Reviewerly/exHarmony huggingface dataset:
ICDAR 2021 Competition on Scientific Literature Parsing

Scientific literature contain important information related to cutting-edge innovations in diverse domains. Advances in natural language processing have been driving the fast development in automated information extraction from scientific literature. However, scientific literature is often available in unstructured PDF format. While PDF is great for preserving basic visual elements, such as characters, lines, shapes, etc., on a canvas for presentation to humans, automatic processing of the PDF format by machines presents many challenges. With over 2.5 trillion PDF documents in existence, these issues are prevalent in many other important application domains as well. A critical challenge for automated information extraction from scientific literature is that documents often contain content that is not in natural language, such as figures and tables. Nevertheless, such content usually illustrates key results, messages, or summarizations of the research. To obtain a comprehensive understanding of scientific literature, the automated system must be able to recognize the layout of the documents and parse the non-natural-language content into a machine readable format. Our ICDAR 2021 Scientific Literature Parsing Competition (ICDAR2021-SLP) aims to drive the advances specifically in document understanding. ICDAR2021-SLP leverages the PubLayNet and PubTabNet datasets, which provide hundreds of thousands of training and evaluation examples. In Task A, Document Layout Recognition, submissions with the highest performance combine object detection and specialised solutions for the different categories. In Task B, Table Recognition, top submissions rely on methods to identify table components and post-processing methods to generate the table structure and content. Results from both tasks show an impressive performance and opens the possibility for high performance practical applications.
ESXi on Arm on Pi and the Post-virtualized Datacenter

In the first chapter, we showed how a multitiered application in the datacenter prior to virtualization required different physical servers, with each server running a single application (Figure 1-2). Once virtualization was introduced, however, that same datacenter could run the same multitiered applications using VMs, consolidating them from eight underutilized servers to three fully utilized servers (Figure 2-1).
TPA Privacy Protection with Blowfish Encryption

In today's technological environment, cloud computing is the latest buzzword. Despite the excitement, many people are unsure what cloud computing is, especially since the term can be used to refer to nearly anything. The next-generation architecture of the IT enterprise has been envisioned as cloud computing. It moves application software and databases to centralised large datacenters, where data and service management may be shaky. This novel paradigm introduces a slew of new security issues that have yet to be fully grasped. This study investigates the problem of ensuring data storage integrity in Cloud Computing. On behalf of the cloud client, we evaluate the job of allowing a third-party auditor (TPA) to verify the integrity of dynamic data stored in the cloud. In order to securely deploy an effective third-party auditor, the following two major needs must be fulfilled (TPA): 1) The TPA should be able to audit cloud data storage efficiently without requiring a local copy of the data or imposing any additional online burden on the cloud user; 2) the third-party auditing process should not introduce any new vulnerabilities affecting user data privacy.
The Impact of Implementing Total Quality Management on Employee Performance while Working from Home in Jordan Banking Sector (Case study in Jordan Commercial Bank Branches)

The study was conducted to find out the impact of implementing (TQM) on employees’ performance working from home (WFH) in the Jordan Commercial Bank. The sample consisted of managers and employees whom have adopted WFH policies. A questionnaire was administered to (250) employees who are working in the bank to examine their performance perception; job satisfaction, productivity, through TQM variables; customer satisfaction, continues improvement and employees involvement. A detailed questionnaire survey was carried out. Regression and correlation methods were also carried out to take account of the results. Results showed that, TQM with its all variables has an impact on employees’ performance WFH.Results also showed that, both continues improvement and customer satisfaction had a higher impact on job satisfaction than on productivity with higher correlation level. While it was found that employee involvement variable had a higher impact on productivity.
From Crisis to Rebirth: Modern Museums as Assessed by Specialists

The article is devoted to conflicting assessments of the state of the museum in contemporary museum criticism: museums are experiencing either a protracted crisis or the Golden Age. The public is also criticized: people prefer to come to museums in order to be entertained with the help of new technologies and to achieve a scientific goal, and not in order to fulfill their civic duty (appreciating national treasure). In general, among the objectives of the museum (to form a “national imaginary”, to support a state ideology, to serve as a scientific and educational center, to entertain and give pleasure), the aesthetic is in the last place, but the current negative views about the state of a museum as a sociocultural institution are caused by the general suspicion that aesthetics has come to the fore as the most important factor. In order to correctly assess what is happening, modern approaches to the duties of the museum and the needs of the general public need to take the existing points of view on this subject in both Russian and international specialized literature into account. The analysis of the situation also involves data from Internet resources specializing in reporting on the modern art market, which is closely tied to museum politics. The article compares various approaches to the current situation: the goals and objectives historically attributed to the museum, a review of current exhibitions and of the technology used, a change in museum policy as a whole (in particular the fact that what was considered a profanation of a great idea 10 years ago has become a cultural norm today). Based on the results of the study, the following conclusion can be made: modern museums are indeed extremely commercialized. The most unexpected souvenirs are sold in museum stores. Para-museums openly parasitize on the tourist flow of large museums. More than that, nobody thinks twice about making a profit in any possible way, while simultaneously grumbling about the fall in cultural demands of the public. However, interest in museums among the general population remains unchanged, so asserting the widespread crisis of the modern museum is a very big stretch.
An Encryption Scheme: ECC and Arnold's Transformation

Messages communicated through the network are vulnerable to unauthorized access. The confidential messages are required to be protected from unauthorized access. The most commonly used form of messages is text and images. Elliptic curve cryptosystem provides more security compared to other cryptosystems. Researchers have proposed many cryptosystems based on elliptic curve cryptography for text as well as for images. We come up with an encryption scheme using Arnold’s transformation and ElGamal encryption scheme based on elliptic curve cryptography in this chapter. The result of the implementation and scrutiny of security features show the strength of the proposed scheme.
The Communication between Students and Teachers…: Barriers and the Ways to Overcome them

The purpose of this article is to explain some of the barriers that teachers can face when they are trying to communicate with their students. At the same time we will suggest some ideas to overcome them, as well as examples of different situations experienced by teachers. Also, there are some cases where we can even use these barriers as a way to improve the learning process. More specifically, we are going to focus on cultural barriers and psychological barriers. Another topic that we will be focusing on is the digital generation and how these differences can generate a new type of barrier.
&lt;br/&gt; AUTOEFICÁCIA NA PESSOA COM DIABETES MELLITUS TIPO 2: REVISÃO INTEGRATIVA DA LITERATURA &lt;br/&gt;

Objective: To identify the relationship between self-efficacy and adherence to the therapeutic regimen in the person with type 2 diabetes mellitus. Method: Integrative literature review according to the Joanna Briggs Institute (JBI) protocol through scientific and electronic database research by the EBSCOhost platform, including articles published in the last five years through the use of Medical Subject Heading (MeSH). Results: After the survey, 342 articles were initially found, four of which met the defined inclusion criteria. The four articles are cross-sectional studies. Conclusion: According to the results of the studies included in this integrative literature review, there is statistically significant evidence that self-efficacy is a predictor of behaviors of adherence to the therapeutic regimen. Keywords: Adherence Therapy Regimen; Diabetes Mellitus Type 2; Self-Efficacy.
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning

Despite recent advances in its theoretical understanding, there still remains a significant gap in the ability of existing PAC-Bayesian theories on meta-learning to explain performance improvements in the few-shot learning setting, where the number of training examples in the target tasks is severely limited. This gap originates from an assumption in the existing theories which supposes that the number of training examples in the observed tasks and the number of training examples in the target tasks follow the same distribution, an assumption that rarely holds in practice. By relaxing this assumption, we develop two PAC-Bayesian bounds tailored for the few-shot learning setting and show that two existing meta-learning algorithms (MAML and Reptile) can be derived from our bounds, thereby bridging the gap between practice and PAC-Bayesian theories. Furthermore, we derive a new computationally-efficient PACMAML algorithm, and show it outperforms existing meta-learning algorithms on several few-shot benchmark datasets.
Evidence Selection as a Token-Level Prediction Task

In Automated Claim Verification, we retrieve evidence from a knowledge base to determine the veracity of a claim. Intuitively, the retrieval of the correct evidence plays a crucial role in this process. Often, evidence selection is tackled as a pairwise sentence classification task, i.e., we train a model to predict for each sentence individually whether it is evidence for a claim. In this work, we fine-tune document level transformers to extract all evidence from a Wikipedia document at once. We show that this approach performs better than a comparable model classifying sentences individually on all relevant evidence selection metrics in FEVER. Our complete pipeline building on this evidence selection procedure produces a new state-of-the-art result on FEVER, a popular claim verification benchmark.
Sentiment Analysis in Kannada

This paper mainly focuses on the idea of Sentiment Analysis in Kannada. As the term ‘Sentiment’ suggests, it may be a joy, sadness, frustration, angry, unhappy, lamentation, if it is said in totality human feelings or emotions. As we know that man is a social animal. He/ She comes with the relationships and goes with the relationships from the day one to day last. Human cannot live alone and it is inevitable to satisfy some natural basic needs to survive and human cannot break the shackles of society. Facebook’s corpus for Sentiment analysis in Kannada which is a South Dravidian Language. The term ‘Sentiment’ suggests a vision or opinion that is expressed in any context. As we are all aware of, the human sentiment is a social behavior and at the same time it could be studied from many perspectives. Innumerable researches are being held in the fields of Natural Language Processing and Information Extraction. In the contemporary era, with the furtherance of web technology and its improvement there is a huge volume of data is available in the web for researchers and everyday a lot of data is generated. There are many works that have been taken place towards Sentiment Analysis. The data that has been used in this paper is collected from the social media like Facebook and Kannada & English mixed in nature. The data has been classified based on how the sentiments are expressed- Positive, Overt Positive & Covert Positive, Negative, Overt Negative & Covert Negative and Neutral.
Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness

Text style can reveal sensitive attributes of the author (e.g. age and race) to the reader, which can, in turn, lead to privacy violations and bias in both human and algorithmic decisions based on text. For example, the style of writing in job applications might reveal protected attributes of the candidate which could lead to bias in hiring decisions, regardless of whether hiring decisions are made algorithmically or by humans. We propose a VAE-based framework that obfuscates stylistic features of human-generated text through style transfer, by automatically re-writing the text itself. Critically, our framework operationalizes the notion of obfuscated style in a flexible way that enables two distinct notions of obfuscated style: (1) a minimal notion that effectively intersects the various styles seen in training, and (2) a maximal notion that seeks to obfuscate by adding stylistic features of all sensitive attributes to text, in effect, computing a union of styles. Our style-obfuscation framework can be used for multiple purposes, however, we demonstrate its effectiveness in improving the fairness of downstream classifiers. We also conduct a comprehensive study on style-pooling’s effect on fluency, semantic consistency, and attribute removal from text, in two and three domain style transfer.
Aspect-Controllable Opinion Summarization.

Recent work on opinion summarization produces general summaries based on a set of input reviews and the popularity of opinions expressed in them. In this paper, we propose an approach that allows the generation of customized summaries based on aspect queries (e.g., describing the location and room of a hotel). Using a review corpus, we create a synthetic training dataset of (review, summary) pairs enriched with aspect controllers which are induced by a multi-instance learning model that predicts the aspects of a document at different levels of granularity. We fine-tune a pretrained model using our synthetic dataset and generate aspect-specific summaries by modifying the aspect controllers. Experiments on two benchmarks show that our model outperforms the previous state of the art and generates personalized summaries by controlling the number of aspects discussed in them.
Human risk factors to measure the potential of digital information leakage

One of the most challenging risk factors of cybersecurity to measure is the human factor. Each individual working for a given organization has a different personality, environmental conditions, and everyone within the organization holds a different position. The various factors are often difficult to quantify, and furthermore, each is highly interdependent from the other.It is necessary to know this risk factor and then build stronger security based on it, as attacks in cyberspace often take advantage of the lack of user knowledge or the mistakes made by developers and operators.To establish a risk factor approaching reality, the authors aim to create a fuzzy system, as this mathematical method is best suited for modeling human logic in addition to handling uncertainties.In this study, the authors present the results of the survey they have conducted, exploring risk factors relevant to digital information leakage.
Design and Simulation of Resource Demand Forecasting Algorithm in Vehicular Edge Network

Vehicle edge computing is a combination of mobile edge computing and traditional vehicular network. The goal of vehicle edge computing is to put communication resource and computing resource near user location. Different from traditional mobile edge computing, vehicle edge computing is characterized by rapid vehicle movement, which results in constant dynamic changes of network topology and computing resources. When resource requirements exceed the load threshold of the edge node, the performance of the service will decrease. In order to solve the lag problem of task allocation, it is necessary to proactively forecast resource demand. The research work is carried out from two aspects. First, the specific scenarios of vehicle network were investigated and analyzed to propose the business resource requirements to be predicted based on the resource consumption of cloud platform and mobile edge computing. Secondly, we designed a multi-dimensional time series prediction algorithm based on deep learning to build a neural network structure in line with business characteristics. The result shows that the prediction accuracy has good application value. The results of this paper will lay a foundation for the research of resource mapping mechanism, edge node scheduling and resource allocation between resource demand and edge stage capability model.
CAPE: Context-Aware Private Embeddings for Private Language Learning

Neural language models have contributed to state-of-the-art results in a number of downstream applications including sentiment analysis, intent classification and others. However, obtaining text representations or embeddings using these models risks encoding personally identifiable information learned from language and context cues that may lead to privacy leaks. To ameliorate this issue, we propose Context-Aware Private Embeddings (CAPE), a novel approach which combines differential privacy and adversarial learning to preserve privacy during training of embeddings. Specifically, CAPE firstly applies calibrated noise through differential privacy to maintain the privacy of text representations by preserving the encoded semantic links while obscuring sensitive information. Next, CAPE employs an adversarial training regime that obscures identified private variables. Experimental results demonstrate that our proposed approach is more effective in reducing private information leakage than either single intervention, with approximately a 3% reduction in attacker performance compared to the best-performing current method.
Adversarial Regression with Doubly Non-negative Weighting Matrices

Many machine learning tasks that involve predicting an output response can be solved by training a weighted regression model. Unfortunately, the predictive power of this type of models may severely deteriorate under low sample sizes or under covariate perturbations. Reweighting the training samples has aroused as an effective mitigation strategy to these problems. In this paper, we propose a novel and coherent scheme for kernel-reweighted regression by reparametrizing the sample weights using a doubly non-negative matrix. When the weighting matrix is confined in an uncertainty set using either the log-determinant divergence or the Bures-Wasserstein distance, we show that the adversarially reweighted estimate can be solved efficiently using first-order methods. Numerical experiments show that our reweighting strategy delivers promising results on numerous datasets.
Training of Elliptical Basis Function NN

The radial basis function neural network (RBF NN) is extended to the elliptical receptive fields of neurons. It is shown that an optimal response function of the elliptical basis function neural network (EBF NN) is the conditional average of the dependent variable with respect to the independent one. An analytical form of the conditional average is calculated for a special case when the probability density function (p.d.f.) is represented as a sum of K multidimensional Gaussian functions. Learning algorithms are derived from relative entropy discrepancy measure between representative and empirical p.d.f. The training is done in batch and adaptive mode. Two-dimensional examples of approximated regression curves are presented and a comparison between radial and elliptical basis function NN is made.
Investigation of heavy metal concentrations in groundwater by geostatistical method (Case study of Gol Gohar iron ore mine)

Simulation in groundwater flow is important for agricultural use and access to high quality groundwater.In many arid countries of the world, especially Iran, the main source of water supply is groundwater reserves.This study, using geostatistical analyzes, has investigated the environmental effects of heavy elements of Gol Gohar Goljan iron ore mine in Sirjan.For this purpose, heavy metal elements (iron, copper, chromium, cadmium, antimony, arsenic) were sampled from 115 groundwater samples.Then, by drawing a histogram, preliminary statistics and exploratory-spatial information obtained from the data of the study area were examined.The experimental half-change of each parameter was calculated using isatis software and fitted with Gaussian, spherical, linear and exponential models.After variography, Gaussian simulation in a block model prepared in the study area was performed 100 times and the map obtained from Gaussian simulation of each of the mentioned variables was prepared. In addition to examining the spatial coherence of the variables, the probability and uncertainty map was prepared according to the EPA drinking water standard and the points where the concentration of heavy metals was within the allowable drinking water limit or above the threshold were determined. The validation results were finally compared with the simulation results and the original values. The results of the studies showed that the highest risk of contamination of heavy metals iron, copper, arsenic and chromium, according to the EPA drinking water standard, is related to Gol Gohar iron ore mineral areas and long wells in Kefahnamak, Khairabad and Qatarbaneh area. Also, the results of studies on the element antimony showed that most of the groundwater resources in the study area are contaminated with this element and are not drinkable according to the EPA limit. Groundwater at points KH1, KH2, KH3 and CHD20 had the highest concentration of cadmium, and probability maps of each element were prepared.
Can Transformers Jump Around Right in Natural Language? Assessing Performance Transfer from SCAN

Despite their failure to solve the compositional SCAN dataset, seq2seq architectures still achieve astonishing success on more practical tasks. This observation pushes us to question the usefulness of SCAN-style compositional generalization in realistic NLP tasks. In this work, we study the benefit that such compositionality brings about to several machine translation tasks. We present several focused modifications of Transformer that greatly improve generalization capabilities on SCAN and select one that remains on par with a vanilla Transformer on a standard machine translation (MT) task. Next, we study its performance in low-resource settings and on a newly introduced distribution-shifted English-French translation task. Overall, we find that improvements of a SCAN-capable model do not directly transfer to the resource-rich MT setup. In contrast, in the low-resource setup, general modifications lead to an improvement of up to 13.1% BLEU score w.r.t. a vanilla Transformer. Similarly, an improvement of 14% in an accuracy-based metric is achieved in the introduced compositional English-French translation task. This provides experimental evidence that the compositional generalization assessed in SCAN is particularly useful in resource-starved and domain-shifted scenarios.