Miracle-dz/UniFAR-FTUs huggingface dataset:
Geodetic Evidence of High Compression Across Seismically Active Kachchh Paleorift, India

In the past 200 years, the Kachchh paleorift in western India, a plate interior region, has witnessed unusually large number of strong earthquakes, namely, the 2001 Bhuj (Mw 7.6), 1956 Anjar (Mw 6.0), 1845 Lakhpat (Mw ~6), and 1819 Allah Bund (Mw ~7.7) earthquakes. We report continuous GPS measurements from the Kachchh and adjoining region. We find that the ongoing deformation during 2009–2016 is inconsistent with the models of postseismic deformation due to the 2001 Bhuj earthquake. The current widespread deformation pattern implies that the paleorift, bounded by the Kachchh Mainland fault and several other faults along its southern flank and the Allah Bund fault and Nagar Parker fault along its northern flank, is under long‐term compression from both sides at a rate of ~4–5 mm/year. We show that most of this compression is released seismically, making this one of the most seismically active paleorifts in the world.
Rule based sentiment analysis system for analyzing tweets

In daily life, our beliefs and perceptions of reality are largely dependent on how other people analyze the world. People mostly share their opinions about different entities like organization, products, services etc. on social media, forums, and blogs. It's very difficult to analyze this enormous amount of data manually. Thus, sentiment analysis is the process which automatically identifies people's attitudes and emotional states. In this paper, sentiment analysis system has been proposed which computes the sentiment and score of each tweet extracted from Twitter about any parameter given by a user. The proposed system has achieved an accuracy of 86% in case of testing on 500 tweets and 94% while testing on 200 English sentences. The results given by the system are very stimulating and in future, it has potential to be used for the benefits of society.
Decentralizing Governance within the European Union’s Framework: Evidence from Greece

ABSTRACT The administrative reform launched in 2010 (“Kallikratis” programme) in Greece stands out as a major decentralization initiative. Notably, amalgamations at the local level were set at the forefront of the reform. This paper aims at shedding light on the issue of decentralizing governance by examining the implications of the “Kallikratis” reform programme on local authorities in terms of their domestic financial mobilization. Drawing empirical evidence from their implementation in European structural programmes, it is argued that the decentralization effort had an asymmetrical impact on local institutions, favouring principally the small-sized local authorities vis-à-vis the bigger in population local bodies.
On the Security of Baruah et al.'s Biometric-based Multi-server Authentication Scheme Using Smart Card

In 2015, Baruah et al. proposed a biometric-based multi-server authentication scheme using smart card. Baruah et al. claimed that their proposed scheme is secure to various cryptographic attacks and can provide forward secrecy. However, this paper points out that Baruah et al.’s scheme not only is still vulnerable to identity guessing attack using stolen smart card and replay attack, but also cannot provide perfect forward secrecy unlike their claims. For this reason, Baruah et al.’s scheme is insecure for practical application.
An Effective Location Privacy using SADLP in Vehicular Ad Hoc Networks

: Vehicular Ad hoc Networks (VANETs) are more influence to a high number of attacks due to its open access and anomalous nature. Security is still a major issue in the VANET. Location privacy of the user should be made as a mandatory property for wireless communication. Once the attacker has the knowledge of the location of the node, it can easily trace its activities. During a long journey, a vehicular user is supposed to cross several Road Side Units (RSUs) that belong to other network communities. An attacker compromises an RSU and easily gets the real identity of a genuine vehicle. It impairs the location privacy of the user. Many of the existing works provides authentication certificates to the vehicles for providing location privacy. The existing works fail to reflect the location privacy, when RSU is compromised. This paper proposes a Sybil Attack Defendant Location Privacy (SADLP) for improving location privacy of the user. In order to provide the location privacy efficiently, a good authentication system is introduced in the proposed work. The SADLP employs a Location Privacy Unit (LPU) for providing good authentication system. The SADLP hides the real identity of a user and provides temporary key and trusted certificate to the users. The user provides the trusted certificate, including consecutive series of random numbers which is gotten from different RSUs for making V2V communication. By verifying the trusted certificate at the interference range of a genuine vehicle and an attacker, the SADLP determines the compromised RSU and thus it provides high location privacy. The simulation results show that compared to existing works, SADLP reduces the Sybil attack and provides high location privacy to the users in VANET.
Transforming mobility: The Dutch smart mobility policy as an example of a transformative STI policy

The recent orientation toward societal challenges in Science, Technology, and Innovation (STI) policies has exposed the limitations of contemporary governance to deal with these endeavors. For this reason, it has been urged a shift toward the so-called ‘transformative change’ approach, drawn from socio-technical transitions literature. This research suggests that this approach can be considered an emerging policy paradigm, and studies the implications of its adoption in STI policy. For doing so, we developed a conceptual framework based on policy sciences and STI policy literature, and used to study a policy resembling this societal challenge orientation: the Dutch smart mobility policy. Our case study suggests that the transformative change approach can be considered a new paradigm in STI policy, as novel theories, approaches, and mechanisms are being used to govern this policy. This research contributes to a better understanding of transformative approaches, by empirically looking the changing role of governance in societal challenge-led STI policies.
Bioremediation to Treat the World’s Worst Ever Recorded Oil Contamination Case

The soil pollution issue in Kuwait was a result of the burning of the oil wells during the 1990 Gulf War. The contaminated soil’s consequences have become more dangerous in many aspects, such as groundwater, dust action, human and animal’s health. In this research, a bio-remediating technology known as the vermi-remediation was chosen to treat the oil contaminated soil by using of earthworms. The objective of this project is to conduct a series of experiments to conclude whether this technique is effective in treating Kuwait’s soil and to what extent. The treatment results were found to surpass the results of the currently being implemented methods by both contamination percentage drops and time of treatment. The system was found to treat up to 62% in the TPH level for highly oilcontaminated soil samples, 55% for medium oil-contaminated soil samples and 71% for low oil-contaminated soil samples within three
The perceived social impacts of the 2006 World Cup on Munich residents

All major sporting events result in a variety of impacts upon the host community. To date, the majority of existing studies have focused upon the wider economic impacts, with few empirical studies of the social impacts upon local residents. This paper explores the perceived impacts of the 2006 Football World Cup upon residents of one of the host cities–Munich. Using a multi-stage sampling technique, 180 Munich residents were randomly selected. Of these, 132 agreed to participate in face-to-face interviews. Findings from the study suggested that the impacts were largely perceived as positive by residents, especially in terms of urban regeneration, increased sense of security, positive fan behaviour and the general atmosphere surrounding the event. Negative impacts, such as increased crime, prostitution, and displacement of local residents were perceived by fewer respondents. Further analysis demonstrates that such perceptions are not dependent upon socio-demographic factors such as age, gender or length of residence in the city.
FACE RECOGNITION USING EDGE INFORMATION AND DCT

This manuscript presents a face identification method based on the theory of Sobel Local Binary Pattern and Laplacian filters edge detectors to represent face images with enriched information. Thereafter, to refine images for less sample size, Discrete Cosine Transform is used to represent these face images in low dimension space. The proposed method of facial recognition performs well to a considerable amount under small sample problem and is also invariant to illumination changes. It shows better results as compared to previously developed face recognition methods after performing experiments on many well known databases.
Do Banking Institutions Respond to Incentives? Banking Awards and Stability Evidence from an Emerging Economy

This study investigates how banking awards influence stability in the banking sector of Ghana. We employ a robust random effect panel model of 30 banks between 2007 and 2014 and the results show that banking institutions respond to incentives in the form of awards. However, banking stability is weakened when previous year awards are concentrated in fewer individual banks. These imply that awards at the industry and individual bank levels promotes and weakens stability, respectively. From these findings, it is obvious that regulators of banks can rely on industry-level focused awards to reinforce stability but not on individual bank level focused awards. Also, organizers of banking awards in conjunction with regulators should structure and design banking awards to avoid concentration of awards in few banks and to be industry oriented.
A study on selecting scenes that match the news contents for news video summarization

Since news videos record daily events, they are valuable sources of real world information. However, when using them as information sources, it is difficult to watch all of the news videos about a topic reported during a long period. Therefore, it is necessary to develop a method for summarizing news videos. Thus, this report studies a method for selecting scenes that match the news contents for news video summarization. The method selects scenes related to news contents by analyzing image features to understand the visual concepts of an image. We confirmed that it was possible to select scenes related to news contents with this method.
Understanding the Probabilistic Latent Component Analysis Framework

Probabilistic Component Latent Analysis (PLCA) is a statistical modeling method for feature extraction from non-negative data. It has been fruitfully applied to various research fields of information retrieval. However, the EM-solved optimization problem coming with the parameter estimation of PLCA-based models has never been properly posed and justified. We then propose in this short paper to re-define the theoretical framework of this problem, with the motivation of making it clearer to understand, and more admissible for further developments of PLCA-based computational systems.
Trade Liberalization and Wage Inequality: A Panel Regression Model.

This paper is trying to answer an old question Does trade liberalization lead to higher wage inequality? To answer this question, I used a sample of 27 countries during 10 years, 1987-1996. Panel data regression model technique is used to estimate this relationship. The results of estimated models show that in low-income and lower-middle income countries, an increase in trade liberalization leads to higher level of wage inequality, and it is significant in case of lower-middle income countries. However, I upper-middle income and high income countries, the higher trade liberalization the lower wage inequality is. Furthermore, the rise in female share in labor market decrease the wage inequality, and higher level of employment in leading sector of production, i.e. industry or agriculture, enhance the wage equality in the economy. Although, the effect of urban population size on wage inequality is ambiguous
AN APPLICATION OF ROUGH SET THEORY TO CLUSTER STUDENT ASSESSMENT AT UNIVERSITIES

Assessment is the last session of a lecture in college. There are several components that form the basis of calculations. A technique to select a clustering attribute based on rough set theory is presented. Dataset is taken from a survey of 150 architectural design studio students. Data are taken on 6th semester students majoring in architecture University of Technology of Yogyakarta Indonesia. Assessment consists of five components, namely three tasks, presentations, midterms and final exams. This assessment was conducted in 2015. The evaluation criteria used range from 0-100. Student name, age, race, and attendance are not required in this assessment. In this study, we show how to determine the dominant attributes of a set of attributes of an assessment list by using the rough set theory (Max-Max roughness). The results obtained can potentially contribute to give a recommendation in awarding the final grade of a course more quickly and accurately. Keywords : Assessment, Clustering, Rough set theory, Attributes
Acoustic Scene Classification on Large Dataset Using Sparse Auto-encoder Based Deep Neural Network

In this paper we study the acoustic scene classification using a large dataset. The spectrogram of the large acoustic samples are extracted and applied with texture feature classification method. First, the acoustic scene database is built including various acoustic events. Second the image texture features on spectrogram are used to represent the acoustic samples. Third the auto-encoder is adopted to build a deep neural network classifier. Finally, we verified the proposed system on a large number of dataset and compared our results with traditional Gaussian mixture model and three-layer neural network. The experimental results show that the proposed method is effective and promising in big acoustic data classification. Keywords—acoustic scene classification; auto-encoder; deep neural network; big data
Tests de l'efficience faible à partir des ondelettes de Haar

Cette these proposee utilise les ondelettes de Haar a creer de nouveaux indicateurs techniques, d’en evaluer leurs performances afin de tester la validite de l’efficience faible des marches financiers. L’approche choisie vise a mettre en œuvre les capacites des indicateurs techniques a capter la memoire longue presente dans les indices boursiers americains et europeens a travers l’estimation de la tendance par le processus de lissage. De plus, cette derniere est une composante importante dans les series economiques et financieres. En effet, elle a fait l’objet d’innombrables investigations tant en analyse technique, qu’en traitement du signal et dans la theorie des cycles economiques. Toutefois, sa presence n’entre pas en ligne de compte dans la theorie classique de la finance, car les principaux modeles utilises se focalisent sur les variations des cours boursiers. A cet effet, la tendance constitue une source de non-stationnarite entrainant des difficultes majeures pour la modelisation econometrique ou financiere. Exploiter cette tendance s’affranchit, dans ce cas, des hypotheses de non-stationnarite tendancielle ou de racine unitaire. En plus, a l’issue des resultats que nous avons obtenus a partir du modele a changement de regime. Nous confirmons qu’il est possible d’exploiter la presence de memoire longue dans les cours, et egalement de battre le marche en presence de couts de transactions sur les marches americains et europeens.
Analyzing the semantic level of outdoor image annotation

Most users of image collections formulate their queries in terms of semantics. The question is which is the level of semantic interpretation the users use to describe certain types of images in order to develop and adjust efficient methods for representing and managing images of a given domain. This paper outlines approaches which in various ways characterise the multi-layering of image semantics. Three semantic categories of concepts are suggested, and these will be analysed in an experiment in which stationary outdoor images are manually annotated. The obtained results are normalized (singular, no numbers, no adjectives, no diminutives, no abstract words that express emotions) and a dictionary is formed, much richer than a dictionary composed solely of concepts recognized in the image. The given dictionary can be a starting point for defining knowledge database, which includes relevant domain knowledge, necessary for image interpretation on a higher semantic level.
Convolutional Neural Networks - Generalizability and Interpretations

In this paper we present a comparison of the robustness of Convolutional Neural Networks (CNN) to other classifiers in the presence of uncertainty of the objects localization in SAR image. We present a framework for simulating simple SAR images, translating the object of interest systematically and testing the classification performance. Our results show that where other classification methods are very sensitive to even small translations, CNN is quite robust to translational variance, making it much more useful in relation to Automatic Target Recognition (ATR) in a real life context.
Tackling undeclared work in the European Union: beyond the rational economic actor approach

ABSTRACT To tackle the undeclared economy, an emergent literature has called for the dominant “rational economic actor” approach, which increases the sanctions and risk of detection, to be replaced and/or complemented by a “social actor” approach that fosters citizens’ commitment to compliance. Reporting two waves of the Eurobarometer survey conducted in 2007 and 2013 across Europe, fixed-effects logistic regression analysis reveals that although both approaches reduce participation in undeclared work, the strength of the impact of deterrents on the likelihood of participation in undeclared work has weakened between 2007 and 2013, but has strengthened for vertical and horizontal trust. The paper concludes by discussing the policy implications of these findings.
Predicting the U.S. Stock Market Return: Evidence from the Improved Augmented Regression Method

We examine whether the stock market return is predictable from a range of financial indicators and macroeconomic variables, using monthly U.S. data from 1926 to 2012. We adopt the improved augmented regression method for parameter estimation, statistical inference, and out-of-sample forecasting. By employing moving sub-sample windows, we evaluate the time-variation of predictability free from data snooping bias and report changes in predictability dynamics over time. Although we may find statistically significant in-sample predictability from time to time, the associated effect size estimates are fairly small in most cases. We also find weak predictability of the stock market return from multistep ahead (out-of-sample) forecasts. In addition, we find that mean-variance investors realize sporadic economic gains in utility based on predictive regression forecasts relative to naive model historic average forecasts
Maintaining Relationships Through Social Media and the Way Forward

This paper examines the influence of social media in our global political, social, and business landscape. It underscores the argument that the value of social media in our 21st century social and business lifestyles cannot be overlooked. It explores some of the ways in which social media has shaped and positively maintained friendship, dating, and family relationships. It also highlights some of the growing challenges and emerging moral risks associated with unregulated social media prevalence in the society and offers ways to minimize threats posed by unfettered social media use in the society.
An Empirical Evaluation of Monetary and Fiscal Policy Effects in Bangladesh

Macroeconomic policy management is crucial for attaining sustainable economic growth with a stable rate of inflation. This working paper examines monetary and fiscal policies in Bangladesh and their effectiveness in achieving sustainable growth and price stability. It concludes that Bangladesh should focus on a combination of expansionary monetary policies and restrictive fiscal policies as the most beneficial approach.
Unpacking Nordic Administrative Reforms: Agile and Adaptive Governments

ABSTRACT This article examines current reform experiences in Denmark, Finland, Iceland, Norway, and Sweden, focusing on the assumed Nordic administrative model. The research questions is: How do Nordic public sector executives perceive reform processes, trends, contents and management instruments? To answer these questions we apply a theory of gradual institutional change. The database is a survey of top civil servants in ministries and central agencies in 19 European countries conducted in 2012–2015 within the COCOPS project funded by the EC 7th Framework Program. We show that the Nordic countries are pragmatic and motivated reformers. There is a high level of reform activity and public involvement. The reforms aim to improve services more than cut costs. The Nordic model emerges as one that is agile and adaptive. New reform elements have been incorporated into the existing welfare state model, which accounts for differences between the five countries.
Does 5-Minute RV Outperform Other Realized Measures in the Cryptocurrency Market?

After the seminal work of Liu et al. (2015) finds that the realized volatility (RV) using 5-minute intervals performs well, economists tend to use this simple measure in applications. Existing literature in the cryptocurrency already relies on 5-minute RV, but no paper has evaluated whether 5-minute RV performs well compared with other realized measures. Following Liu et al. (2015), we show that the 5-minute RV of Bitcoin performs well compared to other realized measures, meaning that this result justifies the existing literature that already uses this simple measure. This paper also indicates that realized measures with longer intervals such as 120-minute RV could provide inaccurate estimates.
Uncertainty, Financial Markets, and Monetary Policy over the Last Century

Abstract What has been the effect of uncertainty shocks in the U.S. economy over the last century? What are the roles of the financial channel and monetary policy channel in propagating uncertainty shocks? Our empirical strategies enable us to distinguish between the effects of uncertainty shocks on key macroeconomic and financial variables transmitted through each channel. A hundred years of data further allow us to answer these questions from a novel historical perspective. This paper finds robust evidence that financial conditions captured by both borrowing costs and the availability of credit have played a crucial role in propagating uncertainty shocks over the last century. However, heightened uncertainty does not necessarily amplify the adverse effect of financial shocks, suggesting an asymmetric interaction between uncertainty and financial shocks. Interestingly, the monetary policy stance seems to play only a minor role in propagating uncertainty shocks, which is in sharp contrast to the recent claim that binding zero-lower-bound amplifies the negative effect of uncertainty shocks. We argue that the contribution of constrained monetary policy to amplifying uncertainty shocks is largely masked by the joint concurrence of binding zero-lower-bound and tightened financial conditions.