Miracle-dz/UniFAR-FTUs huggingface dataset:
THE WONDERS OF THE NORMALIZED COMPRESSION DISSIMILARITY REPRESENTATION

We propose a method to effectively embed general objects, like audio samples, into a vectorial feature space, suitable for classiﬁcation problems. From the practical point of view, the researcher adopting the proposed method is just required to provide two ingredients: an efﬁcient compressor for those objects, and a way to combine two objects into a new one. The proposed method is based on two main elements: the dissimilarity representation and the normalized compression distance (NCD). The dissimilarity representation is an Euclidean embedding algorithm, i.e. a procedure to map generic objects into a vector space, which requires the deﬁnition of a distance function between the objects. The quality of the resulting embedding is strictly dependent on the choice of this distance. The NCD is a distance between objects based on the concept of Kolmogorov complexity. In practice the NCD is based on two building blocks: a compression function and a method to combine two objects into a new one. We claim that, as soon as a good compressor and a meaningful way to combine two objects are available, then it is possible to build an effective feature space in which classiﬁcation algorithms can be accurate. As our submission to the IEEE AASP Challenge, we show a practical application of the proposed method in the context of acoustic scene classiﬁcation where the compressor is the free and open source Vorbis lossy audio compressor and the combination of two audio samples is their simple concatenation .
The Performance Of The Company LQ45 Company Listed In Indonesia Stock Exchange

This study aims to examine how the ethnic influence of the board of commissioners (Chinese) on company performance, family ownership of company performance and company age on company performance. And the effect of all independent variables on dependent. The population of this study is the LQ45 company listed on the Indonesia Stock Exchange from 2013-2017. The sampling technique in this study is Purposive Sampling. Based on observations from 2013-2017 the samples in this study were 23 companies from the LQ45 company. This study uses panel data and the method used is the Hausman Test using Random Effect Model. The results of this study indicate that the Ethnic Board of Commissioners and family ownership do not affect the Company Performance while the Company Age has a positive effect on Company Performance. Taken together,it turns out that Ethnic Board of Commissioners, Family and Company Ownership have a negative influence on the Company's Performance in LQ45 Companies that are listed on the Indonesia Stock Exchange in 2013-2017. Finally, the authors suggest that the performance of the company increases the company must increase the Age of the Company by continuing to carry out or run company activities so that they have more experience and the authors also suggest that further research add variable size companies to get better research results.
Automatic Summary Generation using TextRank based Extractive Text Summarization Technique

- A summary is nothing but a brief statement or main points of the given textual content. The main goal of an automatic text summarization system is to identify the most important parts of sentences in the text and present it to the user. There are two approaches for automatic text summarization 1) Extractive Text Summarization 2) Abstractive Text Summarization. In this paper, we solely focus on extractive text summarization technique. The input is given in the form of an article and the extractive text summarization approach is followed by identifying the most important sentences in the text using graph based approach for text summarization.
A transactional model of expressive control and inhibitory control across childhood

Funding information Preparation of this work was supported by the United States Department of Health and Human Services, National Institutes of Health, National Institute of Mental Health Grant 1F31MH09206001A1 to the first author and by the National Science Foundation Grant 0951775 to the second author Abstract This investigation examined crosslagged relations between expressive control (i.e., management of expressed affect) and inhibitory control (i.e., management of behavior) to elucidate the development of selfregulation across affective and behavioral domains. Regulation was assessed longitudinally at ages four, six, and eight using both observational laboratory tasks and teacher reports in a diverse sample of 250 children. Both observational and teacherreported models produced parallel results, with consistent prospective relations between expressive control and inhibitory control. In both models, better expressive control of affect at age four predicted better inhibitory control of behavior at six whereas better inhibitory control at age six predicted better expressive control at age eight. Crossdomain longitudinal relations controlled for the stability of prior withindomain regulation and concurrent crossdomain relations. The replication of these findings across laboratory and school settings provides particularly robust evidence for these effects. This study demonstrated that clarifying developmental dynamics across domains of selfregulation may be essential to understanding the full regulatory system.
DFT study of HOMO structural map of β-diketones and β-ketoesters; towards prediction of electrochemical oxidation

In this study, anodic oxidation potentials of a training set of the selected 14 β-diketones and β-diketoesters were measured by means of cyclic voltammetry on a glassy carbon electrode and correlated with the highest occupied molecular orbital (HOMO) calculated at the #B3LYP/6-311+G** level. HOMO energy level and HOMO structural map were used to make a powerful model to classify molecules which show similar chemical and electrochemical behaviours. The linear correlation between anodic peak potential, EpA, and EHOMO of classified compounds, certifies that they have the same mechanism of the electron transfer reaction.
Thread Management in the HPX Runtime: A Performance and Overhead Characterization

Bryce Adelstein-Lelbach∗, Patricia Grubel†, Thomas Heller‡, Hartmut Kaiser∗, Jeanine Cook§ ∗Center for Computation and Technology, Louisiana State University †Klipsch School of Electrical and Computer Engineering, New Mexico State University ‡Chair of Computer Science 3, Computer Architectures, Friedrich Alexander University §Sandia National Laboratories blelbach@cct.lsu. edu, pagrubel@nmsu.edu, thomas.heller@cs.fau. de, hkaiser@cct.lsu. edu, jeacook@sandia.gov
A MONITORING SYSTEM OF AGRICULTURAL EQUIPMENT FIELD POSITION 
BASED ON GPS AND GIS

With the development of communication technology and embedded technology, mobile intelligent terminals (mobile GIS) have become the key research direction of monitoring systems. In view of this, the study first constructed the structure frame of the agricultural monitoring system and the system mobile database, and designed the monitoring system terminal, combined with the . Net Compact Framework platform to implement the system functions, and finally the monitoring system based on GPS and GIS. The function of the system was tested. The results show that the system design of this study combines user needs’ analysis, system framework and functional module division. The system design function realizes the function of creating a database, the entry and collection of attribute data and graphic data; and the editing and browsing of map data and management functions; it realizes the processing of data and the output of results, and displays in the form of graphs or reports; it realizes the functional modules of the monitoring system. It is hoped that this research can provide a certain reference and reference for the application of mobile intelligent terminals in our country's agriculture.
The Impact of Economic Freedom on Bilateral Trade: A Cross-Country Analysis

This paper investigates the impact of economic freedom on intra-African bilateral trade. We use an unbalanced panel dataset of total volume of trade among 33 African countries. Using the Fraser Institute’s economic freedom of the world index, we employ an augmented gravity model to estimate the impact of economic freedom on bilateral trade. We find that improvement in both exporter and importer economic freedom tends to induce more trade. Our results also indicate that regional trade agreements have a positive impact on intra-African bilateral trade. JEL codes: F14, F15, G18
Knowledge Graph on Cybersecurity: A Survey

Over the past decade, social networks and discussion forums have become valuable sources for open source cyber security intelligence. However, sorting out relevant and irrelevant information is very complex, as information comes from a variety of sources and in different formats. Different approaches exist for extracting and representing this information. In this review we present a comparative study on knowledge base approaches that consist in extracting relevant cyber security information and representing it in a graphical format for a better understanding and readability of the data. This comparative study reviews the various cyber security knowledge base construction works by identifying the potentials and limitations of each study.
Visual Odometry using Stereo Vision

— Conventional non-vision based navigation systems relying on purely Global Positioning System (GPS) or inertial sensors can provide the 3D position or orientation of the user. However GPS is often not available in forested regions and cannot be used indoors. Visual odometry provides an independent method to estimate position and orientation of the user/system based on the images captured by the moving user accurately. Vision based systems also provide information (e.g. images, 3D location of landmarks, detection of scene objects) about the scene that the user is looking at. In this project, a set of techniques are used for the accurate pose and position estimation of the moving vehicle for autonomous navigation using the images obtained from two cameras placed at two different locations of the same area on the top of the vehicle. These cases are referred to as stereo vision. Stereo vision provides a method for the 3D reconstruction of the environment which is required for pose and position estimation. Firstly, a set of images are captured. The Harris corner detector is utilized to automatically extract a set of feature points from the images and then feature matching is done using correlation based matching. Triangulation is applied on feature points to find the 3D co-ordinates. Next, a new set of images is captured. Then repeat the same technique for the new set of images too. Finally, by using the 3D feature points, obtained from the first set of images and the new set of images, the pose and position estimation of moving vehicle is done using QUEST algorithm.
Using Tweets to Assess Mental Well-being of Essential Workers During the COVID-19 Pandemic

The Covid-19 pandemic has led to large-scale lifestyle changes and increased social isolation and stress on a societal level. This has had a unique impact on US “essential workers” (EWs) – who continue working outside their homes to provide critical services, such as hospital and infrastructure employees. We examine the use of Twitter by EWs as a step toward understanding the pandemic’s impact on their mental well-being, as compared to the population as a whole. We found that EWs authored a higher ratio of mental health related tweets during the pandemic than the average user, but authored fewer tweets with Covid related keywords than average users. Despite this, sentiment analysis showed that, on average, EWs’ tweets yield a more positive sentiment score than average Twitter users, both before and during the pandemic. Based on these initial insights, we highlight our future aims to investigate individual differences in this impact to EWs.
The Effect of Financial Literacy on Small Business Financing Decisions. A Case of Shop Owners at Chelston Big Market

The low uptake of loans has been identified as a major constraint of MSME development and growth in Zambia. This is perturbing since small businesses are touted as critical tools for economic growth and inclusive development. This study sought to determine the effect of financial literacy on small business financing decisions using a case in point of shop owners at Chelstone big Market in Lusaka. The study specifically sought to determine how the level of financial literacy affects the uptake and use of loans in the financing of small businesses. A mixed method study was adopted for this study. Data was collected from shop owners at Chelston big market in Lusaka. Quantitative data was analysed using descriptive statistics while qualitative data was analysed using thematic and content analysis. The study established that the level financial literacy influenced the uptake and use of debt financing through improved financial skills and financial decision making of enterprise owners. This in turn also affected the level of business performance and growth. It was also established that the resentment in the use of debt financing was influenced by collateral demands, high interest rates and tenure of loan products. The study recommends that the government through the local council must facilitate for the enhancement of financial literacy through training so as to improve the quality of financial decisions and business performance. The study recommends that the council could do this by collaborating with colleges and universities to train basic concepts and principles of business financial management to business owners. Furthermore, the study recommends that financial institution and other lenders must provide full information on their loan products to the level of their clients’ comprehension to minimize any information asymmetry problems that lead to the resentment of loan products. Limiting the financing of businesses to owner funds, limits the growth and success of businesses.
Evaluation of BERT and XLNet Models on Irony Detection in English Tweets

Automatically detecting irony is helpful and important for mining fine-grained information from social web data. Therefore, the International Workshop on Semantic Evaluation (SemEval) presented the first shared task on irony detection called "Irony Detection in English Tweets" in 2018. For this task, the system should determine whether the tweet is ironic (Task A) and which type of irony is expressed (Task B). The top teams obtained 𝐹 1 =0.71 for Task A and 𝐹 1 =0.51 for Task B. These teams all used LSTM models exploiting some word embedding features like Glove embeddings. While in recent years more powerful models like BERT and XLNet appeared. Therefore, in our paper, we evaluate the performance of BERT and XLNet models on Irony Detection in English Tweets by two methods: word embedding method and fine-tuning method. Through the experiment, these two models could get relatively high scores showing that BERT and XLNet models are capable to understand the irony to some extent.
Parametrization of two photosynthesis models at the canopy scale in a northern boreal Scots pine forest

In thiswork, eddy covariance data from a northern boreal Scots pine (Pinus sylvestris) stand were used in the parametrization of two up-scaled leaf-level photosynthesis models. The parametrization was carried out by eddy covariance data inversion. The biochemically based Farquhar model parameters, the maximum rate of electron transport (Jmax) and maximum rate of carboxylation [Vc(max)], were both obtained from the temperature responses measured by the eddy covariance. The semi-empirical model, based on optimizing water use and carbon gain, was parametrized according to the seasonal behaviour of the parameter β The parametrization of the models was performed for the year 2001, while 2002 was used as a test year to study the models’ capabilities. Both of the models tracked daily CO2 assimilation fairly well, reaching the high growing-season rates in mid-June and starting the autumn drawdown at the beginning of September. The incapability of the models to track the diminishing of CO2 fluxes during very dry days brings up the issue of combining a soil model into the canopy model. The biochemical model parameters have temperature responses that change during the growing season. The biochemical model responds more to the temperature, whereas the semi-empirical model is strongly driven by the light level.
Fiscal Policy, Factors of Endogenous Growth and Dynamics of Slovenian Exports

In the modern world of rapidly changing technologies, fiscal policy engagement is also needed to promote and adapt to these changes. In order to achieve economic growth, every country needs to ensure an adequate institutional environment and financial incentives for technological development. These investment incentives operate through tax system directly or indirectly. The aim of the paper is to assess the impact of Slovenia's endogenous economic growth factors on exports in the 2009–2016 period. A panel data analysis was applied to obtain empirical results. The analysis showed that a 1% real increase in government subsidies to the economy over three subsequent years increases real investment in research and development by 0.45%, and after a two-year period yields a 0.27% increase in employment of persons with higher education. The latter has a 0.14 % positive impact on the growth of exports after another three-year term. In addition to endogenous factors of economic growth in the Slovenian case, exports are also affected by the dynamics of real world trade, by the dynamics of exchange rates corrected for relative prices, and by the dynamics of wage rates.
Robust and Unsupervised Anomaly Detection for Multivariate Dataset

: Anomaly detection (also outlier detection [1]) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. [1] Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions. [2]
Area Efficient High Speed Multi Operand Adder using 22nm Strained Silicon CMOS Technology

- In this proposal work, the ripple carry adder (RCA)- based binary tree adder (BTA) is analysed to find the possibilities for area minimization. Based on the analysis, critical path of carry is taken into the new logic implementation and the corresponding design of RCA are proposed for the BTA. The RCA is designed for m bits (m=8,16,32 bits) offers better efficiency in terms of area, delay than the existing RCA. Using this RCA design, the multi-operand adder (n=8,16,32) BTA structure is proposed. The synthesis is done at 22nm Cmos technology. Result reveals that the proposed BTA-MOA provides the efficient results in area minimization and also delay efficient structure for multipliers and other applications. Therefore, this binary tree adder based multi-operand design can be a better choice to develop the efficient digital systems for signal and image processing applications.
Thermodynamic properties of mixtures containing ionic liquids. Activity coefficients at infinite dilution of polar solutes in 4-methyl- N-butyl-pyridinium tetrafluoroborate using gas–liquid chromatography

Abstract Activity coefficients at infinite dilution γ i ∞ of the linear and branched C 1 to C 6 alcohols, acetone, acetonitrile, ethylacetate, alkylethers, and chloromethanes in the ionic liquid 4-methyl- N -butyl-pyridinium tetrafluoroborate (C 10 H 16 BF 4 N) were determined by gas chromatography using the ionic liquid as the stationary phase. The measurements were carried out at different temperatures between 313.1 K and 363.1 K. From the temperature dependence of the limiting activity coefficients partial molar excess enthalpies at infinite dilution H i E , ∞ of these polar solutes in the ionic liquid have been derived.
Neutron macromolecular crystallography

Neutron crystallography allows direct determination of the proton and deuteron positions of a macromolecule and its bound solvent. Key advances in neutron macromolecular crystallography have led to an expanding field addressing larger and more complex problems. In particular, improvements in neutron instrumentation, detection systems, data collection methods and perdeuterated sample preparation have dramatically lowered the sample volume requirements to ∼0.1–0.2 mm3 and data collection times to just a few days. This trend towards the study of larger systems using smaller samples is set to continue with instrumentation being built at new sources and upgrades being made to existing instrumentation. These developments make it desirable to summarize the current status of neutron macromolecular crystallography and offer some perspectives for the future. In addition, using examples of the neutron structures determined so far, I will present the wealth of information that can be gained from such studies and illustrate how this can be vital towards understanding macromolecular function and behaviour.
AUDITORY SCENE CLASSIFICATION USING MACHINE LEARNING TECHNIQUES

Audio scene classiﬁcation will play an important role in context-based organization of audio data in the future. With classi-ﬁed and labeled audio data, it will be possible to set up a searchable database where users can retrieve audio ﬁles based on their contents. In this paper, we introduce a system to extract features from such audio scenes and identify the environments in which they were recorded. This system makes use of wavelet and Mel-frequency cepstral coefﬁcient (MFCC) features, and classiﬁes scenes by ﬁrst classifying segments of the scene, and deciding the overall classi-ﬁcation with a vote. The system achieves a classiﬁcation accuracy of 72% for the training dataset provided for the IEEE AASP CASA challenge [1].
Surface treatment of GaN and InN using (NH4)2Sx

The effects of (NH4)2Sx treatment on GaN and InN surfaces were investigated by X-ray photoelectron spectroscopy. It was found that native oxide layers were removed from nitride surfaces by HCl and (NH4)2Sx treatments and that S-terminated surface layers were formed. In addition, the S-terminated layer on the GaN surface was removed by H* irradiation. The usefulness of the combination of (NH4)2Sx treatment and post H* irradiation was shown.
An approach to identifying bots in social networks based on the special association of classifiers

Currently the use of bots, i.e. auto-accounts in social networks which are managed with special programs but disguised as ordinary users, has serious consequences. For example, bots have been used to influence political elections, distort information on the Internet and manipulate prices on the stock exchange. Many research teams concerned with the detection of such accounts have made use of machine learning methods. However, the practical results of detecting social network bots indicate significant limitations because the methodological tools used have language limitation and ineffective criteria for detection. This article presents improved countermeasures in a methodological approach to develop a universal social network account classifier for minimizing the average risk of errors in bot detection. The application of an assembly of classifiers united by a data adaptation criterion and results from the variance of each model found the formation of a universal classifier for social network accounts. The main results obtained by the authors consist of the criteria system and the categorical (nominal) features transformation approach for the formation of the special ensemble of classifiers. In practice, use of the ensemble of classifiers allows us to increase the effectiveness of bot detection compared to other approaches.