Miracle-dz/UniFAR-FTUs huggingface dataset:
“Comparison of Rule based Semantic Web Reasoners for Ontologies in OWL2 Profile”

: The Semantic Web is a vision for the future of the Web in which information is given explicit meaning, making it easier for machines to automatically process and integrate information available on the Web[1]. In semantic web, Knowledge can be encoded using a formal language termed as OWL (Ontology Web Language). To keep this language alive Progression of OWL2 from OWL is one such major step. OWL2 is a strong Modeling language and extends the W3C OWL with a small but useful set of features. The objective of this study is an attempt to highlight those features and to provide and indepth analysis on: a. Introduction to three profiles of OWL2 with a particular focus on how these profiles differ and why the differences are important. b
Evaluating Transformer-Kernel Models at TREC Deep Learning 2020

We tested multiple hypotheses using the Transformer-Kernel neural ranking pattern. The TK model family sits between BERT and previous ranking model in terms of the efficiency-effectiveness trade-off, faster than BERT albeit less effective. In the passage re-ranking task we tested the effectiveness of contextualized stopwords, introduced with TK-Sparse and find that removing 19% of terms after contextualization even slightly increases the model’s effectiveness. In the document re-ranking task we tested if a long-text TKL model is better with 2,000 or 4,000 document tokens and find that our 2,000 token instance outperforms the other. Ourresults confirm the path for new storage saving methods for interpretable ranking models, and give an interesting insight into the questions of how many tokens of a document we need to read for a relevance assessment.
MEEC: Second Workshop on Momentary Emotion Elicitation and Capture

Recognizing human emotions and responding appropriately has the potential to radically change the way we interact with technology. However, to train machines to sensibly detect and recognize human emotions, we need valid emotion ground truths. A fundamental challenge here is the momentary emotion elicitation and capture (MEEC) from individuals continuously and in real-time, without adversely affecting user experience nor breaching ethical standards. In this virtual half-day CHI 2021 workshop, we will (1) have participant talks and an inspirational keynote presentation (2) ideate elicitation, sensing, and annotation techniques (3) create mappings of when to apply an elicitation method.
Lowestcase and uppestcase letters: Advances in derp learning

Have you ever been writing something on the internet and wanted to convey that you ARE FEELING ANGRY? Conversely, have you ever fired back a super quick dm and u wanted to make it clear that it was like super caZ and so u didnt use ne capitals or punctuation dots except 4 that one place where u needed to use the international phonetic alphabet because u dont no how to write caZ as in short for casual without it lol If so, you made use of the fact that all letters have UPPERCASE VERSIONS (e.g. signifying ANGER) and lowercase versions (e.g. signifying u dont care lol). These dimensions have other uses, for example, it is polite to start a person’s name with a capital letter to show that you took the time to regard their humanity (as it takes extra work to press the caps lock key, press the first letter of their name, and then press the caps lock key again to turn it off). In German, Nouns start with uppercase Letters, signifying Superiority over other grammatical Categories like Verbs and Adjectives. Lowercase letters can be used to conserve printer ink. Actually, I’m not sure that lowercase letters have any other uses, but let’s just roll with it. There’s nothing wrong with this (despite the classical advice to use shift to reduce conflict [2]). But the thing is: What if I’m even MORE ANGRY THAN I WAS BEFORE? There are some standard sorts of typographic emphasis, like I can be BOLD ANGRY or BIG BOLD ITALIC UNDERLINE ANGRY or COMBINE A LOT OF THESE ANGERS, each with its own nuances, depending on the cascading style sheet or LaTeX class file. To be even more casual than lowercase, u can learn 2 write like this, and shrink away and also cross out ur words in shame in advance of them even being read, but there are few other options for de-emphasis. Plus, when I’m FEELING PRETTY ANGRY, TOM, how do I capitalize that already-capitalized T in order to show the proper reverence for your humanity? This paper is about unshackling this dimension of human expression by introducing letterforms further along the uppercase and lowercase dimensions. Basically, we want to know what the upperercase version of uppercase T is, and a lowerercase version of lowercase t is.
All Forecasters Are Not the Same: Time-Varying Predictive Ability across Forecast Environments

This paper examines data from the European Central Bank’s Survey of Professional Forecasters to investigate whether participants display equal predictive performance. We use panel data models to evaluate point- and density-based forecasts of real GDP growth, inflation, and unemployment. The results document systematic differences in participants’ forecast accuracy that are not time invariant, but instead vary with the difficulty of the forecasting environment. Specifically, we find that some participants display higher relative accuracy in tranquil environments, while others display higher relative accuracy in volatile environments. We also find that predictive performance is positively correlated across target variables and horizons, with density forecasts generating stronger correlation patterns. Taken together, the results support the development of expectations models featuring persistent heterogeneity.
Investigation of Factors Influencing the Implementation of Smart Fire Management using the Fuzzy Approach in Yazd, Iran

INTRODUCTION: The development and application of new firefighting technologies are inevitable considering an increase in the number of smart technologies, the widespread use of technologies in various industries and all domains of human societies, utilization of polymeric materials in the tools and infrastructure of societies, as well as the production of new structures with diverse uses, and their aggregation together. The occurrence of massive disasters due to the lack of development and application of safety along with technology is the result of neglecting safety in the development process and social progress. This study aimed to investigate the factors influencing the implementation of smart fire management using the fuzzy approach. METHODS: This descriptive-analytical study was designed based on an applied research method. Initially, all factors influencing the implementation of smart fire management were extracted from the literature. They were then evaluated based on experts and university professorschr('39') opinions. Following that, three types of questionnaires were distributed among the experts. The study population included 15 university experts and managers in the Fire Organization in Yazd, Iran. FINDINGS: The results showed that "command and operations", "integration of smart firefighting systems", "clear business plan and vision", "effective change management", "equipment", "cooperation between the business association and information technology" and "management support" obtained positive D-R and were considered causes. CONCLUSION: The "integration of smart firefighting systems" was regarded as the most effective factor, followed by "organizational resource planning", "exchange system", and "communication system" that ensure the success of these systems. This improves information flow and decision-making process, especially in a crisis.
Sentiment Analysis of Major Mutual Fund Related News Articles in India Amid the COVID-19 Outbreak, to Obtain Investor Sentiment in Mutual Funds & to Forecast Assets under Management (AUM), a Mutual Fund Market Indicator

The outbreak of the COVID-19 Pandemic caused widespread panic among people around the world. Many countries enforced a fierce lockdown to curb the spread of the virus. But the lockdown had other plans for Businesses as many of them were forced to shut shop leading to highly volatile market conditions and Bearish Economic Conditions in most countries. But was this, the perception of the Investors as well? This Paper aims to find out the sentiment of Investors on Mutual Funds in the Indian Market with Assets Under Management (AUM), a predominant performance gradient for Mutual Funds, as a proxy to analyse the impact of news articles related to the Mutual Fund Industry amid the COVID-19 Outbreak. The paper also goes on to establish a significant mathematical relationship between Sentiment Scores &amp; AUM and to using Regression to generate a Forecasting model that could be used to forecast future AUM given a sentiment score. <br>
Fuck

This Article is as simple and provocative as its title suggests: it explores the legal implications of the word fuck. The intersection of the word fuck and the law is examined in four major areas: First Amendment, broadcast regulation, sexual harassment, and education. The legal implications from the use of fuck vary greatly with the context. To fully understand the legal power of fuck, the nonlegal sources of its power are tapped. Drawing upon the research of etymologists, linguists, lexicographers, psychoanalysts, and other social scientists, the visceral reaction to fuck can be explained by cultural taboo. Fuck is a taboo word. The taboo is so strong that it compels many to engage in self-censorship. This process of silence then enables small segments of the population to manipulate our rights under the guise of reflecting a greater community. Taboo is then institutionalized through law, yet at the same time is in tension with other identifiable legal rights. Understanding this relationship between law and taboo ultimately yields fuck jurisprudence.
Macroeconomic Forecasting with Neural Network Reinforced Factor Models

In many macroeconomic forecasting applications factor models are used to cope with large datasets. This study aligns variational autoencoders with macroeconomic factor modeling and proposes an extension to adapt this framework for forecasting exercises. Variational autoencoders are well suited for nonlinear dimensionality reduction. They estimate the distribution of the common latent variables by combining a statistical factor model with a purely data-driven neural network approach. It is demonstrated that the resulting deep factor model can be interpreted as a flexible nonlinear extension of the standard factor model. In the empirical part, it is analyzed whether factor models augmented by neural networks can achieve superior forecasting power. The results suggest significant improvements in the forecasting accuracy of four major US macroeconomic time series.
Encapsulation of Citronellal from Citronella Oil using β-Cyclodextrin and Its Application as Mosquito (Aedes aegypti) Repellent

Mosquito (Aedes aegypti) is a threat to human health due to its capability to spread dengue fever. Citronellal in citronella oil is one ofnatural active compound that has repellent activity. Essential oil is a sensitive material whichiseasy to degrade. Encapsulation is coating technology use to avoid essential oil from degradation problems. β-Cyclodextrin is frequently used as acoating material in encapsulation. The aims of this study wereto prepare the citronellal encapsulation and to evaluate its control-released and repellency. In this study, encapsulated citronellal was prepared using 83.65% citronellal and encapsulation were prepared with the theemulsion-based method and dried using freeze-dryer. The best-controlled release was performed in citronellal encapsulate with a weight ratio of 1:1 (citronellal : β-Cyclodextrin). The morphology of encapsulated citronellal was analyzed using SEM. SEM result showed it has three dimensions random shape and agglomerate in some part with thebrighter spot. Citronellal encapsulate showed the highest repellent effect at 84,67% for 5 minutes in mosquito repellency test although it has lower result compared with citronellal inliquid form.
Overvaluation Distortion

This paper studies how firms optimally design mechanisms to transmit information to outside investors in order to obtain capital for growth opportunities. The value of issued securities depends on information about future cash flows that outside investors are unable to verify. Firms claim to have better projects in order to receive cheaper financing. However, these projects optimally require more investment since they are more productive. This creates an incentive for the firm to overinvest. This overvaluation distortion is mitigated when the firm pays low dividends, the level of collateral is high, and the firm's equity ownership is sufficiently dispersed to allow appropriately designed share repurchase programs. I develop a model of a risk-neutral firm with assets-in-place and a growth opportunity. The optimal amount of capital to allocate to the growth opportunity depends on its productivity, which is unknown to outside investors. The firm designs a mechanism through which it credibly transmits information to outside investors. The mechanism identifies its investment policy and the securities being issued to finance its investment. Credible information transmission requires the firm to overinvest in the growth opportunity. Since the firm has an incentive to overstate the quality of the growth opportunity to secure funding with the lowest possible dilution of insider ownership, there must be a cost associated with overstating quality in the firm's investment policy. In the underinvestment region, however, the firm would benefit from overstating quality both because it (i) reduces dilution and (ii) moves its investment policy closer to first-best; thus, the firm cannot credibly transmit information in the underinvestment region. Conversely, in the overinvestment region, the firm does incur a cost of overstating quality - committing to a less efficient investment policy.
Bayes’ Factors for Event-Study: The Case of A Single Security

Event Studies have been a subject matter of interest in corporate finance for quite sometime. While the frequentist literature of event-study is fairly matured, the same cannot be said about its Bayesian counter-parts. This article derives the Bayes’ Factors for the hypotheses testing problems of event study, in the context of a single security, involving both its abnormal as well as cumulative abnormal returns. The priors needed for the computation of the Bayes’ Factors, are constructed using the estimation period data. The developed methodologies are illustrated to study the behavior of the Volkswagen stock returns since the recent news, as well as the phenomenon of derivatives introduction in the Indian stock market. The study also conducts a simulation experiment involving the returns of all the stocks listed in India since 1998, to compare the performances of the existing frequentist methods with the proposed Bayesian ones. It is found that the Bayes’ Factor based tests are the best specified ones, with powers better or comparable to the non-parametric ones, but somewhat lesser than the corresponding t-tests.
Why the HAF Is Not the Head of the Actuarial Function?

In this paper we investigate the role of the Head of the Actuarial Function (HAF) and its implementation in the South African insurance market, two years after the passing of the new Insurance Act in 2017. We compare the current role in South Africa to actuarial roles in other jurisdictions and the prior statutory actuary role based on the Long-term Insurance Act. Based on an industry survey conducted by us, we highlight the differences in the interpretation and implementation of the role in different segments of the market. We discuss areas that have been problematic when embedding the HAF role in an organisation and investigate those from a professional, organisational and regulatory perspective. Based on this analysis we propose best practices for the implementation of the HAF role including a minimally compliant interpretation for smaller insurers and outline how the role can be interpreted, clarified or changed to achieve further improvements for the South African insurance market.
Bayesian Inference for Quantal Response Equilibrium in Normal-Form Games

This paper develops a framework for estimating Quantal Response Equilibrium models from experimental data using Bayesian techniques. Bayesian techniques offer some advantages over the more commonly-used maximum likelihood approach: (i) the accuracy of the posterior simulation is limited by (increasingly plentiful) computational resources, both in hardware and software, rather than the validity of an asymptotic assumption that may not be reasonable with typical experimental sample sizes; (ii) Bayesian hierarchical models are a useful way to organize heterogeneity in one's data; and (iii) Bayesian inference allows us to test whether Quantal Response Equilibrium better organizes data than does (say) Nash equilibrium or purely random behavior, without rigging the test in favor of one of these by calling it the null hypothesis. <br><br>As Quantal Response Equilibrium is a non-linear model, I also discuss some issues with choosing appropriate priors. Namely, choosing a very flat prior for the choice precision parameter implies a prior on choice probabilities with too much mass near Nash equilibrium and/or random choice. I propose a prior calibration process which seeks to avoid this problem by targeting the implied prior distribution of equilibrium choice probabilities.
Euro Area Banking and Monetary Policy Shocks in the QE Era: A Structural Credit Risk and Vector-autoregression Approach

This paper assesses the effects of monetary policy shocks on the macroeconomy and the euro area banking sector after the global financial crisis. Financial risk-return indicators of the banking sector based on a compound option-based structural credit risk model are embedded in a large macro-financial quarterly database covering the period 2008Q4-2019Q4. A SFAVAR identifies and estimates the shocks’ responses relating them to the endogenous build-up of banks’ vulnerabilities. The study finds that unconventional monetary policy, in particular the Asset Purchase Program of the European Central Bank, seems to have been more successful than conventional monetary policy in raising output and inflation. The desired boost to bank lending has been muted and loan cyclicality has varied across countries and loan types. The performance of the banking sector following monetary policy shocks can be characterized by a drop in expected ROE and ROA, a relaxation of lending conditions and increased correlation between banks’ assets return and the market return, a mechanism pointing to enhanced risk-taking. While banks’ probabilities of default fall following monetary policy shocks, financial leverage and the price of risk increase. Banks’ net worth rises via higher market capitalization and implied assets value together with lower volatility, albeit often incurring more debt. Risk-taking in the banking sector, such as the one observed in the run-up to the global financial crisis, may pose a risk to financial stability, especially if its effects on banks’ vulnerability spread to systemic risk. The endogenous build-up of macro-financial vulnerabilities may need to become part of monetary policymaking.
Work commitment and Employee Job Satisfaction: Study of Commercial Banks

The competition in commercial banking sector in Pakistan is at the peak and expectations of every owner of the institution is to be the leader of market, in this context they are eager to find out the determinants of employee job satisfaction, keeping in view the above situation researchers meet together from different disciplines to work out the issue. In this connection study of commercial banks of Sukkur region, Sindh Pakistan was carried out through collecting the primary data from the officers and employees of different commercial banks, used the same through SPSS, and results states that over all model is fit and significant and work commitment is positively and significantly correlated/related to each other.
Allosteric Modulation of GABAB Receptors

c-Aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the mammalian central nervous system (CNS). A great number of major CNS-active, investigational, recreational, or clinically used drugs exert their effects through the GABAergic system. The antiepileptic drugs tiagabine (a GABA-transport blocker) and vigabatrine (c-vinyl GABA, a GABAtransaminase inhibitor) both act by increasing GABA concentrations in the nervous tissue. However, most pharmacologically active compounds interacting with GABAergic neurotransmission are targeted at the receptors for GABA (1,2). The hallucinogenic and CNS-depressant drug muscimol is a selective agonist at the GABAA receptor, a ligand-gated ion channel, which by enabling the influx of chloride ions hyperpolarizes the postsynaptic neuronal membrane. On the other hand, the convulsant drugs bicuculline and picrotoxin inhibit this receptor. Barbiturates (such as pentobarbital) and benzodiazepines (diazepam, chlordiazepoxide) are in clinical use as anesthetic, anxiolytic, anticonvulsant, and muscle-relaxant agents. Both classes of drugs enhance the chloride conductance of the GABAA receptor, albeit through different mechanisms. Most barbiturates prolong the open time of the chloride channel also in the absence of GABA, whereas the