• User agency: The self-perceived abilities of users to operate and control their devices.
• Device agency: The capacities that users attribute to their smart devices.

The study introduces four agency profiles that characterize different types of human-smart device relationships:

1. Controller: High user agency, low device agency
2. Collaborator: High user agency, high device agency
3. Victim: Low user agency, high device agency
4. Detached: Low user agency, low device agency

Figure below presents an intuitive idea of the research:

The researcher is validated by conducting an exploratory factorial analysis, tag cloud analysis, and sentiment analysis on survey data to investigate these agency profiles. In this work we examined also how various background variables (such as age, gender, education, and device type) relate to user and device agency.

Key findings include:

1. The type of smart device significantly affected user agency, with household cleaning device users reporting the highest user agency and personal assistant device users reporting the lowest.
2. Most background variables (age, gender, education, professional field, work experience) did not significantly impact user or device agency.
3. The tag cloud analysis revealed distinct patterns of language use associated with each agency profile, providing insights into how users perceive their relationships with smart devices.

This research contributes to the fields of Human-Computer Interaction (HCI) and Information Systems (IS) by offering a framework for understanding the varied ways humans interact with and perceive smart devices. The findings have implications for designing more effective and user-friendly smart device interfaces and experiences. The impact of this paper on human-smart device relationships and related fields includes:

1. Theoretical contribution: The study introduces a novel framework for understanding human-smart device relationships through the lens of user and device agency. This conceptualization provides a new perspective for researchers in Human-Computer Interaction (HCI) and Information Systems (IS).
2. Design implications: The findings can inform the design of smart devices and their interfaces. By understanding different agency profiles, designers can create more user-friendly and effective devices that cater to various user perceptions and needs.
3. User experience enhancement: The research helps in identifying factors that influence user satisfaction and engagement with smart devices. This knowledge can be applied to improve overall user experience.
4. Industry relevance: The study’s insights are valuable for companies developing and marketing smart devices. Understanding user perceptions can guide product development and marketing strategies.
5. Educational impact: The research provides a foundation for educating users about their relationship with smart devices, potentially leading to more informed and empowered consumers.
6. Future research directions: The paper opens up new avenues for research in human-smart device interactions, encouraging further exploration of agency concepts in technology adoption and use.
7. Societal implications: By shedding light on how people perceive and interact with smart devices, the study contributes to broader discussions about the role of technology in society and its impact on human behavior.
8. Methodological contribution: The combination of exploratory factorial analysis, tag cloud analysis, and sentiment analysis provides a multi-faceted approach to studying human-smart device relationships, which can be applied in future studies.

This research has the potential to influence how we design, use, and think about smart devices, ultimately advancing our understanding the human-technology interactions. Overall, you should read this paper for several compelling reasons:

• Novel perspective: The paper introduces a unique framework for understanding human-smart device relationships through the concepts of user agency and device agency. This approach offers a fresh perspective on how people interact with and perceive their smart devices.
• Practical implications and industry relevance: The research has direct applications for designers and developers of smart devices. By understanding different agency profiles, they can create more user-friendly and effective products that cater to various user perceptions and needs. Thus facilitating the development and marketing of smart devices.
• Insight into user experience: The study provides valuable insights into factors that influence user satisfaction and engagement with smart devices. This knowledge can be applied to enhance overall user experience across different types of smart devices.
• Societal impact: The research contributes to broader discussions about the role of technology in society and its impact on human behavior. It helps us understand how people perceive and interact with increasingly prevalent smart devices.
• Methodological innovation: The paper employs a multi-faceted approach combining exploratory factorial analysis, tag cloud analysis, and sentiment analysis. This methodology provides a comprehensive understanding of human-smart device relationships and could be applied in future studies.
• Future research directions: The study opens up new avenues for research in human-smart device interactions, encouraging further exploration of agency concepts in technology adoption and use.

In summary, by reading this paper, you’ll gain a deeper understanding of the complex relationships between humans and smart devices, which is increasingly relevant in our technology-driven world. The insights provided can be valuable whether you’re a researcher, designer, industry professional, or simply interested in how technology shapes our daily lives.

Link to the paper: https://doi.org/10.3390/fi16030090

Dataset for the study of Human and Device Agency

The relationship we have with our smart devices is more complex than we think! This research reveals how user and device agency influence our interactions. #TechResearch #HCI #IoT #AI
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Human-computer interaction (HCI) is a multidisciplinary field that explores the interaction between humans and computers, emphasizing especially the design and use of computer technology. Within this domain, the notion of sense of agency, usually defined as the users perceiving their actions as influencing the system, is of crucial importance. Another central notion is that of socio-technical systems, referring to the interplay between social and technical elements. In this work, we delve into users’ perceptions of their interactions with smart devices, drawing insights from a dataset collected during the COVID-19 outbreak in 2020.

The dataset, gathered through an expansive survey, was distributed in professional and personal networks, offers a window into the perspectives of 587 respondents, predominantly highly educated professionals with significant technological familiarity. While the sample’s specificity limits the generalizability of findings, it offers valuable insights into how tech-savvy users perceive their interactions with smart devices, illuminating the nuanced dynamics of HCI within socio-technical contexts.

One of the survey’s notable aspects was its focus on users’ choice of a single smart device to consider throughout their responses. This deliberate framing encourages a deeper exploration of users’ relationships with specific technologies, shedding light on the intricacies of their interactions. For respondents who did not own multiple smart devices, the instruction was to choose their primary device, such as a computer or mobile phone, thus ensuring consistency in the examination of user experiences.

Within the dataset, various dimensions of users’ interactions with smart devices emerged, encompassing cognitive, emotional, and behavioral aspects. Users’ perceptions of agency, defined as their sense of control and influence over the device, are particularly intriguing. Analysis of the dataset reveals diverse manifestations of agency, ranging from feelings of empowerment and efficiency to frustrations stemming from perceived limitations or complexities in device operation.

Moreover, the socio-technical lens allows us to delve deeper into the contextual factors shaping users’ interactions. The dataset captures not only users’ individual experiences but also the broader socio-cultural and technological landscapes influencing these interactions. Factors such as educational background, professional expertise, age, and gender are among the variables examined, offering insights into how these socio-demographic factors intersect with users’ perceptions of smart device interactions.

The dataset’s utilization in previous research endeavors underscores its significance in advancing our understanding of human-computer interaction, user behavior analysis, and agency. By building upon existing research and leveraging the data available, researchers can uncover deeper insights into the complexities of smart device interactions and contribute to the development of more user-centered technologies.

The COVID-19 context during which the data were collected adds another layer of complexity to the analysis. Due to the increased time spent at home and the increase in remote work, the global pandemic catalyzed increased reliance on technology for work, education, and socialization. Understanding users’ interactions with smart devices within this unique context provides valuable implications for designing technologies that accommodate evolving user needs and preferences.

In conclusion, users’ notions of their interactions with smart devices offer a rich tapestry of insights that intersect with key concepts in HCI, including agency and socio-technical systems. Through the analysis of a dataset collected during the COVID-19 pandemic, we gain valuable insights into users’ perceptions, behaviors, and experiences with smart devices within the context of a rapidly evolving technological landscape. This dataset serves as a springboard for further exploration in the field, driving forward our understanding of human-computer interactions in an increasingly digital world.

Where to find more:

• Agency in Human-Smart Device Relationship: A work in progress description of a research done with this dataset

Initial call for collecting the responses

Link to the dataset (Open Access)

https://doi.org/10.3390/data9040056

https://doi.org/10.34894/traony

Main Personal Related Publication

https://doi.org/10.3390/fi16030090

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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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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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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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Abstract:
With technology in reach of everyone and the technology sector in ascendance, it is central to investigate the relationship people have with their devices. We use the concept of agency to capture aspects of user’s sense of mastery and control in relation
to their device. This study gives preliminary evidence of the existence of two dimensions of agency for modelling the interaction between humans and smart devices: (i) user agency and (ii) device agency. These constructs emerged from an exploratory factorial analysis conducted on a survey data collected from 587 participants. Mapping the users’ dynamics with their devices into user agency and device agency advances our understanding of how digital technology shapes human cognition and emotion and how
users interact with technology. This approach can assist in designing interfaces that match the beliefs users have regarding their capabilities and the capacities of their devices.

Key words: Agency, Human-Computer Interaction, smart devices, user agency, device agency, exploratory factorial analysis, survey.

we show that agency in human-smart device interactions is a twofold and graded construct, consisting of user agency and device agency. The attributing of agency to themselves and to their device frames how the users are perceiving and leveraging the affordances of the smart device. The attribution of user and device agency can be used as a measure for understanding if the user masters the device’s potentials to the fullest or not. Thus, user agency and device agency can be used as proxies for understanding how users experience their relationships with their devices.

The conceptualization of agency proposed here should be further refined in relation to other existing constructs. We should investigate whether user and device agency act as mediators for other factors of human-device interaction such as anthropomorphism and other concepts mentioned in this article. Furthermore, our exploratory study should be developed further to a point where we can perform confirmatory factor analysis. As the next step, an expert panel will be summoned to review the items used in this study, in preparation for refining the survey before collecting new data for the second set of statistical analyses. Furthermore, we should continue exploring the nuances of agency in different contexts of smart device use, such as work vs. personal.
Brad Pitt as Tyler Durden in Fight Club (1999) states that “the things you own end up owning you”. In the context of smart devices, this is true only if we are unaware of the agency we attribute to our devices and to ourselves. Perhaps the secret of a balanced relationship with our gadgets is in making an aware decision of when and how to be “owned” by the device and when not.

Full article available at this link.

Check out the presentation:

Your help in sharing the idea is very welcome!

Agency in Human-Smart Device Relationships: An Exploratory Study #HCI #IoT #smarthome #homeautomation #survey #softwaredevelopment #softwareengineering
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Cite this paper as:

Toivonen, Heidi and Lelli, Francesco, “Agency in Human-Smart Device Relationship: an Exploratory Study” (2021). PACIS 2021 Proceedings. 145. https://aisel.aisnet.org/pacis2021/145

Check out the Related Dataset: Lelli, Francesco; Toivonen, Heidi, 2021, “A Survey for investigating human and smart devices relationships“,  https://doi.org/10.34894/TRAONY, DataverseNL, V1

• Full list of Articles
• Full list of Conference Publications

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Abstract:
The growing synergy between Web Services and Grid or cloud based technologies is enabling profound, dynamic interactions between applications dispersed in geographic, institutional, and conceptual space. Such deep interoperability requires the simplicity, robustness, and extensibility for which XML has been conceived, making it a natural lingua franca for the network. Along with these advantages, there is a degree of inefficiency that may limit the applicability of XML.
In this paper, we investigate the limitations of XML for high-performance and high-interactive distributed computing. Our experimental results clearly show that focusing on parsers, that are routinely used for desterilize XML messages exchanged in these system, we can improve the performance of a generic the end to end web services based solution. Then we present a new parser, the Cache Parser, which uses a cache to reduce the parsing time by reusing information related to previously parsed documents/messages similar to the one under examination. Finally, we will show how our fast parser can improve the global throughput of any application based on Web or Grid or Cloud Services, or also RPC calls in general.

Key words: Web Service, Cloud Services, 3 tier architecture, SOA, RPC, Service Oriented Architecture

The may Web Services fully solve the “global enterprise integration” problem, but the proposed solution seems to exhibit a poor performance, and we believe that this could pose serious limitations on their actual applicability, as the number of commercial users will increase.
As shown in paragraph 2 we also believe that one of the limitations in using a full WS approach for implementing complex and highly interactive systems, comes from the large de-marshalling costs incurred on the receiver sides.
To solve this problem, we have designed a new parser: the Cache Parser. It is able to “well and quickly” retrieve information from XML documents, using previous knowledge about the document syntactic tree. In particular, our Cache Parser, which is used to de-marshal XML messages on the receiver side:

• Uses a checksum to detect if a new document is “well formatted”, and to know if it shares the syntactic tree with an already parsed one.
• Takes advantage of this XML-Document syntactic tree stored in a cache.
• It is based on a strict collaboration between sender and receiver.
• Consistently reduces the receiver parse time, without increasing the sender document creation time.

This algorithm is 25 time faster than a pull parser and if used in a WS scenario it can allow a 1.54 performance improvement factor in term of request handled per second. Finally it can be applied in any scenario where the client and the server decide to cooperate.

Full article available at this link.

Your help in sharing the idea is very welcome!

Improving the #performance of XML based technologies by caching and reusing #Information. #Webseries #RealTime #CloudComputing #API #APIs #programming #softwareengineering
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Cite this paper as:

F. Lelli, G. Maron, and S. Orlando. Improving the Performance of XML Based Technologies by Caching and Reusing Information.. IEEE International Conference on Web Services (ICWS), Chicago, September 2006.

• Full list of Articles
• Full list of Journal Publications

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Is it possible to achieve a Client Side Estimation of a Remote Service Execution?

Abstract:
Many use cases, concerning the monitoring and controlling of real physical instruments, demand deep interaction between users and services that virtualize the access to such instruments/devices. In addition, in order to realize high interoperable solutions, SOA-based Web/Grid Service technologies must be adopted. When the access to one of these services is performed via internet using a Web Service call, the remote invocation time becomes critical in order to understand if an instrument can be controlled properly, or the delays introduced by the wire and the serialization/ deserialization process are unacceptable. This paper thus presents methodologies and algorithms, based on a 2k factorial analysis
and a Gaussian Majorization of previous service execution times, which enables the estimation of a generic remote method execution time. Furthermore it suggests three different software architectures, where the developed algorithms and methodology could be integrated in order to automatically profile the end-to-end service. It is worth noting that our proposals are validated using suitable benchmarks and extensive tests coming out from a real (not simulated) environment. In addition, the outcome of this paper have been used in the realization of a service for remote control, monitor, and manage of a pool of instruments/devices.

Key words: Web Service, Quality of Service, QoS, Grid, Cloud Services, Response Time

In this paper you will understand the state of the art on Quality of Service in today digital services with particular attention to Web Services. In this scenario, we proposes a set of solutions that enable a remote service invocation time to be predicted in a general scenario. The contribution of this paper can be divided in three different parts:

• Section 3 formalizes the problem while Sections 5 and 5.1 validate this formalization by providing a dataset for web service QoS study. In particular, the dataset consists of 2304000 samples (of 9 significant values each) organized in 2304 different tests.
• In the second part of this paper (Section 4, 6, and 7) we analyze the collected dataset in a more exhaustive way. Two variants of a methodology that utilizes a Gaussian approximation of the dataset distributions, in combination with a regression model of the key factors that influence the average and the standard deviation, have been developed in Section 4.
• Then, in Section 6 our estimators are validated with experimental results in several scenarios. In the last part of this paper (Section 7), we propose three different software architectures that can utilize the developed methodologies and algorithms. It is worth noting that the presented solutions have been already adopted in a Grid context during the design and implementation of our IE

We have also experienced that in case of server overload, the number of deadline misses increases. This suggests a possible approach for an automatic organization of the clients that balances the servers’ load, and that could be exploited to solve the use cases mentioned in the introduction of this paper. If the number of deadline misses is greater than the expected
ones, the server machine is probably overloaded. So clients could/should decide to use a different machine, in which an identical copy of the invoked service is deployed.

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Client Side Estimation of a Remote Service Execution. #Webseries #RealTime (sort of) #CloudComputing #API #APIs #programming #SoftwareEngineering
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Cite this paper as:

F. Lelli, G. Maron and S. Orlando. Client Side Estimation of a Remote Service Execution, 15th Annual Meeting of the IEEE International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Istanbul, Turkey October 2007

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A significant health concern is addiction, a brain disease that can affect anyone. 

The most severe form of substance use disorder (SUD), addiction manifests as a compulsion for a substance or behavior. If addiction is left untreated, it can affect your life in multiple ways: financially, socially, physically, and mentally. 

There are various approaches to fight addiction. You can use traditional psychotherapy, technological methods, or a combination of both.

The Internet of Things (IoT)—a network of Internet-connected devices—is a technological innovation that can help fight addiction and facilitate online recovery. 

As of July 2020, more than 4.5 billion people globally were active Internet users. 

How Can the Internet of Things Help Fight Addiction?

There are various ways the Internet of Things helps to provide online recovery. The Internet of Things offers:

Anonymity

A 2014 study of public attitudes by the Johns Hopkins Bloomberg School of Public Health found that society is more hostile towards people with substance use disorders (SUD) than those with mental illnesses. This animosity—even among family members or people close to us, makes people uncomfortable seeking help for their SUD. 

Celebrities and other well-known people may feel they can’t be seen seeking help without risking legal trouble or loss of professional standing. That’s one reason why 12-step organizations like Alcoholics Anonymous are anonymous; they can help people recover from addiction without feeling judged.

The Internet of Things helps to maintain the anonymity of those fighting their addiction. You can create an anonymous profile on an online recovery program or anonymously get information on how to get help for addiction. Many 12-step groups have long offered online meetings. Since the COVID-19 pandemic, these online meetings have increased. 

Some addiction professionals also offer online consultations.

Technology-Based Interventions

Traditional methods are still instrumental in addiction recovery. The Internet of Things uses technology-based interventions to supplement and support traditional addiction treatments, not replace them, assist online recovery, and prevent relapse.

Technology-based interventions include:

• Technology-assisted behavioral therapies like individual counseling.
• Education on substance and behavioral addiction.
• Recovery support programs.
• Prevention resources.
• Information resources.
• Wellness monitoring.

The Internet of Things provides access to these interventions through various methods which include:

• Zoom and other types of video conferencing.
• Telephone counseling.
• Web-based text communication, such as chat, emails, and online forums.
• Self-directed therapeutic tools.
• Self-assessment tools.

While such technology-based interventions have little impact on addressing behavioral addictions, it is still significant.

Centralized Prescriptions

Prescription drugs are a concern because of the high abuse, addiction, overdose, and fatalities. Opioids are one of the significant prescription drugs raising a lot of concern. 

Currently, the United States is in the middle of an opioid epidemic. In 2012, there were more than 250 million opioid prescriptions. By 2018, however, the number had decreased to fewer than 170 million prescriptions. Still, 128 people lost their lives every day due to opioid overdose.

The Internet of Things provides prescription monitoring programs that help healthcare officials monitor and control prescriptions. The monitoring programs can also store your prescription history to indicate if you are responsible enough to take the prescriptions home.

Awareness

The Internet is an extensive information resource that young people prefer to brick-and-mortar libraries. The Internet of Things increases awareness of and access to information about addiction recovery, such as:  

• Types of addiction. 
• Causes. 
• Symptoms. 
• Effects. 
• How to get help.  

The Internet of Things is all about communication. You can use various platforms to ask questions, learn about campaigns, present your views, debate, and seek addiction support. Since it’s online, you can have few interruptions when addressing your thoughts and can take your time to give responses.

Monitoring Apps and Use of Wearables

The Internet of Things has also made it possible to fight addiction through artificial intelligence applications and wearables. There are smartphone apps available that create addiction awareness and monitor the chances of relapse.

Wearables have sensors that track your habits. For example, a smartwatch can detect hand movements used in smoking, create awareness of the effects of smoking, and give out reward points when you abstain.

You can also use a wearable breathalyzer to monitor your alcohol consumption and the alcohol content in your body. 

Other wearables offer physical stimuli like a tiny zap to prevent you from engaging in addictive habits.

Final Thoughts

Addiction is a disease characterized by a compulsion to engage in a particular behavior or use a substance. There are various treatment and prevention measures for addiction. Treatment is more effective if you keep up on trends and combine various methods.

The use of the Internet of Things is one of the trends. It will help you fight addiction in various ways, including anonymous online recovery, use of wearables, prescription control, technology-based interventions, and addiction awareness.

About the Author

Patrick Bailey is a professional writer mainly in the fields of mental health, addiction, and living in recovery. He attempts to stay on top of the latest news in the addiction and the mental health world and enjoy writing about these topics to break the stigma associated with them. 

Twitter: https://twitter.com/Pat_Bailey80

LinkedIn: www.linkedin.com/in/patrick-bailey-writer

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