Miracle-dz/UniFAR-FTUs huggingface dataset:
In Defense of Information Leadership Share: A Response to Shrestha and Lee (2023)

Shrestha and Lee (2023) is a short note arguing that the information leadership share (ILS) measure by Putni¸nˇs (2013), building off of the information leadership (IL) measure by Yan and Zivot (2010), is flawed and not appropriate for measuring price leadership, and they argue for the continued use of information share (IS) and component share (CS) related measures instead of the ILS measure. In this paper, we argue that their critiques are flawed and their examples are misleading. Our analysis supports the usage of the ILS measure.
The PIPr Dataset of Public Infrastructure as Code Programs

With Programming Languages Infrastructure as Code (PL-IaC), developers implement IaC programs in popular imperative programming languages like Python and Typescript. Such programs generate the declarative target state of the deployment, i.e., they describe what to set up, not how to set it up. Despite the popularity of PL-IaC, which has grown more than ten times from 2020 to 2023, we know little about how developers apply it and how IaC programs differ from other software. Such knowledge is essential to effectively use existing software engineering techniques and develop new ones for PL-IaC. To shed light on PL-IaC in practice, we present PIPr , the first systematic PL-IaC dataset. PIPr is based on 37712 public IaC programs on GitHub from August 2022 and includes initial analyses, assessing the programming languages, testing techniques, and licenses of the IaC programs. Beyond the metadata and analysis results of all IaC programs, PIPr contains the code of all 15504 IaC programs whose licenses permit redistribution. PIPr sets the ground for future in-depth investigations on PL-IaC in practice.
Research on the Dimensional Model of Tourist Perceived Value in the Dabie Mountain Ecotourism and Its Empirical Research

Based on the view of tourists' experience, this paper builds the conceptual model of tourist perceived value in the Dabie Mountains ecotourism from the four tourist perceived value dimensions of environment, resources, service and cost. Selecting Daofeng Scenic Area as the case study, this paper applies SPSS22.0 and AMOS22.0 analysis tools to dispose of research data, builds the model for analyzing tourist perceived value dimensions, and conducts the measurement research. The results show that the tourist perceived values for the Dabie Mountains ecotourism decreases progressively from the resource perceived value, the environment perceived value, the cost one to the service one. The results indicate the direction for improving the quality of the Dabie Mountains ecotourism from the perspective of tourist perceived values, as well as provide theoretical support and practical guidance for sound and sustainable development of the ecotourism. Keywords—ecotourism; tourist perceived value; Bo Daofeng
DIFFERENTIAL PRIVACY TECHNIQUES IN MACHINE LEARNING FOR ENHANCED PRIVACY PRESERVATION

: The increasing reliance on machine learning models has prompted growing concerns regarding the privacy of sensitive information used in the training process. As a result, using differential privacy techniques has become a viable paradigm for attaining strong privacy preservation without sacrificing the models' usefulness. This study investigates and summarises important approaches in the field of machine learning differential privacy. Adding controlled noise at various points in the machine learning pipeline is the first class of approaches. To avoid unintentionally revealing private information, Laplace and Gaussian noise are deliberately added to training data, predictions, and model parameters. By employing strategies like randomised response mechanisms, data perturbation can enhance privacy for each individual without compromising the model's quality. Collaborative model training is made easier by privacy-preserving aggregation techniques like Secure Multi-Party Computation (SMPC), which protects raw data. By adding noise to gradients during training, Differential Privacy Stochastic Gradient Descent (DP-SGD) provides privacy guarantees during the optimization stage. When differential privacy is combined with federated learning, it allows for decentralized model training across devices while maintaining the security and localization of sensitive data. By allowing computations on encrypted data or safely aggregating model updates, advanced cryptographic approaches like homomorphic encryption and secure aggregation protocols give another degree of privacy. When taken as a whole, these methods add to a thorough framework for machine learning differential privacy that strikes a balance between the need to protect individual privacy and the drive to create accurate models.
Aggregates versus pores? A survey among soil scientists about their perspectives on soil structure

Despite mostly uniform deﬁnitions of soil structure, two perspectives on it have been juxtaposed in the past: the “aggregate perspective” and the “pore perspective”. The debate as represented in the literature appears to be polarised. To test whether this is also the case in the wider soil science community, an online survey was conducted asking about soil scientists’ view on these perspectives. There was a strong bias towards participants from Germany (139 of 251) but all career stages and different sub-disciplines were well-represented, so the results are assumed to be representative at least for the German 5 soil science community. As expected, the largest group of participants (49 %) equally agreed with both perspectives and both concepts were judged as important to understand soil structure. A clear correlation between career stage and strength of opinion could not be found. The survey is not meant to decide democratically on these questions but to give an impression of the status quo of the debate in the wider community. This snapshot is by far not complete. To make best use of research resources, it may be helpful to identify potential barriers to effective communication, like misunderstandings or irrational belief perseverance, in 10 further studies.
Effects of terraces on submerged aquatic vegetation in shallow marsh ponds in coastal southwest Louisiana

The wetlands of coastal Louisiana are disappearing at a rate of 65 to 80 kmyr. Most of the loss is the conversion of emergent marsh to shallow marsh ponds. Terracing is one restoration technique that has been used frequently in recent years. Terraces are small intertidal ridges built in shallow marsh ponds to reduce wave action. It is assumed that this will slow erosion of adjacent emergent marsh and increase Submerged Aquatic Vegetation (SAV) production, a key habitat component for many marsh fauna. Yet both relevant previous studies failed to show that terraces increased SAV abundance. In April of 2004 this study was initiated to test this assumption. Three study sites with paired terraced and unterraced ponds were selected in southwest Louisiana; two at Rockefeller State Wildlife Refuge and one at Sabine National Wildlife Refuge. SAV abundance was estimated every other month for one year. SAV biomass and frequency were significantly higher in terraced ponds. SAV frequency in unterraced ponds averaged 20% (SE 13 to 33%) but frequency for unterraced ponds was 9% (SE 5 to 14%). Terraced ponds had approximately three and half times the biomass of unterraced ponds. This indicates that terraces improve SAV production as had been suspected. Turbidity and organic matter content were lower in terraced ponds indicating a possible causal mechanism. My results confirm some assumptions of wetland restoration planners who have used terraces.
An update of data compilation on the biological response to ocean acidification and overview of the OA-ICC data portal

Studies investigating the effects of ocean acidification on marine organisms and communities are increasing every year. Results are not easily comparable since the carbonate chemistry and ancillary data are not always reported in similar units and scales, and calculated using similar sets of constants. To facilitate data comparison, a data compilation hosted at the 15 data publisher PANGAEA was initiated in 2008 and is updated on a regular basis (https://doi.pangaea.de/10.1594/PANGAEA.962556, Ocean Acidification International Coordination Centre, 2023). By November 2023, a total of 1501 data sets (over 25 million data points) from 1554 papers have been archived. To easily filter and access relevant biological response data from this compilation, a user-friendly portal was launched (https://oa-icc.ipsl.fr) in 2018. Here we present the updates of this data compilation since its second description by Yang et al. (2016) and provide 20 an overview of the “OA-ICC portal for ocean acidification biological response data” launched in 2018. Most of the study sites from which data have been archived are in the North Atlantic Ocean, North Pacific Ocean, South Pacific Ocean and Mediterranean Sea, while polar oceans are still relatively poorly represented. Mollusca and Cnidaria are still the best represented taxonomic groups. The biological processes most reported in the datasets were growth and morphology. Other variables that can potentially be affected by ocean acidification and are often reported include calcification/dissolution, 25 primary production/photosynthesis, and biomass/abundance. The majority of the compiled datasets have considered ocean acidification as a single stressor, but their relative contribution decreased from 68% before 2015 to 57% today, showing a clear tendency towards more data archived from multifactorial studies
Can ETFs affect U.S. financial stability? A quantile cointegration analysis

This study evaluates whether exchange traded funds (ETFs) threaten financial market stability by testing two hypotheses relating the growing importance of ETFs to increased market volatility and rising equity valuations. We estimate quantile cointegration models using Standard & Poor’s 500 Index (S&P 500) and Chicago Board Options Exchange volatility Index (VIX) data for 1994–2020. We found that an increase in ETFs is positively and significantly related to the long-term valuation of the S&P 500 for quantile values above the median. By contrast, ETFs have only a negative and significant effect on the VIX for quantiles around the median. Ultimately, two novel results were obtained. First, the distortion in the value of the S&P 500 relative to its fundamentals is driven by investor flow into ETFs during a bull market. Second, the impact of equity ETFs on the VIX is only affected when fundamental factors are in play, decreasing it. Therefore, ETFs contribute to forming equity bubbles and support valuation market dynamics. Both regulators and policymakers should consider these conclusions.
CLASSIFICATION AND OBJECT DETECTION FOR ARCHITECTURAL PATHOLOGY: PRACTICAL TESTS WITH TRAINING SET

: Image classification and object detection techniques have been widely discussed and developed in recent years; they are the basis of various prosperous applications, for example, real-time mapping. Promising as it is, the practical test in the cultural heritage field encountered multiple problems. In this paper, the authors attempt to share the research experimentations and the empirical knowledge focusing on the classification and detection of architectural pathology. The tests are built on elaborated training sets annotated with analysed and in-advance defined categories. The trained models were examined from the perspective of evaluation sets, model explanation and unseen datasets. The outcomes indicated the mistakes and confusions behind things and stuff in the object detection efforts, to which cultural heritage and architectural field are closely related. The model also reveals specific visual patterns for recognition from thousands of instances in the training set. By digging into different aspects of model performance, the potential and limitations of these techniques in practical applications can be better understood.
Applying Multivariate Multilevel Models to Explore Arable Land Quality in Sub-Saharan Africa: A Case Study in Kenya

Controversy exists on the magnitude and variability of farm nutrient balances and quality of arable land in sub-Saharan Africa with Kenya among those affected negatively. This study investigates quality of arable land by fitting multivariate multilevel model to farm nutrient balance data collected from five agro-climatic zones of Kenya (arable lands). Objectives of the study were to investigate the magnitude and variability of Nitrogen, Phosphorus and Potassium (NPK) farm nutrient balances in arable lands of Kenya, study effects of agro-climatic zones on nutrient balances and to determine effects of household resource endowments on NPK nutrient balances. The study concludes that agro-climatic zones differ with respect to farm nutrient balances; that livestock resource endowments and hired labour have positive effects on the magnitude and direction of farm nutrient balances; and that household ownership of large capital resources do not guarantee a positive effect on farm nutrient balances. The study recommends integration of sound livestock practices and application of agro-climatic zone differentiated interventions in future strategies for addressing farm nutrient balances and arable land quality, and the use of large sample sizes and relevant factors/covariates in future analysis to shed additional insights on farm nutrient balances and on how arable land quality can be improved.
A Classification Model For Phishing Detection System Based On Machine Learning Algorithms

Recently, phishing attacks have risen to the top of the social engineering assaults that affect organizations, governments, and the general public. A phishing assault cost roughly $1.5 billion in 2012, according to an internet analysis. As the worldwide impact of phishing attempts grows, more effective phishing detection systems will be required to combat this threat. This paper has examined six classifiers to identify the top machine learning classifiers for phishing attack detection from a dataset of 48 features from 5000 legitimate websites retrieved from the Kaggle repository. This work is implemented using CatBoostClassifier (CB). , light gradient-boostingmachineClassifier(LGBM). , eXtremeGradientBoosting (XGB), GradientBoostingClassifier (GBA), AdaBoostClassifier and RandomForestClassifier (RF) algorithms. The algorithms have achieved the XGBClassifier's accuracy result of 99.05% and the highest f1-core result of 99.0331%. The outcomes show that XGBClassifier is a reliable classifier for phishing attack detection.
Machine-learning-based technique to establish ASE or Kerr impairment dominance in optical transmission

Data extraction from optical networks has increased substantially with the evolution of monitoring and telemetry methods. Using data analysis and machine learning, this paper aims to derive insights from this data, contributing to the development of self-optimized optical networks. More particularly, it focuses on predicting the Kerr and amplified spontaneous emission dominance by examining the fluctuations in the signal-to-noise ratio due to polarization-dependent loss. Building on previous work, which used the SNR statistic as the input feature of machine learning, our primary goal is to enhance prediction precision while concurrently decreasing the computational model’s complexity. After refining the selection parameters of the input features, we observed a 70% reduction in the input feature length with respect to our previous work. The model reached a 98% accuracy rate, and it was able to successfully classify the regimes in a limited set of unseen experimental instances.
The impact of characteristic factors of the direct-to-consumer marketing model on consumer loyalty in the digital intermediary era

In the era of digital intermediaries, the direct-to-consumer (DTC) marketing model is gaining prominence in the retail and brand marketing domains. This model exhibits a distinct advantage over traditional models in cultivating loyalty. Consequently, this study employs a survey-based approach and utilizes the Stimulus-Organism-Response (SOR) theory to construct a structural equation model and investigate the relationship between the DTC marketing model’s characteristic factors and consumer loyalty. The results indicate that cost-effectiveness and social media marketing directly and positively influence consumer loyalty, while product features indirectly contribute to loyalty through perceived emotional value. Multi-channel integration indirectly influences loyalty through perceived functional value. Additionally, the varying degrees of influence highlight social media marketing as the most impactful factor and product features as the least influential. The research findings underscore the importance of strengthening social media marketing, optimizing product features, reducing information asymmetry, and integrating multiple channels to enhance consumer loyalty. This study enriches the understanding of the DTC theoretical framework in the field of marketing and provides new perspectives for formulating marketing strategies.
A Behavioral Analysis of Households Using Reclaimed Water in Irrigation: Case of Pinellas County, Florida

vii Chapter 1: Introduction . 1 Goal of the Study . 3 Chapter 2: Literature Review . 4 Integration of Reclaimed Water . 4 Reclaimed Water: Advantages and Disadvantages . 5 Wastewater treatment in Florida: comparison and effects . 6 Regulations: Pinellas County vs. the city of St. Pete . 8 Public Perception . 12 Chapter 3: Theoretical Framerwork . 15 Chapter 4: Study Area . 18 Population/Land Use . 18 Climate/Soils . 18 Joe’s Creek Watershed: Miles Creek and Bonn Creek . 20 Chapter 5: Research Design/Methods . 22 Questionnaire . 22 Data Analysis . 25 Chapter 6: Findings . 26 ii Socio-Economic Demographics . 26 Behavior . 28 Knowledge . 31 Attitude . 35 Awareness of Regulations vs. Different Predictors . 38 Awareness of Irrigation Regulations. 38 Awareness of Fertilizer Regulations . 47 Chapter 7: Discussion . 55 Research Questions . 55 Main Factors Affecting Awareness/Knowledge of Irrigation Regulations . 59 Social Demographics. 59 Behavior. 61 Attitude . 62 Main Factors Affecting Awareness/Knowledge of Fertilizer Regulations . 65 Social Demographics. 65 Behavior. 67 Attitude . 68 Problem Statement . 71 Perceptions. 73 Behavior. 73 Attitude . 74 Chapter 8: Conclusion and Recommendations . 76 References . 79 Appendix A: Questionnaire . 84 Appendix B: IRB Letter of Approval . 88 Appendix C: Letter of Consent . 89 Appendix D: Permissions . 90
Diagnosis, Management, and Prognosis of Cystic Fibrosis-Related Liver Disease in Children

Cystic fibrosis (CF) is a multifaceted disorder predominantly investigated for its pulmonary manifestations, yet patients with CF also exhibit a spectrum of extrapulmonary manifestations, notably those involving the hepatobiliary system. The latter constitutes the third leading cause of morbidity and mortality in individuals with CF. Cystic fibrosis-related liver disease (CFLD), with an escalating prevalence, manifests diverse clinical presentations ranging from hepatomegaly to cirrhosis and hepatopulmonary syndrome. Consequently, early detection and appropriate management are imperative for sustaining the health and influencing the quality of life of CF patients afflicted with CFLD. This review aims to consolidate existing knowledge by providing a comprehensive overview of hepatobiliary manifestations associated with CF. It delineates the clinical hepatobiliary manifestations, diagnostic methodologies, incorporating minimally invasive markers, and therapeutic approaches, encompassing the impact of novel CFTR modulators on CFLD. Given the exigency of early diagnosis and the intricate management of CFLD, a multidisciplinary team approach is essential to optimize care and enhance the quality of life for this subset of patients. In conclusion, recognizing CF as more than solely a pulmonary ailment, the authors underscore the imperative for further clinical investigations to establish a more robust evidence base for CFLD management within the continuum of this chronic disease.
Enhancing Insider Threat Detection in Imbalanced Cybersecurity Settings Using the Density-Based Local Outlier Factor Algorithm

In today’s interconnected world, cybersecurity has emerged as a critical domain for ensuring the integrity, confidentiality, and availability of digital assets. Within this sphere, insider threats represent a unique and particularly insidious class of security risks, originating not from external hackers but from within the organization itself. These threats are perpetrated by individuals with inside information concerning the organization’s security practices, data, and computer systems. Traditional security measures like firewalls, intrusion detection systems, and antivirus software are often inadequate for tackling insider threats effectively, owing to their focus on external threats. This inadequacy underscores the urgent need for the development and implementation of more sophisticated, targeted detection techniques for insider threats. In response to this challenge, our research introduces an innovative approach that employs the Density-Based Local Outlier Factor (DBLOF) algorithm, fine-tuned to specifically tackle the challenges posed by the imbalanced nature of the CERT r4.2 insider threat dataset. This dataset is characterized by a highly skewed distribution, with a significant majority of benign instances and only a minimal proportion of malicious activities. Conventional detection algorithms often fail to effectively identify these rare but dangerous instances, leading to a high rate of false negatives. Our methodology capitalizes on the algorithm’s ability to focus on the local density deviation of data points, thereby enabling the precise identification of outliers that are indicative of potential insider threats. Through rigorous testing and validation processes, we have achieved outstanding results, with an of F-score 98%. These remarkable outcomes not only affirm the effectiveness of the DBLOF algorithm as a powerful tool for combating insider threats but also contribute valuable insights to the broader academic and professional discourse on cybersecurity. Importantly, our findings have practical implications, offering organizations actionable recommendations for boosting their internal security mechanisms against the complex and evolving landscape of insider threats.