Miracle-dz/UniFAR-FTUs huggingface dataset:
The Feasibility of Comic Media on Narrative Texts Based on Local Wisdom in VII Grade Student of Junior High School (SMPN) 3 Kotapinang

The aims of this study is to find out the feasibility of comic media on narrative texts based on local wisdom. The research was conducted in the second semester 2018/2019. The subjects in this study were students of VII grade of Junior High School (SMPN) 1 Kotapinang. The results of the material expert validation on the feasibility of material aspects gained an average of 89.37% with the category "Very Good", the aspect of language feasibility obtained an average of 83.33% with the category "Very Good", the feasibility of presentation aspects obtained an average of 89, 58% with the category "Very Good. The results of the validation from the illustrated expert obtained an average of 100% with the category "Very Good", while the results of the validation of the design of the learning media from the visual appearance aspect obtained an average of 80.28% with the category "Good", while from the aspect of design learning obtained on average 75% with the category "Good". Therefore, comic media in narrative texts based on local wisdom are developed for VII Grade Students of Junior High School (SMPN) 3 Kotapinang that are suitable for use in the learning process.
Reducing Interaction Cost: A Mechanism Deisgn Approach

In this thesis we study the problem of requiring self-interested agents need to interact with some centralized mechanism where this interaction is costly. To improve their utility, agents may choose to interact with neighbours in order to coordinate their actions, potentially resulting in savings with respect to total interaction costs for all involved. We highlight the issues that arise in such a setting for the mechanism as well as for the agents. We use a mechanism-design approach to study this problem and present a model for self-interested agents to form groups with neighbours in order to reduce the total interaction cost. Our model focuses on two aspects: reward-distribution and cost-sharing. We look at two scenarios for reward-distribution mechanisms and proposed a core-stable payoff as well as a fair payoff mechanism. We then propose a cost-sharing mechanism that agents can use to coordinate and reduce their interaction costs. We prove this mechanism to be incentive-compatible, cost-recovery and fair. We also discuss how agents might form groups in order to save on cost. We study how our final outcome (the total percentage of savings as a group) depends on the agents’ interaction topology and analyze different topologies. In addition we carry out experiments which further validate our proposal.
Parameter Estimation, Bias Correction and Uncertainty Quantification in the Vasicek Credit Portfolio Model

This paper is devoted to the parameterization of correlations in the Vasicek credit portfolio model. First, we analytically approximate standard errors for value-at-risk and expected shortfall based on the standard errors of intra-cohort correlations. Second, we introduce a novel copula-based maximum likelihood estimator for inter-cohort correlations and derive an analytical expression of the standard errors. Our new approach enhances current methods in terms of both computing time and, most importantly, direct uncertainty quantification. Both contributions can be used to quantify a margin of conservatism, which is required by regulators. Third, we illustrate powerful procedures that reduce the well-known bias of current estimators, showing their favorable properties. Further, an open-source implementation of all estimators in the novel R package AssetCorr is provided and selected estimators are applied to Moody’s Default & Recovery Database.
Virtual Full Duplex Wireless Broadcast via Sparse Recovery

A novel solution is proposed to undertake a frequent task in wireless networks, which is to let all nodes broadcast information to and receive information from their respective one-hop neighboring nodes. The contribution is two-fold. First, as each neighbor selects one message-bearing codeword from its unique codebook for transmission, it is shown that decoding their messages based on a superposition of those codewords through the multiaccess channel is fundamentally a problem of compressed sensing. In the case where each message consists of a small number of bits, an iterative algorithm based on belief propagation is developed for efficient decoding. Second, to satisfy the half-duplex constraint, each codeword consists of randomly distributed on-slots and off-slots. A node transmits during its on-slots, and listens to its neighbors only through its own off-slots. Over one frame interval, each node broadcasts a message to neighbors and simultaneously decodes neighbors' messages based on the superposed signals received through its own off-slots. Thus the solution fully exploits the multiaccess nature of the wireless medium and addresses the half-duplex constraint at the fundamental level. In a network consisting of Poisson distributed nodes, numerical results demonstrate that the proposed scheme often achieves several times the rate of slotted ALOHA and CSMA with the same packet error rate.
Statistical modelling of Zimbabwe’s international tourist arrivals using both symmetric and asymmetric volatility models

Orientation: Modelling of international tourist arrivals’ volatility is vital for marketing, planning, policy formulation and investment purposes among others. Symmetric and asymmetric tourism volatility models for years 2000–2017 were fitted to assess the impact of good and bad news on Zimbabwe’s international tourist arrivals’ volatility. Research purpose: In this article, we model Zimbabwe’s monthly international tourist arrivals’ volatility using both symmetric and asymmetric volatility models and investigate the impact of good and bad news on international tourist arrivals’ volatility. Motivation for the study: Very few, if any, research papers have looked at volatility clustering and the impact of good and bad news on Zimbabwe’s international tourist arrivals’ volatility. These are important aspects for tourism managers, investors and the government in decision-making processes. Research design, approach and method: The ARMA-GARCH and ARMA-EGARCH models were used to model tourism volatility. Main findings: The ARMA(1,1)-GARCH(1,1) model under the t -distribution (STD) innovations shows unexpected tourism shocks having a strong impact, that persists for significant periods of time, on Zimbabwe’s tourist arrivals. The ARMA(1,1)-EGARCH(1,1) model under STD innovations indicated less impact of bad news on future tourism volatility than good news of the same size because the symmetry coefficient gamma (γ) is statistically significant and is above zero. Practical/managerial implications: Tourism volatility models unearth the impact of news on international tourist arrivals’ volatility. Good news attracts tourism investors, while bad news discourages tourism investors from investing. Tourism stakeholders, government and investors can use these informative volatility models to the success of the tourism industry in Zimbabwe. Contribution/value-add: This article highlights the importance of volatility models in the tourism industry.
Graduate migration in Germany: New evidence from an event history analysis

"We use administrative social security records and event history methods to investigate graduate migration in Germany. The results indicate that most migration events happen up to seven years after graduation. Work experience gathered before and during the studies influences the migration decision, pointing to the importance of labour market contacts and social networks. In contrast to previous studies we do not detect a genuine negative duration dependence for the probability of leaving the region of study. When labour market entry outside the university region is considered there is some indication for cumulative stress. " (Author's abstract, IAB-Doku) ((en))
The Exit Behavior of Private Equity Firms in Eastern Europe: An Empirical Analysis of the Main Exit Strategies

The author examines the two main exit routes preferred by private equity funds, initial public offerings (IPOs) and mergers and acquisitions (M&A), using quarterly data for Eastern European countries and covering the period 2000–2014. This article follows the work of Black and Gilson (1998), who confirmed the existence of a positive correlation between the number of IPOs (in year t) and the willingness of institutional investors to allocate funds to private equity firms (in year t + 1). Additionally, this article studies M&A as a second exit strategy for private equity investors by mobilizing the economic techniques that are used in time series analysis. The results show that Eastern European private equity firms prefer M&A exits followed by IPOs. The Granger causality test shows the existence of a unidirectional causality of the number of M&A to the volume of private equity investments in Eastern Europe. TOPICS: Private equity, emerging markets, statistical methods
A picture says more than a thousand words : the influence of congruence in Visual Brand identity on Brand Preferences

Objective: A consumer is exposed to more than 5.000 advertisements in one day, which makes it a real challenge for a brand to draw attention and be preferred over other brands. Awareness can be gained by the visual presence of brands, else stated as the Visual Brand Identity (VBI). Literature suggests a relationship between VBI and brand preference: the congruence between the VBI-elements influences a positive attitude, which in turn leads to brand preference. The associations of the VBI-elements were projected on luxury- and ordinary brands and two product types were used: accommodations and accessories. Brand preference was operationalized into two constructs: price-quality ratio and attitude. Method: This study was conducted with a 2 (black vs yellow) x 2 (angular vs circular) x 2 (luxury vs ordinary) between-subjects experimental research design, which led to eight different conditions. In the online questionnaire, each respondent was randomly assigned to one of the eight conditions, consisting of two manipulations for two product types. Here, questions were answered (on a 7-point Likert scale) about the perceived price-quality ratio of- and attitude towards the fictional brand, and the perceived congruence in the VBI. In total, 220 Dutch respondents filled out the questionnaire of which the majority was 20-25 years old and highly educated. Findings: The results of the study showed the elements effects of VBI did not have a significant effect on attitude. This was significantly influenced by the amount of overlap between the identity of the respondent and the identity of the brand, so self-congruity is a significant predictor for one’s brand evaluation. Also, no congruence appeared and thus, did not influence brand preference. However, the luxury brand positioning appeared to be perceived as a positive price-quality ratio than the ordinary brand positioning. Conclusion: This research concludes that the concept of congruence within VBI and on brand preference needs more elaboration. The context of the brand, the environment of the respondent and the respondent sample are important aspects to take into account when conducting further research. Also, the concept of incongruence deserves attention when it comes to brand preference.
Servant as Leader: The Effects of Servant-Leaders on Trust, Job Satisfaction, and Turnover Intentions in Intercollegiate Athletics

This study examined the possible influence of servant leadership in NCAA Division III intercollegiate athletics. Using a survey research design, 326 athletic department employees were asked to provide their perceptions of their athletic directors’ servant leadership characteristics and respond to questions on trust in leader, turnover intentions, and job satisfaction. Results from structural equation modeling suggested servant-leaders significantly and positively impacted perceptions of trust in leader and job satisfaction. Additionally, there was a significant effect of perceptions of servant leadership on turnover intentions as mediated by job satisfaction. Sport leaders taking a servant leadership approach in their organizations could help nurture a trusting, collaborative, and more satisfying work
CodeReco - A Semantic Java Method Recommender

The increasing volume and complexity of software systems and the growing demand of programming skills calls for efficient information retrieval techniques from source code documents. Programming related information seeking is often challenging for users facing constraints in knowledge and experience. Source code documents contain multi-faceted semi-structured text, having different levels of semantic information like syntax, blueprints, interfaces, flow graphs, dependencies and design patterns. Matching user queries optimally across these levels is a major challenge for information retrieval systems. Code recommendations can help information seeking and retrieval by proactively sampling similar examples based on the users context. These recommendations can be beneficial in improving learning via examples or improving code quality by sampling best practices or alternative implementations. In this thesis, an attempt is made to help programming related information seeking processes via pro-active code recommendations, and information retrieval processes by extracting structural-semantic information from source code. I present CodeReco, a system that recommends semantically similar Java method samples. Conventional code recommendations found in integrated development environments are primarily driven by syntactical compliance and auto-completion, whereas CodeReco is driven by similarities in use of language and structure-semantics. Methods are transformed to a vector space model and a novel metric of similarity is designed. Features in this vector space are categorized as belonging to types signature, structure, concept and language for user personalization. Offline tests show that CodeReco recommendations cover broader programming concepts and have higher conceptual similarity with their samples. A user study was conducted where users rated Java method recommendations that helped them
A Proposal for a Decision-Making Tool in Third-Party Logistics (3PL) Provider Selection Based on Multi-Criteria Analysis and the Fuzzy Approach

The selection of a third-party logistics (3PL) provider is an important and demanding task for many companies and organizations dealing with distribution activities. To assist in decision making, this paper proposes the implementation of fuzzy logic. To design a fuzzy inference system (FIS), the first prerequisite is to determine a set of evaluation criteria and sub-criteria and to find the relationship between them. This task was solved by an extensive review of the literature and expert opinions on implementing the Fuzzy Analytic Hierarchy Process (AHP) approach. The results obtained in the first part of the research, together with data collected from 20 3PL providers, were further used in the second part, which was related to the implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Finally, a decision-making tool for 3PL provider selection was designed as an FIS structure, where the inputs were the previously defined criteria and the output was a preference for 3PL selection. The fuzzy rules were generated on the basis of the collected empirical data, the preferences obtained by the TOPSIS method, and expert opinion using the Wang–Mendel method. The proposed fuzzy model is particularly suitable when input data are not crisp values but are provided descriptively through linguistic statements.
Political institutions and collective attachments

We argue that the geographic distribution of power across different levels of government influences citizens’ attachments to alternative collective groups. In federal states where authority is shared between the center and regions, individuals are more likely to have attachments to multiple groups. We present evidence for the impact of state structure on collective attachments using the case of supranational European identity. We show that in countries where state structure empowers sub-state governments, individuals manifest stronger attachments to a supranational European community than they do in unitary states. This study indicates that when we examine individuals’ attachments to a collective group, the political-institutional context in which these individuals are embedded is an important factor to consider.
C Projects

C Projects [Free] C Projects [EPUB] [PDF] 3 Section 1 Basic Types and Operators C provides a standard, minimal set of basic data types. Sometimes these are called "primitive" types. More complex data . za, 25 mei 2019 08:44:00 GMT Catalogs Archiexpo Public Projects Manual Norfolk Southern C.3 Stormwater Handbook scvurppp-w2k.com C.3 Stormwater Handbook Guidance for Implementing Stormwater Requirements for New Development and Redevelopment Projects June 2016 Campbell • Cupertino • Los . CREATE Project Status Summary 190506 CREATE Program Overall Project Status Summary Revised 6-May-2019 Current Project Status 6-May-2019 Proj. No. Project Name Municipality Railroads Affected Lead . Publications (Department of Transport and Main Roads) The Caboolture to Maroochydore Corridor Study proposed a passenger rail service branching off the North Coast railway line at Beerwah and extending through Caloundra . Table 310.15(B)(2)(a) Adjustment Factors for More Than . Copyright © 2008, National Fire Protection Association®. All Rights Reserved Table 310.15(B)(2)(a) Adjustment Factors for More Than Three Current-Carrying c-icap download | SourceForge.net Download c-icap for free. c-icap is an implementation of an ICAP server. It can be used with HTTP proxies that support the ICAP protocol such as the Squid 3.x HTTP . About Our Construction Projects | Campus Services From Design To Completion. The Planning, Design and Construction (PD&C) and Facilities Operations & Management (FO&M) teams manage Dartmouth’s construction projects .
Election Manipulation on Social Networks with Positive and Negative Messages

We study the problem of election control through social influence when the manipulator is allowed to use the locations that she acquired on the network for sending both positive and negative messages. We investigate the tie-breaking rules that can emerge in this scenario and explore various generalizations, widely extending the previous results available in the literature. In particular, we provide a tight characterization of the settings in which the maximum increase in the margin of victory can be efficiently approximated and of those in which any approximation turns out to be impossible. We also show that, in simple networks, a large class of algorithms, mainly including all approaches recently adopted for social-influence problems, fail to compute a bounded approximation even on very simple networks, as undirected graphs with every node having a degree at most two or directed trees.
Registration & Modeling of Shapes with Uncertainties: Contributions and Applications to Knowledge Based Segmentation

In the recent years, we have witnessed a revolution on new non-invasive means for human and biological tissues imaging. The use of computer aided-techniques has emerged naturally as an efficient pre-screening and post treatment evaluation procedure. This often involves mathematical modeling of organs which usually refers to the three following steps: (i) determine/extract the structure of interest, (ii) provide a mathematical model to describe these structures and (iii) estimate the variations of the parameters in the proposed model. In this thesis we propose novel means to address the above tasks. The modeling of organs is performed with the description of the deformations. It therefore involves shape registration with the definition of a reference shape and a deformations space. In this thesis we introduce the use of uncertainties in the registration, defined as covariance matrices in the deformations space which indicate the amount of confidence in the obtained registration. The next step consists in modeling the shape variations based on a training set representing various instances of the organ under study. In this thesis we extend the state of the art that does not account for registration errors and introduce a method that propagates registration uncertainty to the modeling step. The last contribution of the thesis is in the area of knowledge based segmentation and consists of introducing a segmentation-by-deformation approach where the use of uncertainties both in the model as well as image space are considered. Segmentation of the cardiac left ventricle on CT scan and of the corpus callosum on MR-images using the above mentioned-methods are considered as applications to demonstrate the extreme potentials of our approach.
CLOUD WORKFLOW SCHEDULING BASED ON STANDARD DEVIATION OF PREDICTIVE RESOURCE AVAILABILITY

: Cloud computing has emerged as a new paradigm that provides services to the user on pay-as-you-use basis. Cloud computing’s Infrastructure as a service provides various resources to user such as computing, storage and bandwidth etc., where Computing services can be provided in the form of Virtual Machines. Parallel applications submitted for execution on these Virtual Machines can be completed without any delays only when the Virtual Machines are available for execution. In general, resources in cloud are shared by various users and their availability is dynamic and unpredictable due to various reasons such as capacity of underlying hardware and number of users sharing the resources and various other reasons. Hence, non-availability of these allocated resources may cause delays in completion of execution of parallel applications. In this scenario, existing algorithms without considering the delays that may occur due to non-availability of Virtual Machines may not perform better and are less reliable in terms of completion of jobs. In this paper, we propose a new heuristic called Workflow Scheduling based on Standard Deviation of Predictive Resource Availability in cloud computing considers the dynamic nature of cloud resources and its dynamic availability in scheduling decisions and produces reliable schedules.
A Distributed Clustering Algorithm without an Explicit Neighbourhood Knowledge

Ad-hoc networks have received significant attention over the last few years as these emerging networks provide a fast deployable multi-hop wireless infrastructure for a growing number of applications when a wireline network is neither available nor economical to build. To overcome the scalability problem, creating hierarchies among the nodes seems to be a promising approach and an effective way to organize the network as the number of nodes increases. In this paper, we present a novel distributed clustering algorithm for ad-hoc networks. Our algorithm is based on a synchronized and layered process. Its main objective is to minimize the number of control messages exchanged during the clustering process.We evaluate our algorithm using the Visidia simulation test bed and show that it outperforms one of the most known clustering algorithms in minimizing both the number of control messages and the number of elected clusterheads.