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From shocks to solidarity and superstition: Exploring the foundations of faith
Get PDFAdditive shocks can substantially increase cooperation in otherwise standard public goods game experiments. We study shocks that randomly adjust players’ earnings by a fixed positive or negative amount reported at the end of each round. These adjustments change neither the return to players’ contributions nor the information about other group members. We compare results across four treatments that employ the same group-level adjustment algorithm but frame it differently, with pre-play descriptions that range from omitting all useful information to accurately revealing its 50/50 random nature. In each treatment, overall contributions run about 50% higher than those obtained in the standard no-adjustment game. Contributions run higher still, nearly 100% over baseline, in a treatment that individualizes the adjustments, truthfully describing them as 50/50 random and separately calculated for each player. Our results contrast with those of previous studies, which add risk to public goods games in ways that directly interact with players’ contributions and typically reduce cooperation. Players’ contributions and post-play feedback strongly suggest that our results trace back to a pair of deep-rooted impulses that boost solidarity in response to external risk and rationalize the response with superstitious thinking
Spartan Daily, October 8, 2025
Get PDFVolume 165, Issue 20https://scholarworks.sjsu.edu/spartan_daily_2025/1063/thumbnail.jp
Spartan Daily, October 9, 2025
Get PDFVolume 165, Issue 21https://scholarworks.sjsu.edu/spartan_daily_2025/1064/thumbnail.jp
Spartan Daily, October 15, 2025
Get PDFVolume 165, Issue 23https://scholarworks.sjsu.edu/spartan_daily_2025/1066/thumbnail.jp
Sensing the Pulse of a Data Stream in Real Time
Get PDFIn an era where data never sleeps, streaming algorithms offer a powerful toolkit for extracting meaningful insights from high-velocity data flows. This talk explores some foundational techniques that enable efficient, real-time analytics with minimal memory requirements. The algorithms covered include a clever bit-based strategy for approximating the count of 1s in a sliding window, ideal for binary streams where space efficiency is paramount. Another algorithm helps estimate statistical moments (mean, variance, skewness) using compact sketches, enabling a deeper understanding of stream distributions without storing the entire dataset. One other algorithm identifies trending items with exponential decay, giving more weight to recent data, a crucial method for dynamic environments like social media or sensor networks. Techniques like these form the backbone of intelligent stream processing. Through intuitive examples and practical applications, this session will demystify how these algorithms work, why they matter, and how they can be used to monitor, summarize, and react to data in motion.https://scholarworks.sjsu.edu/oer/1018/thumbnail.jp
“Burned Through”: Performing Social Justice in the Academy
No full textIn this chapter, we discuss and interrogate our experiences of infusing social justice into our research, teaching, and service amidst sexism, racism, and other intersectional forms of oppression that are prevalent within the academy and how this tension has tended to elicit performative social justice. First, we situate this chapter by summarizing our individual journeys with social justice in sport and exercise psychology (SEP). Second, we describe our collective journey as former graduate students and early career professionals. Third, we discuss the challenges that we encountered with respect to our social justice work. Fourth, we share the ways that we used tomanage and circumvent these challenges by performing social justice in the academy. Fifth, we conclude by offering what we see as critical future directions in this area
When the Audience Takes the Controller: How Streaming Is Changing the Way We Play Games
Get PDFFor decades, video games have been understood as solitary experiences. A player would sit alone or with a small group, controller in hand, navigating digital worlds where every action, decision, and outcome was shaped by their own input. At its core, gaming was personal. The lines between the audience and player were clearly drawn. The player was the one actively involved in the game, while everyone else was an observer, watching from the sidelines. This traditional model has seen significant transformation in the last decade, as the rise of streaming platforms like Twitch, YouTube Gaming, and other similar services radically reshaped how we engage with games. Suddenly, watching others play became not just a passive activity but a cultural phenomenon—a space where fans could immerse themselves in the gaming experience without touching a controller. This paper will examine how streaming has blurred the lines between performer and audience, creator and consumer, fundamentally altering what it means to play a game
SECURED DATA STORAGE MANAGEMENT WITH DEDUPLICATION IN CLOUD COMPUTING AND LOCAL GPT INTEGRATION
No full textExponential growth in cloud computing has brought enormous changes in data storage and processing, but also raised several questions on the security, privacy, and efficient storage of data. This report provides a dual-focused approach toward solving these challenges. First, we try to build an application securely and efficiently using data deduplication and Proxy Re-Encryption for optimization of storage and enabling secure data sharing. Deduplication ensures that redundant data is removed before encryption for maximum efficiency in storage, while PRE enables the safe sharing of encrypted data by re-encrypting the keys for specified recipients without the leakage of sensitive information. We further propose developing a local version of GPT. LocalGPT is a solution for privacy and being absolutely offline to interact with documentation in a privacy-preserving manner. Indeed, all data is local to the user’s device. By combining Large Language Models and Document Ingestion with local embedding generation using the Instructor Embedding model and storing them in a Chroma vector database, one can efficiently and securely create and query contextual documents without relying on an external server
Coral Vision – Crustose Coralline Algae Detection with Computer Vision
No full textCrustose coralline algae (CCA) are a group of red algae that are vital contributors to the health of coral reef ecosystems. Monitoring CCA abundance can serve as an indicator for coral reef health and improve reef conservation efforts. Autonomous Reef Monitoring Structures (ARMS) are artificial structures that can be deployed into coral reef ecosystems and retrieved to gather ecological data without harming reef structures. Traditional methods of calculating CCA abundance require manual analysis and are labor-intensive. Recent developments in computer vision and deep learning technology have provided an avenue to fully automate this task. This research aims to train a machine learning model capable of automatically segmenting CCA in ARMS plate images by applying the Mask R-CNN algorithm. To accomplish this, ARMS plate images were annotated and used to create datasets. Numerous models were trained using varying parameters and datasets to establish best practice training methods. The best performing model in this work achieved, 0.6903 precision, 0.5538 recall, and an F1 score of 0.6146. These results demonstrate preliminary success with using the Mask-R CNN framework to identify CCA, showing potential for automatic CCA coverage calculation
PhysioTrack: A Gamified Physiotherapy System
No full textTraditional physiotherapy methods tend to be non-interactive and provide little to no personalized instruction, even though physiotherapy is critical to stroke recovery. This thesis explores a fully adaptive, sensor-based, feedback architecture intended for stroke patients which remotely supervises movement and personalizes exercises enabled by multimodal sensors. The system uses filtering and windowed segmentation of accelerometer and skeletal data to compute features like jerk, speed, and joint movement angular range. A game engine applies accelerometer and skeletal features together with optimized, lightweight ML models to drive adaptive feedback, scoring, and difficulty adjustment. The architecture supports responsive continuous sensor streaming within the bounds of low-latency inference and real-time parallel processing. This sensor-based augmented reality system has significant potential as a scalable foundation for individualized rehabilitation in clinical and home environments