Reviewerly/exHarmony huggingface dataset:
The Location Determinants of FDI in Sudan. Time Series – Empirical Analysis From 1980-2018

This paper study the location determinants of FDI inflow in Sudan, the study applied multivariate time series, Vector auto-regression using time-series data of 1980-2018. Variable include; market size, infrastructure, inflation rate, Debt, Dummy of investment incentive, and Terms of Trade openness. The empirical results revealed that there is a short-run causality relationship running from independents variable to FDI as specified in the study. Also, GDP, External debt, investment incentive, and inflation are important location determinants of FDI inflows to Sudan. The Granger Causality shows Uni-directional causality running from GDP, Inflation, TOP, inflation, and Dummy investment incentives to FDI. These results were consistent with the short-run vector autoregression results. Therefore for a country to be declared as attractive by MNC, it means that the above factors have to be significant, since the variable for infrastructure and terms of trade openness are insignificant, investors would choose to neglect Sudan. The outcome shows that GDP is a very important variable in determining the location choice of FDI. This suggests that authorities need to take into account factors that hamper the growth of the economy given its importance in attracting FDI inflows. Finally, Sudan should implements policies to encourage a fast-growing economy by improving its debt service payment, infrastructure, and its terms of trade openness and international cooperation.
Fundamentación del empleo de las redes sociales educativas en la formación de los profesionales universitarios

Este articulo persigue como principal objetivo, fundamentar el empleo de las redes sociales educativas en la formacion de los profesionales universitarios, acorde con las exigencias de la sociedad tecnologica en la que conviven, de modo que los mismos puedan aplicar los conocimientos, habilidades, destrezas, valores, actitudes, desarrollados en el trabajo con estas redes, en el abordaje y solucion eficiente y efectiva de las distintas situaciones profesionales que se les puedan presentar.Lo anterior fundamenta la necesidad de perfeccionamiento de la dinamica del proceso de formacion de los profesionales universitarios a partir del empleo de las redes sociales educativas con un significado y sentido de transformacion, lo cual presupone cambios en la concepcion y el modo de conducir dicho proceso, a partir de un nuevo modo de pensar y actuar por parte de los participantes en el mismo.Palabras clave: Procesos formativos universitarios, redes sociales educativas. ABSTRACTThis article pursues as its main objective, to base the use of educational social networks in the training of university professionals, according to the requirements of the technological society in which they live, so that they can apply knowledge, skills, skills, values, attitudes, developed in the work with these networks, in the efficient and effective approach and solution of the different professional situations that may arise. The aforementioned bases the need to perfect the dynamics of the process of training university professionals based on the use of educational social networks with a meaning and a sense of transformation, which presupposes changes in the conception and manner of conducting said process, from a new way of thinking and acting on the part of the participants in it.Keywords: University training processes, educational social networks.
Love and Mathemetics. Sofia Kovalevska – Science and Struggle for Happiness

The author follows the professional successes and the personal experience of the one of the first women in the modern science – the Russian mathematician Sofia Kovalevska. The dialogue between her strong will, energy and intellect in the male world of science and her delicate sensibility and deep emotionality in her literary works has been analysed and the conflict points has been outlined. The author tries to answer to the question why despite of her professional successes Sofia Kovalevska felt unhappy, why the question of happiness is key question in her literary works and memoirs. Due to the tension between the profession-al and personal discourses a sense of polyphony and lack of satisfaction is marking her narratives. An attempt has been made to place Kovalevska in the trends of the Russian feminism in the second half of the 19th century.
Multi-Agent Programming Contest 2019 FIT BUT Team solution

During our participation in MAPC 2019, we have developed two multi-agent systems that have been designed specifically for this competition. The first of the systems is pro-active system that works with pre-specified scenarios and tasks agents with generated goals designed for individual agents according to assigned role. The second system is designed as more reactive and employs layered architecture with highly dynamic behaviour, where agents select their own action based on their perception of usefulness of said action.
Mechanization of coherence and adequacy: Being extrinsic extended to subtyping

Operational and denotational are two common approaches to specify the semantics of programming languages: the former is more suitable for expressing a particular evaluation strategy and the latter is more abstract. If there are both an operational and a denotational semantics for a programming language, one expects that the operational semantics be adequate with respect to the denotational one (this ensures that a program whose denotational semantics is some mathematical function computes that function). Moreover, there are two styles of denotational semantics for typed languages: the extrinsic one gives a denotation for any expression (even if ill-typed), while in the intrinsic only well-typed expressions are meaningful. In this paper we mechanize in Coq a typed, call-by-value language by specifying its operational semantics and giving intrinsic and extrinsic denotational semantics, both using domain theory. We also prove that the denotational semantics are equivalent; this is interesting because it leads to a direct proof of coherence for the intrinsic semantics. Finally, we prove the adequacy of the operational semantics with respect to the denotational semantics. As far as we know, this is the first mechanization of Reynolds' bracketing theorem and also the use of biorthogonality with extrinsic semantics instead of intrinsic semantics. We also show that the methodology is modular by extending those results for a larger language with more types (Booleans and products) and subtyping.
Comparative Analysis of Sentiment Analysis Between All Bigrams and Selective Adverb/Adjective Bigrams

Increase in the amount of unstructured data across different platforms serves as a valuable resource for predicting market trends, analyzing product features, and considering the customer sentiment in designing new features/products. The sentiment of unstructured data such as tweets, Facebook comments, and web reviews is calculated by using the polarity and intensity of the words, whereas polarity indicates positive or negative sentiment, and intensity indicates the strength of polarity. In this paper, a comparative study of sentiment analysis performance and accuracy between all bigrams and selective adverb/adjective bigrams is done. The outcome of this research will serve as a metric for both academia and industry to implement sentiment analysis projects.
Survey Paper on Descriptive Document Clustering using on the basis of Predictive Network

The descriptive grouping consists of automatically organizing data instances into groups. The description should inform a user about the content of each group without further examination of the specific instances, allowing a user to quickly scan relevant groups. Selection of descriptions is often based on heuristic criteria. We modelling the descriptive cluster as an auto-coder network that predicts the characteristics of cluster assignments and predicts the cluster assignments of a subset of characteristics. The subset of functionality used to predict a cluster serves as a description. For the text documents, appearance or counting of words, phrases or other attributes provides a representation of the dispersed features with interpretable feature labels in the proposed network, cluster predictions are performed and feature forecasts are based on machine learning framework. The optimization of these models leads to a completely self-regulating descriptive grouping approach that automatically selects the number of clusters and the number of functions for each cluster. We apply the methodology to a variety of text documents and has shown that the selected grouping, as demonstrated by the subsets of the selected features, is associated with significant topical organization.
For interpolating kernel machines, the minimum norm ERM solution is the most stable.

We study the average $\mbox{CV}_{loo}$ stability of kernel ridge-less regression and derive corresponding risk bounds. We show that the interpolating solution with minimum norm has the best $\mbox{CV}_{loo}$ stability, which in turn is controlled by the condition number of the empirical kernel matrix. The latter can be characterized in the asymptotic regime where both the dimension and cardinality of the data go to infinity. Under the assumption of random kernel matrices, the corresponding test error follows a double descent curve.
The Extraordinaries by TJ Klune

Reviewed by: The Extraordinaries by TJ Klune Alaine Martaus Klune, TJ The Extraordinaries. Tor Teen, 2020 [400p] (The Extraordinaries) Trade ed. ISBN 9781250203656 $18.99 E-book ed. ISBN 9781250203670 $9.99 Reviewed from galleys R Gr. 8-12 As Shadow Star's biggest fan and writer of his most popular fanfic, sixteen-year-old Nick Bell is sure he knows everything about Nova City's favorite Extraordinary. Now Nick sets out to become Extraordinary himself, only to find himself distracted by his ADHD, his seemingly ever-present ex, and his new fascination with adorable best friend Seth Gray. His plans also put him in the middle of the ongoing battle between Shadow Star and nemesis Pyro Storm, both of whom are hiding more than their identities. Soon, dastardly plots unfold and secret identities are revealed, and Nick learns he knows far less than he realized about his hero, his friends, and his own heart. Part superhero homage, part sigh-worthy romance, part action-packed adventure, this novel is all kinds of comical fun. Klune skillfully strings together the tropes of the genre for an enjoyably fresh take that celebrates superhero fandom even as it playfully mocks its excesses. The real draw here is Nick himself and his myriad relationships, whose playful banter full of snark and sass gives the novel its heart and its humor, alongside plenty of genuine laugh-out-loud moments; his navel-gazing, which humorously lacks any real self-awareness, just makes him more likable. This will be a strong recommendation for genre fans who will be glad to see a post-"credits" scene that offers extra reveals and sequel potential. Copyright © 2020 The Board of Trustees of the University of Illinois
The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions

This paper introduces a new algorithm for the fundamental problem of generating a random integer from a discrete probability distribution using a source of independent and unbiased random coin flips. We prove that this algorithm, which we call the Fast Loaded Dice Roller (FLDR), is highly efficient in both space and time: (i) the size of the sampler is guaranteed to be linear in the number of bits needed to encode the input distribution; and (ii) the expected number of bits of entropy it consumes per sample is at most 6 bits more than the information-theoretically optimal rate. We present fast implementations of the linear-time preprocessing and near-optimal sampling algorithms using unsigned integer arithmetic. Empirical evaluations on a broad set of probability distributions establish that FLDR is 2x-10x faster in both preprocessing and sampling than multiple baseline algorithms, including the widely-used alias and interval samplers. It also uses up to 10000x less space than the information-theoretically optimal sampler, at the expense of less than 1.5x runtime overhead.
Design och konstruktion av distorsionseffekt för gitarrer

This project has been done as a part of a bachelors degree in engineering at Uppsala university. The project was supervised by Jorgen Olsson at the division of solid state electronics.The goal of t ...
PANDA: Privacy-Aware Double Auction for Divisible Resources without a Mediator

Auction mechanism generally requires a trusted-third party as the market mediator to coordinate bidding and resource allocation via collecting private data from the agents, which may arouse severe privacy concerns and high computation overheads. To address such issues, we propose a novel privacy-aware double auction framework (namely PANDA) by designing an efficient cryptographic protocol to privately execute double auction for divisible resources among all the agents. To ensure privacy and truthfulness, PANDA delicately co-designs VCG auction and cryptographic protocol, which is equivalent to a mediator for sealed-bid auction of divisible resources.
DETECTION OF PHISHING WEBSITES USING DEEP LEARNING AND MACHINE LEARNING

Phishing can be described as a way by which someone may try to steal some personal and important information. By appearing as a trusted body. Many websites, which look perfectly legitimate to us, can be phishing and could well be the reason for various online frauds. These phishing websites may try to obtain our important information through many ways, for example: phone, calls, messages, and pop up windows. When a user opens a fake webpage and enters the username and protected password, the credentials of the user are acquired by the attacker which can be used for malicious purposes . Phishing websites look very similar in appearance to their corresponding legitimate websites to attract large number of Internet users.
Analysis and Detection of Unreliable Users in Twitter: Two Case Studies

The emergence of online social networks provided users with an easy way to publish and disseminate content, reaching broader audiences than previous platforms (such as blogs or personal websites) allowed. However, malicious users started to take advantage of these features to disseminate unreliable content through the network like false information, extremely biased opinions, or hate speech. Consequently, it becomes crucial to try to detect these users at an early stage to avoid the propagation of unreliable content in social networks’ ecosystems. In this work, we introduce a methodology to extract large corpus of unreliable posts using Twitter and two databases of unreliable websites (OpenSources and Media Bias Fact Check). In addition, we present an analysis of the content and users that publish and share several types of unreliable content. Finally, we develop supervised models to classify a twitter account according to its reliability. The experiments conducted using two different data sets show performance above 94% using Decision Trees as the learning algorithm. These experiments, although with some limitations, provide some encouraging results for future research on detecting unreliable accounts on social networks.
Bitcoin Sentiment Index, Performance and US Dollar Exchange Rate

This study introduces a comprehensive Google search volume based Bitcoin sentiment index (BSI) by following the methodology of Da, Engelberg, and Gao (2014). BSI is investigated for its association with Bitcoin returns, trade volume, volatility, and United States dollar exchange rates (USD). Results of the simple OLS regression clarify that BSI has a positive impact on Bitcoin returns and trade volume while it has a negative impact on the volatility of Bitcoin returns. Furthermore, the Auto-regressive distributed lag (ARDL) model has been used to capture the relationship between BSI and Bitcoin price with USD. Empirical results of the ARDL model demonstrate that Bitcoin sentiments affect USD in the short-run but not long-run. However, Bitcoin's price can negatively affect USD in short as well as in the long-run. Error correction term represents a 38.50% adjustment towards the long-run equilibrium in one month if there is a deviation in the short-run. The results imply that investors in FOREX or public in general interested in Bitcoin trading should foresee sentiments of the public towards Bitcoin.
Combination of flood models with weather research and forecast based on extreme rainfall for hazard mitigations

Abstract Semarang city in central java Indonesia has a strategic location as the capital of Central Java Province and developed into the fifth largest metropolitan city in Indonesia. The city of Semarang, which is traversed by the north coast route (pantura) is often flooded during the rainy season. This is because the intensity of rainfall is high and some areas in the city of Semarang are rob areas so that when there is a tide of sea water this area will be flooded. The combination of the Flood model and the weather prediction model will illustrate future conditions. The results showed that the prediction of flood distribution based on rainfall for a period of 10 years. With the flood model the period pattern of rainfall intensity values obtained is processed into a relationship graph between the duration of rain in minutes with the intensity of rain in the return period of 2 years, 5 years, 10 years and 25 years. The rainfall intensity value obtained will then be used to determine the peak discharge value
The Socio-Economic Conditions of Women in Iran

Women’s increasing presence in the economic and social activities is one of the main pillars of the economic development. In Iran, especially after the Islamic Revolution, special efforts were made to increase the role of women in the society. As a result, the fields of health and education enjoyed a significant improvement. However, despite the fact that the development of all the predisposing factors has contributed to the greater participation of women in the economy and society in previous years, a large proportion of the population of Iranian women, especially women with academic achievements, are not actively engaged in social activities. The non-economic attitude toward education, lack of success in finding suitable jobs, the inconsistency of education with the needs of the market and low wages seem to be among the barriers in this area. Nevertheless, it is expected that Iranian women’s participation in economic and social activities will be enhanced by taking appropriate measures in the area of entrepreneurship development and empowerment of women.
An Online Topic Modeling Framework with Topics Automatically Labeled

In this paper, we propose a novel online topic tracking framework, named IEDL, for tracking the topic changes related to deep learning techniques on Stack Exchange and automatically interpreting each identified topic. The proposed framework combines the prior topic distributions in a time window during inferring the topics in current time slice, and introduces a new ranking scheme to select most representative phrases and sentences for the inferred topics. Experiments on 7,076 Stack Exchange posts show the effectiveness of IEDL in tracking topic changes.
Privacy Preserving File Auditing Schemes for Cloud Storage using Verifiable Random Function

Users leverage the cloud storage to store data by uploading files to the cloud storage. They can use it to share files to work on collaborative projects. However, during administrative operations by cloud storage service provider (CSSP), there may be some inadvertent corruption of files during data migration and backup. Due to heavy demands, the cloud service provider may not update the desired files immediately when requested by the owner. As a result, a user of the file may receive an obsolete file. To ensure integrity and freshness of files, third party auditing (TPA) services should be supported by CSSP while maintaining the confidentiality of user files and preserving privacy of users. In this paper, three privacy preserving auditing schemes for files stored in cloud has been proposed. Verifiable random function, merkle hash tree and ciphertext-policy attribute-based encryption has been used to achieve the desired goals.
Classification of Medieval Documents: Determining the Issuer, Place of Issue, and Decade for Old Swedish Charters.

Classification of Medieval Documents: Determining the Issuer, Place of Issue, and Decade for Old Swedish Charters
ENHANCING THE LEARNING OF BASIC CONCEPTS IN BIOLOGY WITH THE UTILIZATION OF MOBILE BIO-APPS AMONG BIOLOGY STUDENTS IN COLLEGE OF EDUCATION, IKERE, EKITI STATE, NIGERIA

The study examined the utilization of a mobile application that is biology-based in the learning of some basic concepts among NCE II students in the Department of Biology, College of Education, Ikere, Ekiti State, Nigeria. Among all the NCE Part II students which constituted the population, 60 students were purposively chosen and eventually randomly sampled into individualized Bio-Apps and collaborative Bio-Apps of 30 students each. These Bio-Apps groups have functional smart phones and their smartphones have biology dictionary downloaded on them. The third group of 30 students are of the traditional classroom and were randomly selected from the population. In all, 90 students formed the sample for this study. All the groups were exposed to the same instructional contents in BIO 214 (Plant Pathology). The individualized mobile Bio-Apps group was made to check basic concepts in plant pathology from the biology dictionary on their smart phones and participated as the classes progressed. The collaborative Bio-Apps group was made to check those basic concepts in plant pathology from their mobile Bio-Apps and collaborated among themselves in clusters while the classes progressed; while the traditional group was without mobile bio-apps and taken through traditional instructional strategy. Standardized Biology Achievement Test was administered on the three groups. Two research questions and two hypotheses were raised and answered descriptively. The results from the analyses show that students in Mobile Bio-Apps groups performed better that their counterparts in traditional class. Meanwhile, students in the collaborative performed than those in the individualized mobile bio-app. It was therefore recommended among others that, Mobile Bio-App should be integrated into students’ learning of basic concepts in biology in a collaborative form so as to avoid distraction in using the mobile phones for something else other the learning while the teaching-learning lasted.
Mini-batch Bayesian Inverse Reinforcement Learning for Multiple Dynamics

Inverse reinforcement learning is a method that estates a reward function from experts demonstrations. Most existing inverse reinforcement learning methods assume that an expert gives demonstrations in a fixed environment, although the expert can provide demonstrations for a specific objective in multiple environments. In such cases, normal practice is to use demonstrations in multiple environments to estimate the expert's reward. Herein, we formulate this problem based on a Bayesian inverse reinforcement learning framework and propose a mini-batch Markov chain Monte Carlo method. An advantage of our method is scalability. Our proposed method is scalable with respect to a number of environments in which expert demonstrations are generated. Experimental results show quantitatively that the proposed method outperforms existing inverse reinforcement learning methods.