Miracle-dz/UniFAR-FTUs huggingface dataset:
The Determinants of Corporate Social and Environmental Disclosure in the Nigerian Oil and Gas Industry: An Empirical Investigation

This paper investigates whether corporate size, profitability, leverage, management efficiency, liquidity and tax can explain the level and quality of social and environmental disclosure (SED) in the Nigerian oil and gas industry. Time­series cross­ section (TS­CS) data for a sample of eight listed Nigerian oil and gas companies are collected for the period 2004–2013. We use word count and compliance­oriented content analysis to measure the quantity and quality of SED respectively. We use pooled ordinary least squares (OLS) with panel corrected standard errors to examine the relationship between SED quantity and quality, on the one hand, and the six regressors, on the other hand. Results documented show that listed companies in the Nigerian oil and gas industry make low disclosures on few Gloabal Reporting Initiative (GRI) items which exhibit low quality. While the low nature of the disclosure quantity is explained by size and to some extent management efficiency, the poor disclosure quality is jointly and individually explained by corporate size, management efficiency and liquidity. The paper provides empirical evidence showing that listed Nigerian oil and gas companies are exploiting the vulnerabilities of Nigeria as a resource­rich less­developed country. Furthermore, our findings imply that legitimacy theory is useful in explaining SEDs in Nigeria through the extent of management efficiency and liquidity.
A Fast and Precision Initial Alignment Method for Strapdown Inertial Navigation System

Although the tradition muliposition alignment technique made the strapdown inertial navigation system (SINS) having high alignment accuracy, the poor observability of SINS on stationary base, the estimation of the azimuth error will converge very slowly in initial alignment by means of Kalman filter. In this paper, a fast and accuracy estimation method of the multiposition alignment azimuth error is creatively proposed for the initial alignment of SINS on stationary base. On the basis of the fast converge of the leveling error, the azimuth error can be directly calculated. By means of this fast and accuracy initial alignment method, not only the precision of initial alignment is increased, but also the speed of initial alignment is greatly improved. The computer simulation results illustrate the efficiency of this method.
A survey on Internet of Things (IoT) security : Challenges and Current status

: When Internet of Things (IoT) applications become a part of people’s daily life, security issues in IoT have caught substantial attention in both academia and industry. Compared to traditional computing systems, IoT systems have more inherent vulnerabilities, and in the intervening time, could have higher security requirements. However, the current design of IoT does not successfully address the higher security requirements postured by those vulnerabilities. Many recent attacks on IoT systems have shown that novel security solutions are needed to defend this emerging system. This paper purposes to examine security challenges resulted from the special characteristics of the IoT systems and the new features of the IoT applications. This could help pave the road to better security solution design. Furthermore, three architectural security designs are suggested and analyzed. Examples of how to implement these designs are discussed. Finally, for each layer in IoT architecture, open issues are also identified.
A Spherical Far Field HRIR/HRTF Compilation of the Neumann KU100

A full sphere far field HRIR/HRTF (head related impulse response and its Fourier transform head related transfer function) [1] compilation of a Neumann KU 100 dummy head is presented. The compilation can be used for different purposes; it is, for instance, very convenient for applications in the field of spherical Fourier acoustics [2]. Different sampling configurations were captured and the compilation on the whole involves around 22.000 post-processed binaural impulse responses. The data is organized and stored in MATLAB R © objects, offering different processing features and including gateways to the SOFiA sound field analysis toolbox [3] and to the Sound Scape Renderer [4]. Associated headphone compensation filters for around 20 common headphones were created. Additionally, a method to optimize and extend the low frequency response of measured HRIRs/HRTFs is discussed.
# 2102 July 2021 1 The Methodology for Assessing Interest Rate Policy Rules : Reply

This note is a brief reply to Watts (2021), who has been critical of the conceptual foundations and methodology in a discussion of the impact of different interest rate policy rules on inflation in Smithin (2020). I conclude that the case for a ‘zero real policy rate of interest’ (ZRPR), rather than a ‘zero interest rate policy’ (ZIRP), emerges unscathed.
Amelioration of altered adipokines expression and serine/threonine phosphorylation of IRS1 in alloxan-induced diabetes by Potentilla fulgens and its phytochemical constituents

The present study is aimed at investigating the modulation of serine/threonine phosphorylation of IRS1 and the gene expression oftotal IRS1 and adipokines including TNF-α, IL-6 and adiponectin by the plant Potentilla fulgens and its phytochemical constituents catechin and (-)-epicatechin. Alloxan-induced diabetic mice with a two-to three-fold increase in their blood glucose levels were taken for the study. The level of protein expression of total (tIRS1), tyrosine (pIRS1), and serine phosphorylated IRS1 (pIRS1 ser307) was analysed by western blot, and the gene expression level of tIRS1, IL-6, TNF-α, and adiponectin was analysed by real-time PCR. Since evidences strongly suggest that adiponectin, TNF-α, and IL-6 are implicated in insulin resistance and type 2 diabetes, therefore these three adipokines have been targeted in our study with an aim to investigate the anti-inflammatory and antidiabetic effects of our plant Potentillafulgens(PF) andits phytochemicals. The results strongly demonstrates the capability of PF and its phytochemicals to modulate the ser/thr phosphorylation state of IRS1 by downregulating the serine 307 phosphorylation while simultaneously upregulating the tyrosine phosphorylation of IRS1. The results also indicate the ability of the same to alleviate inflammation in alloxan induced diabetes by modulating the expression of the insulin sensitizing hormone adiponectin and the pro-inflammatory cytokines L-6 and TNF-α.
Conventional Extraction Methods Use in Medicinal Plants, their Advantages and Disadvantages

Medicinal plants are gaining much interest because of their use to treat and cure common as well as chronic diseases. The study on medicinal plants started with extraction procedures that play a critical role. A wide range of technologies with different methods of extraction is available nowadays. These techniques are conventional (traditional using from long ago) and Modern (developed more recently). The conventional extraction methods use solvents and require long extraction time whereas modern extraction methods have also been applied in natural products extraction. The modern extraction methods are complex, not easily available and costly whereas Conventional extraction methods are simple, easily available and low cost. A domestic application of conventional extraction is quite familiar to everybody in daily life from the making of coffee or tea at home. It is very crucial to develop effective and selective conventional extraction methods so that practitioners and researcher inspire and improve efficiency. This review presents a detailed description of the various Conventional extraction methods for better understanding and summarizes the potential, to help evaluating the suitability and economic feasibility of them. A comparison of the performance, advantage and disadvantage of these methods is also presented.
Stock Prediction using Machine Learning

Stock trading is one of the foremost activity in finance world. Stock market prediction is used to find the long run values of the stock and other financial factors influenced on a financial exchange. The technical and fundamental or the statistical analysis is employed by most of the stockbrokers while making the stock predictions. Python programming language in machine learning is used for the stock market prediction. In this paper we have proposed a Machine Learning (ML) approach which trains from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. In stock market prediction, the aim is to predict the longer term value of the financial stocks of a corporation [1]. The recent trend in market prediction technologies is that the use of machine learning approach which makes predictions supported the values of current stock market indices by training on their previous values. Machine learning itself employs different models to form prediction easier and authentic. This paper focus on Regression and Long Short Term Memory (LSTM) based Machine learning to predict stock values. The factors that are being considered include re-open, close, low, high and volume [2,3].
Artificial intelligence: Future for Polyp Diagnosis!

Colorectal polyp is an independent risk factor of colorectal cancer, as well as a common disease in children with serious effects on normal growth and development. Colonoscopy is the gold standard for the detection and diagnosis of colonic polyps, while the diagnosis quality various due to operator’s diagnosis experience and their visual fatigue. Therefore, it is important to improve the diagnosis accuracy of colonic polyp from the colonoscopy. An artificial intelligence model which can identify colorectal polyp patients at early stage would be useful for earlier intervention. However, the full connection layer in the existed deep learning models not only brings a large number of parameters, but also easily makes these networks fall into over fitting, resulting in weak network generalization ability. In this study, we collected two children colonic polyp RGB image datasets from public database, CP-CHILDA dataset and CP-CHILD-B, as training sets and testing sets respectively. By using global average pooling, we proposed the improved approaches: ResNets-GAP, which has much fewer parameters than origin ResNets. The accuracy, sensitivity and specificity of the model in the testing dataset are all above 95%. The experimental results show that the proposed approach has good effect on the automatic detection of colonic polyps.
ShadowDroid: Practical Black-box Attack against ML-based Android Malware Detection

Machine learning (ML) techniques have been widely deployed in the field of Android malware detection. On the other hand, ML-based malware detection also faces the threat of adversarial attacks. Recently, some research has demonstrated the possibility of such attacks under the settings of white-box or grey-box. However, a more practical threat model - black-box adversarial attack has not been well validated and evaluated. In this paper, we bridge this research gap and propose a black-box adversarial attack approach, ShadowDroid, against ML-based Android malware detection. On a high level, ShadowDroid tries to construct a substitute model of the target malware detection system. Utilizing this substitute model, we can identify and modify the key features of a malicious app to generate an adversarial sample. During the experiment, we evaluated the effectiveness of ShadowDroid against nine ML-based Android malware detection frameworks. It achieved successful malware evading on five platforms. Based on these results, we also discuss how to design a robust malware detection system to prevent adversarial attacks.
THREE NOTIONS OF DIMENSION AND AN APPLICATION

The Lebesgue outer measure is a notion for the size of a set in Rn. Dimension is a more generalized notion of size which includes non-integer values and helps differentiate between certain sets which outer measure cannot. In this paper, three notions of dimension are introduced and several facts about them are proven. In the last section, we discuss how dimension can be applied to asset prices.
Deep generative models create new and diverse protein structures

We explore the use of modern variational autoencoders for generating protein structures. Models are trained across a diverse set of natural protein domains. Three-dimensional structures are encoded implicitly in the form of an energy function that expresses constraints on pairwise distances and angles. Atomic coordinates are recovered by optimizing the parameters of a rigid body representation of the protein chain to ﬁt the constraints. The model generates diverse structures across a variety of folds, and exhibits local coherence at the level of secondary structure, generating alpha helices and beta sheets, as well as globally coherent tertiary structure. A number of generated protein sequences have high conﬁdence predictions by AlphaFold that agree with their designs. The majority of these have no signiﬁcant sequence homology to natural proteins. Most designed proteins are variations on existing proteins. It is of great interest to create de novo proteins that go beyond what has been invented by nature. A line of recent work has explored generative models for protein structures [1, 2, 3, 4, 5, 6]. The main challenge for a generative model is to propose stable structures that can be realized as the minimum energy state for a protein sequence, i.e. the endpoint of folding. The space of possible three-dimensional conformations of a protein sequence is exponentially large [7], but out of this set of possible conformations, most do not correspond to stable realizable structures. In this work we explore the use of modern variational autoencoders (VAEs) as generative models of protein structures. We ﬁnd that the models can produce coherent local and global structural organization while proposing varied and diverse folds. We use AlphaFold to assess the viability of sampled sequences, ﬁnding that many sequences are predicted to fold with high conﬁdence to their designed structures. To assess the novelty of the generated sequences, we search sequence databases including metagenomic
Decision Tree Models Induced by Membrane Systems

This paper focuses on an application of membrane systems to solve classiﬁcation problems. Decision tree technique has been widely used to construct classiﬁcation models because such models can closely resemble human reasoning and are easy to understand. A novel membrane computing-based decision tree induction algorithm is developed in this paper. An extended tissue membrane system with tree-like objects is considered as the computing framework, in which each object in cells expresses a feasible decision tree and the transformation-communication mechanism is applied to deal with the tree-like objects. The extended tissue membrane system with tree-like objects can eﬃ-ciently induce a best decision tree model for a given data set. The proposed decision tree induction algorithm is evaluated on some data sets and compared with two classical methods
Feature Extraction and Feature Evaluation Framework for Document Representation

This thesis studies stages for extracting features from unstructured text data to generate representation of document. Feature extraction aims to transform text into useful representations, a measurable property representing the text. While there are various methods available for feature extraction, the performance of the approach highly depends on the data itself. As such, this study investigates 2 main important stages in utilizing features from text document: (1) representing document features into continuous vector space with classical and state-of-the-art methods and (2) evaluating the methods that generate those features as a framework. Previously bag-of-words (BOW) method is most commonly used for text representations, which represents each word by its frequency of occurrences. BOW model is easy to implement and fast, but it discards many information, e.g., grammar, semantic contexts. A new method to represent words is quite popular in recent two years, word embedding, which is able to catch semantic meaning by representing each word with continuous floats. Word embedding based neural model is harder to train and requires more data, while traditional BOW model cannot catch semantic meaning, as a result, we implement both models for feature extraction and evaluate to make comparison with different datset. In evaluation stages classical BOW methods based models are still very competitive, such as LSA, especially on document categorization tasks. For document categorization task, the weight of topic related key words is important, while for classical feature extraction method frequency of occurrences of a word is quite relevant to the weight. Sequence to sequence LSTMs autoencoder performs well, compared to other word embedding based models, but it takes longer training time and do not deal well with long sequences, e.g., sequence containing more than 1000 tokens.
Exploring Best Lossy Compression Strategy By Combining SZ with Spatiotemporal Decimation

—In today’s extreme-scale scientiﬁc simulations, vast volumes of data are being produced such that the data cannot be accommodated by the parallel ﬁle system or the data writing/reading performance will be fairly low because of limited I/O bandwidth. In the past decade, many snapshot-based (or space-based) lossy compressors have been developed, most of which rely on the smoothness of the data in space. However, the simulation data may get more and more complicated in space over time steps, such that the compression ratios decrease signiﬁcantly. In this paper, we propose a novel, hybrid lossy compression method by leveraging spatiotemporal decimation under the SZ compression model. The contribution is twofold. (1) We explore several strategies of combining the decimation method with the SZ lossy compression model in both the space dimension and time dimension during the simulation. (2) We investigate the best-ﬁt combined strategy upon different demands based on a couple of typical real-world simulations with multiple ﬁelds. Experiments show that either the space-based SZ or time-based SZ leads to the best rate distortion. Decimation methods have very high compression rate with low rate distortion though, and SZ combined with temporal decimation is a good tradeoff.
An Experimental Investigation on Using Pedagogical Conversational Agents: Effects of Posing Facilitation Prompts in Oral-Based Peer Learning

: This study investigates the use of pedagogical conversational agents (PCAs) that intervene in learner-learner collaborative learning activities. In addition, this study investigates how the quality of learning performance in a simple concept explanation task may change due to the use of multiple PCAs that pose different types of facilitation prompts to the learners. A controlled experiment was performed by comparing a condition using multiple PCAs wherein each PCA provided different types of facilitations with a condition using multiple PCAs wherein each PCA provided a mixture of the two types of facilitations. Using the WOZ method, this study reports the preliminary results of an analysis of oral-based peer learning. Lexical network analysis was used to understand the complexity of learner’s semantic knowledge over two-time series. The results of the analysis show that when multiple PCAs were used with different facilitation prompts, the lexical network became more complex, showing that the learners developed a more sophisticated knowledge about the concept throughout their explanations. The significance of using multiple PCA is that it allows different types of contents to be considered during interaction.
Medicinal plants possess sedative and anxiolytic effect with emphasis on their mechanisms of action

Insomnia and anxiety are worldwide medical problems. Plant extracts possessed sedative and anxiolytic effect via different mechanisms included interactions with Na+ channels, γ-aminobutyric acid type A receptors, N-methyl-Daspartate receptors and chatecholamines. In the current review, Web Science, PubMed, Scopus and Science Direct, were searched to investigate the plants with sedative and anxiolytic effects
Estimating an Economic and Social Value for Healthy Forests: Evidence from Tree Mortality in the American West∗

Linkages between healthy forests and human well-being are often theorized, yet the magnitude of benefits remains unknown. This paper uses a natural experiment to assess the welfare consequences of changes in forest health across the American West. My empirical analysis relies on plausibly random variation in tree mortality generated by the thermal threshold at which cold-induced mortality occurs in bark beetles. I find that forest die-off has significant and economically meaningful impacts on both the market value of forests and the non-market benefits these ecosystems provide. I estimate that over the last two decades, tree mortality in the American West decreased the value of timber tracts by $1.1 billion, decreased home values by $16.5 billion, and increased damages from air pollution, wildfire, and floods by a combined $921 million. In a back-of-the-envelope calculation, I find that the death of a tree in my sample costs society $43 in foregone benefits. ∗I am deeply grateful to Solomon Hsiang, Joseph Shapiro, and Maximilian Auffhammer for their advice, support, and guiding discussions. I also thank Wolfram Schlenker, Michael Anderson, Meredith Fowlie, David Anthoff, Reed Walker, Lara Kueppers, Jonathan Proctor, Andrew Hultgren, and seminar participants at UC Berkeley, Stanford, and Columbia for helpful comments and suggestions. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. 1106400. Any opinions, findings, or conclusions expressed in this material are those of the author and do not necessarily reflect the views of the NSF.
Negative capacitance devices: sensitivity analyses of the developed TCAD ferroelectric model for HZO

This work aims to investigate the suitability of innovative negative capacitance (NC) devices to be used in High Energy Physics experiments detection systems, featuring self-amplified, segmented, high granularity detectors. Within this framework, MFM (Metal-Ferroelectric-Metal) and MFIM (Metal-Ferroelectric-Insulator-Metal) structures have been investigated within the Technology-CAD environment. The strength of this approach is to exploit the behavior of a simple capacitor to accurately ad-hoc customize the TCAD library aiming at realistically modeling the polarization properties of devices fabricated with ferroelectric materials. The comparison between simulations and measurements in terms of polarization as a function of the applied electric field for both MFM and MFIM devices has been used for modeling and methodologies validation purposes. The analyses and results obtained for MFIM capacitors can be straightforwardly extended to the study of NC-FETs. This work would support the use of the TCAD modeling approach as a predictive tool to optimize the design and the operation of the new generation NC-FET devices for the future High Energy Physics experiments in the HL-LHC scenario. The NC working principle will be employed for particle detection applications in order to exceed the limits imposed by current CMOS technology in terms of power consumption, signal detectability and switching speed.