Reviewerly/exHarmony huggingface dataset:
A Prediction Method of Ground Deformation in Subway Tunnel Construction based on Gaussian Process Regression

With the continuous development of urban traffic, the coverage area of the subway has gradually expanded, and the number of subway tunnel construction has increased significantly. To ensure that the construction process does not affect the ground, the ground deformation prediction method of subway tunnel construction is often used to predict the ground conditions. The method is not strong enough to analyze the ground deformation mechanism, which causes the problem that the predicted result is quite different from the measured result. Therefore, a prediction method of ground deformation based on Gaussian process regression is proposed. Set the ground displacement monitoring process, combined with the characteristics of tunnel construction, complete the monitoring and analysis of ground displacement data. The mechanism of ground deformation is analyzed, and combined with the grey prediction model, the ground deformation is predicted. Based on Gaussian process regression equation and covariance calculation results, a ground deformation prediction model is established. So far, the design of ground deformation prediction method for subway tunnel construction based on Gaussian process regression is completed. Constructing the experimental link, the comparison shows that the prediction result of this method is closer to the actual measurement result, which shows that this method is more effective for ground deformation prediction.
Disentangling Adversarial Robustness in Directions of the Data Manifold

Using generative models (GAN or VAE) to craft adversarial examples, i.e. generative adversarial examples, has received increasing attention in recent years. Previous studies showed that the generative adversarial examples work differently compared to that of the regular adversarial examples in many aspects, such as attack rates, perceptibility, and generalization. But the reasons causing the differences between regular and generative adversarial examples are unclear. In this work, we study the theoretical properties of the attacking mechanisms of the two kinds of adversarial examples in the Gaussian mixture data model case. We prove that adversarial robustness can be disentangled in directions of the data manifold. Specifically, we find that: 1. Regular adversarial examples attack in directions of small variance of the data manifold, while generative adversarial examples attack in directions of large variance. 2. Standard adversarial training increases model robustness by extending the data manifold boundary in directions of small variance, while on the contrary, adversarial training with generative adversarial examples increases model robustness by extending the data manifold boundary directions of large variance. In experiments, we demonstrate that these phenomena also exist on real datasets. Finally, we study the robustness trade-off between generative and regular adversarial examples. We show that the conflict between regular and generative adversarial examples is much smaller than the conflict between regular adversarial examples of different norms.
New educational trends and challenges. Determination of the level of digital competence

The development of information technology leads to a change in old paradigms in all areas. The state and employers set new requirements for specialists, such as digital competencies (DC). The article presents the results of a study of the general interest and interest of the academic community in the DC. Additionally, we made a primary analysis of the existing methods of DC level detection, presented in the Russian-language academic (RLA) literature within 2015-2020 in the work. In preliminary the work conclusion was estimated that this issue has not been considered enough in RLA.
ParsEL 1.0: Unsupervised Entity Linking in Persian Social Media Texts

Social media users have exponentially increased in recent years, and social media data has become one of the most populated repositories of data in the world. Natural language text is one of the main portions of this data. However, this textual data contains many entities, which increases the ambiguity of the data. Entity linking targets finding entity mentions and linking them to their corresponding entities in an external dataset. Recently, FarsBase has been introduced as the first Persian knowledge graph, containing almost 750,000 entities. In this study, we propose ParsEL, the first unsupervised end-to-end entity linking system specially designed for the Persian language, and utilizes contextual and graph-based features to rank the candidate entities. To evaluate the proposed approach, we publish the first entity linking dataset for the Persian language, created by crawling social media text from some popular Telegram channels and contains 67,595 tokens. The results show ParsEL records 86.94% f-score for the introduced dataset, and it is comparable with one other entity linking system which supports the Persian language.
Minorities and COVID-19. European Academy Bozen/Bolzano (EURAC research) Webinar Series (May–July 2020)

The report presents the webinar series “Minorities and COVID-19”, launched by the Institute for Minority Rights of EURAC research in spring 2020. The webinars attracted almost 2000 viewers, with the most popular episodes attracting 150 Zoom attendees and over 600 viewers on YouTube and Facebook, where all episodes can be found.
Moving your containers to the Cloud with Microsoft Azure

What a journey so far, right? If you knew nothing about containers when you started this book, you should feel proud now. I’m positive at this point you can face the challenge to go through a project that includes containerizing an existing application with Windows Containers. So far, you learned so much on this platform, and there’s not much to cover from an infrastructure standpoint in regard to how Windows Containers work, how to deploy it, how to containerize an application, how to manage the container platform itself, and so on.
Meta-Learning of Structured Task Distributions in Humans and Machines

In recent years, meta-learning, in which a model is trained on a family of tasks (i.e. a task distribution), has emerged as an approach to training neural networks to perform tasks that were previously assumed to require structured representations, making strides toward closing the gap between humans and machines. However, we argue that evaluating meta-learning remains a challenge, and can miss whether meta-learning actually uses the structure embedded within the tasks. These meta-learners might therefore still be significantly different from humans learners. To demonstrate this difference, we first define a new meta-reinforcement learning task in which a structured task is generated using a compositional grammar. We then introduce a novel approach to constructing a task distribution with the same statistical complexity as this structured task but without the explicit rule-based structure used to generate the structured task. We train a standard meta-learning agent, a recurrent network trained with model-free reinforcement learning, and compare it with human performance across the two task distributions. We find a double dissociation in which humans do better in the structured task whereas agents do better in the null task -- despite comparable statistical complexity. This work highlights that multiple strategies can achieve reasonable meta-test performance, and that careful construction of control task distributions is a valuable way to understand which strategies meta-learners acquire, and how they might differ from humans.
THE OBJECT-ORIENTED VISUAL PROGRAMMING SYSTEM «LAZARUS ONLINE»

В статье рассматривается методика обучения программированию на уроках информатики в 9-м классе с помощью системы Lazarus и тренажера "Лазарус онлайн", разработанного автором статьи. The article discusses the method of teaching programming in computer science lessons in the 9th grade using the Lazarus system and the "Lazarus online" simulator, that was developed by the author of the article.
TRANSVERSAL ENVIRONMENTAL COMPETENCE OF TEACHER AS A RESULT OF EDUCATION FOR SUSTAINABLE DEVELOPMENT

Introduction. The relevance of developing a transversal environmental competence in teachers of various subjects is determined by the global challenges of the time, and the current state of educational development. The study aims to justify the need to form the transversal environmental competence in teachers of different subjects. Materials and methods. The paper discusses theoretical foundations of the phenomenon of transversal environmental competence of the teacher, and substantiates the independence of this competency. Experiments have been made to prove the effectiveness of the development of a transversal environmental competence in the teacher. The methods employed in the study also included questionnaires “Questions to yourself,” and a monographic description. The results of the study. An analysis of empirical material concluded that most teachers do not have sufficient knowledge and skills related to the issues of education to promote sustainable development. This is due to the fact that the environmental competence of the teacher is considered separately from their professional competencies. Therefore, it is necessary to build such environmental competence of teachers, which can help them solve worldview, methodological and axiological problems of self-identification, self-determination, self-actualization, and self-development in a society in the interests of sustainable development. In this regard, the appropriateness of introducing the concept of a “transversal environmental competence” in the pedagogical thesaurus has been experimentally proved. Conclusion. The practical value of the study lies in the improvement of the environmental competence in teachers of various subjects. Keywords: professional competence, transversal environmental competence, environmental education, construction, sustainable development.
Mineralogy and Geochemistry of Shale-Hosted Copper of the Middle Buanji Group, Chimala Area, South-Western Tanzania

The mineralogical and geochemical assessments are presented in this paper to constrain the mineralogy and copper concentrations of the shale-hosted copper in the Middle Buanji Group of the Upper Paleoproterozoic (1.67 Ga). The XRD analysis revealed that illite and chamosite are the major clay minerals present in the shales together with pseudomalachite, quartz, and muscovite that constitute over 95% by proportion of the sample. The minerals biotite, birnessite, ferroselite, bearsite, chloritoid, and anatase are present in association with shale-hosted copper in low amounts (i.e., <5% by proportion of the sample). The pseudomalachite [Cu5(PO4)2(OH)4] is considered as ore mineral of copper in the shales of the Middle Buanji Group. Field observations revealed the presence of different shale layers within the Middle Buanji Group such as red, grey and green/blue in which copper mineral is distributed. On average, the copper concentrations in the shale layers were unevenly distributed throughout the red-grey-green shales layers with values of 0.31 wt%, 5.2 wt% and 13.19 wt%, respectively. A noticeable amount of copper mineralization up to 25.7 wt% was restricted within the green shale layer as compared to red (0.31 wt%) and grey (10.9 wt%) layers.Keywords: Pseudomalachite; Supergene enrichment; Shale-hosted copper; Green shale layer
Analysis on lending service of paper books of double first-class construction university libraries in COVID-19

[Purpose/significance] How to carry out lending services of paper books during the COVID-19 pandemic to meet the needs of teachers and students for teaching and scientific research is an important issue that deserves attention. [Method/process] With the method of bibliometric analysis, the lending service of paper books of double first-class construction university libraries in China was investigated after the outbreak. It has been sorted into key elements from the investigation, and shows three characteristic features which are process specification of “online” appointment, “offline” book delivery service, and “pandemic prevention” requirements in place. [Result/conclusion] Three aspects are enlightened, including taking collection “electronification” into consideration, innovating lending service as “online appointment + offline delivery” model, and promoting the public health safety as the focus of library management. It provides a reference for domestic and foreign university libraries to carry out lending services of paper books during the pandemic.
Multimedia Assisted Sharing Platform of Higher Education Resources in the Era of Internet of Things

In the establishment of multimedia aided sharing platform of educational resources in Colleges and universities, B / S architecture is often used for database management, but the response time of database in updating and querying users is greatly affected by the number of users and the number of users. Therefore, this paper puts forward the research on Multimedia Assisted sharing platform of higher education resources in the era of Internet of things. In the logical construction of sharing platform, three levels are constructed, and DRM protocol is applied to manage the resource copyright. Hadoop is chosen to manage and establish the database. Finally, the rest architecture is used to allocate and share resources. In order to verify the feasibility of the design platform, design experiments, the experimental results show that the design platform has higher response time and less impact when performing multi-user or multi operation, which meets the design intent.
Extract Local Inference Chains of Deep Neural Nets

We study how to explain the main steps/chains of inference that a deep neural net (DNN) relies on to produce predictions in a local region of data space. This problem is related to network pruning and interpretable machine learning but the highlighted differences are: (1) fine-tuning of neurons/filters is forbidden: only exact copies are allowed; (2) we target an extremely high pruning rate, e.g., ≥95%; (3) the interpretation is for the whole inference process in a local region rather than for individual neurons/filters or on a single sample. In this paper, we introduce an efficient method, \name, to extract the local inference chains by optimizing a differentiable sparse scoring for the filters and layers to preserve the outputs on given data from a local region. Thereby, \name~can extract an extremely small sub-network composed of filters exactly copied from the original DNN by removing the filters/layers with small scores. We then visualize the sub-network by applying existing interpretation technique to the retained layer/filter/neurons and on any sample from the local region. Its architecture reveals how the inference process stitches and integrates the information layer by layer and filter by filter. We provide detailed and insightful case studies together with three quantitative analyses over thousands of trials to demonstrate the quality, sparsity, fidelity and accuracy of the interpretation within the assigned local regions and over unseen data. In our empirical study, \name~significantly enriches the interpretation and makes the inner mechanism of DNNs more transparent than before.
Disperse Access Considered Energy Inefficiency in Intel Optane DC Persistent Memory Servers

The Intel Optane DC Persistent Memory Module (AEP), which is the first commercial available Non-Volatile Memory (NVM) product, offers comparable performance with DRAM while providing larger capacities and data persistence. Existing researches that substitute NVM with DRAM or hybridize them are either emulator-based or focused on how to improve the energy efficiency for writes. Unfortunately, the energy efficiency of the real AEP system is less explored. Based on real AEP, we observe that even though eliminating the DRAM-like refresh energy consumptions, AEP consumes significant different energy at different performance levels. Specifically, requests with time intervals (dispersed) underperform in both performance and energy efficiency when compared with the case of requests without time intervals (compact). This disparity and parallelism exploitation potentials motivate us to propose Sprint-AEP, an energy-efficiency-oriented scheduling method for AEP-equipped servers. Sprint-AEP fully activates adequate AEPs to serve most of the requests by deferring the write requests and prefetching the hottest data. The remaining AEPs will stay in idle mode with a low idle power to save energy. Besides, we also utilize the read parallelism to accelerate the sync and prefetching processes. Compared with energy-unaware AEP usages, our experimental results show that Sprint-AEP saves up to 26% energy with little performance degradation.
The Shift to Information System Continuous Monitoring (ISCM)

Cyber risk management is a concept difficult to understand, let alone implement effectively. In light of this challenge, it may come at no surprise that our current methods of managing risk in cybersecurity are a complete failure (Hubbard & Seiersen, 2016, p. 15). There is something unsettling about the concept of being unable to fully secure a network infrastructure, but perhaps, more unsettling is the inability to accurately quantify the risk associated in the cyber domain to drive informed decisions. The global community at large needs leaders who consistently make better choices through better analysis (Hubbard & Seiersen, 2016, p. iii). An organization is able to throw as much money and manpower at the problem and still fall short and succumb to a breach. This is largely in part due to a flawed risk management approach or inability to monitor the maintenance of the risk management program. The capability to monitor the implementation of the risk management program while providing the flexibility to provision on-demand alterations to the plan is not an easy task.
Redefining The Self-Normalization Property

The approaches that prevent gradient explosion and vanishing have boosted the performance of deep neural networks in recent years. A unique one among them is the self-normalizing neural network (SNN), which is generally more stable than initialization techniques without explicit normalization. The self-normalization property of SNN in previous studies comes from the Scaled Exponential Linear Unit (SELU) activation function. %, which has achieved competitive accuracy on moderate-scale benchmarks. However, it has been shown that in deeper neural networks, SELU either leads to gradient explosion or loses its self-normalization property. Besides, its accuracy on large-scale benchmarks like ImageNet is less satisfying. In this paper, we analyze the forward and backward passes of SNN with mean-field theory and block dynamical isometry. A new definition for self-normalization property is proposed that is easier to use both analytically and numerically. A proposition is also proposed which enables us compare the strength of the self-normalization property between different activation functions. We further develop two new activation functions, leaky SELU (lSELU) and scaled SELU (sSELU), that have stronger self-normalization property. The optimal parameters in them can be easily solved with a constrained optimization program. Besides, analysis on the activation's mean in the forward pass reveals that the self-normalization property on mean gets weaker with larger fan-in, which explains the performance degradation on ImageNet. This can be solved with weight centralization, mixup data augmentation, and centralized activation function. On moderate-scale datasets CIFAR-10, CIFAR-100, and Tiny ImageNet, the direct application of lSELU and sSELU achieves up to 2.13% higher accuracy. On Conv MobileNet V1 - ImageNet, sSELU with Mixup, trainable λ, and centralized activation function reaches 71.95% accuracy that is even better than Batch Normalization.(code in Supplementary Material)
Natural algorithms for image and video enhancement

Through evolution, nature has developed the most energy efficient method for achieving a certain goal. In contrast to the traditional hand-crafted algorithms pervasive in computational imaging, our approach takes inspiration from nature, and in particular optical physics, to craft qualitatively new algorithms. Here, we show that propagation of light through an optical medium has properties that can be exploited for development of a new class of image and video enhancement algorithms that offer unprecedented improvements in performance over conventional counterparts. In certain cases, these algorithms also have the potential to be implemented physically compared to synthetically fashioned computational techniques in practice today.
A novel Based-Approach Composed of Clustering Algorithm &amp; Cosine Similarity for Products Recommendation

There are huge tons of transactions being accomplished online every day. This implies that ecommerce is facing the problem of data and information overloads. While customers are shopping via websites, they spend a lot of time to search for the required products based on their needs. This problem can easily be alleviated by having an accurate recommendation system based on a strong algorithm and confident measures in this regard. There are two main techniques for products recommendation; content-based filtering and collaborative filtering. If one of these two techniques implemented on the e-commerce system, a lot of limitations and weak points will appear. This paper aims at generating an optimal list of product, which, in turn, generates an accurate and reliable list of items. The new approach is composed of three components; clustering algorithm, user-based collaborative filtering, and the Cosine similarity measure. This approach implemented using a real dataset of past experienced users. The accuracy of the search results is a matter to users, it recommends the most appropriate products to users of the e-commerce website. This approach shows trustworthy results and achieved a high level of accuracy for recommending products to users.
Heterogeneous identity trust management method based on risk assessment

The cross-trust domain environment in which heterogeneous identity alliances are located often does not have a completely trusted centralized trust root, and different trust domains and entities also have specific security requirements. In view of the above problems, we believe that trust measurement of cross-domain identities based on risk assessment is an effective method to achieve decentralized proof of user identities in heterogeneous cyberspace. There are various risk assessment models. We choose the more mature attack graph theory in the existing research to apply to the new field of cross-trust domain management of heterogeneous identities. We propose an attribute attack graph evaluation model to evaluate cross-domain identities through risk measurement of attributes. In addition, heterogeneous identity alliances also have architectural risks, especially the risk of decentralized underlying structures. In response to this problem, we identify the risk of the identity alliance infrastructure, and combine the risk assessment and presentation system design to verify the principle.