The job of a revenue manager is a classic example of a digital decision-making (DDM) problem because it involves the use of complex information and communication technologies (ICT) to make informed decisions that significantly impact the organization. Here’s how it aligns with the principles of DDM:

• Data Collection and Analysis: Revenue managers collect vast amounts of data from various sources, including historical booking data, market trends, competitor pricing, and customer behavior. This data is then analyzed to identify patterns and trends that inform pricing and inventory decisions.
• Modeling and Forecasting: Using sophisticated algorithms and models, revenue managers forecast future demand and set optimal prices. This involves modeling human behavior (e.g., booking patterns) and system-generated data (e.g., market conditions) to predict outcomes and make strategic decisions.
• Human-Machine Cooperation: Revenue management systems (RMS) leverage both human expertise and machine intelligence. While automated systems provide real-time data and pricing recommendations, human revenue managers interpret this information, make final decisions, and adjust strategies as needed. This cooperation ensures that decisions are both data-driven and contextually appropriate.
• Optimization and Implementation: Revenue managers continuously optimize pricing and inventory strategies to maximize revenue and occupancy rates. This involves filtering, segmenting, and augmenting information to refine models and improve decision-making processes. The goal is to devise techniques that support human-driven tasks (e.g., setting prices) and design systems that can autonomously adjust prices based on predefined criteria.
• Impact on Organizations and Society: Effective revenue management promotes significant changes within the organization by improving financial performance, enhancing customer satisfaction, and maintaining competitive advantage. These changes can also have broader societal impacts, such as influencing market dynamics and consumer behavior.

In summary, the role of a revenue manager exemplifies digital decision-making by integrating data collection, analysis, modeling, human-machine cooperation, and optimization to make strategic decisions that drive organizational success.

Note on Software and Human Machine Cooperation:

Revenue Management Systems (RMS) are specialized software tools designed to help businesses optimize their pricing strategies, manage inventory, and maximize revenue. These systems use data analysis, market trends, and forecasting to make informed decisions about pricing and availability. For short-term rental businesses, RMS can be particularly valuable in adjusting rates dynamically based on demand, seasonality, and other factors.

Examples of RMS for Short-Term Rentals

1. Beyond Pricing: This tool offers dynamic pricing solutions that automatically adjust rental rates based on market demand and other factors.
2. PriceLabs: Known for its user-friendly interface, PriceLabs provides dynamic pricing and revenue management solutions tailored for short-term rentals.
3. Wheelhouse: Wheelhouse offers advanced pricing algorithms and analytics to help property managers optimize their revenue.
4. AirDNA: While primarily a market data provider, AirDNA also offers tools for dynamic pricing and revenue management.
5. Lodgify: This all-in-one vacation rental software includes revenue management features to help optimize pricing and increase bookings.
6. Hostfully: Hostfully integrates with various RMS tools and provides dynamic pricing options to maximize revenue.
7. Rentals United: This platform offers channel management and revenue optimization tools for short-term rental businesses.
8. Guesty: Guesty provides a comprehensive property management solution with built-in revenue management features.

These RMS tools help short-term rental businesses, and their revenue managers stay competitive by ensuring their pricing strategies are aligned with market conditions and demand trends. Consequently, by leveraging these systems, property managers can increase occupancy rates and overall profitability.

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In short, three different fellowships are available:

1. Research Fellowship: For individuals without a PhD who are considering pursuing research studies.
2. PhD Scholarship: For those who wish to pursue a PhD in Switzerland for a period of 3+ years.
3. Post-Doc Scholarship: For individuals with a PhD who are looking to spend one year in Switzerland conducting postdoctoral research.

Please note that all fellowships are intended for individuals who have not previously spent time in Switzerland. Consequently, if you are awarded one of these fellowships, you will be ineligible to apply for the others in the future as you technicaly already have a Phd or Postdoc in Switzerland.

Let’s see each one of them in dettail:

Research Fellowship

Research Fellowships are 12-month programs aimed at highly qualified postgraduate researchers from all academic disciplines. Applicants must have obtained a master’s degree or equivalent by July 31, 2024, except for ETH Zurich applicants, who must meet this requirement by June 30, 2024. Candidates should be born after December 31, 1988. The application requires a support letter from an academic host professor at a Swiss university, including a brief CV (maximum 2 pages), explaining their willingness to supervise and support the research. This letter differs from the confidential recommendation letter also required. Additionally, applicants must submit a research proposal with a defined time frame. Notably, this fellowship is not available to those who already hold a PhD degree

PhD Scholarship

PhD Scholarships are designed for highly qualified PhD students from all academic disciplines, offering funding for 12 to 36 months, extendable in three 12-month increments based on academic progress. Applicants must have obtained a master’s degree or equivalent by July 31, 2024, except for ETH Zurich applicants, who must meet this requirement by June 30, 2024. Eligible candidates should be born after December 31, 1988. The application requires a support letter from an academic host professor at a Swiss university, including a brief CV (maximum 2 pages), detailing their commitment to supervise and support the PhD throughout its duration. If an assistant professor supports the application, a full professor must co-sign the letter. This support letter is distinct from the confidential recommendation letter also required. For PhD studies at a University of Applied Sciences, an additional support letter from a faculty member of the host research university is mandatory. The doctoral degree must be awarded by a research university. A detailed research proposal with a defined time frame is the centerpiece of the application and requires thorough attention.

Post-Doc Scholarship

Postdoctoral Scholarships are 12-month programs aimed at highly qualified early career scientists from all scientific fields. Eligible candidates must have completed their PhD between December 31, 2020, and July 31, 2024 (June 30, 2024, for ETH Zurich), and primarily for those who have not previously studied in Switzerland. The program does not fund sabbaticals. Applicants need a support letter from an academic host professor at a Swiss university, including a brief CV (maximum 2 pages), explaining their willingness to supervise the research. Former PhD mentors and co-directors for co-tutelle PhDs cannot be supervisors. A detailed research proposal with a timeframe is the centerpiece of the application. This scholarship is exclusively for full-time postdoctoral research in Switzerland, prohibiting fieldwork outside the country, and recipients are expected to reside in Switzerland during the grant period. For double doctorates, the date of the first degree is considered. The scholarship is not valid for mixed research conducted partly in Switzerland and partly in another country.

Where to find more Information

The official link of the scholarship contains all the relevant information and is the “GO TO” resources:

• https://www.sbfi.admin.ch/sbfi/en/home/education/scholarships-and-grants/swiss-government-excellence-scholarships.html

Additionally, each country has specific information and details regarding the application procedure, which can be found at the following link.

• https://www.sbfi.admin.ch/dam/sbfi/en/dokumente/2022/07/all-countries.pdf.download.pdf/2023_ESKAS_all_country_contacts_A_to_Z_for_application_documents.pdf

I recommend relying on the official information provided at the link above. The program itself warns about potential scams, and while there are numerous YouTube videos on the topic, some are generated by ChatGPT and others are, for lack of a better word, “dubious.” Here, I share videos from two YouTubers who, in my opinion, offer a reliable overview of the program. From Jama, part 1:

And part 2:

As well as from Aatiya:

My personal suggestions:

If you are interested in applying for a Phd or Postdoc in Switzerland, here are my personal suggestions:

1. Start Early (Paperwork): The application process is deliberately complex to filter out unmotivated candidates. The responsibility for managing the paperwork falls entirely on you, so begin early to ensure you have ample time to complete everything accurately.
2. Start Early (Connections in Switzerland): A critical aspect of the application is obtaining a support letter from a professor in Switzerland who is willing to endorse your candidacy and confirm that your proposal aligns with their and their institution’s research. Building these connections early is essential.
3. Start Early (Your Research Proposal): Develop a meaningful and well-written research proposal as soon as possible. Without a solid proposal, it will be challenging to secure support from a professor. You are solely responsible for writing this proposal. Once you have a polished version formatted according to the required templates, use it as your business card when reaching out to potential professors in Switzerland. Without this document, your communication is likely to be ignored or flagged as spam.

In other words, your commitment and the importance of contacting your potential supervisor as early as possible, armed with all the relevant information, cannot be overstated. This official video from the program provides additional valuable suggestions:

In addition, please note that each professor will not/cam not endorse more then one (or two) candidates per year. Therefore many of the request may simply be not answered.

Can I help you further for your Phd or Postdoc in Switzerland?

It really depends. For this specific grant you (the applicant) are really responsible for doing the heavy lifting as well as for dealing with the dettails. As for the rest, (i) if you believe that you have what it takes for this fellowship, (ii) you fulfill all the requirements, (iii) you have a nice idea to pursue and (iv) it is related to one of the following areas:

1. Internet of Things (IoT): Refers to the network of interconnected devices and objects that can communicate and share data. IoT enables smart homes, industrial automation, and more.
2. Extended Reality (XR): Encompasses virtual reality (VR), augmented reality (AR), and mixed reality (MR). XR technologies blend digital content with the real world, enhancing user experiences.
3. Human Behavioral Informatics: The study of human behavior using data analytics and computational methods. It involves understanding patterns, preferences, and decision-making based on digital interactions.
4. Computer Vision: A field of artificial intelligence that focuses on enabling machines to interpret visual information from images or videos. Applications include facial recognition, object detection, and medical imaging.
5. Language Processing: Also known as natural language processing (NLP), it involves teaching computers to understand and generate human language. NLP powers chatbots, language translation, and sentiment analysis.
6. Security and Privacy: Addresses safeguarding data, systems, and networks from unauthorized access or breaches. It includes encryption, authentication, and privacy-preserving techniques.
7. Big Data Processing: Deals with managing and analyzing large volumes of data. Technologies like Hadoop and Spark enable efficient processing of massive datasets.
8. Distributed Systems: Involves designing and managing systems that run on multiple interconnected computers. Examples include cloud computing, peer-to-peer networks, and distributed databases.
9. Serverless Computing: A cloud computing model where developers focus on writing code without managing servers. Serverless platforms automatically scale resources based on demand.
10. Digital Decision Making: The process of using data-driven insights and algorithms to make informed choices in various domains, such as business, healthcare, and finance.

You can consider dropping me a line! Are you still finishing your thesis instead? These guides may help!

Good Luck!

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In the realm of business and AI, Supervised Learning (yet another AI technique that is becoming a bit old fashioned nowadays) and Generative AI emerge as pivotal techniques offering transformative potential. They are effective especially when approached as development tools tailored to specific domains rather than mere products or services to be integrated into existing business frameworks. This perspective advocates for leveraging AI technology not only as a tool but as a toolbox containing customizable instruments for domain-specific innovation. By understanding the intricacies of these techniques, businesses can harness their capabilities more effectively, thereby maximizing their impact on society and fostering sustainable growth. In essence, it’s about not just using the tool but understanding and utilizing the toolbox itself for the betterment of society and business alike.

The video below presents the business and AI view according to the AI Fund perspective:

Moreover, I would personally (i.e. this is my opinion) advocate that the combination of both, the “old fashion” supervised learning and the popular generative AI (with LLM leading the pack), coupled with sound understanding of information enrichment techniques will probably offer the best cocktail for a successful venture capable to create value for society.

In the rest of this post I will try to expand on this point by first looking at what Large Language Models are and how they function. Next, I will share a pointer to a comprehensive (and free!) resource for familiarizing with deep learning tools and techniques.

Large Language Models: What They Are, How to Make Your Own, and How to Engineer an Application

Let’s start by looking at Large Language Models using the following two videos. LLM are text manipulation tools that are capable of both summarizing and creative writing (witting code can be considered as a creative endeavor). Thanks to recent progresses (that we can date with the launch of ChatGPT), the structure and the consistency of such generated text is increasing in accuracy and, consequently, in usefulness. The video below can serve as a good introduction to how Large Language Models work and their capability of guessing the next word:

However, there is a notable gap in research concerning the integration of such approaches into everyday industrial practices. For example (to name the one that I hinted at before), the potential fusion of structured knowledge graphs that are typical of databases-oriented information systems with AI-based semantic embedding, remains largely untapped. Furthermore, the exploration of multiagent aspects and memory-resilient LLMs holds promise for improving business processes, yet systematic empirical validation of their efficacy is lacking. The video below is an introduction to how to engineer Large Language Models in order to perform tangible tasks of value:

Promising directions of investigation include:

• Exploring diverse applications of Large Language Models (LLMs) tailored to specific subtasks in composing a comprehensive global capability model.
• Investigating optimal development configurations and orchestrating multiple-agent LLMs to enhance solution effectiveness.
• Assessing the potential of memory-based agents in facilitating the synthesis of various capabilities.
• Establishing best practices for presenting semantically enriched data to LLMs in a meaningful manner.
• Integrating embeddings and implicit semantics with explicit knowledge from knowledge graphs to enrich the understanding and inference capabilities of LLM.

While numerous methods exist for grasping the utility of a tool, I contend that learning its construction can accelerate mastery and unlock its full potential. This video provides an insightful overview on (re)implementing a transformer architecture, as detailed in the seminal paper “Attention is all you need,” which underpins the success of ChatGPT.

This concludes the conversation on Large Language Models that are a part of the “Generative AI” family. What follows is an introduction to the old fashioned deep learning that, as I mentioned at the beginning of this post, will still probably cover an important role in the next years

A Few Notes on Deep Learning

Perhaps now that we know the details of the transformer architecture (and everything else related to LLM), we can zoom out from Generative AI (and LLM) and take a look at a larger context taking into account other aspects of AI. Deep Learning and Generative AI are intertwined fields within artificial intelligence, each serving distinct yet complementary purposes. Deep Learning, a subset of machine learning, employs neural networks with multiple layers to learn representations from data, excelling in tasks like classification, regression, and pattern recognition. Generative AI, on the other hand, focuses on creating new data samples that resemble those in the training data, utilizing techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs). The relationship between Deep Learning and Generative AI is evident in how Deep Learning techniques, like convolutional and recurrent neural networks, form the foundation for building generative models. For instance, GANs employ adversarial training between a generator and discriminator network, while VAEs use encoder-decoder architectures, both rooted in Deep Learning principles. Together, Deep Learning and Generative AI enable the development of sophisticated models capable of learning from data, generating new insights, and advancing artificial intelligence across various domains.

The video below presents the book “Understanding Deep Learning”. It has been published in December 2023 by MIT Press and is presenting itself as a comprehensive guide for learning modern machine learning.

However, as mentioned in the video, the field is currently growing at the rate of 4000 papers a month. Therefore, is almost impossible to be able to cover all the relevant aspects. However, the book is free and you download it at the following link:

https://udlbook.github.io/udlbook/

A Final Note on AI, LLM, Generative AI, Supervised Learning and Everything in Between

In conclusion, the surge of interest in Generative AI and Large Language Models (LLMs) across academic and industrial spheres underscores their potential to revolutionize various sectors. Embracing Supervised Learning and Generative AI as developmental tools tailored to specific domains, rather than mere commodities, holds promise for driving transformative innovation in business and beyond. By comprehending the intricacies of these techniques, businesses can harness their capabilities effectively, thereby maximizing societal impact and fostering sustainable growth. The exploration of diverse applications, optimal configurations, memory-based agents, semantic data presentation, and knowledge integration mark promising directions for future research. While understanding the construction of tools accelerates mastery, the broader context of Deep Learning and Generative AI highlights their intertwined roles in advancing artificial intelligence.

A No-#Nonsense Approach to #deeplearning , #LLM (#LLMs), Supervised Learning, #GenerativeAI, and Everything in Between
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It was a really fun and formative experience where we had the possibility to interact with many colleagues across Europe. We can clearly declare the workshop successful as we had to cap the contribution to only 11 despite the very large contribution that we received (total 22, acceptance rage 50%). If that is not enough for declaring it a success, it also was the largest (most participated) event of the conference.

A big thanks also goes to our sponsors and keynote speaker that stimulated the conversation as well as provided a valuable industrial experience.

Here you can find a set of useful links in case you would like to profit of the outcomes:

• Call for paper for the first workshop on AI Beyond Efficiency
• Modafferi, S., Nuñez, M. J., Lelli, F., & Dalle Carbonare, D. (2022). Artificial Intelligence beyond Efficiency. CEUR Workshop Proceedings, 3214

Full report can be downloaded here: PDF Version of the report on the workshop

The paper that where discussed are available at the following links:

• Introducing Building Blocks for Industry 4.0, an Analytics Application for the Federated EFPF Platform
  Jose Maria Gonzalez Castro Robert Sanfeliu Prat, Francesc Bonada, Fernando Gigante Valencia
• AI Ethics for Industry 5.0 – From Principles to Practice
  Alexandru Constantin Ciobanu, Gabriela Meșniță
• A Practical Experience of AI Solution Used to Improve Varnishing Process Efficiency in Furniture Manufacturing
  Juan Del Agua, Gabriel Modia
• Industry5.0 and Sociotechnical Theory: Theoretical Underpinnings
  Nicholas Fair
• Relevance of Visualization and Interaction Technologies for Industry 5.0
  Ander Garcia, Marco Quartulli, Igor G. Olaizola, Iñigo Barandiaran
• Teaming.AI: Enabling Human-AI Teaming Intelligence in Manufacturing
  Thomas Hoch, Bernhard Heinzl, Gerald Czech, Maqbool Khan, Philipp Waibel, Stefan Bachhofner, Elmar Kiesling, Bernhard Moser
• On Exploring the Possibilities and the Limits of AI for an Interoperable and Empowering Industry 4.0
  Francesco Lelli
• Artificial Intelligence from Industry 5.0 perspective: Is the Technology Ready to Meet the Challenge?
  Igor García Olaizola Marco Quartulli, Ander Garcia, Iñigo Barandiaran
• Towards Zero-Defect Manufacturing: Machine Selection through Unsupervised Learning in the Printing Industry
  Sotirios T. Spantideas, Anastasios E. Giannopoulos, Nikolaos C. Kapsalis, Angelos Angelopoulos, Stamatis Voliotis, Panagiotis Trakadas
• Towards Industry 5.0 – A Trustworthy AI Framework for Digital Manufacturing with Humans in Control
  Usman Wajid, Alexandros Nizamis, Victor Anaya
• On Developing Human Centric Digital Twins in Industry 4.0 and Beyond
  Akpofure A. Enughwure, Francesco Lelli

As organizer I hope that the connections that the participants build will remain strong, will grow, and will materialize in a tangible advance of the field.

First Workshop on AI Beyond Efficiency: proceedings of the event. #ArtificialIntelligence #workshops #interoperability #Industry40 #BigData #MachineLearning
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In both cases you have my respect and support for trying to understand how HPC, AI, Big Data and Industry 4.0 can be used in your business.

I am honored and humbled that you selected me as part of your portfolio of experts for understanding how to leverage AI based technologies in your company. I am also expressing my gratitude to the StarwAI for supporting candidature as expert.

Please consider the following, prior contacting me:

• StairwAI is using a matchmaking tool to propose to each SME the list of AI Experts that fit the pilot’s scope and needs. Obviously you make the final decision on the Expert or Experts that would like to cooperate with. In addition, you free to select any Experts from the list of validated Experts, not proposed by the matchmaking tool. However, you may want to give a shot to the matchmaking tool first.
• You may want to drop me a line informally before moving forward, I will be happy to answer to your questions and take a virtual (or physical) coffee and discuss your ideas. In this way I may be able to understand if we are a good fit for each other and if I can really provide value to you.
• StairwAI is planning a second support program in 2023. Therefore if we will not have the possibility to work together in this round, do not despair; we will be able to do it at the next iteration.

Where to know more:

• AI4Europe
• StarwAI Project

Finally, let me thanks AI4EU for bring together AI stakeholders and resources, overcoming fragmentation issues and encouraging the acceleration of AI-based innovations. In particular for supporting SMEs that may be left behind due to their limited capability to acquire competences in this current market.

Happy to support the StarwAI Project and AI4EU in their mission of supporting SMEs in their strategies for #DataScience #AI #industry40 and #hpc .
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The benefit of getting familiar with Data Science Concepts

There are several benefits of getting familiar Data Science concepts. Here, I will list the most obvious:

• You will have another methodology in your portfolio of tools for conducting research.
• The certifications that you will obtain will be part of your CV and will be able to differentiate your profile.
• If you plan a a programming intensive and prototype driven approach for conducting your thesis (or research in general) you will be able to focus on the most interesting aspect from day one. This will prevent the unfortunate situation where you will be spending your time in catching up with your programming skills instead of really conducting research.

Courses Related to Data Science In DataCamp

This is a Python 101 course to perform Data Science:

• https://app.datacamp.com/learn/courses/introduction-to-data-science-in-python

This course is a bit more advanced compared to the previous one and focuses on Python specifically for data science:

• https://app.datacamp.com/learn/courses/intro-to-python-for-data-science

Here you learn to write your own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments:

• https://app.datacamp.com/learn/courses/python-data-science-toolbox-part-1

This one complements the previous one and lets you practice doing a case study:

• https://app.datacamp.com/learn/courses/python-data-science-toolbox-part-2

With this you will learn how to improve the efficiency and readability of your code:

• https://app.datacamp.com/learn/courses/writing-efficient-python-code

And this one provides you with techniques for data cleaning and data (pre)elaboration:

• https://app.datacamp.com/learn/courses/cleaning-data-in-python

Finally, the following 2 courses will let you familiarize yourself with Tensorflow

• https://www.datacamp.com/tutorial/tensorflow-tutorial
• https://app.datacamp.com/learn/courses/introduction-to-tensorflow-in-python

If you like the idea of learning Data Science, consider sharing and spreading the voice:

A collection of #courses in DataCamp for getting familiar with #DataScience concepts using #Python . They are free if you are a #student. #DataScientist #pythonprogramming
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Bonus Course (in OOP):

This course is not strictly related to Data Science skills. However, the more the complexity of your code will grow the more you will need to organize it properly. If you are not familiar with OOP at all, this post is a good introduction to Object Oriented Programming.

• https://app.datacamp.com/learn/courses/object-oriented-programming-in-python

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A knowledge graph is also known as a semantic network and represents a network of real-world entities. In other words represent “reality” as a combination of objects, events, situations, or concepts taking into account the relationship between them. This information is usually stored in a graph database and visualized as a graph structure, prompting the term knowledge “graph.”. The figure below can give you a visual idea of how these entities interlink among each other.

Intuitively, we can note that knowledge graphs take advantages of triples: subject-verb-qualifier in order to infer relationships among the data.

The knowledge graphs is at the core of the semantic web vision and standards like RDF and query languages like SPARQL are maintained by the w3c. A set of pointers for more information is available in this paper that I wrote specifically for interoperability in industry 4.0 and internet of things. However sections 2.3. (of the paper) give an overview of the semantic stack.

Existing tools and technologies that use knowledge graph

A core set of technologies that are currently in productions and used companies like Google, Facebook, Apple includes the following:

• Wikidata: Wikidata is a collaboratively edited multilingual knowledge graph hosted by the Wikimedia Foundation. It is a free and open knowledge base that can be read and edited by both humans and machines.
• schema.org is an organization that publishes documentations and guidelines to using structured data mark-up on web-pages. Its main objective is to standardize HTML tags to be used by webmasters for creating rich results about a certain topic of interest.
• Google Knowledge Graph is a knowledge base used by Google and its services to enhance its search engine’s results with information gathered from a variety of sources.

These technologies are strictly focusing on the data instead and the video below share a practical approach that focus on the data and how to combine them. Note that it does not consider any AI approach but focus on how to combine existing available data and their relationships.

However, it contains very nice examples and give an intuitive idea on how Wikidata, Schema.org and Knowledge graph works.

Research on Knowledge Graph and Artificial Intelligence

Now that we have an idea of what are the basic techniques for representing information we need to understand how we can use machine learning techniques for enriching these information and the expressiveness of the models. What follows is a set of seminars that discuss the knowledge graph from a more theoretical point of view and consider open research questions. This is the first part:

Below you can find the second part. It also contains very nice real life case studies. Including how Siri works!

If you are curious and would like to know more about the topic I encourage you in browsing the YouTube channel for more videos.

On #Knowledge Graph and #ArtificialIntelligence : how to combine data and models for better information. #BigData #DataScience
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• After the initial pain, and maybe a bit of a slow start, your thesis will advance ten times faster and you will be capable to achieve more than you can imagine.
• What you will be learning will not be lost. Many companies use a similar environment (if not the same).
• Many of these resources are free and you will be able to use them for other projects after the end of this academic endeavor.

In other words, you have nothing to lose. All you have to do is to endure a bit of the initial annoyance that is required for a proper warm up of your programming environment. However, the reward is much greater than the initial investment.

In this article, I list only a very few resources and only the ones that are free. In the internet you can find many more.

Programming skills: no matter how much you know, there is always room for knowing more. Most libraries, both public and university, have free subscriptions to courses. For example, the library in my university gives free access to DataCamp to all the students that request it.

If you are not familiar with the platform, it contains a set of courses at all levels and many of them have code examples. As they cover a large range of cases, some of them will be relevant for your thesis. All you have to do is to dig into the list and familiarize with the code that is most promising for the work that you plan to do with your supervisor.

Get a student account in GitHub, it is a free repository that you can use for storing your code. Calming student status will grant you a pro version for free as well as many discounts for products of other companies. In addition, you will also not loose these perks after the graduation – some of them could be useful for you in the future.

Get an account in Kaggle, again, it is a free service that has courses and datasets. They also offer a web based programming environment with the idea that if you will find yourself in need of additional computation, you will use their services. As a general rule, I would recommend to have a local working environment instead of a cloud based like the one of Kaggle. However, a cloud based environment may be useful under certain conditions.

Set up Anaconda (or Eclipse) in your laptop. An Integrated Development Environment is extremely useful when you have to do some professional programming. Anaconda is free, open source and perfect for Python. Eclipse is its counterpart for Java. In the article below you can read about some of the benefits of an IDE. Once you will start using one you will never go back:

This article is a mini-tutorial on how to set up the environment for Anaconda:

Finally, in the article below, you can familiarize with the debug functionality. It is one of the most useful features of an IDE and can really save you a tremendous amount of time:

I would strongly encourage you in enduring the pain of getting familiar with the tools and the resources that I shared with you in this article. In the long run your future self will be extremely thankful.

A working environment is a very personal thing and I can imagine that every supervisor has their own. Consequently, I would encourage you to discuss the details during your first meeting. At the same time, I can imagine that these tools will be, in a way or an other, useful for your future work. In addition, the fact that you will have your working environment ready will allow you to advance conversations that are more specific to your thesis and you will result in a better use of the (limited) time that you will share with your supervisor.

#free #Resources that will #SHARP your #programming #environnement. #Java #pythonprogramming #pythoncode #DataScience #DataAnalytics #data
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By the way, here you can fine my profiles in GitHub Kaggle and DataCamp:

• https://github.com/Nym77
• https://kaggle.com/francescolelli
• https://www.datacamp.com/profile/flelli
• https://www.freecodecamp.org/francesco-lelli

Are you really committed in improving your programming skills?

Free Code Camp is an open source free course that will guide you in a project based journey towards advanced programming skills. It contains also a session on how to pass a programming oriented job interview.

Introduction to Computer Science from Harvard This is a free edX course from Harvard. You can also get a certificate but this will cost you something. Nevertheless the entire course is free.

Something else:

This article (Free Resources That Will Warm Up Your Programming Environment) is part of the miniseries on how to do a good thesis, you can see the full list of post at the following link:

This article is also part of a miniseries on how to become a better programmer. You can see the full list of posts at the following link:

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Objectives of the Workshop:

Industry 4.0 is the integration of IoT, artificial intelligence (AI), cyber-physical systems, and Cloud that brought us smart factories, putting technology at the forefront of manufacturing. It heavily focusses on maximizing given set of mainly technology and economic KPIs: in other words, the systems efficiency. Industry 5.0 provides a vison of industry that aims beyond efficiency and productivity as the sole goals and reinforces the
role and the contribution of industry to society.
The workshop will discuss what are the drivers, the barriers and the implications of leveraging the technological solutions developed in Industry 4.0 (I4.0) to address relevant societal impacts, so aligning with the EC agenda.

With the final goal of further interconnecting Civil Society, Government & Policy Makers, Academia and Industry, the workshop will discuss how to shape the Industry 5.0 (I5.0) paradigm to enable a human-centric, resilient society, the green transition, and the related awareness, what is the role of interoperability, and how the SMEs can contribute to this process.
It will discuss methodological approaches and models to describe from both an ICT and Social Science perspective the ever-increasing complexity of socio-technical systems that are core of I4.0 and I5.0 and foresee an extensive use of AI-based solutions. This will include, but not limit to, how to identify, measure, monitor and feedback key features and attributes to keep all the stakeholders informed and satisfied.
The workshop will also touch upon the key concepts of Data Spaces and Testing and Experimentation Facilities as a way to answer current challenges in I4.0 and support the transition to I5.0 exploring interoperability challenges reaching solutions to industry.

Topics of interest:

• Interoperability in the transition to the human centric approach.
• Interoperable socio-technical systems.
• SME supporting interoperability in I5.0.
• AI as a support to Civil Society, Government & Policy Makers.
• Digital Education and impact of ICT and data sharing on the green agenda.
• Multi-Stakeholders monitoring models and tools.
• The role of AI in governing the transitioning I4.0 to I5.0.
• The workforce in I5.0.
• Methodologies, prototypes, and tools addressing the human-centric approach in socio-technical systems.
• SME vs large industry approach to provide tools in interoperable socio-technical systems.
• Addressing privacy and trust in interoperable human-centered systems.
• Best practices and guidelines to foster the collaboration among Social and ICT Scientists.
• Practical experiences of AI solution used beyond efficiency improvement.
• Experiences, insights, and vision on setting up and use of Data Spaces to support I5.0.
• Experiences on setting up and use of Testing and Experimentation Facilities to support I5.0

AI Beyond Efficiency: Paper Submission

Papers must be in English, describes original work and must follow the CEUR guidelines for authors. Paper length should be 6 pages for a full paper and 4 pages for a short paper.
Authors should submit their contributions by email to the Workshop Chairs: sdm2d11@soton.ac.uk, mjnunez@aidimme.es, F.Lelli@tilburguniversity.edu, davide.dallecarbonare@eng.it

Important Dates:

• January 31st, February the 7th, 2022 First draft paper submission
• February 15th, 2022 Notification of acceptance
• March 23rd, 24th, 2022 I-ESA 2022 Workshops
• April 18th, 2022 Camera-ready
• June 20th, 2022 Publication of Proceedings

Further Information about the co-location with I-ESA, venues and proceeding

Since several years, I-ESA is the major event regarding Enterprise Interoperability and the official conference of Virtual Laboratory for Enterprise Interoperability (INTEROP-VLab) with the sponsorship of the International Federation for Information Processing (IFIP).
Accepted and presented papers will be published by CEUR Workshops Proceedings

Please, consider that at least one author per paper must register to the Workshops. If none of the authors is registered, the paper will not be included in the workshop program and in the proceedings

Workshop Chairs:

• Stefano Modafferi (University of Southampton, UK),
• Maria José Nunez (AIDIMME, SP),
• Francesco Lelli (University of Tilburg, NL),
• Davide Dalle Carbonare (Engineering, IT)

Program Committee (alphabetical order):

• Emiel Caron (University of Tilburg, NL);
• Luca Caviglione (CNR-IMATI, IT);
• Francesco D’Andria (Atos ARI, SP);
• Angelo Di Iorio (Universita’ di Bologna, IT);
• Akpofure Enughwure (Nigeria Maritime University, NG);
• Nicholas Fair (University of Southampton, UK);
• Alessia Focareta (FIDIA SPA, IT);
• Simone Fratini (Solenix Enineering GmbH, DE);
• Javed Khan (Sappi Europe, BE);
• Florian Kunneman (Vrije Universiteit Amsterdam, NL);
• Manuel Mazzara (Innopolis University, RU);
• Rashid Mehmood (King Abdulaziz University, SA)
• Raffaele Montella (Universita’ di Napoli Partenope, IT);
• Alexandros Nizamis (CERTH/ITI, GR);
• Antonio Nocera (Universita’ di Pavia);
• Joao Sarraipa (Universidade NOVA de Lisboa, PT);  
• Usman Wajid (Information Catalyst, UK).

Help us spread the voice:

First #workshop on #AI Beyond Efficiency: Interoperability towards Industry 5.0.#Industry40 #BigData #MachineLearning #artificialintelligence #iot #DataScience #coding #pythoncode #Software #service
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Service Encapsulation is often used to hide the internal representation, or state, of an object from the outside. In this way, we can present to the outside world a cleaner, simpler, and more coherent view of the internal systems. Many services which were not initially planned for a Service Oriented Architecture (SOA) may get encapsulated or become a part of a SOA in this way.

In the example of ACME Social and University of Nowhere, both the parties agreed on exchanging information in order to improve the existing geolocation service of ACME Social. In particular, ACME Social would like to receive the following information from the University of Nowhere:

• Retrieve a list of unique IDs of the students
• Retrieve the email addresse(s) of each student
• Get the class that the student is supposed to follow
• Retrieving the exact coordinates of the location of a given student according to the University of Nowhere.

Service Encapsulation: Can we design it as a stateless service?

Probably yes, even though students and their respective locations will change. From the point of view of the methods, we can expect that their behavior will not change over time. A possible UML diagram that describes the interface of the service could be as follows:

The class represents all the functionalities requested by ACME Social in 4 methods. It does not have attributes and this approach will keep the design simple. IDs, emails, as well as geocoordinates will be shared as Strings in order to use one of the basic types of a service.

In the case we will decide to implement a WS-I compliant service, we can assume that the calls of the methods can fail and may trigger exceptions on the service consumer’s side.

What about the concerns of ACME Social and the documentation of the service?

As the service represents the entry point of the systems of the university of Nowhere, we need to carefully balance the complexity of the interface that we need to design. On the one hand, it must be able to mask all the idiosyncrasies of the IT of the university in order to facilitate its consumption by ACME Social. On the other hand it must be able to deliver everything that is needed and agreed. The figure below shows how this interface sits at the intersection points of two different worlds with different needs and goals:

ACME Social, i.e. the client of the service needs a clear answer to the following questions:

• What does the service do? Answering to this question will require a proper documentation of the methods. The name may be intuitive, but some of its behavior may not. Therefore, it needs to be carefully documented.
• How can we use the service? The best way to answer this question is with an example of use. ACME social may use different languages so the example(s) need to be written in Java and Python (for example) and the code must be well documented (see these best practices).
• Where can we locate the service? The URL of the service, together with the way to get the authorization of use, should be part of the documentation. In addition, the service should be part of an UDDI registry as well.

ACME Social is not at all interested in:

• How to represent internally the request received from ACME social
• How to implement the service
• Where to host the server needed for running the service
• How to publish the service in the Internet
• How to keep it running

These aspects are of fundamental importance for the University of Nowhere. However, these concerns should not be shared with the consumers of the service.

A #service Encapsulation example using a location service. #servicedesign #programming
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