual activities, based on a pre-experimental study method. The most representative results indicate that the intervention of this resource allowed a significant number of students to reach levels of performance with outstanding achievement.
Covid-19 preventive action intention determinants-focused on the expanded health belief model

The world is experiencing a COVID-19 pendemic fear. Accordingly, Korean university students, who were active in k quarantine, studied what they think about the COVID-19 pendemic.Perceived sensitivity, perceived seriousness, perceived benefit, and perceived disability, which are the main variables of the health belief model, were measured. In addition, questions about self-efficacy, subjective norms, and preventive behavior intention were read and answered online. It was done in a way. This study focused on the perceived sensitivity and perceived severity, perceived benefit, perceived disability, self-efficacy, and extended variables of subjective norms of the health belief model. Perceived sensitivity had a negative effect on preventive behavior intention. it is judged that the more information about COVID-19, the less active it will be in infection prevention actions. Therefore, hypothesis 1 was adopted. The perceived severity had a positive effect on preventive behavior. This means that as the number of COVID-19 infections increases and the number of local outbreaks increases, more proactive actions will be taken. Hypothesis 2 was also adopted. The perceived benefit did not affect the intention of preventive behavior, which seems to have little effect on mask hand washing. Hypothesis 3 was rejected. Perceived disability also did not affect preventive behavior intention, which means that the prolonged COVID-19 infection was not significantly affected by increased indifference to infection and the cost of prevention such as masks.Hypothesis 4 was also rejected. Self-efficacy had a positive effect on the intention of preventive behavior, which means that they are positively and proactively engaged in their own prevention efforts, and their satisfaction with these behaviors is high. The results of this study are expected to be helpful in effectively explaining the preventive behavioral intention of risk of diseases such as COVID-19.