CMS Data Analysis: Current Status and Future Strategy

We present the current status of CMS data analysis architecture and describe work on future Grid-based distributed analysis prototypes. CMS has two main software frameworks related to data analysis: COBRA, the main framework, and IGUANA, the interactive visualisation framework. Software using these frameworks is used today in the world-wide production and analysis of CMS data. We describe their overall design and present examples of their current use with emphasis on interactive analysis. CMS is currently developing remote analysis prototypes, including one based on Clarens, a Grid-enabled client-server tool. Use of the prototypes by CMS physicists will guide us in forming a Grid-enriched analysis strategy. The status of this work is presented, as is an outline of how we plan to leverage the power of our existing frameworks in the migration of CMS software to the Grid.Comment: 4 pages, 3 figures, contribution to CHEP`03 conferenc

Sentiment analysis in context: Investigating the use of BERT and other techniques for ChatBot improvement

openIn an increasingly digitized world, where large amounts of data are generated daily, its efficient analysis has become more and more stringent. Natural Language Processing (NLP) offers a solution by exploiting the power of artificial intelligence to process texts, to understand their content and to perform specific tasks. The thesis is based on an internship at Pat Srl, a company devoted to create solutions to support digital innovation, process automation, and service quality with the ultimate goal of improving leadership and customer satisfaction. The primary objective of this thesis is to develop a sentiment analysis model in order to improve the customer experience for clients using the ChatBot system created by the company itself. This task has gained significant attention in recent years as it can be applied to different fields, including social media monitoring, market research, brand monitoring or customer experience and feedback analysis. Following a careful analysis of the available data, a comprehensive evaluation of various models was conducted. Notably, BERT, a large language model that has provided promising results in several NLP tasks, emerged among all. Different approaches utilizing the BERT models were explored, such as the fine-tuning modality or the architectural structure. Moreover, some preprocessing steps of the data were emphasized and studied, due to the particular nature of the sentiment analysis task. During the course of the internship, the dataset underwent revisions aimed to mitigate the problem of inaccurate predictions. Additionally, techniques for data balancing were tested and evaluated, enhancing the overall quality of the analysis. Another important aspect of this project involved the deployment of the model. In a business environment, it is essential to carefully consider and balance resources before transitioning to production. The model distribution was carried out using specific tools, such as Docker and Kubernetes. These specialized technologies played a pivotal role in ensuring efficient and seamless deployment.In an increasingly digitized world, where large amounts of data are generated daily, its efficient analysis has become more and more stringent. Natural Language Processing (NLP) offers a solution by exploiting the power of artificial intelligence to process texts, to understand their content and to perform specific tasks. The thesis is based on an internship at Pat Srl, a company devoted to create solutions to support digital innovation, process automation, and service quality with the ultimate goal of improving leadership and customer satisfaction. The primary objective of this thesis is to develop a sentiment analysis model in order to improve the customer experience for clients using the ChatBot system created by the company itself. This task has gained significant attention in recent years as it can be applied to different fields, including social media monitoring, market research, brand monitoring or customer experience and feedback analysis. Following a careful analysis of the available data, a comprehensive evaluation of various models was conducted. Notably, BERT, a large language model that has provided promising results in several NLP tasks, emerged among all. Different approaches utilizing the BERT models were explored, such as the fine-tuning modality or the architectural structure. Moreover, some preprocessing steps of the data were emphasized and studied, due to the particular nature of the sentiment analysis task. During the course of the internship, the dataset underwent revisions aimed to mitigate the problem of inaccurate predictions. Additionally, techniques for data balancing were tested and evaluated, enhancing the overall quality of the analysis. Another important aspect of this project involved the deployment of the model. In a business environment, it is essential to carefully consider and balance resources before transitioning to production. The model distribution was carried out using specific tools, such as Docker and Kubernetes. These specialized technologies played a pivotal role in ensuring efficient and seamless deployment

Client-Clinician Texting: An Expansion of the Clinical Holding Environment

While there has been a surge in the texting literature related to the innovative uses of mobile technology in clinical social work practice, there is a dearth of knowledge related to the use of texting between clients and clinicians. Regardless of a clinician’s individual preference for using texting, cultural paradigm shifts in communication and interpersonal expectations will require incorporation of texting technology to meet client demands. This two-part dissertation provides a critical review of the literature that chronicles the rapid diffusion of texting into American culture and identifies its current use in psychotherapy. It demonstrates a significant gap related to its impact on the therapeutic relationship, as well as the absence of theoretical evolution to guide practice. An accompanying article expands relational theory as a way to conceptualize texting and texting behaviors in order to make responsible and purposeful decisions when integrating this technology. Composite case vignettes will demonstrate how “theoretical knowing” can be translated into “clinical doing” to address this current gap between theory and practice