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This paper is under CC 4.0 license. available on arxiv : Authors (1) Linda Pigureddu, University of Turin, Italy, 332079@edu.unito.it; (2) Cristina Gena, Dept. of Computer Science, University of Turin, Italy, cristina.gena@unito.it. Table of Links Introduction The Project Memes Conclusion and References CONCLUSION The analysis of the interactions and dialogues that took place during the therapeutic laboratory for autonomies endorsed with the inclusion of the Pepper robot provided an interesting perspective on the communication dynamics of children in the lab. The results obtained showed the effectiveness of the robot in promoting autonomy and functional acquisition, demonstrating that the therapeutic method based on assistive robotics is a valuable resource to support the rehabilitation needs of communication and social skills of autistic children. The analysis highlights new possibilities for their engagement and active participation as a co-designer, as we already experienced in the past with neuro-typical children [19], representing an innovative opportunity to promote the development and wellness of autistic people and opening to new perspectives for therapeutic intervention more aware of the needs of autistic minds. In conclusion, this paper highlights the need to provide the robot with the ability of adapt to the children’s peculiarity and features and sharing her/his vocabulary, especially by recognizing and using memes during interactions, to inspire greater trust in children and allowing the use of common slang, already in use with classmates, allowing them to consider Pepper a peer and effectively insert it in the role of mediator. In addition, it would allow children to use a simplified type of communication during meetings to compensate for the typical communication deficits associated with autism spectrum disorders. In the future we will work in this directions (co-design, user’s adaptations, using and recognizing memes during children-robot communication) having the robot able to communicate and express adapting to user tastes and preferences and and try again to field a real-world evaluation that takes into account the effectiveness of different levels of user adaptation [23]. REFERENCE [1] M. Biondi e M. Maj, DSM-5: diagnostic and statistical manual of mental disorders: text revision, 5. ed. Milano: Raffaello Cortina. [2] E. Hollander, R. Hagerman, e C. Ferretti, Textbook of Autism Spectrum Disorders, Second Edition. American Psychiatric Association Publishing, 2022. [3] ISS - Istituto superiore di sanità, «Osservatorio Nazionale Autismo», OssNA. https://osservatorionazionaleautismo.iss.it [4] P. Pennisi et al., «Autism and social robotics: A systematic review», Autism Research, vol. 9, fasc. 2, pp. 165–183, 2016, doi: 10.1002/aur.1527. [5] S. Shamsuddin, H. Yussof, S. Mohamed, e F. A. Hanapiah, «Design and Ethical Concerns in Robotic Adjunct Therapy Protocols for Children with Autism», Procedia Computer Science, vol. 42, pp. 9–16, gen. 2014, doi: 10.1016/j.procs.2014.11.027. [6] K. Dautenhahn e I. Werry, «Towards Interactive Robots in Autism Therapy: Background, Motivation and Challenges», Pragmatics & Cognition, vol. 12, pp. 1–35, giu. 2004, doi: 10.1075/pc.12.1.03dau. [7] C. Li, Q. Jia, e Y. Feng, «Human-Robot Interactoin Design for Robot-Assisted Intervention for Children with Autism Based on E-S Theory», in 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China: IEEE, ago. 2016, pp. 320–324. doi: 10.1109/IHMSC.2016.103. [8] A. Peca et al., «Robot Enhanced Therapy for Children with Autism Disorders: Measuring Ethical Acceptability», IEEE Technol. Soc. Mag., vol. 35, fasc. 2, pp. 54–66, giu. 2016, doi: 10.1109/MTS.2016.2554701. [9] B. Robins, K. Dautenhahn, e J. Dubowski, «Does appearance matter in the interaction of children with autism with a humanoid robot?», IS, vol. 7, fasc. 3, pp. 479–512, nov. 2006, doi:10.1075/is.7.3.16rob. [10] E. S. Kim, «Robots for social skills therapy in autism: evidence and designs toward clinical utility», 2013, doi: 10.13140/2.1.3038.2083. [11] «Autismo», Neuraxpharm Italia. https://www.neuraxpharm.com/it/disordini/autismo [12] L. Hull, K. V. Petrides, e W. Mandy, «The Female Autism Phenotype and Camouflaging: a Narrative Review», Rev J Autism Dev Disord, vol. 7, fasc. 4, pp. 306–317, dic. 2020, doi: 10.1007/s40489-020-00197-9. [13] D. Cohen et al., «NAO, a humanoid robot as a therapeutic mediator for young people with autism». [14] Cristina Gena, Claudio Mattutino, Andrea Maieli, Elisabetta Miraglio, Giulia Ricciardiello, Rossana Damiano, Alessandro Mazzei: Autistic Children's Mental Model of an Humanoid Robot. UMAP (Adjunct Publication) 2021: 128-129 [15] Rossana Damiano, Cristina Gena, Andrea Maieli, Claudio Mattutino, Alessandro Mazzei, Elisabetta Miraglio, Giulia Ricciardiello: UX Personas for Defining Robot's Character and Personality 213-216. IUI Workshops 2022: 213-216. [16] Cristina Gena, Claudio Mattutino, Stefania Brighenti, Andrea Meirone, Francesco Petriglia, Loredana Mazzotta, Federica Liscio, Matteo Nazzario, Valeria Ricci, Camilla Quarato, Cesare Pecone, Giuseppe Piccinni: et al., Sugar, Salt & Pepper - Humanoid robotics for autism, in Joint Proceedings of the ACM IUI 2021 Workshops co-located with 26th ACM Conference on Intelligent User Interfaces (ACM IUI 2021), College Station, United States, April 13-17, 2021, D. Glowacka e V. R. Krishnamurthy, A c. di, in CEUR Workshop Proceedings, vol. 2903. CEUR-WS.org, 2021. [17] Cristina Gena, Rossana Damiano, Claudio Mattutino, Alessandro Mazzei, Andrea Meirone, Loredana Mazzotta, Matteo Nazzario, Valeria Ricci, Stefania Brighenti, Federica Liscio, Francesco Petriglia: Preliminary results of a therapeutic lab for promoting autonomies in autistic children. arXiv, doi: 10.48550/arXiv.2305.02982. [18] B. C. Carbo, «The Use of Social Stories with Individuals with Autism Spectrum Disorders», University of Delaware ProQuest Dissertations Publishing, Newark, DE, 2005. [Online]. Disponibile su: https://udspace.udel.edu/server/api/core/bitstreams/cd5e8850-e57c-41d2-a7b4ac5e25a4b659/content [19] V. Cietto, C. Gena, I. Lombardi, C. Mattutino and C. Vaudano, "Co-designing with kids an educational robot," 2018 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), Genova, Italy, 2018, pp. 139-140, doi: 10.1109/ARSO.2018.8625810. [20] A. Mousa, «Sherry Turkle, Reclaiming Conversation. The Power of Talk in a Digital Age», Questions de communication, fasc. 34, Art. fasc. 34, dic. 2018, doi:10.4000/questionsdecommunication.16891. [21] H.-M. Chiang e M. Carter, «Spontaneity of Communication in Individuals with Autism», J Autism Dev Disord, vol. 38, fasc. 4, pp. 693–705, apr. 2008, doi: 10.1007/s10803-007-0436-7. [22] Gena, C., Cena, F., Vernero, F., Grillo, P. The evaluation of a social adaptive website for cultural events. User Model User-Adap Inter 23, 89–137 (2013) https://doi.org/10.1007/s11257-012-9129-9 [23] Gena, Cristina, and Ilaria Torre. "The importance of adaptivity to provide onboard services: A preliminary evaluation of an adaptive tourist information service onboard vehicles." Applied Artificial Intelligence 18.6 (2004): 549-580.

The Power of Memes: The Pepper Robot as a Communicative Aid for Autistic Children - Conclusion

Quality-Diversity through AI Feedback: Appendix

Revolutionizing Creative Text Generation with Quality-Diversity through AI Feedback

THE CRICKET: THE BURROW; THE EGG

: Jupiter in Ultraviolet from Hubble The APoD for 10/16/2018 : The sky is not the limit ☁️ Me : Pics or I don’t believe you Stranger 🙄 Based on the above conversation, I’m going to show you how to get NASA’s highly coveted (APoD) with 🌑 ⭐️ Astronomy Picture of the Day Standard Library If you’re not familiar with , we’re a serverless platform that enables (yes, ! Calling all PMs, designers, marketing teams, sales people, oh and engineers… 🙋) to build, ship, and integrate scalable APIs in a matter of seconds. With the help of our online text editor, , you can do all of this right from your browser. Standard Library everyone everyone Code.xyz Requirements account (free) Standard Library API key (free) NASA provides a plethora of API templates that are readily usable. In order to retrieve the APoD API template, go to and search for 💁 Standard Library Code.xyz astronomy-picture-of-the-day by @eileenzhong APoD template Name your API: Name your API One last step before sending the APoD to your phone! You’ll need to properly configure the file. You’ll notice that there are two sets of variables; however, we will be deploying our code to the environment, so we will only need to provide the variables. env.json dev dev First, insert your Standard Library library token ( ): STDLIB_LIBRARY_TOKEN Right click to access this context menu Then, insert your API key. When finished, your file should look like this: NASA env.json That’s all the setup you have to do! To run the program, click “Debug” to bring the parameter input area into view. Enter your phone number, including the country code: Don’t forget the country code! When you click “Run”, you’ll receive NASA’s astronomy picture of the day! 🚀 That’s not even the coolest part. We don’t want to click “Run” every single day to get this text delivered to our phone. Thankfully, has a task scheduler where you can customize a schedule to auto_magic_ally invoke your newly created API. Standard Library In , click the “Tasks” tab. You can then enter a phone number and customize a schedule to your liking: Code.xyz Create a scheduled task for your API Suggestion: schedule the task to execute once a day since APoD is an acronym for astronomy picture of the day , and hence, the picture won’t change throughout the day. When you’re happy with the settings, click the blue “Schedule Task” button to schedule your API. That’s it! If you know one ☝️, two ✌️, or five 🖐 other people who would appreciate a daily astronomy picture, you can schedule a different task (one for each phone number) for all of them. Bonus section If you’ve made it this far, you deserve a reward 🌈 Now, don’t get too excited (actually — do get excited 🙃). With just 2 more lines of code, you can send your friend a random quote along with the APoD! APoD and a random Paul Jacques Grillo quote We’ll be using WordPress’s API to pull a randomized quote: [http://quotesondesign.com/wp-json/posts?filter[orderby]=rand&filter[posts_per_page]=1](http://quotesondesign.com/wp-json/posts?filter[orderby]=rand&filter[posts_per_page]=1.) . Add lines and to send a random quote with the APoD text 11 12 Don’t worry about all the RegEx (regular expressions) sitting on line 12. The WordPress API just returns some HTML tags that won’t play nicely in an MMS 📱 Alternatively, you can just replace the value of the variable with any chunk of text you’d like to send your friend: quote const quote = "Hi friend." Enjoy!

Text Yourself the NASA Picture of the Day with Standard Library and Node.js

Authors:
(1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu);
(2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Table of Links Abstract and I. Introduction II. Overview of PING System and Gridlock III. Design Insights and Educational Impact IV. Conclusion and References II. OVERVIEW OF PING SYSTEM AND GRIDLOCK A. Intelligent Tutoring in the PING System This section provides a non-technical overview of the PING system and, more specifically, the ITS we integrate for personalized learning. For more technical information, we ask that readers refer to our other recent publication [11, 12]. The PING system is designed as a modular intelligent tutoring system (ITS) that can be integrated with any serious game (SG). To make the ITS modular, the system developer first partitions the game’s educational domain into certain subject-specific sections, referred to here as content blocks. Within each content block, the student can then be tested and supported on a specific topic. For example, a game dealing with math might have a block for multiplication and a block for division. Then, as the student interacts with game components and completes in-game content, the system receives a constant feed of that student’s performance, as well as any relevant data. For example, Gridlock collects timing information on how long students took to complete sections. In addition, Gridlock also records a score on each section, various data on mouse movement and keyboard interaction, as well as estimated emotional response through facial emotion recognition and webcam images. All of this data is collected through quizzes and mini-games, which are covered in the next section. Then, the data are fed into a set of reinforcement learning agents, each of which is trained to support a student on a specific content block [12]. With reinforcement learning, the system learns optimal behavior by exploring choices, so the system needs some student interaction before providing acceptable student support. But after initial training, the reinforcement learning back-end allows the system to adapt to trends and changes in student behavior without any human intervention. To learn, reinforcement learning requires feedback about the quality of the decisions made. As such, student performance is used as a “reward” for the agent. If a student performs better after receiving assistance from the system, the system receives a positive reward, encouraging the chosen assistance for future students. Likewise, if a student performs poorly after receiving some assistance, the specific assistance chosen will be less likely to appear to similar students in the future. B. Gridlock Gridlock is a game that has been iteratively redesigned and improved over the past several years. Through in-classroom testing, pilot evaluations, and student feedback, numerous changes have been made to improve both the usability of the game and to make the game more enjoyable and educational for students. The game places students in the position to redesign the logic controller for a traffic light, which is a typical lab assignment for courses that teach digital logic design. When the game starts, students are quizzed on their entry knowledge to establish a benchmark for future assistance. Then, students explore a 3D environment and unlock a door by completing tasks specific to the content the game intends to teach. The tasks given to students vary between concepts. For example, one block focuses on the specific design specifications students need for the traffic light problem. In this section, students must complete a miniature design problem which tests their ability to set the order of the traffic light changes. This problem is structured more as a puzzle or a minigame, and students’ number of actions, time taken, and correctness of their submitted solution all give the system insight into the student’s knowledge of this section’s content. Then, the system can give the student feedback to bolster their content knowledge. As shown in Figure 1, students are given dialogue from the game, videos, images, and other relevant materials based on their performance. Once students have mastered the concepts presented by the game, they are tasked with programming their final traffic light logic and submitting it into the game. To help minimize instructor time used by this assignment, the game has full functionality to let students program and debug and simulate their solutions, all within the game, as shown in Figure 2 and Figure 3. With this approach, students have a seamless experience, and the game can observe student performance as they write their solutions. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Authors: Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); Dept. of Electrical and Computer Engineering, Rowan University, (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Dept. of Electrical and Computer Engineering, Rowan University, Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Overview of PING System and Gridlock II. Overview of PING System and Gridlock III. Design Insights and Educational Impact III. Design Insights and Educational Impact IV. Conclusion and References IV. Conclusion and References II. OVERVIEW OF PING SYSTEM AND GRIDLOCK A. Intelligent Tutoring in the PING System A. Intelligent Tutoring in the PING System This section provides a non-technical overview of the PING system and, more specifically, the ITS we integrate for personalized learning. For more technical information, we ask that readers refer to our other recent publication [11, 12]. The PING system is designed as a modular intelligent tutoring system (ITS) that can be integrated with any serious game (SG). To make the ITS modular, the system developer first partitions the game’s educational domain into certain subject-specific sections, referred to here as content blocks. Within each content block, the student can then be tested and supported on a specific topic. For example, a game dealing with math might have a block for multiplication and a block for division. Then, as the student interacts with game components and completes in-game content, the system receives a constant feed of that student’s performance, as well as any relevant data. For example, Gridlock collects timing information on how long students took to complete sections. In addition, Gridlock also records a score on each section, various data on mouse movement and keyboard interaction, as well as estimated emotional response through facial emotion recognition and webcam images. All of this data is collected through quizzes and mini-games, which are covered in the next section. Then, the data are fed into a set of reinforcement learning agents, each of which is trained to support a student on a specific content block [12]. With reinforcement learning, the system learns optimal behavior by exploring choices, so the system needs some student interaction before providing acceptable student support. But after initial training, the reinforcement learning back-end allows the system to adapt to trends and changes in student behavior without any human intervention. To learn, reinforcement learning requires feedback about the quality of the decisions made. As such, student performance is used as a “reward” for the agent. If a student performs better after receiving assistance from the system, the system receives a positive reward, encouraging the chosen assistance for future students. Likewise, if a student performs poorly after receiving some assistance, the specific assistance chosen will be less likely to appear to similar students in the future. B. Gridlock B. Gridlock Gridlock is a game that has been iteratively redesigned and improved over the past several years. Through in-classroom testing, pilot evaluations, and student feedback, numerous changes have been made to improve both the usability of the game and to make the game more enjoyable and educational for students. The game places students in the position to redesign the logic controller for a traffic light, which is a typical lab assignment for courses that teach digital logic design. When the game starts, students are quizzed on their entry knowledge to establish a benchmark for future assistance. Then, students explore a 3D environment and unlock a door by completing tasks specific to the content the game intends to teach. Gridlock The tasks given to students vary between concepts. For example, one block focuses on the specific design specifications students need for the traffic light problem. In this section, students must complete a miniature design problem which tests their ability to set the order of the traffic light changes. This problem is structured more as a puzzle or a minigame, and students’ number of actions, time taken, and correctness of their submitted solution all give the system insight into the student’s knowledge of this section’s content. Then, the system can give the student feedback to bolster their content knowledge. As shown in Figure 1, students are given dialogue from the game, videos, images, and other relevant materials based on their performance. Once students have mastered the concepts presented by the game, they are tasked with programming their final traffic light logic and submitting it into the game. To help minimize instructor time used by this assignment, the game has full functionality to let students program and debug and simulate their solutions, all within the game, as shown in Figure 2 and Figure 3. With this approach, students have a seamless experience, and the game can observe student performance as they write their solutions. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Enhancing Learning with Gamified Instruction: Overview of PING System and Gridlock

What if I ask you what you want to have right now? If I were you, I would surely say hamburger. Nothing is better than a juicy, cheesy, perfectly grilled hamburger. However, not everyone can make a perfectly grilled hamburger. You need to nail every step and cook the meat at the right temperature to make a perfect hamburger. This article is the ultimate hamburger grilling guide with step by step process to make the perfect hamburger. Step 1 Let’s start with the meat selection. When it comes to meat, it is always best to select a fatty cut. High-fat content meat is perfect for the juiciness and the moisture of the burger. To be precise, 15-20% fat content will give the best-grilled patty for your hamburger. However, ground chuck beef is highly recommended and will give the best for making hamburgers. Step 2 We have to make proper and perfect patties. Hence, the preparation of the patty is very vital. To begin with, the patties should be refrigerated to make it stick together. It is better to keep the mass of the patty between 170-200 grams; it will give a perfect bun to meat ratio.  You need to marinate the patty with soya sauce, chili paste and a pinch of lime. Make sure to marinate the patty for 5-6 hours before grilling it. Step 3 You need to prepare the grill before you start grilling the patty. Olive oil should be brushed over the grills so that it does not stick with the patties. To add flavor, you can even use butter to sear the meat on all sides. A common mistake that everyone makes is that they press the patty too hard; don’t press it too hard as it makes the patty dry. The best way to grill is to let the patty sear for 2-3 minutes on each side, and then flip it. It is best to flip it only once, and hence, the searing time needs to be properly matained. The cooking approach To overview the whole cooking process, it is always best to use a thermometer. It allows checking the internal temperature of the burger. Moreover, maintaining the temperature allows the patty to be moist and juicy. Now, finish cooking the patty in the oven; the burger needs to be cooked within (365-400) degrees. However, try to maintain the internal temperature to be around 160 degrees. Additionally, cooking time varies according to the burger type. For instance: Rare: 4-5 minutes Medium-Rare: 6-7 minutes Medium: 8-9 minutes Once the patty is cooked, let it sit for 3-5 minutes. Then use dressing and sauce of your choice and enjoy the perfectly cooked patty, and a delicious hamburger. It is never hard to make the perfect hamburger if one just knows the little tricks. Moreover, religiously following the above steps, one can avoid the little mistakes to experience the juicy, tender, and flavourful taste that everyone wants in a perfectly grilled hamburger. So, when you have friends or family coming over, or a casual gathering, make sure to cook the ultimate grilled hamburger and awe-struck everyone with its amazing taste. Thanks to for share my article. hackernoon.com Related Post: 7 best 6 burner Gas grill review

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