The current study proposes an innovative methodology for the profiling of psychological traits of Operations Management (OM) and Supply Chain Management (SCM) professionals. We use innovative methods and tools of text mining and social network analysis to map the demand for relevant skills from a set of job descriptions, with a focus on psychological characteristics. The proposed approach aims to evaluate the market demand for specific traits by combining relevant psychological constructs, text mining techniques, and an innovative measure, namely, the Semantic Brand Score. We apply the proposed methodology to a dataset of job descriptions for OM and SCM professionals, with the objective of providing a mapping of their relevant required skills, including psychological characteristics. In addition, the analysis is then detailed by considering the region of the organization that issues the job description, its organizational size, and the seniority level of the open position in order to understand their nuances. Finally, topic modeling is used to examine key components and their relative significance in job descriptions. By employing a novel methodology and considering contextual factors, we provide an innovative understanding of the attitudinal traits that differentiate professionals. This research contributes to talent management, recruitment practices, and professional development initiatives, since it provides new figures and perspectives to improve the effectiveness and success of Operations Management and Supply Chain Management professionals.

Through Ecological Momentary Assessment (EMA) studies, a number of time-series data is collected across multiple individuals, continuously monitoring various items of emotional behavior. Such complex data is commonly analyzed in an individual level, using personalized models. However, it is believed that additional information of similar individuals is likely to enhance these models leading to better individuals' description. Thus, clustering is investigated with an aim to group together the most similar individuals, and subsequently use this information in group-based models in order to improve individuals' predictive performance. More specifically, two model-based clustering approaches are examined, where the first is using model-extracted parameters of personalized models, whereas the second is optimized on the model-based forecasting performance. Both methods are then analyzed using intrinsic clustering evaluation measures (e.g. Silhouette coefficients) as well as the performance of a downstream forecasting scheme, where each forecasting group-model is devoted to describe all individuals belonging to one cluster. Among these, clustering based on performance shows the best results, in terms of all examined evaluation measures. As another level of evaluation, those group-models' performance is compared to three baseline scenarios, the personalized, the all-in-one group and the random group-based concept. According to this comparison, the superiority of clustering-based methods is again confirmed, indicating that the utilization of group-based information could be effectively enhance the overall performance of all individuals' data.

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Millions of people across the around the world are wondering, what is the difference between data scientist and data engineer. These are exciting new fields that seemed like prosperous avenues for college students and older individuals who are looking for a career change. Many of these newcomers often do not know the specific difference between the two fields. They are seen as almost interchangeable and are usually referred to in the same breath. But the fields are in fact quite different.

The influence of one's peers significantly affects individual actions. The dynamics of a group affect its members' propensity to break the law, use violence, or aid those in need. Studies have shown that looking at a group of people has a powerful effect on people's focus. The things we pay attention to significantly impact how we react. The conventional explanation is that this behavior is adaptive; when we observe many people fixating on the same object, we reason that this must be significant and we decide to follow the group's gaze.

With the advent of the ability of artificial intelligence ("AI") to alter an individual's voice and image (whether in deepfakes or expressly fictional works), it is critical to determine who – if anyone – owns the right to do so, particularly when the voice or image is clearly identified with a fictional character from an existing film. This issue is highlighted by the recent license by James Earl Jones (the voice of Darth Vader) of his voice to an AI company. While articles state that the license of his voice was for use by Disney (the owner of the Star Wars franchise), the transaction raises the following questions: (a) could anyone use his voice without permission and (b) could James Earl Jones have licensed his voice to third parties for use in other films, particularly if used in the distinctive manner of Darth Vader? This article will refer to the individual whose voice or image is at issue as the "Individual," the licensee of AI rights as the "AI Licensee," the new AI work incorporating the voice or image as the "AI Work," and any prior work that the voice or image is taken from, or resembles elements of, as the "Prior Work." Let's first deal with the right of publicity.

This morning, the White House Office of Science and Technology Policy released a long-awaited "Blueprint for an AI Bill of Rights" ("AI Bill of Rights") that, when implemented, would apply to automated systems that have the potential to meaningfully affect the American public's rights, opportunities, or access to critical resources or services. The AI Bill of Rights is designed to provide protections to apply broadly to all automated systems that "have the potential" to significantly affect individuals or communities, from civil rights/civil liberties (including privacy), to equal opportunities for healthcare, education, and employment, as well as access to resources and services. The AI Bill of Rights contains five broad categories of practices designed to "guide the design, use, and deployment of automated systems to protect the rights of the American public in the age of artificial intelligence." Safe and Effective Systems: Individuals "should be protected from unsafe or ineffective systems." In addition, "[a]utomated systems should be developed with consultation from diverse communities, stakeholders, and domain experts to identify concerns, risks, and potential impacts of the system."

You must have less than 3 years of paid postdoctoral experience.

So if you're like me just beginning out at NLP after finishing a few months building Computer Vision models as a beginner then surely this story has something in supply for you. BERT is a deep learning model that has given state-of-the-art results on a wide variety of natural language processing tasks. It stands for Bidirectional Encoder Representations for Transformers. It has been pre-trained on Wikipedia and BooksCorpus and requires (only) task-specific fine-tuning. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks, including Question Answering (SQuAD v1.1), Natural Language Inference (MNLI), and others.

Preferences can be described with the Utility Function (Microeconomics) 13 14. Use Machine Learning to Learn an Individual's Preferences 15 [Bouza et al., 2009], [Bouza, 2012] 16. 16 - Good - Bad 17. Represent Preferences, e.g., as Decision Tree 17 [Bouza, 2012] 18. Let's be pragmatic: Machine Learning Model approximates Utility Function 18 [Bouza, 2012] 19. Based on a personal true story in 2008 21. People who share similar prefernces in the past continue to do so in the future. People who have similar preferences in the past, continue to do so in the future.