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Redefining Fun: How Realism and Effort Engage Players in Video Games
Get PDFIn the current era of video game development, the gaming industry frequently pursues instant gratification, creating a landscape for immediate and empowering rewards. Current trends clearly show that experiences allow players to become superhuman or extraordinary beings with very little difficulty, satisfying a common desire for escapism and power. This suggests that “fun” is commonly associated with ease, speed, and constant positive feedback. However, my counter-argument is the emergence of games such as Kingdom Come: Deliverance and Red Dead Redemption 2. These games operate with a different game design and philosophy. In fact, they embrace slowness, hardship, and the detailed nuances of their worlds not as obstacles to enjoyment, but rather as crucial ingredients for a more profound and lasting form of engagement
Nothing Is Off Limits, But Not Everything Is Done Right: Exploring Ineffability
Get PDFGames are capable of addressing absolutely anything. No topic is too big, too controversial, or too emotional. but not everything is done well; the problem isn\u27t the subject, it\u27s always the delivery. When the developers approach heavy topics without care, they risk doing harm. bwhen they do their research and consult communities and write with purpose, the result can be transformative. Representation, when handled with attention, isn\u27t political correctness; it\u27s simply good storytelling. it\u27s how games become more than entertainment. They become tools for empathy, education, and more valuable connection
Fuel Cell System Development for Heavy Duty Vehicles
Get PDFAs California advances its ambitious goals for transportation electrification to combat climate change, hydrogen-powered fuel cells are emerging as a viable solution for overcoming the challenges of heavy-duty vehicles, offering an efficient alternative to lithium-ion batteries because they produce minimal chemical, thermal, and carbon emissions. One type of hydrogen fuel cell technology called proton exchange membrane fuel cells (PEMFCs) has garnered the most attention due to its distinct advantages, including relatively low operating temperatures (60–80 °C) and reliable performance at high current densities. However, despite their promise, PEMFCs face challenges, including in optimizing stack power output and safety concerns. To tackle these issues, accurate modeling and control strategies are essential. This study focuses on using data-driven modeling (specifically using a process known as “closed-loop system identification” under proportional controller and pseudo-random binary sequence excitation methods) to better understand and manage PEMFC systems. Various transfer functions models were analyzed, including first-order, first-order plus time delay, second-order, and second-order plus time delay models. The resulting closed-loop identification approach was applied on the humidifier, cooling, and oxygen supplier subsystems of simulated PEMFC to build their models under controlled operations. The results of this study highlight the potential of closed-loop system identification techniques to improve fuel cell vehicle performance in power supply, water, and heat management, without interrupting PEMFC operations. These findings demonstrate the significance of precise modeling as a cornerstone for advancing PEMFC control strategies and optimizing their application in renewable transportation and a more sustainable future
Archeota, Spring/Summer 2025
Get PDFArcheota is a platform for SJSU iSchool students to contribute to the archival conversation. It is written BY students, FOR students. It provides substantive content on archival concerns and issues and promotes professional development in the field of archival studies. Archeota upholds the core values of the archival profession. Contents: Happy 10th Birthday Archeota! Celebrating Archeota\u27s 10 Year Anniversary by Gwendolyn Smith Beyond Survival: The Fragility of Context in Digital Archives by Nicholas Haynes “Records Doulas” and the Case for Patient Records Advocacy: An Emerging Role for Archivists by Stacy L. Vandenput Preserving Play: Memory, Meaning, and the Soul of Games by Jesse Jacobs Farewell to Spring \u2725 Graduates Meet the 2025 Archeota Team Farewell to Managing Editor, Katie Burns SAASC 2025-2026 Team SAASC Spring 2025 Eventshttps://scholarworks.sjsu.edu/saasc_archeota/1022/thumbnail.jp
Transforming Chatbot Text: A Sequence-to-Sequence Approach to Human-Like Text Generation
No full textWith the advancements in Large Language Models (LLMs) such as ChatGPT, the boundary between text written by a human and that created by AI has become blurred. This poses a threat to systems designed to detect AI-generated content. In this work, we adopt a novel strategy to adversarially transform GPT-generated text using sequence-to-sequence (Seq2Seq) models, to make the text more human-like. We concentrate on improving GPT-generated sentences by including significant linguistic, structural, and semantic components that are typical of human-authored text. The goal is to then use this transformed data to train a more robust detection model. Our experiments show that our Seq2Seq approach makes it difficult for classifiers to detect AI-generated text. On the other hand, once retrained on this augmented data, models become more robust with improved accuracy in distinguishing AI-generated from human-generated content. This work adds to the ongoing conversation of text transformation as a tool for both attack (in the sense of defeating classification models) and defense (in the sense of improved classifiers), thereby advancing our understanding of AI-generated text detection
EduRAG: Improving AI Teaching Assistants with Retrieval-Augmented Generation
No full textThis paper is based on the emerging need for AI-driven teaching assistants to deliver personalized, effective, and responsive educational assistance. Earlier proposals for educational technology such as rule-based systems and adaptive learning platforms have been limited by the lack of flexibility, domain accuracy, and slow, non-interactive support. With the launch of large language models (LLMs) like GPT-3, GPT-4, they have demonstrated that they can generate the kind of human-responsive text. When it comes to more substantive education matters, though, such models suffer from domain accuracy, in addition to whether accurate information can be imparted. And that is where the aspect of staying accurate comes into play. One such potential solution was identified as the Retrieval-Augmented Generation (RAG). These problems can be addressed by fusing the generative capability of LLMs with modern retrievals that allow it to refer to well-curated knowledge bases in that domain, thus making the responses more accurate and appropriate. The present study develops RAG further as an AI teaching assistant to enhance the value of education for the individual. Its objective is to remain abreast of the more current retrieval methodologies together with Large Language Models (LLMs) that attempt to reduce the divide between general knowledge and subject-specific expertise. This, therefore, empowers AI assistants by providing information that is not only relevant to the context but also customized to the unique requirements of students. The paper discloses how RAG brings benefits to a solution that conventional AI teaching assistants would have otherwise restricted. It further discusses about the development of dependable, precise, scalable, AI-driven solutions for educational settings
Dynamic Pricing for Revenue Maximization in 5G Networks with Elastic Network Slicing and Reinforcement Learning
No full textNetwork slicing is a concept that enables a diverse array of network applications with lots of unique service requirements. For the scope of this research, we delve into elastic network slicing, which has a potential benefit for both the providers and users through cost-effective resource utilization. Dynamic pricing of these slice resources is a method for operators to realize the balance of different types of network slices by implicitly communicating the current network state to slice users. We have designed a custom pricing scheme using Deep Reinforcement Learning for elastic network slices that maximizes the revenue of slice providers while meeting the users’ slice service requirements. Our experiment results indicate that a balanced distribution of different slice types, which is realized by our dynamic pricing, increases the total revenue of a slice provider without violating the service level agreement with slice users
Faux Capabilities: A novel approach for code analysis
Get PDFWhen using third-party packages or libraries, it is crucial to understand their behavior. Typically, this requires developers to either conduct code reviews or set up sandbox environments for testing or write unit tests with mocked values for every function used in their code. However, these approaches are often inefficient and time-consuming. A more effective solution would provide developers with a broad understanding of the functionality required by the code they plan to import. This can be done using object capabilities, where a particular functionality is the capability that an object must possess, in order to be able to perform the required actions. Examples of these functionalities of capabilities could be something like File I/O, Network access etc. This research explores using the object-capabilities system to create faux capabilities. The faux capabilities possess the same methods and attributes as the original capabilities, however the way they behave is different. These capabilities can either mock results for the operations used by the third-party code or log those operations to a defined log file, helping developers gain insight into the code’s behavior without having to set up a sandbox environment or creating mocked unit tests
Retrieval-Augmented Generation (RAG) Chatbots: A Comparative Study of Claude, GPT-4o, DeepSeek, and Llama
No full textThe use of Retrieval-augmented generation (RAG) in chatbot platforms has transformed academic spaces by significantly improving information accessibility. RAG has become a viable approach to upgrading Large Language Models (LLMs) with external knowledge access in real time. With the growing availability of advanced LLMs such as GPT, DeepSeek, Claude, Gemini, and Llama, there is a growing need to compare RAG systems based on different LLMs. This study compares the responses of four different RAG chatbots using popular LLMs against a uniquely designed evaluation dataset. Specifically, the study compares the responses and performance of closed-source (GPT-4o and Claude) and open-source models (DeepSeek and Llama) against questions requiring inference from multiple scientific corpora with intricate content and structure. All RAG models in this research use a Chroma vector database to store embeddings. The retrieved documents and the query are provided as input prompts to the LLMs, thus allowing contextually grounded response construction. Each chatbot is evaluated based on ten complex research papers from various domains in computer science. The specifically designed evaluation dataset contains 75 questions derived from these research papers, with a wide variety of questions ranging from simple yes/no questions to questions requiring an understanding of multiple papers. The responses provided by each chatbot are measured quantitatively using standard measures, including Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation (ROUGE), and Bidirectional Encoder Representations from Transformers (BERT) scores, to evaluate response quality comprehensively
Retrieval-Augmented Generation for Survival Analysis in Cancers: Methods and Evaluation on the Surveillance, Epidemiology, and End Results Database
No full textHealthcare is one of the most important fields that benefits from advancements in Artificial Intelligence (AI). From classic models like linear regression to cuttingedge transformers, AI is applied across various healthcare subdomains, such as drug discovery, predictive analytics, and personalized medicine, to name a few. These techniques enable medical practitioners to make more informed decisions, significantly improving both the speed and accuracy of diagnoses and treatments. Machine learning has played a transformative role in oncology, especially in areas like early detection, diagnosis, treatment planning, and patient monitoring, by analyzing medical images, clinical information, genomic data, sensor information. Our research aims to develop a solution for predicting cancer patient survivability using an emerging approach in AI, called Generative AI. Specifically, we will leverage Large Language Models, a powerful subset of Generative AI, including other facets of AI, namely Natural Language Processing (NLP), Machine Learning (ML), and Rule based systems. We also look into how Retrieval Augmented Generation can be used to derive insights from larger datasets, followed by evaluation of the responses given by LLMs, using the LLM as a Judge approach. This research enables us to capture insights from unstructured data that can go unnoticed in traditional machine learning pipelines. By integrating generative models with retrieval mechanisms, our goal is to deliver more context-aware and clinically relevant insights. Additionally, the evaluation process involves comparing multiple responses and evaluating accuracy across different models