Reviewerly/exHarmony huggingface dataset:
Incorporating Bidirection-Interactive Information and Semantic Features for Relational Facts Extraction (Student Abstract)

The interaction between named entity recognition and relation classification is quite essential for the extraction of relational triplets. However, most of jointly extraction works only consider unidirectional interaction between the two sub-tasks. They even neglect the interactive information totally. In order to tackle these problems, we propose a novel unified joint extraction model which considers bidirection-interactive information between the two subtasks. Our model consists of two modules. The first module utilizes Bi-LSTM and GCN to capture the sequential and the structure-semantic features of a sentence, The second module utilizes two layers to capture bidirection-interactive information between the two subtasks and generates relational triplets respectively. The experimental results show that our proposed model outperforms the state-of-the-art models on two public datasets.
Complementing Static Scholarly Editions with Dynamic Research Platforms

In 2000 the Wittgenstein Archives at the University of Bergen (WAB) published the CD-ROM edition of Wittgenstein's Nachlass: The Bergen Electronic Edition (BEE).However, since the publication, it has become increasingly obvious that this edition does not meet all demands of the community that uses Wittgenstein's manuscripts (his "Nachlass") for research and learning.WAB has, therefore, for more than a decade now worked towards complementing the static CD-ROM edition with an interactive web platform that allows a more comprehensive, yet also a more tailored and user-specific utilization, of WAB's Nachlass resources.The paper describes and discusses two specific web service tools of this platform: Interactive Dynamic Presentation (IDP) of the Wittgenstein Nachlass, and Semantic Faceted Search and Browsing (SFB) of Wittgenstein metadata.The paper argues that it is only when these two tools are fully implemented and functional that WAB can adequately serve the scholarly needs of the Wittgenstein Nachlass user community.The paper describes some selected features and functionalities of these two tools in detail.While pilot versions of both tools are already in use on the platform, they need substantial extension and optimization.This upgrade is being implemented within the Norwegian CLARINO+ project. Data and Metadata for Use of Wittgenstein in Research and EducationDuring his lifetime, the Austrian-British philosopher Ludwig Wittgenstein (1889-1951) published only one philosophical book, the Logisch-philosophische Abhandlung / Tractatus logicophilosophicus (1 st ed.1921/22), and a Dictionary for Elementary Schools (1 st ed.1926).However, on his death in 1951, he left behind a significant 20,000 page corpus of unpublished philosophical notebooks, manuscripts, typescripts and dictations.This oeuvre, called "the Wittgenstein papers" or "Wittgenstein's Nachlass" (von Wright, 1969), was early recognized to be one of the most important philosophy archives of all times.It was subsequently brought to the wider public through posthumous book publications such as Philosophical Investigations (1 st ed.1953) and Culture and Value (1 st
Holistic Multi-View Building Analysis in the Wild with Projection Pooling

We address six different classification tasks related to fine-grained building attributes: construction type, number of floors, pitch and geometry of the roof, facade material, and occupancy class. Tackling such a remote building analysis problem became possible only recently due to growing large-scale datasets of urban scenes. To this end, we introduce a new benchmarking dataset, consisting of 49426 images (top-view and street-view) of 9674 buildings. These photos are further assembled, together with the geometric metadata. The dataset showcases various real-world challenges, such as occlusions, blur, partially visible objects, and a broad spectrum of buildings. We propose a new \emph{projection pooling layer}, creating a unified, top-view representation of the top-view and the side views in a high-dimensional space. It allows us to utilize the building and imagery metadata seamlessly. Introducing this layer improves classification accuracy -- compared to highly tuned baseline models -- indicating its suitability for building analysis.
Building integrated photovoltaics- an overview

From the older concept of photovoltaic installation, which includes the addition of solar panels to a building’s roof, the construction technology has merged with the photovoltaics technology. The result is Building Integrated Photovoltaics (BIPV), in which integrating the architectural, structural and aesthetic component of photovoltaics into buildings. Building integration of photovoltaics (BIPVs) has been recognized worldwide as a pivotal technology enabling the exploitation of innovative renewable energy sources in buildings, acting as electric power generators within the new framework of smart cities. The standard semitransparent photovoltaic (PV) modules can largely replace architectural glass installed in the building envelopes such as roofs, skylights, and facade of a building. Their main features are power generation and transparency, as well as possessing a heat insulating effect. PV glass shows the same mechanical properties as a conventional, architectural glass used in construction. Additionally, it provides free and clean energy. Given these properties, PV Glass maximizes the performance of the building’s envelope. The cost of the PV system and its implementation is still significantly high in comparison to solar thermal systems.&#x0D; Keywords: Building Integrated Photovoltaics, renewable energy, power generation, heat insulating effect
DevOps for Better Software Security in the Cloud

The DevOps model ensures that the organization's growth and processes are work together. As far as security is concerned, DevOps security tools are significant in relaying information on external attackers which can be traced back to development, allowing for quicker elimination of potential vulnerabilities. It is a process that is particularly critical in cloud deployments, whereby release intervals can be less than24 hours. This paper will explore how DevOps can be used in cloud solutions to increase the overall security of systems and applications. DevOps delivers improved automation across the product delivery chain, prevents errors, nullifies risk and downtime, and enables security to be further integrated into the cloud environment. The paper covers the main facets of DevOps in securing cloud solutions with examples provided on practical applications by companies like IBM. The significance of the research to the U.S is also covered especially for organizations looking to secure their serverless systems.
Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural network

Prostate cancer (PCa) arises from the glandular epithelium. To confirm the presence of cancer, a pathologist uses stained tissue samples taken either from biopsy or prostatectomy. Due to the relationship between epithelium and cancer, special consideration is given to regions identified as epithelium. Histomophometric techniques have long been used to identify areas of epithelium within the tissue for automated detection and classification pipelines; however, they are often rigid in their implementation and their performance suffers on large datasets where variation in staining, imaging, and preparation is difficult to control. The recent development and popularity of deep learning methods for image processing and segmentation offer great promise for developing robust classification pipelines for such ends; however, they require large labeled datasets for training. The goal of this study was to combine weakly labeled datasets generated using histomophometric techniques, and high‐quality labeled datasets from human observers to train a convolutional neural network. In doing so we developed a pixel‐wise segmentation algorithm for classification of stromal, epithelium, and lumen (SEL) regions for use on both biopsy core and whole‐mount bright‐field H&amp;E stained tissue. We provide evidence that by simply training a deep learning algorithm on weakly labeled data, we can improve the robustness of the classification. Finally, we show that not only does this method carry primary improvement on SEL labeling within tissue, but that the information provided by the deep learning generated labels improves cancer classification in a higher‐order algorithm over the histomorphometric labels that it was trained on. Support or Funding Information Funding was provided by the State of Wisconsin Tax Check‐off Program for Prostate Cancer Research (RO1CA218144 and R01CA113580) and the National Center for Advancing Translational Sciences (NIH UL1TR001436 and TL1TR001437). This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
Pengaruh Kemandirian Belajar dan Motivasi Belajar Terhadap Prestasi Belajar Siswa Kelas VIII A SMP Negeri 1 Lakudo

The formmulation of the problems in this study were: (1) whether independent learning afected the learning achievement of class VIl A students of SMP Negeri 1 Lakudo, (2) whether learning motivation affected the learning achievement of classVIll A students of SMP Negeril Lakudo; and (3) whether independent learning andlearning motivation affected the learning achievement of class VIlI A students of SMPNegeri 1 Lakudo. This stucdy was Er-Post Facto research, which was conducted in class VIll A of SMP Negeril Lakudo, even semester of the 2019 2020 school year. Data collectionim this stucy was carried out using a research instrument in the form of aquestionnaire. Population in this research were l08 students. Sampel in this researchwere 33 students by using random sampling technique. The data obtained were thenamalyzed by descriptive anahsis and inferential analysis and tested the hypothesis inthe form of the t-test. The study results obtained: (1) the minimum and maximum scores of the independent learning questionnaire were 94 and 121, the minimum and maximum Scores of the learning motivation questionnaire were 91 and 136, and the minimum and maximum scores of student learning achie vement were 60 and 83; (2) the mean Score of the learming independence questionnaire was 67, the mean score of thelearning motivation questionnaire was 71 and the student achievement average score was 68; and (3) based on the results of hypothesis testing with t-test, the value of tount 7.867 with a significance value of p = 0.001 was obtained. So that there was asignificant relationship between learning independence and learning motivation onstudents' achievement.
Compressing Deep Convolutional Neural Networks by Stacking Low-dimensional Binary Convolution Filters

Deep Convolutional Neural Networks (CNN) have been successfully applied to many real-life problems. However, the huge memory cost of deep CNN models poses a great challenge of deploying them on memory-constrained devices (e.g., mobile phones). One popular way to reduce the memory cost of deep CNN model is to train binary CNN where the weights in convolution filters are either 1 or -1 and therefore each weight can be efficiently stored using a single bit. However, the compression ratio of existing binary CNN models is upper bounded by ∼ 32. To address this limitation, we propose a novel method to compress deep CNN model by stacking low-dimensional binary convolution filters. Our proposed method approximates a standard convolution filter by selecting and stacking filters from a set of low-dimensional binary convolution filters. This set of low-dimensional binary convolution filters is shared across all filters for a given convolution layer. Therefore, our method will achieve much larger compression ratio than binary CNN models. In order to train our proposed model, we have theoretically shown that our proposed model is equivalent to select and stack intermediate feature maps generated by low-dimensional binary filters. Therefore, our proposed model can be efficiently trained using the split-transform-merge strategy. We also provide detailed analysis of the memory and computation cost of our model in model inference. We compared the proposed method with other five popular model compression techniques on two benchmark datasets. Our experimental results have demonstrated that our proposed method achieves much higher compression ratio than existing methods while maintains comparable accuracy.
HyperSec: Visual Analytics for Blockchain Security Monitoring

Today, permissioned blockchains are being adopted by large organizations for business critical operations. Consequently, they are subject to attacks by malicious actors. Researchers have discovered and enumerated a number of attacks that could threaten availability, integrity and confidentiality of blockchain data. However, currently it remains difficult to detect these attacks. We argue that security experts need appropriate visualizations to assist them in detecting attacks on blockchain networks. To achieve this, we develop HyperSec, a visual analytics monitoring tool that provides relevant information at a glance to detect ongoing attacks on Hyperledger Fabric. For evaluation, we connect the HyperSec prototype to a Hyperledger Fabric test network. The results show that common attacks on Fabric can be detected by a security expert using HyperSec's visualizations.
The Nexus between Financial Development and Economic Growth in China

The relationship between financial development and economic growth has been subject to the considerable debate in the literature of development and growth.Hence, this study aims to investigate the long-run relationship between financial development and economic growth of China over the period 1988 to 2018.This study employed Johansen's Cointegration and Granger Causality analysis to achieve the objectives of the study.The findings of this study indicate there is a long-run relationship between financial development and economic growth in China.
Study on gamma spectrum wavelet noise reduction based on scintillation detector

Abstract Wavelet transform is a time-scale signal analysis method, which holds the characteristics of the same window size, arbitrary shape change and multi-resolution analysis. Most applications of wavelet transform need to be based on signal decomposition and reconstruction, that is, through the combination of low-pass and high-pass filters to achieve frequency division, the signal is decomposed into signal detail components and large-scale approximate components. In the measurement process of the original energy spectrum data, the denoting process plays a role in reducing the incidence of statistical fluctuations to a certain extent, which is of profound significance to improve the data accuracy. In this paper, wavelet transform is used as the theoretical basis to denoise the actual energy spectrum, at the same time based on the MATLAB platform, simulation of signal with noise, using the different parameters on the signal processing. Firstly, the signal-to-noise ratio (SNR) of the hard and soft thresholds to the signal processing under the same conditions was compared, and then the change of the SNR was observed when the number of decomposition layers was changed under the wavelet basis of db3 (soft threshold). The experimental results show that: 1) under the same conditions, the soft threshold method has higher SNR than hard threshold; 2) The number of decomposition layers and the elimination moment affect the signal-to-noise ratio and smoothness of the processed signal respectively, and when the number of decomposition layers is 7-8 layers, the signal-to-noise ratio is about 29, the signal processing effect is the best. This study has shown that Wavelet method is an effective way to process energy spectrum.
Increasing Arabic Vocabulary Mastery Through Gamification; is Kahoot! Effective?

Vocabulary (mufradat) mastery is decisive in the success of foreign language learning. Mastering many vocabularies can improve students’ listening, speaking, reading, and writing skill. Various strategies have been applied to improve vocabulary mastery, but the results are not satisfactory. They were only able to achieve an average score of 42, far below the minimum criteria of mastery leaning, which is 75. It was because the students are less motivated to master it. Quantitative research with experimental design was carried out to test Kahoot! media, one of the game-based learning methods to improve students’ vocabulary learning outcomes in class X MAN 4 HST. The results showed that students' learning outcomes in the experimental class which used Kahoot! Gamification method is significantly higher than the control class, which used reading aloud and guessing the meaning method. The average learning outcomes in the experimental class were 28,269 points higher than that of the control class. The average value of the control class N-Gain is 25.95, which is categorized as ineffective. While the average N-Gain value of the experimental class is 74.31, categorized as quite effective category. The results indicated that Kahoot! implementation can increase the average score of students in learning mufradat to 85.
Outlier Impact Characterization for Time Series Data

For time series data, certain types of outliers are intrinsically more harmful for parameter estimation and future predictions than others, irrespective of their frequency. In this paper, for the first time, we study the characteristics of such outliers through the lens of the influence functional from robust statistics. In particular, we consider the input time series as a contaminated process, with the recurring outliers generated from an unknown contaminating process. Then we leverage the influence functional to understand the impact of the contaminating process on parameter estimation. The influence functional results in a multi-dimensional vector that measures the sensitivity of the predictive model to the contaminating process, which can be challenging to interpret especially for models with a large number of parameters. To this end, we further propose a comprehensive single-valued metric (the SIF) to measure outlier impacts on future predictions. It provides a quantitative measure regarding the outlier impacts, which can be used in a variety of scenarios, such as the evaluation of outlier detection methods, the creation of more harmful outliers, etc. The empirical results on multiple real data sets demonstrate the effectivenss of the proposed SIF metric.
Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models

Publicly available, large pretrained Language Models (LMs) generate text with remarkable quality, but only sequentially from left to right.As a result, they are not immediately applicable to generation tasks that break the unidirectional assumption, such as paraphrasing or text-infilling, necessitating task-specific supervision.In this paper, we present REFLECTIVE DE-CODING, a novel unsupervised algorithm that allows for direct application of unidirectional LMs to non-sequential tasks.Our 2-step approach requires no supervision or even parallel corpora, only two off-the-shelf pretrained LMs in opposite directions: forward and backward.First, in the contextualization step, we use LMs to generate ensembles of past and future contexts which collectively capture the input (e.g. the source sentence for paraphrasing).Second, in the reflection step, we condition on these "context ensembles", generating outputs that are compatible with them.Comprehensive empirical results 1 demonstrate that REFLECTIVE DECODING outperforms strong unsupervised baselines on both paraphrasing and abductive text infilling, significantly narrowing the gap between unsupervised and supervised methods.REFLECTIVE DECODING surpasses multiple supervised baselines on various metrics including human evaluation.
CURIOSITY-DRIVEN REINFORCEMENT LEARNING AGENT FOR MAPPING UNKNOWN INDOOR ENVIRONMENTS

Abstract. Autonomously exploring and mapping is one of the open challenges of robotics and artificial intelligence. Especially when the environments are unknown, choosing the optimal navigation directive is not straightforward. In this paper, we propose a reinforcement learning framework for navigating, exploring, and mapping unknown environments. The reinforcement learning agent is in charge of selecting the commands for steering the mobile robot, while a SLAM algorithm estimates the robot pose and maps the environments. The agent, to select optimal actions, is trained to be curious about the world. This concept translates into the introduction of a curiosity-driven reward function that encourages the agent to steer the mobile robot towards unknown and unseen areas of the world and the map. We test our approach in explorations challenges in different indoor environments. The agent trained with the proposed reward function outperforms the agents trained with reward functions commonly used in the literature for solving such tasks.
Formation and Assessment of Skills of Independent Preparation of Written Works in General

Purpose of research. Today, the informatization of school education has reached a qualitatively new level, schools now have access to fast Internet, modern computer equipment, etc. However, the quality of i nde p endent w ri tten w or k o f s tudent s may sig n i f ic a ll y fa ll , due t o op en and free access to fabricated texts (essays, reports, essays), as well as the lack of basic skills of independent work and curiosity among the younger generation. In this regard, the purpose of the study is to pos e the probl em of f or mati o n and eva l uat io n o f s k ills f or i nde pe ndent preparation of written work in General education, to consider the current state of affairs in this area of primary education, to develop proposals and solutions to this problem. Materials and methods. To solve this problem, the authors of the article have analyzed the works of Russian and foreign authors in the field of pedagogy, as well as works on the digital generation and the information society formation in the Russian Federation and legislative acts (Federal law «on education in the Russian Federation» dated 29.12.2012 N 273-FZ, national project «Education», order of the Ministry of education and science of the Russian Federation dated December 17, 2010 No. 1897 «on approval of the Federal state educational standard of basic General education», The procedure for admitting citizens to study in educational programs of primary General, basic General and secondary General education, approved by order of the Ministry of education and science of the Russian Federation of January 22, 2014, No. 32, etc.). Results. The study has revealed the current situation in teaching and explaining the basic skills of independent and creative work of students: the lack of methodological foundations and practice of teaching the basics of academic ethics and academic writing, the lack of automated means of monitoring the independent performance of written works. Conclusion. In t h e a rticle , th e au t h ors pro vide b a sic rec ommendat i on s and suggestions for improving the current situation, such as the introduction of elements of informing the school system about the requirements for independence in the performance of educational work as part of academic ethics, elements of independence control (systems for detecting borrowings in written works), the development of programs for teachers to master the means of controlling the independence of students’ written work.