Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Affordances of Social Media Use in Election Campaigns: What Political Parties Want to Facilitate, Project and Create

In recent years, social media have become omnipresent and highly important for social networking and content sharing. Lately we have witnessed how also political parties adopt social media as part of their political campaign strategy. The purpose of this work-in-progress paper is to investigate this tendency by posing two research questions: 1) what do political parties perceive as affordances of social media use in their campaign strategy? And 2) how are these affordances reflected in the political parties’ actual actions during the campaign? To address the two questions, we conducted a qualitative case study of the political parties’ use of Facebook in the Danish general election in 2011. Our preliminary findings reveal three main categories of affordances that the political parties wish to pursue: 1) they want to facilitate direct communication to promote political interests and create room for dialogue in a controlled environment, 2) they want to project an image of authenticity through an informal media, and 3) they want to create interaction and involvement through dynamic relationships with voters. A closer look at the parties’ actual use of Facebook reveals that their intention of generating interaction and involvement is limited by their actions as most of them do not engage with the users’ posts and comments. The tensions between perceived affordances and actual use prompt further investigation of what political parties should consider when engaging in social media activities as part of their campaign strategy.
How cultural and organizational conditions impact on policy entrepreneurship: evidence from Thailand

ABSTRACT Kingdon's framework has limited power to explain policy changes in the Global South. By investigating Thai cases, cultural and organisational conditions are found to play an important role in creating opportunities for policy entrepreneurship and in the successful formation and implementation of inter-local collaboration policy. The strong hierarchical social system prevalent in Thai culture caused the mayors of powerful councils to perceive themselves as the benevolent brother, with a responsibility to help those with limited resources to enhance their service-delivery capabilities. Hence, they played entrepreneurial roles to form collaboration. Then, they play managerial roles to sustain and push forward it.
OPTIMAL PERFORMANCE AND SECURITY BY DIVISION AND REPLICATION OF DATA IN CLOUD

The third-party administrative control is done in cloud computing which gives rise to security concerns the attacks may happens by data of other users and nodes within the cloud hence, high security measures are required to protect data within the cloud. In this paper we propose (DROPS) Division and Replication of Data in the Cloud for Optimal Performance and Security that will collectivel1y approaches the security and performance issues. Here we divide a file into fragments and replicate the fragmented data over the cloud nodes. The nodes stores only a single fragment of a particular data file that ensures that even in case of a successful attack and so no meaningful information is revealed to the attacker. Furthermore, the traditional cryptographic techniques for the data security is not used by DROP which reduces cost. Then we also compare the performance of the DROPS methodology with ten other schemes for providing higher level of security.
User-Centric Analytics for Expert Crowds

Pervasiveness and permissiveness of social media made content verification an integral part of any analysis involving online generated data. The most natural and convenient way to assess content trustworthiness and validity is to examine it from the perspective of a user that authored it. With more studies switching their strategies of data collection from event-oriented to user-focused, it is crucial to outline and address the challenges pertaining to this approach. We propose a user-centric analytics paradigm as one of the solutions to this problem. It is casted as a three-tier framework, consisting of detection of topical experts, extraction and interpolation of their opinions and utilization of the latter in social filtering. The first is concerned with automatic identification of user’s topical attribution on Twitter: while the platform is so popular among professionals, it does not support an explicit mechanism for community membership. We present three models exploiting semantic signature of a group and examine their performance on a case study of Twitter investment community. Interpolation of missing opinions is intended to handle mass amounts of periods with no activity peculiar to user streams. We introduce a number of community-based models exploiting user’s historical activity, content and opinions of his immediate network, and also verify their feasibility to serve as an initialization scheme in low-rank matrix approximation. We also analyze how predictability changes from user to user and build a model based on his characteristics to assess this value beforehand. Finally, we
Research of Sentiment Classification Based on the Chinese Stock Blog

According to the characteristics of the financial Sina stock blog,this paper constructs a stock sentiment dictionary by using pointwise mutual information( PMI) method and analyses the financial Sina blog sentiment by employing the classical Bayesian methods based on the constructed stock sentiment dictionary and the existing Internet National Taiwan University sentiment dictionary. Based on Chinese word segmentation and natural language processing( NLP),the paper discusses the influence of the text syntactic structure on the stock blog text sentiment classification. Considering the syntactic details of the text,such as collocations,negations or conjunctions,the experimental results show that the macro average accuracy is increased from 60. 19% to 80. 50%,the macro average recall is increased from the original 59. 35% to the current 78. 70%and the macro average F1 value is also improved from the previous 59. 77% to the present 79. 60% by using PMI stock sentiment dictionary and the Bayesian method.
Content-Based Natural Image Classification and Retrieval Using SVM

In the traditional approach of content-based image retrieval, the wide image domain results in the wide semantic gap between the low-level features and the high-level concepts. We propose to narrow the image domain and use machine learning methods to automatically construct models for image classes, thus providing users with a conceptualized way to image query. In this paper, support vector machines are trained for natural image classification. The resulting image class models are incorporated into image retrieval system, so that the users can search natural images by classes. The experimental results are promising.
Comments on Ontology-Based Semantic Annotation Prototypes

The key to realizing the semantic Web is to mark up the Web resources,such as Web pages and services,using ontology terms.Ontology-based annotation prototypes support content providers to add semantic metadata to Web pages and make machines and humans understand.This paper first makes a chief introduction of current ontology-based semantic annotation prototypes,then makes a survey on them from several facets and compares them,and finally points out their shortcomings.
Pricing and Hedging Equity-Linked Life Insurance Contracts Beyond the Classical Paradigm: The Principle of Equivalent Forward Preferences

Abstract By applying the principle of equivalent forward preferences, this paper revisits the pricing and hedging problems for equity-linked life insurance contracts. The equity-linked contingent claim depends on, not only the future lifetime of the policyholder, but also the performance of the reference portfolio in the financial market for the segregated account of the policyholder. For both zero volatility and non-zero volatility forward utility preferences, prices and hedging strategies of the contract are represented by solutions of random horizon backward stochastic differential equations. Numerical illustration is provided for the zero volatility case. The derived prices and hedging strategies are also compared with classical results in the literature.
Neighborhood Defined Feature Selection Strategy for Improved Face Recognition in Different Sensor Modalitie

A novel feature selection strategy for improved face recognition in images with variations due to illumination conditions, facial expressions, and partial occlusions is presented in this dissertation. A hybrid face recognition system that uses feature maps of phase congruency and modular kernel spaces is developed. Phase congruency provides a measure that is independent of the overall magnitude of a signal, making it invariant to variations in image illumination and contrast. A novel modular kernel spaces approach is developed and implemented on the phase congruency feature maps. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The unique modularization procedure developed in this research takes into consideration that the facial variations in a real world scenario are confined to local regions. The additional pixel dependencies that are considered based on their importance help in providing additional information for classification. This procedure also helps in robust localization of the variations, further improving classification accuracy. The effectiveness of the new feature selection strategy has been demonstrated by employing it in two specific applications via face authentication in low resolution cameras and face recognition using multiple sensors (visible and infrared). The face authentication system uses low quality images captured by a web camera. The optical sensor of the web camera is very sensitive to environmental illumination variations. It is observed that the feature selection policy overcomes the facial and environmental variations. A methodology based on multiple training images and clustering is also incorporated to overcome the additional challenges of computational efficiency and the subject's non involvement. A multi-sensor image fusion based face recognition methodology that uses the proposed feature selection technique is presented in this dissertation. Research studies have indicated that complementary information from different sensors helps in improving the recognition accuracy compared to individual modalities. A decision level fusion methodology is also developed which provides better performance compared to individual as well as data level fusion modalities. The new decision level fusion technique is also robust to registration discrepancies, which is a very important factor in operational scenarios. Research work is progressing to use the new face recognition technique in multi-view images by employing independent systems for separate views and integrating the results with an appropriate voting procedure.
Call Admission Control Using Adaptive-MMOSPRED for Resource Prediction in Wireless Networks

This paper presents adaptive-MMOSPRED method for prediction of resource demands requested by multimedia calls, and shows the performance of the call admission control based on proposed resource prediction method in multimedia wireless networks. The proposed method determines (I-CDP) random variables of the standard normal distribution by using LMS algorithm that minimize errors of prediction in resource demands, while parameters in an existing method are constant all through the prediction time. Our simulation results show that prediction error in adaptive-MMOSPRED method is much smaller than in fixed-MMOSPRED method. Also we can see via simulation the CAC performance based on the proposed method improves the new call blocking performance compared with the existing method under the desired handoff dropping probability.
Optimization and Scalable Constrained Clustering Performances

Objectives: To achieve the accuracy of clustering performances higher and to optimize the scalable approaches. Methods: Constrained spectral clustering and optimization algorithms are used to analyze and evaluate the large dataset. It is used to produce quality of clustering results. Findings: The proposed method achieves high performance in terms of precision, recall and accuracy. Application/Improvements: The proposed system is done by using optimization algorithm and pairwise constraints concepts. The optimization algorithm is used to increase the clustering accuracy and produce more optimal performances.
Explaining Financial Uncertainty through Specialized Word Embeddings

The detection of vague, speculative, or otherwise uncertain language has been performed in the encyclopedic, political, and scientific domains yet left relatively untouched in finance. However, the latter benefits from public sources of big financial data that can be linked with extracted measures of linguistic uncertainty as a mean of extrinsic model validation. Doing so further helps in understanding how the linguistic uncertainty of financial disclosures might induce financial uncertainty to the market. To explore this field, we use term weighting methods to detect linguistic uncertainty in a large dataset of financial disclosures. As a baseline, we use an existing dictionary of financial uncertainty triggers; furthermore, we retrieve related terms in specialized word embedding models to automatically expand this dictionary. Apart from an industry-agnostic expansion, we create expansions incorporating industry-specific jargon. In a set of cross-sectional event study regressions, we show that the such enriched dictionary explains a significantly larger share of future volatility, a common financial uncertainty measure, than before. Furthermore, we show that—different to the plain dictionary—our embedding models are well suited to explain future analyst forecast uncertainty. Notably, our results indicate that enriching the dictionary with industry-specific vocabulary explains a significantly larger share of financial uncertainty than an industry-agnostic expansion.
Does Fake News Affect Voting Behaviour?

We study the impact of fake news on votes for populist parties in the Italian elections of 2018. Our empirical strategy exploits the presence of Italian- and German-speaking voters in the Italian region of Trentino Alto-Adige/Sudtirol as an exogenous source of assignment to fake news exposure. Using municipal data, we compare the effect of exposure to fake news on the vote for populist parties in the 2013 and 2018 elections. To do so, we introduce a novel indicator of populism using text mining on the Facebook posts of Italian parties before the elections. We find that exposure to fake news is positively correlated with vote for populist parties, but that less than half of this correlation is causal. Our findings support the view that exposure to fake news (i) favours populist parties, but also that (ii) it is positively correlated with prior support for populist parties, suggesting a self-selection mechanism.
Knowledge-based methods handling complex dependency structures : applications to gene expression data

Gene Set Enrichment Analysis (GSEA) is a method for analysing gene expression data with a focus on a priori defined gene sets. The permutation test generally used in GSEA for testing the significance of gene set enrichment involves permutation of a phenotype vector and is developed for data from an indirect comparison design, i.e. unpaired data. In some studies the samples representing two phenotypes are paired, e.g. samples taken from a patient before and after treatment, or if samples representing two phenotypes are hybridised to the same two-channel array (direct comparison design). In this paper we will focus on data from direct comparison experiments, but the methods can be applied to paired data in general. For these types of data, a standard permutation test for paired data that randomly re-signs samples can be used. However, if the sample size is very small, which is often the case for a direct comparison design, a permutation test will give very imprecise estimates of the p-values. Here we propose using a rotation test rather than a permutation test for estimation of significance in GSEA of direct comparison data with a limited number of samples. Our proposed rotation test makes GSEA applicable to direct comparison data with few samples, by depending on rotations of the data instead of permutations. The rotation test is a generalisation of the permutation test, and can in addition be used on indirect comparison data and for testing significance of other types of test statistics outside the GSEA framework.
Sustaining Isolation Security for PreservingUsers Seclusion in Web Search Engines

We apply a PWS support called UPS that can adaptively simplify profiles by queries as regarding user specific privacy necessities. Our runtime simplification aims at conspicuous a equilibrium among two analytical metrics that estimate the service of personalization and the isolation danger of revealing the general outline. We present two greedy algorithms, specifically Greedy DP and Greedy IL, for runtime simplification. We also present an online prediction machinery for deciding whether personalizing a query is useful. Wide-ranging experiments demonstrate the effectiveness of our framework. The experimental results also expose that Greedy IL significantly outperforms Greedy DP in terms of efficiency. In this paper we present a novel procedure specifically calculated to protect the users’ privacy in front of web search profiling. Our system provides a distorted user profile to the web search engine. We offer implementation details and computational and communication results that show that the proposed protocol improves the existing solutions in terms of query delay. Our scheme provides an reasonable overhead while contribution seclusion benefits to the users.
Does Forecast‐Accuracy‐Based Allocation Induce Customers to Share Truthful Order Forecasts?

Through a behavioral study, we investigate buyers’ strategic order forecasting behavior in a multi‐period setting under a forecast accuracy‐based allocation policy, where the supplier allocates (proportionally) more inventory to the buyer with the better order forecast accuracy in case of scarce supply. We developed an interactive game that simulates a supply chain in which one supplier sells a key component to two buyers, who in turn sell to consumers. In each period, buyers share forecasts of future orders with their supplier. The participants in the game play the role of a buyer, while the supplier is automated. Our experimental findings suggest that rewarding forecast accuracy in allocating inventory can significantly improve the order forecast accuracy of the buyers and reduce their forecast inflation behavior. Interestingly, even without communication of the policy, buyers learn over time that more accurate forecasts lead to better service from their supplier and improve their order forecast accuracy.
Empirical and Theoretical Frontiers of Subnational Research in Comparative Politics

Subnational research (SNR) plays a growing and increasingly prominent role in comparative politics. It advances knowledge about fundamental themes that define the field, including regimes, states, and development. The contributions to this volume show how looking inside countries provides a powerful way to discover new and humanly important phenomena hidden from view by the dominant national lens. Moreover, SNR spurs theoretical innovation, especially multilevel theory building; offers new strategies for comparative research; and can be combined fruitfully with vanguard methodologies. These achievements of SNR point to key areas where more work needs to be done. In this concluding chapter, we discuss empirical and theoretical challenges for future research.
The Impact of Credit Risk Mispricing on Mortgage Lending During the Subprime Boom

We provide new evidence that mispriced mortgage insurance shifted credit supply and contributed to the mortgage boom and bust of the early 2000s. Original data on private mortgage insurance premiums from 1999 to 2016 reveal that before 2008, premiums did not vary across loans with widely different indicators of default risk. We quantify the mispricing of premiums before 2008 and show that even allowing for more optimistic beliefs, the flat premium structure resulted in cross‐subsidies and substantial adverse selection.
aggregate production function

We want to understand why some countries are richer than others, in the sense of having higher GDP per capita. Since rich means they produce more output, the question becomes where the output comes from. Here we describe a tool for answering that question: a production function that relates the quantity of output produced to the quantities of inputs and the efficiency or productivity with which they’re used. Doing this for an entire economy takes a leap of faith, but the reward is a quantitative summary of the sources of aggregate economic performance.
Efficient Feature Extraction for Fear State Analysis from Human Voice

Background/Objectives: Analysis of human speech emotion has been continued since long. As the study and recognition helps the society in many respects, we intend to analyze the similar type of emotions. Methods/Statistical Analysis: ‘Fear’ and ‘Nervousness’ are being analyzed in comparison with normal voice. The correlation between these two emotions found to be very close. These voices belong to Oriya language. The popular features of speech, Mel-frequency cepstral coefficients (MFCCs) are used. As the fundamental frequency is unique from voice to voice, it is a suitable feature in case of similar voice signals. Findings: The combination of these two features outperformed the single feature based classification. In addition, the performance has been measured using log-likelihood ratio parameter. For recognition purpose, Gaussian mixture model (GMM) has been selected, and tested for these features. Novelty/Improvement: The individual MFCCs show 81.33%, whereas the combined features show 86.01% of accuracy. It is clearly evidenced in the result section.
Identity and Representation within the Contemporary Brazilian LGBT Movement

Social movements engage in processes of identity work to construct and reconstruct collective identities. Within movements characterized by identity differences, such as contemporary LGBT movements, demands for representation of individual identities challenge the collective movement to perform hard identity work. What strategies do trans activists utilize to achieve representation within the collective Brazilian LGBT movement? This research argues that processes of movement institutionalization within the State condition opportunities and strategies for conducting hard identity work. At the meso-level, institutionalization within State-apparatus of participatory governance provide new opportunities and strategies for the LGBT movement to negotiate representation. At the micro-level, institutionalization within State-sponsored public policy leads to innovative discursive strategies for contending and negotiating representation. Together, meso and micro level processes offer important strategies for the collective LGBT movement to address some of its most divisive internal conflicts in productive ways. This research employs a multi-method research approach through analysis of quantitative policy measurements, archival policy data, semi-structured interviews, and participant-observation during the 2014 calendar year.
Digital Australia: seizing the opportunity from the Fourth Industrial Revolution

2 Introduction . 5 Digital in key sectors of the Australian economy . 27 Digital in healthcare . 31 Digital in the public sector . 45 Digital in retail . 57 Digital in the arts . 77 Digital in banking and insurance . 85 Digital in mining . 111 Digital in utilities . 123 Seizing the digital opportunity . 145