Miracle-dz/UniFAR-FTUs huggingface dataset:
How Can Entrepreneurial Self-Efficacy, Proactivity and Creativity Enhance Sustainable Recognition Opportunity? The Effect of Entrepreneurial Alertness Is to Mediate the Formation of Sustainable Entrepreneurial Intention

Entrepreneurship plays an important role in solving student employment problems and promoting sustainable social and financial development through a new form of sustainable entrepreneurship. However, the previous studies did not provide sufficient reasons for how the psychological factors of the simultaneous generation of social, ecological and economic value affect the intention-forming process of sustainable entrepreneurs. Such an understanding has led to a developing interest in the exploration of entrepreneurial intentions across different types of entrepreneurship, particularly from the perspective of sustainability. The aim of the study is to observe the effects of entrepreneurial self-efficacy, proactive personality and creativity variables, the recognition of opportunities as well as sustainable entrepreneurial intention. This study quantitatively analyzed the data of 500 university students from Punjab, Pakistan. These data were used for structural equation modeling. The results of the study indicated that entrepreneurial alertness has a mediating effect between entrepreneurial self-efficacy, proactive personality, creativity, and has an impact on the recognition of opportunities as well as the intention of sustainable entrepreneurship. These findings further spotlight the importance of recognition of opportunities in the implementation of sustainable entrepreneurship. In fact, the results suggest that scholars and practitioners can use social cognitive theory and personality traits to stimulate the development of techniques, thereby promote sustainable entrepreneurial intention.
Continuous wavelet transform based time-scale and multi-fractal analysis of the nonlinear oscillations in a hollow cathode glow discharge plasma

Continuous wavelet transform (CWT) based time-scale and multi-fractal analyses have been carried out on the anode glow related nonlinear floating potential fluctuations in a hollow cathode glow discharge plasma. CWT has been used to obtain the contour and ridge plots. Scale shift (or inversely frequency shift) which is a typical nonlinear behaviour, has been detected from the undulating contours. From the ridge plots, we have identified the presence of nonlinearity and degree of chaoticity. Using the wavelet transform modulus maxima technique we have obtained the multi-fractal spectrum for the fluctuations at different discharge voltages and the spectrum was observed to become a monofractal for periodic signals. These multi-fractal spectra were also used to estimate different quantities like the correlation and fractal dimension, degree of multi-fractality and complexity parameters. These estimations have been found to be consistent with the nonlinear time series analysis.
Automated Detection of Cyberbullying Using Machine Learning

— Increasing the use of Internet and facilitating access to online communities such as social media have led to the emergence of cybercrime. Cyberbullying is very common now a days. which have no tracking like it may harm any individual, business, society, country in past few days it seems that riots were happened due to some statement used by one community on another its important to identify such content which spreads hate or harm community text processing, NLP (natural language processing) is an emerging field with the help of NLP and machine learning algorithms such as naive bayes, random forest, SVM we are going to identify cyberbullying in twitter. Objectives of this implementation written in objective section. Image character with the help of OCR will be done by us to find image - based cyberbullying the impact on individual basis thus will be checked on dummy system. Machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely supervised learning, lexicon-based, rule-based, and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naïve Bayes to develop predictive models for cyberbullying detection.
Hosting Exceptional Singularities of Different Orders in Photonic Systems for Unconventional Device Applications

‘Singularity’ is a mathematical term that refers to a critical point where a given mathematical function is infinite or not differentiable. Analogically, a mathematical model of a physical system hosts some singular points or singularities in its parametric space, which are fundamentally different from their neighboring points and at which the behavior of the respective physical system is not well-defined. The hosting of such singularities in different physical systems has attracted enormous attention due to their unconventional functionalities and new physical insights. The most famous example of a singularity is studied in the context of gravitational-singularities in the general theory of relativity, which is the center of a black-hole that accommodates a huge mass in an infinitely small space (can be considered as a one-dimensional point). Here, we have explored the intriguing features of a new class of singularities, namely Exceptional Points (EPs), which usually appeared in open systems, i.e., the systems that interact with their surroundings. The open systems are welldefined by the non-Hermitian formulation in quantum mechanics, and in this context, the term "Exceptional Point" was first introduced by T. Kato in 1966 while studying the perturbation theory of a non-Hermitian operator [1].
Estimating the instantaneous survival rate of digital advertising and marketing IDs: LIFESPAN by Cox-Proportional

Finding the active and inactive device IDs 1 (ID) in the digital advertising and marketing domain is one of the most crucial tasks in terms of the cost and quality aspect. Keeping the IDs for a longer time will increase the load for the downstream pipelines that incur more storage and computation cost. This can also lead to digital campaigns(advertising or marketing) with low active users thus degrading the performance. Though quality can be improved by putting a constant time to leave(TTL) to each of the IDs, determining an optimal TTL is a tedious task. These IDs are the unique identifiers for the digital domain hence treated as the currency. It also plays an important role in the engineering framework for keeping all other attributes in the storage being linked to it. So, by putting a smaller TTL, losses of ID prematurely can lead to multiple loss of information. This can affect the segment 2 volume export for a campaign largely. On the contrary, if higher TTL is proposed, it can lead to the original problem of cost and computation. Checking an individual ID is active or not in realtime is almost impossible. While most of the non-feedback systems run on TTL based methods to purge the IDs and clean the database, in our paper we propose a granular machine learning-based approach which learns from implicit feedback. We take the bid request from DSPs 3 as feedback which can act as a proxy for individual ID’s activity. We created multiple duration parameters from this implicit feedback and experimented with different techniques such as Kaplan-Meier and Cox-Proportional Hazard models to build a robust, learnable, and incremental model. We considered the attributes present for the IDs as covariates and built a Cox Proportional Hazard model with 0.9 concordance score. For a billion scale profile store this is an excellent benchmark.
Contexts of recognition of prior learning: A comparative study of RPL initiatives in Brazil, Portugal, and Germany

The main purpose of this paper is to reflect on and compare the different 'realities' of Recognition of Prior Learning (RPL) regarding informal and non-formal learning contexts in three countries: Brazil, Portugal, and Germany. In Brazil, the Certific Network is a public policy that addresses the recognition and certification of prior learning. In Portugal, the Qualifica Programme is dedicated to the recognition of learnings and skills acquired throughout life. In Germany, the ProfilPass Project focuses on the values or skills acquired informally and non-formally. Regarding the international-comparative analysis methodology, the paper studies and compares these initiatives considering three comparative axes: 1) central aims, 2) target groups, 3) formats and structures of action. Such comparative research shows the importance of understanding RPL concepts and practical realities in different social, political and economic contexts.
Reducing Cannibalization Due to Line Extensions: The Role of Brand Architecture, Product Positioning within the Portfolio

Profitably managing a brand portfolio involves decisions related to brand architecture, product positioning within the current portfolio, and planning its future. This paper quantifies the effect of different brand architectures and product positioning strategies (with varying levels of attribute similarities between products) in moderating the impact of line extensions on total portfolio sales. The authors ground their hypotheses in behavioral decision theory and test them with a sales decomposition model using seemingly unrelated regression methods. The results show substantial differences in net portfolio sales depending on the brand architecture and product positioning within the portfolio for line extensions. Controlling product feature similarity within a portfolio is more important than controlling price similarity to limit cannibalization in the context of line extensions. In addition, it is crucial to factor in the role of attribute similarity in generating spillover (additional sales for the non-focal product) in addition to cannibalization to maximize sales.
How COVID‐19 vaccine supply chains emerged in the midst of a pandemic

Abstract Many months after COVID‐19 vaccines were first authorised for public use, still limited supplies could only partially reduce the devastating loss of life and economic costs caused by the pandemic. Could additional vaccine doses have been manufactured more quickly some other way? Would alternative policy choices have made a difference? This paper provides a simple analytical framework through which to view the contours of the vaccine value chain. It then creates a new database that maps the COVID‐19 vaccines of Pfizer/BioNTech, Moderna, AstraZeneca/Oxford, Johnson & Johnson, Novavax and CureVac to the product‐ and location‐specific manufacturing supply chains that emerged in 2020 and 2021. It describes the choppy process through which dozens of other companies at nearly 100 geographically distributed facilities came together to scale up global manufacturing. The paper catalogues major pandemic policy initiatives – such as the United States' Operation Warp Speed – that are likely to have affected the timing and formation of those vaccine supply chains. Given the data, a final section identifies further questions for researchers and policymakers.
Demand-Driven Corporate Social Responsibility and Accounting Quality: Evidence on Symbolic vs Substantive Change after Large Technological Disasters

We examine a set of technological disasters with large negative effects on the environment. The disasters are each caused by a single firm, and trigger stakeholders’ attention for corporate social responsibility (CSR) at the industry level. We find that firms in the affected industries increase CSR performance, but not in a substantive manner. We refer to this CSR performance as socially enforced or demand-driven CSR. We document that demand-driven increases in CSR performance do not improve environmental performance, nor do they reduce CSR concerns, or entail costly actions for firms. CSR increases in our setting lead to lower quality financial reporting and lower quality narrative disclosure. Overall, our evidence suggests that socially enforced CSR practices are symbolic and may lead to unfavourable outcomes, including impaired transparency.
Endogenous Technology Space

We use probabilistic machine learning to span a new endogenous technology space from patent texts. We then rely on information-theoretic methods to construct measures of technological firm distances -- both fixed and time-varying. Using the latter, we present three sets of findings. First, we observe that industries are becoming more technologically specialised and segregated over time. Second, we identify the emergence of internet companies in the mid-1990s as a distinct group of firms with roots in traditional information and communication technologies. Third, we determine the unique set of time-varying rivals surrounding a focal firm in the endogenous technology space. We demonstrate the validity of this approach by means of a case study of the software company Oracle.
When Do Investors Freak Out? Machine Learning Predictions of Panic Selling

Using a novel dataset of 653,455 individual brokerage accounts belonging to 298,556 households, the authors document the frequency, timing, and duration of panic sales, which they define as a decline of 90% of a household account’s equity assets over the course of one month, of which 50% or more is due to trades. The authors find that a disproportionate number of households make panic sales when there are sharp market downturns, a phenomenon they call freaking out. The authors also show that panic selling and freak-outs are predictable and fundamentally different from other well-known behavioral patterns such as overtrading or the disposition effect.
Efficiency and equity in a society-economy integrated model

A model that only focuses on economic relations, and in which efficiency and equity are defined in terms of resource allocation may miss an important part of the picture. We propose an integrated model that embeds economic activities in a larger game of social interactions, and define comprehensive notions of efficiency and equity. Social interactions provide additional sources of inefficiencies and inequalities, including through the mere lack of coordination between the regulation mechanisms of the economic sphere and the social sphere. Remedies may require more than market solutions and redistribution of resources. In particular, social norms are likely to play an important role.
Inter-linkage between Indian Stock, Commodity and Foreign Exchange Markets

Study of market inter-dependency helps the investors, financial managers, policy makers and other stake holders to formulate their financial strategies according to the direction and strength of dependence of one market with another market. In this study we analyse the integration and inter-relationship among commodity, stock and foreign exchange markets in India. This study extends other studies by including all indices of commodity market since their birth till recent days, major stock market indices of India and exchange rates of rupee against US dollar. We use cross- correlation, residual based co-integration tests, Granger causality test and AR-EGARCH (p, q) models to capture the inter-linkage between the markets. The study found bidirectional relation between some of the commodity indices and stock indices and strong unidirectional relationship between commodity and foreign exchange market.
Examining the Effectiveness of Machine Learning Algorithms as Classifiers for Predicting Disease Severity in Data Warehouse Environments

Recent trends in the incorporation of computational science into medical and biological field of research have led to the accumulation of huge amounts of data regarding medical and experimental information. The application of data mining in healthcare sectors enables early prediction of patient conditions and their behaviors by performing data analysis and discovering relations from seemingly unrelated large volume of collected data. There is also increasing popularity of data mining in healthcare operations due to its ability to benefit all parties. For instance, data mining application in this sector aids in ensuring that patients receive more affordable and better healthcare services, physicians identify the best practices and effective treatments, healthcare firms make informed decisions about customer relationship management, and healthcare insurers detect abuse and fraud. Despite these promising trends, however, the resultant and huge data amounts that healthcare transactions generate prove voluminous and too complex to process and analyze using traditional approaches. Also, the conventional mechanism involved in the extraction of information from the data warehouse does not identify the hidden patterns involved and hence a new approach is adopted in this research to classify the data to predict the medical conditions of a patient. Moreover, in this research, prediction of severity of brain-related diseases is described based on the medical attributes using advantages of machine learning algorithms as classifier. This was implemented by utilizing the data obtained from the medical data warehouse (DWE). The extract, transform, load (ETL) process and the Online Analytical Processing (OLAP) approach were used for feature extraction, training and testing of data. Machine learning algorithms such as the Artificial Neural Network (ANN) and Support Vector Machine (SVM) are applied to generate optimized input arguments (weights and bias) for the selection of best kernel to classify the data for further diagnosis. The proposed model is found to provide quick response time and least error rate in the identification of diseases. Thus, the proposed framework can be used to predict the conditions of the patients and to provide optimal decisions in the treatment of diseases in healthcare institutions or organizations.
Extracting Semantics from Maintenance Records

Rapid progress in natural language processing has led to its utilization in a variety of industrial and enterprise settings, including in its use for information extraction, specifically named entity recognition and relation extraction, from documents such as engineering manuals and field maintenance reports. While named entity recognition is a well-studied problem, existing state-of-the-art approaches require large labelled datasets which are hard to acquire for sensitive data such as maintenance records. Further, industrial domain experts tend to distrust results from black box machine learning models, especially when the extracted information is used in downstream predictive maintenance analytics. We overcome these challenges by developing three approaches built on the foundation of domain expert knowledge captured in dictionaries and ontologies. We develop a syntactic and semantic rules-based approach and an approach leveraging a pre-trained language model, fine-tuned for a question-answering task on top of our base dictionary lookup to extract entities of interest from maintenance records. We also develop a preliminary ontology to represent and capture the semantics of maintenance records. Our evaluations on a real-world aviation maintenance records dataset show promising results and help identify challenges specific to named entity recognition in the context of noisy industrial data.
DOI: Divergence-based Out-of-Distribution Indicators via Deep Generative Models

To ensure robust and reliable classification results, OoD (out-of-distribution) indicators based on deep generative models are proposed recently and are shown to work well on small datasets. In this paper, we conduct the first large collection of benchmarks (containing 92 dataset pairs, which is 1 order of magnitude larger than previous ones) for existing OoD indicators and observe that none perform well. We thus advocate that a large collection of benchmarks is mandatory for evaluating OoD indicators. We propose a novel theoretical framework, DOI, for divergence-based Out-of-Distribution indicators (instead of traditional likelihood-based) in deep generative models. Following this framework, we further propose a simple and effective OoD detection algorithm: Single-shot Fine-tune. It significantly outperforms past works by 5~8 in AUROC, and its performance is close to optimal. In recent, the likelihood criterion is shown to be ineffective in detecting OoD. Single-shot Fine-tune proposes a novel fine-tune criterion to detect OoD, by whether the likelihood of the testing sample is improved after fine-tuning a well-trained model on it. Fine-tune criterion is a clear and easy-following criterion, which will lead the OoD domain into a new stage.
Session-based Recommendation with Heterogeneous Graph Neural Network

—The purpose of the Session-Based Recommendation System is to predict the user’s next click according to the previous session sequence. The current studies generally learn user preferences according to the transitions of items in the user’s session sequence. However, other effective information in the session sequence, such as user proﬁles, are largely ignored which may lead to the model unable to learn the user’s speciﬁc preferences. In this paper, we propose a heterogeneous graph neural network based session recommendation method, named SR-HetGNN, which can learn session embeddings by heterogeneous graph neural network (HetGNN), and capture the speciﬁc preferences of anonymous users. Speciﬁcally, SR-HetGNN ﬁrst constructs heterogeneous graphs containing various types of nodes according to the session sequence, which can capture the dependencies among items, users, and sessions. Second, HetGNN captures the complex transitions between items and learn the item embeddings containing user information. Finally, to consider the inﬂuence of users’ long and short-term preferences, local and global session embeddings are combined with the attentional network to obtain the ﬁnal session embedding. SR-HetGNN is shown to be superior to the existing state-of-the-art session-based recommendation methods through extensive experiments over two real large datasets Diginetica and Tmall.
Identifying poverty traps based on the network structure of economic output

In thiswork, we explore the relationship betweenmonetary poverty and production combining relatedness theory, graph theory, and regression analysis. We develop two measures at product level that capture short-run and long-run patterns of poverty, respectively. We use the network of related products (or product space) and both metrics to estimate the influence of the productive structure of a country in its current and future levels of poverty. We found that poverty is highly associated with poorly connected nodes in the PS, especially products based on natural resources. We perform a series of regressions with several controls (including human capital, institutions, income, and population) to show the robustness of our measures as predictors of poverty. Finally, by means of some illustrative examples, we show how our measures distinguishes between nuanced cases of countries with similar poverty and production and identify possibilities of improving their current poverty levels.