Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Quality of Web-Based Data Imputation With Crowd Intervention

Data incompleteness is a common data quality problem in databases. Recent work proposes to retrieve missing string values from the World Wide Web for higher imputation recall, but on the other hand, takes the risk of introducing web noises into the imputation results. So far there lacks an effective way to control the quality of web-based data imputation, given the complexity of the quality model and lacking of enough ground truth data. In this article, an EM-based quality model is first built for web-based data imputation which investigates three key factors jointly, i.e., precision of web sources, correlation among web sources, and precision and recall of the employed extractors. However, the accuracy of the EM-based quality model could be harmed when the EM (Expectation Maximization) assumption that “the majority agree on the truth” does not hold in some cases. To solve this problem, we introduce crowd intervention to help improve the quality model. While a straightforward but expensive way is to let the crowd to identify all these undesirable cases and provide the right imputation values for these blanks, a most crowd-economic way is to select a small set of blanks for crowd-based imputation, whose results could help to adjust the EM-based quality model towards a better one. To achieve this, an adaptive blank selection strategy is proposed to select a sequence of blanks for crowd-based imputation. Also, we work on finding a proper time to stop further crowd intervention for the balance of crowd efficiency and quality improvement. Our experiments performed on three real world and one simulated data collections prove that the proposed quality model can effectively help improve the quality of the web-based imputation results by more than 15 percent, while our crowd cost saving strategy saves more than 75 percent crowd cost.
Failed or Stealth Innovation?*

Capital market research routinely uses patents to capture innovation or R&D productivity. Two conflicting contentions about non-patenting R&D firms persist, implicitly depicting them as either failed innovators or trade-secret-focused firms. Testing the failed innovation hypothesis, we find that both patentand non-patent-seeking R&D firms subsequently announce significant stockreturn increasing products. Non-patenting R&D firms exhibit lower product efficiency but higher cost efficiency than their patenting peers. Strikingly, executives in non-patenting R&D firms engage in greater opportunistic insider trading than their patenting peers. Both patenting and non-patenting R&D firms experience greater returns than non-R&D firms, with comparable consequences to tariffinduced competition shocks. Exploiting an exogenous shock to corporate patenting, we find this disclosure choice significantly influences financial analysts and institutional investor scrutiny. Overall, our results are inconsistent with the hypothesis that non-patenting R&D firms denote failed innovation, suggesting the need to incorporate this disclosure choice in evaluating corporate decisions to engage in and finance innovation. JEL Classifications: M41, G32; O34;
Pushing Data-Induced Predicates Through Joins in Big-Data Clusters

Using data statistics, we convert predicates on a table into data induced predicates (diPs) that apply on the joining tables. Doing so substantially speeds up multi-relation queries because the benefits of predicate pushdown can now apply beyond just the tables that have predicates. We use diPs to skip data exclusively during query optimization; i.e., diPs lead to better plans and have no overhead during query execution. We study how to apply diPs for complex query expressions and how the usefulness of diPs varies with the data statistics used to construct diPs and the data distributions. Our results show that building diPs using zone-maps which are already maintained in today's clusters leads to sizable data skipping gains. Using a new (slightly larger) statistic, 50% of the queries in the TPC-H, TPC-DS and JoinOrder benchmarks can skip at least 33% of the query input. Consequently, the median query in a production big-data cluster finishes roughly 2x faster.
Make Social Networks Clean Again: Graph Embedding and Stacking Classifiers for Bot Detection

The paper introduces a novel approach to the detection of social bots using ensembling of classiﬁers. We also studied the impact of diﬀerent feature sets and demonstrated the power of graph embedding which is underused by the existing methods. The main contribution of this work is a creating of a stacking based ensemble, which eﬀectively exploits text and graph features. Empirical evaluation proved the eﬀectiveness of the proposed method for bots detection and showed improvement in comparison to existing solutions by 4-9 points of AUC.
AUTOMATING THE CORRECTION OF USGS DIGITAL ELEVATION MODELS USING FOURIER ANALYSIS AND THE MEAN PROFILE FILTER

In the late 1990s, i-cubed, a value-added image pro cessing company, produced a seamless mosaic of USGS 30meter 7.5-minute digital elevation models (DEMs), k nown as eDEM. A few years later, USGS created a si milar product, the National Elevation Dataset (NED). In assembling the NED, USGS used an innovative techniq ue to remove regular striping and quilting artifacts foun d i some of the older DEM files. The filter elimi nated many problems associated with these patterns while prese rving almost all of the high-frequency topographic information. i-cubed advanced the use of this mean-profile filte r by using Fourier analysis to identify the existen c , interval and direction (horizontal and/or vertical) of striping in each DEM file. The interpretation of the Fourie r analysis was completely automated and did not require human inte rvention. The identified artifact information was then used as inputs to the mean profile filter in the correction f each file. The end result was to be used in a new version of eDEM, known as eDEM-II. This paper presents the me thodology and results of this method as used in eDE M-II. Although the inputs to the NED have largely been su perseded by newer, higher-resolution, artifact-free DEMs, this research may have practical applications beyond the original purpose.
The Determinants of Large and Medium Industrial Sectors Productivity Growth in Indonesia

The study aims to analyze the determinants of labor productivity growth in the large and medium industrial sub-sectors in Indonesia related to changes in economic structure that lead to the dominant role of the industrial sector in national GDP formation. The data used are combined between cross-section from 62 large and medium industrial sub-sectors, and time series, during 1990-2014, which are divided into 5 sub-periods of research. The data includes value added, number of workers, FDI, and Wages. Data sources are the Central Bureau of Statistics, Bappenas, and the Ministry of Industry of the Republic of Indonesia. The analytical method used is the panel data regression model, using secondary data. The model is analyzed by estimating the Least Square Dummy Variable (LSDV) method. The results of the study show that: (1) Changes in the share of labor as a result of the process of reallocating labor between sub-sectors have a negative effect on labor productivity growth. (2) Determinants of labor productivity growth, in addition to changes in the share of labor: those are investment variables (capital deepening), both short and long-term, and FDI does not affect labor productivity growth, while wages have a significant positive effect.
Zoumpalidis, Changing attitudes

The present paper examines the linguistic behaviour of the first wave of Pontic Greek immigrants to Cyprus based on their internalized language attitudes and dominant language ideologies. Since the time of its settlement in Cyprus in the early/mid 1990s, the predominantly Turkish-speaking community of Pontic Greeks has experienced a rapid linguistic and cultural transformation. This occurred primarily due to the local population’s (i.e. Greek-Cypriots’) reluctance to recognize the Turkish-speaking Pontic Greeks as belonging to the Greek linguistic and cultural ‘world’ in light of the former’s historical and socio-political tensions with the Turkish-Cypriot minority. More specifically, I will analyse the factors that have contributed to this rapid language shift and show what (non-) linguistic means are employed by the members of the Pontic Greek community to index their ethnic identity and belonging.
Stress Relief through Leisure and Complementary and Alternative Medicine

When thinking about Complementary and Alternative Medicine (CAM), many times people tend to think about health. This article discusses the need to start thinking about these health practices as leisure pursuits as well. This qualitative study used interviews of CAM practitioners to learn why people seek alternative therapies and to show the close relationship between leisure activities and CAM. The leisure literature shows gaps by not including various alternative modalities that are considered as leisure by others. This article aims to fill this gap between leisure and health, or more specifically, the relationship between leisure and Complementary and Alternative Medicine.
PR ] 1 1 N ov 2 01 9 Markov chains in random environment with applications in queueing theory and machine learning ∗

We prove the existence of limiting distributions for a large class of Markov chains on a general state space in a random environment. We assume suitable versions of the standard drift and minorization conditions. In particular, the system dynamics should be contractive on the average with respect to the Lyapunov function and large enough small sets should exist with large enough minorization constants. We also establish that a law of large numbers holds for bounded functionals of the process. Applications to queuing systems and to machine learning algorithms are presented.
NUMERICAL SIMULATION OF INELASTIC CYCLIC RESPONSE OF HSS BRACES UPON FRACTURE

Concentrically braced frames (CBFs) with a tension-compression bracing system dissipate hysteretic energy when braces yield in tension and buckle in compression, whereas the hysteretic response of hallow structural section (HSS) braces varies with brace slenderness, width-to-thickness ratio, and yield strength. Modelling the nonlinear response of braces upon the fracture requires an assigned brace fracture model and implicitly calibrated input material parameters. The selected brace fracture models are those that are compatible to nonlinear analysis and fiber-based elements formulation suited to OpenSees framework. To replicate the brace response, nonlinear beam-column elements that encompass distributed plasticity and discretized fiber cross-sections were used, whereas to simulate brace fracture, the strain fatigue model was considered. In this study, in order to predict the failure strain for a single reversal value that is required as input parameter in the strain fatigue model, regression analysis was employed and the proposed equation was given for square HSS braces and validated against experimental test results for a wide range of brace slenderness ratios, 50  kL/r  150 and types of displacement loading history. The predicted failure strain value is expressed in terms of slenderness ratio, width-to-thickness ratio and yield strength of steel. Comparisons to existing brace fracture models, such as the strain-range and end-rotation of braces at fracture, are provided. All aforementioned brace fracture models were evaluated against experimental tests results, while replicating fourteen specimens that were found in the literature.
Scaling Text Processing Pipelines using Apache Spark

Big data, which is derived from humans or machines, starting with social media and extending to smartphones or sensors, in forms of texts, images or transactions, is a continuously evolving ﬁeld. Thus, the ongoing increase of data generated creates a need for knowledge extraction from it, through data analysis. Several areas are engaged in data mining, and in particular the area of machine learning which has been well established over the past years. Various techniques and methods of machine learning are trying to solve big data problems and these two areas consist now an integral part. This particular combination is the main subject of this study, which aims to implement a large-scale text processing architecture. More speciﬁcally, this architecture focuses on processing streaming texts derived from Reddit in real-time and the classiﬁcation thereof as sarcastic or non-sarcastic through a machine learning model. The architecture uses the latest technologies in the ﬁeld of information processing through distributed platforms such as Apache Kafka and Spark as well as state-of-the-art but also simple and powerful ML algorithms, i.e Random Forests, Naive Bayes and Logistic Regression. After comparing the methodology and design of each individual piece forming the ﬁnal layout, a selection of the most appropriate model is made followed by the implementation of the framework. Success rates exported were quite close to the relevant literature and sometimes higher, depending on each technique examined. Finally, results are indexed in the distributed search engine Elasticsearch and are evaluated through the Kibana plugin.
Determining the Relationship between Symptoms of Post Traumatic Growth and Post Traumatic Stress Disorder in Three Groups of Pulmonary Patients with Cancer, Their Families and Pulmonary Patients without Cancer

Many pulmonary patients, cancer patients and their families experience post traumatic stress disorder (PTSD) and post traumatic growth (PTG). Since no study has been conducted with such an objective, this study designed to compare the relationship between PTSD and PTG in three groups of pulmonary patients, cancer patients and their families. Methods: In this case-control study, 180 samples in three mentioned groups selected by accessible method. The study population consisted of three mentioned groups referred to Massih Daneshvari Hospital (2013). Data collected by PTSD, PTG and demographic questionnaires. Anova, Tukey, independent T tests and SPSS-21 used for analysis. Results: Mean age was 44.92. Patients encounter the most psychological trauma during the first year of diagnosis. Cancer patients and their families had a higher level of PTG. The more ambiguous and hopeless views they had, the higher was PTSD. A negatively significant correlation was seen between PTSD and PTG in cancer patients. There was a significant difference in PTG between males and females. Conclusion: Results show the relationship between variables and their difference in three groups. It is important to provide psychological care beside medical treatment, after studying psychological factors which affect the cope way with the disease.
Deep Learning Approach for Handwritten Image Recognition Based on a High Dimensionality Reduction and Feature Selection Using Stacked Sparse Auto-Encoder Structure

In this paper, we implement and analyze the performance of one of the “state of the art" approaches in machine learning. Deep Learning-based approach is proposed in this paper as one of the “state of the art” approaches for the high dimensionality feature space classification scheme using (softmax classifier), comparing with other machine learning approach using K-Nearest Neighbor (KNN) classifier. We use MNIST dataset, and stacked sparse autoencoder as a dimensionality reduction and feature selected approach with the KNN classification algorithm once, and comparing the result with the Deep Learning using CNN structure. Our proposal tests two different feature spaces using different approaches. The first uses the stacked sparse autoencoder on the original feature space and the second one with the Deep Learning space. Two different classification approaches are applied in this paper to recognize the handwritten images, and do a comparison between them. The first approach relies on using the KNN classifier algorithm with the new feature space. This space is produced depending on using the stacked sparse autoencoder on the original dataset feature. The second one relies on using CNN approach (softmax classifier) with the different feature space that has been also produced by using stacked sparse autoencoder on the Deep Learning feature space. We analyze that the two approaches are significantly different according to the two different feature spaces using the same reduction approach which uses the stacked sparse autoencoder. The experiment results show that Deep Learning feature space is more robust and can generate a good reconstruction feature space. Furthermore, the experimental results show that the feature space is significantly low in errors after using the stacked sparse autoencoder. Also, the Deep Learning feature space using stacked sparse autoencoder is more powerful than using the original feature space with the KNN. We notice that the stacked sparse autoencoder feature extraction is more efficient when it is used with the Deep Learning feature space. It satisfies 98.629% with a dimension 1000×144 after applying the stacked sparse autoencoder on the CNN feature space. Stacked sparse autoencoder feature extraction using KNN classifier satisfies 97.27% using 1 hidden layer with the original feature space which is 10000×784.
TOWARDS THE RETRIEVAL OF ICE CRYSTALS PROPERTIES WITHIN MIXED-PHASE CLOUDS USING DUAL POLARIZATION SPECTRAL RADAR MEASUREMENTS

Among mid-level clouds, the coexistence of ice particles with tiny but dense water droplets are easily observed when the temperature goes down 0°C . They can occur in the form of layers of few hundreds thick either above or embedded within thicker ice clouds . Because of their notable presence as well as their microphysical specifications, mixedphase clouds (and mainly the water phase) are potentially considered of great importance on the earth radiative budget [2] and cloud evolution. Nowadays such clouds are still poorly represented in Global Climate Models (GCMs) and Forecast Models. Therefore, in order to evaluate and develop cloud parameterization schemes, observation of particles size distribution (PSD), shapes, orientations, size-mass relation and phase distributions are required. Up to now observational datasets are mainly available from aircraft in-situ measurements . To be able to get a wider time and spatial scale, remote sensing retrieval schemes are also under development . Among them a new technique is built in the university of Delft for remotely determining the properties of the ice particles within mixed-phase clouds from measurements taken by the S band Doppler Polarimetric radar TARA and combining it with a microphysical model which characterizes the bulk of atmosphere being probed .
Q A ] 5 O ct 2 01 7 HOMOLOGICAL PROPERTIES OF QUANTUM PERMUTATION ALGEBRAS

We show that As(n), the coordinate algebra of Wang’s quantum permutation group, is Calabi-Yau of dimension 3 when n ≥ 4, and compute its Hochschild cohomology with trivial coefficients. We also show that, for a larger class of quantum permutation algebras, including those representing quantum symmetry groups of finite graphs, the second Hochschild cohomology group with trivial coefficients vanishes, and hence these algebras have the AC property considered in quantum probability: all cocycles can be completed to a Schürmann triple.
Quality of life of Iranian married women: a comparative study of women who had experienced infertility with fertile ones through Health, Wellness and Quality of Life Questionnaire

Background: The treatment of infertility and the quality of life in women is now considered equally important. The aim of the study was to determine the quality of life (QoL) of married women aged 20-45 years and to compare QoL domain in fertile and infertile women. Methods: In a community based study, the Wellness and Quality of Life Questionnaire (WHOQOL) was used to assess the QoL of married women. A total of 1,140 women with the age range of 20-45 were selected using standard cluster sampling technique in Babol, Iran. Among those women who attempted conception, 168 of the women experienced difficulty conceiving at some stage in their lives. Then, the QoL of the women who had experienced infertility were compared with those of other fertile women. The questionnaire containing 55 items comprised five domains: physical state, mental/emotional state, stress evaluation, life enjoyment, and overall quality of life. Lower scores in three domains: physical state, mental/emotional state, and stress evaluation mean better QoL. Higher scores in life enjoyment and overall quality of life mean better QoL. Results: The means of the QoL of married women in physical status, mental/emotional state, stress evaluation, life enjoyment, and overall QoL were 19.0 ± 5.3, 20.0 ± 6.8, 23.4 ± 7.8, 32.6 ± 5.3, and 61.5 ± 10.1, respectively. There was no significant relationship in the mean scores in the five domains of QoL between the fertile and infertile groups. Logistic regression also indicated that infertility was associated with higher (worse) scores in the mental/emotional state domain (p= 0.018), and fertility was associated with higher scores (better) in overall score QoL (p= 0.003). Conclusion: The results can be regarded as useful findings to make more efficient interventions for infertile women.
Standing out from the crowd via corporate social responsibility: Evidence from non-fundamental-driven price pressure

Prior literature proposes that firms can use corporate social responsibility (CSR) activities to signal their quality to investors and other stakeholders. We formally test this prediction by identifying situations where firms deem it necessary to signal via CSR. Using two independent quasi-natural experiments that impose non-fundamental-driven negative pressure on firms’ stock prices, we find that firms under such adverse circumstances engage more in CSR than otherwise similar firms. These effects are more pronounced among affected firms with stronger signaling needs (i.e., those facing a more opaque information environment and more intense product market competition) and among firms with better signaling capabilities (i.e., those with fewer financial constraints and higher profitability). Further, we find supporting evidence that signaling via CSR under downward price pressure helps firms attract more socially responsible investors, indicating benefits of such signaling efforts.
Efficient Progressive Minimum k-core Search

As one of the most representative cohesive subgraph models, k -core model has recently received significant attention in the literature. In this paper, we investigate the problem of the minimum k -core search: given a graph G , an integer k and a set of query vertices Q = { q }, we aim to find the smallest k -core subgraph containing every query vertex q ϵ Q. It has been shown that this problem is NP-hard with a huge search space, and it is very challenging to find the optimal solution. There are several heuristic algorithms for this problem, but they rely on simple scoring functions and there is no guarantee as to the size of the resulting subgraph, compared with the optimal solution. Our empirical study also indicates that the size of their resulting subgraphs may be large in practice. In this paper, we develop an effective and efficient progressive algorithm, namely PSA , to provide a good trade-off between the quality of the result and the search time. Novel lower and upper bound techniques for the minimum k -core search are designed. Our extensive experiments on 12 real-life graphs demonstrate the effectiveness and efficiency of the new techniques.