Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Structural Breaks in the Money Demand Function in Search of the Possible Effect of Financial Innovations: Applying VECM to the Case of China

In this paper, we estimated a conventional money demand model with money demand (MD) as dependent variable and gross domestic product GDP, interest rate (IR) as independent variables over the period 1990-2016 in China. We apply Vector Error Correction Model to estimate money demand. Chow breakpoint test is used to test whether financial innovations has impacted the demand for money. Based on the estimates of the VAR system, we conclude that there are no breaking points in money demand which confirms the result of the stability tests (CUSUM and CUSUM of Squares tests) and is in line with the result of Johansen cointegration test meaning that money demand in China during 1990-2016 was stable. It provides us with the evidence that financial innovations did not cause any breaks in the demand for money. As a by-product of this research, we also conclude that the speed of adjustment toward long run equilibrium is 0.0698. There is not long run causality running from GDP and IR to MD meaning that these variables do not have influence on MD in the long-run. There is short run causality running from GDP to MD and there is no short run causality running from IR to MD. In other words, in the short-run, IR does not affect money demand.
#Selfie: Instagram and the United States Congress

This article is based on a content analysis of the 17,811 Instagram posts made by all 534 members of the United States Congress who were seated for the duration of the first 6 months of the 115th session. I find that women are significantly more likely than men to have an Instagram account. Senators and women post significantly more times to their accounts. And a member’s personal characteristics, such as their chamber, party, and age, had significant effects on the type of content posted to Instagram. I conclude that members of Congress use Instagram similarly to how they use other social media platforms, that parties in and out of power use Instagram in substantively different ways, and that the more personal accounts of younger members suggest future changes in Congressional representation.
Topic modelling: Location-based offline advertising using Twitter

Targeting the desirable audience for offline advertisements may be challenging. Unlike online where user profiling is common practice, offline advertising can only target a broader, less appropriate audience. Offline is generally more expensive than online marketing, but your advertisements may be overshadowed with the greater amounts of online advertising content, therefore, offline advertising is still valuable. This study researches an alternative approach to offline advertising with the use of online publicly available information from Twitter. The aim of this research is to investigate to what extent interest topics can be identified from Twitter content in a geographical region. Tweets located in Dublin County were collected and used to identify specific interest zones across Dublin. The main method for topic modelling in this research is with a Latent Dirichlet Allocation algorithm. The algorithm runs into obstacles on smaller sized documents to produce consistent, coherent and non-subjective topic labels. It is evident from the research that topic modelling on tweets may not produce consistent topic categories, to represent the Dublin population and interest zoning adequately. The research provides a well-designed framework from which future work in interest zoning using topic modelling can be done. The paper proposes additional measures to improve on location-based offline advertising using online content.
Effects of Combined CO2 and O3 Exposures on Net CO2 Assimilation and Biomass Allocation in Seedlings of the Late-Successional Fagus Crenata

We examined the effects of elevated CO2 and elevated O3 concentrations on net CO2 assimilation and growth of Fagus crenata in a screen-aided free-air concentration-enrichment (FACE) system. Seedlings were exposed to ambient air (control), elevated CO2 (550 µmol mol−1 CO2, +CO2), elevated O3 (double the control, +O3), and the combination of elevated CO2 and O3 (+CO2+O3) for two growing seasons. The responses in light-saturated net CO2 assimilation rates per leaf area (Agrowth-CO2) at each ambient CO2 concentration to the elevated CO2 and/or O3 treatments varied widely with leaf age. In older leaves, Agrowth-CO2 was lower in the presence of +O3 than in untreated controls, but +CO2+O3 treatment had no effect on Agrowth-CO2 compared with the +CO2 treatment. Total plant biomass increased under conditions of elevated CO2 and was largest in the +CO2+O3 treatment. Biomass allocation to roots decreased with elevated CO2 and with elevated O3. Elongation of second-flush shoots also increased in the presence of elevated CO2 and was largest in the +CO2+O3 treatment. Collectively, these results suggest that conditions of elevated CO2 and O3 contribute to enhanced plant growth; reflecting changes in biomass allocation and mitigation of the negative impacts of O3 on net CO2 assimilation.
A framework for economic analysis of network architectures (Ağ mimarilerinin ekonomik analizi için çerçeve)

Karakus, Murat PhD, Purdue University, December 2018. A Framework for Economic Analysis of Network Architectures. Major Professor: Arjan Durresi. This thesis firstly surveys and summarizes the state-of-the-art studies from two research areas in Software Defined Networking (SDN) architecture: (i) control plane scalability and (ii) Quality of Service (QoS)-related problems. It also outlines the potential challenges and open problems that need to be addressed further for more scalable SDN control planes and better and complete QoS abilities in SDN networks. The thesis secondly presents a hierarchical SDN design along with an inter-AS QoSguaranteed routing approach. This design addresses the scalability problems of control plane and privacy concerns of inter-AS QoS routing philosophies in SDN. After exploring the roots of control plane scalability problems in SDN, the thesis then proposes a metric to quantitatively evaluate the control plane scalability in SDN. Later, the thesis presents a general framework for economic analysis of network architectures and designs. To this end, the thesis defines and utilizes two metrics, Unit Service Cost Scalability and Cost-to-Service, to evaluate how SDN architecture performs compared to MPLS architecture in terms of unit cost for a service and cost of introducing a new service along with giving mathematical models to calculate Capital Expenditures (CAPEX) and Operational Expenditures (OPEX) of a network. Moreover, the thesis studies the problem of optimal final pricing for services by proposing an optimal pricing scheme for a service request with QoS in SDN environment while aiming to maximize benefits of both service providers and customers. Finally, the thesis investigates how programmable network architectures, i.e. SDN, affect the network economics compared to traditional network architectures, i.e. MPLS, in case of fail-
Technical analysis gives you courage, but not money-von the relationship between technical analisys usage, overconfidence and investment performance

This paper investigates the psychological background of the use of technical analysis in the financial markets. Such a background may give additional explanations to the popularity and common usage of technical analysis as an investment decision tool. This popularity is puzzling due to the missing theoretical and scientific background of technical analysis and the contradictory empirical evidence on its effectiveness. We postulate that overconfidence (regardless of its manifestation: calibration, better than average, and illusion of control) reinforces technical analysis usage. This hypothesis is tested in the present study conducted with professionals (traders in a proprietary trading company) and novices (finance students). The hypothesis received partial empirical support. Overconfidence – in particular the illusion of control – explains technical analysis usage in both investigated groups.
Tolerable Level of Corruption for Foreign Direct Investment in Africa

Corruption has become endemic in many African countries and is difficult to eradicate completely; therefore, reducing corruption to a tolerable level that will not deter foreign investors must be the aspiration of all political leaders and stakeholders. This study tries to identify the level of corruption that is tolerable to foreign investors, which is referred to as the Tolerable Level of Corruption for Investment (TLCI). The study proposes that below the TLCI, corruption plays the role of “sand in the wheels of commerce” and thus has a negative impact on FDI inflows, but above the TLCI, corruption functions to “grease the wheels” and has a positive impact on FDI inflows. The study is based on secondary data collected from the World Bank World Development Indicators. Using a dynamic panel data estimation technique while controlling for other variables, the estimated TLCI in Africa is -0.27 on the control of corruption scale, which ranges from approximately -2.5 (weak) to 2.5 (strong). Therefore, all African leaders and stakeholders, especially in countries that fall below the TLCI, should intensify their efforts in the fight against corruption to reduce corruption in their respective countries to at least the TLCI to attract foreign investors.
Firm Value and Corporate Cash Holdings. Empirical Evidence from the Polish Listed Firms

In the context of imperfect markets, it is important to understand the relationship between nonearning assets and firm value, in order to evaluate the corporate financial policies and to attain the right equilibrium between liquidity and profitability. The aim of our paper is to assess the relationship between corporate cash holdings and firm value for a sample of 719 Polish listed firms over the period 2007-2016. The study reports an inverted U-shape relationship between cash holdings and firm value, irrespective of whether we use static regression methods or dynamic panel regression. Our results confirm the existence of an optimum level of cash holdings at 27.06% of total assets. Furthermore, the nonlinear relationship between firm value and corporate cash holdings is found for all Polish listed firms, financially and less financially constrained. We report two breakpoints of the cash-value relationship, in the context of financial constraints, and the results indicate that the optimum level of cash holdings is much higher for financially constrained firms than less financially constrained ones. Finally, we show that the financial crisis has no additional impact on the nonlinear relationship between cash holdings and firm value. We validate the financial constraints as having a more pronounced effect on the relationship between corporate cash holdings and firm value, compared to the financial crisis, as intervening effects, in the context of the Polish economy. This study holds important microeconomics policy implications – firm-level financial policies should evaluate the tradeoff between cash holdings and market value in order to maintain the firm financial performance.
Secured and anonymous data transmission in Manet environment using zone‐based intrusion detection system

Zone‐based intrusion detection is a supporting protection mechanism for mobile adhoc environment consisting of large volume of distributed number of nodes. With the presence of malicious nodes, security will be violated, which is focused in proposed research by introducing Secured and Anonymous Zone‐Based Intrusion Detection System (SAZIDS). Here, anonymous rule sharing is guaranteed by introducing the Anonymous Location‐Aided Routing in MANET in which group signatures would be generated by all zone members based on group public key provided by the intra zone leader. Thus, the exact location of zone members cannot be revealed. To ensure the secured rule sharing, Enhanced Adaptive ACKnowledgment is utilized, which would ensure the presence of malicious nodes. This is done at the time of rule sharing by the intra zone leaders. After secured sharing of rule set, in order to optimize the memory storage, in this work, rule set aggregation is performed, which would merge the rules, thus resolving the bandwidth and associated memory issues. The overall evaluation of SAZIDS is performed in the NS2 simulation environment, which proves secured and reliable transmission of data without the involvement of malicious activities.
SAGS: A SLA-Aware Green Scheduling in Heterogeneous Cloud Using Hadoop YARN

: The Apache Hadoop with cloud had become an emerging and popular service. Irrespective of its huge dominance in large scale data processing, it has challenges yet to be addressed. The primary challenges in yarn scheduler are the abilities to automate and control the resource allocation to different workloads in order to meet the deadline-based Service Level Agreement (SLA) in the cloud environment with optimal energy consumption. Our study with the Hadoop YARN addresses this problem in a controlled homogeneous environment. In cloud data-centers, heterogeneity had become a normal phenomenon. Hence, this paper proposes the problem of energy-aware heterogeneous Hadoop Yarn cloud with deadline based SLA. We proposed a SLA-Aware Green Scheduling (SAGS), a Dynamic Voltage/Frequency Scaling (DVFS) based approach along with SLA-Aware scheduling algorithm in the heterogeneous environment. We evaluated SAGS by using benchmark datasets and, compared its performance with previously proposed solutions. Our observation with experimental results shows that, the proposed approach outperforms existing approaches.
Qui sont les Gilets jaunes et leurs soutiens

Ce travail propose pour la premiere fois d’etudier les clivages caches du soutien aux Gilets jaunes a partir des donnees de l’enquee du Barometre de la confiance du CEVIPOF. Nous montrons que le soutien au mouvement enterine l’ effacement de l’axe droite-gauche traditionnel. Les Gilets jaunes reunissent des personnes dont les taux de satisfaction dans la vie sont tres faibles, independamment de leur accord sur les moyens d’y repondre. Ce sont majoritairement d’anciens eecteurs de Marine Le Pen, de Jean-Luc Melenchon ou des abstentionnistes (dans cet ordre). Ils partagent une critique plus radicale de l’Etat et du gouvernement que l’un et l’autre de ces electorats, tout en ayant des positions plus medianes sur des questions morales comme la tolerance a l’ egard des minorites. L’analyse de la geographie des ronds-points confirme le caractere original de ce mouvement. Le Nord-Est et le Sud-Ouest sont les points forts de la mobilisation, soit les deux regions ou Marine Le Pen et Jean-Luc Melenchon ont fait leurs meilleurs scores en 2017
Deep Reinforcement Learning with Temporal Logic Specifications

In this thesis, we propose a model-free reinforcement learning method to synthesize control policies for mobile robots modeled by Markov Decision Process (MDP) with unknown transition probabilities that satisfy Linear Temporal Logic (LTL) specifications. The key idea is to employ Deep Q-Learning techniques that rely on Neural Networks (NN) to approximate the state-action values of the MDP and design a reward function that depends on the accepting condition of the Deterministic Rabin Automaton (DRA) that captures the LTL specification. Unlike relevant works, our method does not require learning the transition probabilities in the MDP, constructing a product MDP, or computing Accepting Maximal End Components (AMECs). This significantly reduces the computational cost and also renders our method applicable to planning problems where AMECs do not exist. In this case, the resulting control policies minimize the frequency with which the system enters bad states in the DRA that violate the task specifications. To the best of our knowledge, this is the first model-free deep reinforcement learning algorithm that can synthesize policies that maximize the probability of satisfying an LTL specification even if AMECs do not exist. We validate our method through numerical experiments.
The Role of Stakeholders in Development of Social Economy Organizations in Poland: An Integrative Approach

The aim of this article is to explore key changes in the mode of operation of Polish social economy organizations (SEOs) that result from a social policy targeted at strengthening their independence and sustainability. The activities of SEOs are largely supported by public institutions, but their opportunities for assistance of capacity building are considered insufficient. Owing to the current policy, not only an economic independence, but also the structure and behavior of supported social organizations, especially in their relations with other stakeholders, can be strengthened. Based on the exploratory analysis on how SOEs change their independence and sustainability as a result of implementation of the public policy, a conceptual model of value co-creation will be used. The model enables analyzing the scope and scale of stakeholder engagement in the development of SEOs. The empirical research was conducted using a survey among 112 Polish social economy organizations. The results of the study show that the market-oriented approach not only reduces the scale of relations between SEOs and their stakeholders but also affects the way SEOs work, transforming them to be more like traditional businesses.
An Improved Extreme Learning Machine Based on Full Rank Cholesky Factorization

Extreme learning machine (ELM) is a new novel learning algorithm for generalized single-hidden layer feedforward networks (SLFNs). Although it shows fast learning speed in many areas, there is still room for improvement in computational cost. To address this issue, this paper proposes an improved ELM (FRCF-ELM) which employs the full rank Cholesky factorization to compute output weights instead of traditional SVD. In addition, this paper proves in theory that the proposed FRCF-ELM has lower computational complexity. Experimental results over some benchmark applications indicate that the proposed FRCF-ELM learns faster than original ELM algorithm while preserving good generalization performance.
A Prediction-Based User Selection Framework for Heterogeneous Mobile CrowdSensing

Mobile CrowdSensing is a new paradigm in which requesters launch tasks to the mobile users who provide the sensing services. The tasks, in practice, are usually heterogeneous (have diverse spatial-temporal requirements), which make it hard to select an efficient subset of users to perform the tasks. In this paper, we present a point of interest (PoI) based mobility prediction model to obtain the probabilities that tasks would be completed by users. Based on it, we propose a greedy offline algorithm to select a set of users under a participant number constraint. Furthermore, we extend the user selection problem to a more realistic online setting where users come in real time and we decide to select or not immediately. We formulate the problem as a submodular $k$k-secretaries problem and propose an online algorithm. Finally, we design a distributed user selection framework Crowd UserS and implement an Android prototype system as proof of the concept. Extensive simulations have been conducted on three real-life mobile traces and the results prove the efficiency of our proposed framework.
Demand Function for Industrial Electricity: Evidence from South Korean Manufacturing Sector

Electricity is a crucial input to the industrial production of South Korea. Estimating the demand function for electricity in the manufacturing sector is an important task because electricity consumption in the manufacturing sector accounts for 56.3% of total electricity consumption in South Korea. Thus, this article tries to estimate the demand function for industrial electricity in the manufacturing sector of South Korea using cross-sectional data for analyzing the influence of manufacturing firms’ characteristics. To this end, 946 observations collected from a nationwide survey of manufacturing firms in 2018 are used and analyzed. As a robust approach, the least absolute deviations estimation method is applied to obtaining the demand function. The results show that the price elasticity and the sales amount elasticity of the industrial electricity demand are estimated to be −0.9206 and 0.2568, respectively, which are statistically significant at the 1% level. Furthermore, the economic benefits of industrial electricity consumption are computed to be 1.46 times as great as the price of electricity. The results of this study can be utilized in policy planning, making, and evaluation.
Design, construction and evaluation of a distributed 48 V inverter for a mild hybrid vehicle

This thesis deal with the design, construction and evaluation of a prototype of an integrated inverter intended for integrated modular motor drive in a 48 V mild hybrid vehicle in order to reduce the overall volume of the inverter. The prototype is developed for a crankshaft integrated starter generator. Different semiconductor devices are evaluated theoretically with regards to performance, losses and thermal development. Moreover, different topologies for an integrated inverter are analyzed and PPB-II is selected as the platform to build the prototype of. Two prototypes are built, the first is a single phase half bridge which is used to verify and investigate different circuit designs. The second one is a three phase half bridge inverter which represents one module of eight integrated inverters. This second prototype is designed on a PCB and will have water cooling integrated. How to design and optimized a DC capacitor bank is studied. By implementing ceramic capacitors on the second prototype the size of the DC capacitor bank is reduced. Furthermore, an optimization program is used to present the optimal combination of capacitors with regards to cost, volume and losses. A switching frequency of 100 kHz is achieved.
Capital Structure Variation across Europe: Decomposing Country-, Industry- and Firm-Specific Effects on Leverage

I find that corporate European leverage variation between 2007 and 2015 is largely driven by firm and industry characteristics. Conventional, time-varying firm characteristics explain as much leverage variation as country and industry fixed effects combined. Cross-sectional leverage disparities are more distinct between industries than between countries. Corporate tax rate does have a significant positive effect on leverage, as predicted by the traditional tradeoff theory. The impact is however negligibly small relative to firm- and industry-specific effects. Evidence on both the tradeoff and pecking order model is, at best, mixed. Moreover, macroeconomic conditions are largely insignificant and unable to explain leverage differences in a linear regression context. Macroeconomic effects on capital structure might however channel through firm and industry determinants, thus affecting financing choice indirectly. A more dynamic, possibly nonlinear model specification is needed. Effects of the financial crisis of 2008 and the subsequent European debt crisis are apparent across all of corporate Europe and are most severe for Southern European firms.