A theory for the sparsity emerged in the Forward Forward algorithm

This report explores the theory that explains the high sparsity phenomenon \citep{tosato2023emergent} observed in the forward-forward algorithm \citep{hinton2022forward}. The two theorems proposed predict the sparsity changes of a single data point's activation in two cases: Theorem \ref{theorem:1}: Decrease the goodness of the whole batch. Theorem \ref{theorem:2}: Apply the complete forward forward algorithm to decrease the goodness for negative data and increase the goodness for positive data. The theory aligns well with the experiments tested on the MNIST dataset

Dumping the Closet Skeletons Online: Exploring the Guilty Information Disclosure Behavior on Social Media

Privacy issues on social media are becoming an increasing area of concern. Paradoxically, some netizens are actively divulging their privacy online. Noticeably, some information is specifically guilt-related, though confession online is considered irrational. This preliminary study strives to understand this guilty information disclosure behavior through a mixed-approach. Analyzing posts and comments in a confession forum on Reddit, we find that sex-related and recreation-related topics prevail. Our qualitative investigation produces a thematic model with 71 codes, 17 concepts, 4 frames, 3 categories, and 9 relationships, capturing the intents, content, influencers of this behavior, and the interactions among users. Our contribution relies on the investigation of this peculiar behavior to better understand people’s privacy behavior. Also, we render a sophisticated framework around guilt-inducing behaviors useful for future work. We also suggest it as a mixture of conformity and counter-conformity, a modern “technology of self” and a variant of Adaptive Cognitive Theory

When Power Goes Wild Online: How Did a Voluntary Moderator’s Abuse of Power Affect an Online Community?

Online moderation is apropos to community curation as a way to fight against malicious behaviors bristling on User-Generated Content (UGC)-based Social Network Sites (SNS). Given the current research gap on voluntary moderation from the perspectives of power misuse, we investigate how power abuse by a moderator would affect the community dynamics in terms of participation indicators, linguistic characteristics, and network structure in a computational fashion. An event on Reddit is chosen for a case study. Using interrupted time-series analysis and social network analysis, we find moderation fueled short-term feuds and brought potential prolonged destruction to the community. People’s linguistic patterns remained stable while the liberation from “tyranny” brought the community back to life and the power competition entailed negative repulsion. We also find an “Exodus” phenomenon as netizens voted with their feet and migrated to a mirror community when facing severe moderation. This preliminary research expands the connotation of moderation by addressing more forms of power abuse. We also refer to social movement and community choice theories in relevant fields and provide the insights of online moderation from interdisciplinary perspectives

Managing Code Debt in Open Source Software Development Projects: A Digital Options Perspective

In this study, we examine the impact of three commonly used digital options on the accumulation of code debt in open source software development (OSSD) projects. Further, we examine the impact of code debt on three measures of OSSD project performance. Specifically, we hypothesize that increased use of defer options and abandon options is negatively related to the accumulation of code debt, while increased use of growth options is positively related to the accumulation of code debt. Further, we hypothesize that while the accumulation of code debt is negatively related to a project’s market success and the engagement of peripheral developers, it is positively related to the engagement of core developers. To test our hypotheses, we plan to collect and analyze project data from a leading OSSD platform. We expect our findings to provide new theoretical perspectives for researchers and actionable insights for software practitioners

AIoT-Based Drum Transcription Robot using Convolutional Neural Networks

With the development of information technology, robot technology has made great progress in various fields. These new technologies enable robots to be used in industry, agriculture, education and other aspects. In this paper, we propose a drum robot that can automatically complete music transcription in real-time, which is based on AIoT and fog computing technology. Specifically, this drum robot system consists of a cloud node for data storage, edge nodes for real-time computing, and data-oriented execution application nodes. In order to analyze drumming music and realize drum transcription, we further propose a light-weight convolutional neural network model to classify drums, which can be more effectively deployed in terminal devices for fast edge calculations. The experimental results show that the proposed system can achieve more competitive performance and enjoy a variety of smart applications and services

Numerical Simulation of Welding Process for Q235 Steel Plate

In this paper, the weld of Q235 steel pipe with diameter of 60 mm and thickness of 4 mm is simulated and analyzed by MSC. Marc software. After modeling, applying material physical properties, setting weld bead and welding path, and applying boundary conditions, the operation results are analyzed and processed by submitting the work. According to the simulation results, different joints are selected to study the thermal cycle process and temperature distribution of joints under different joints

Friends or Foes: Understanding Communication and Interaction Patterns of Homogeneous and Cross-Cutting Spaces in Online Activism

The understanding of people’s communication structure is crucial to answering the ongoing debates about whether social media is a “filter bubble”. Focusing on this topic, our work investigated people’s preference for homogeneous or cross-cutting communications in an online activism setting, also the difference in language use and hyperlink use in different communications types. We used the tweets with #Silent Sam to classify communication types of user interactions. After the metric generation, significance tests, and subgraph mining, we found that people are 15 times more likely to communicate with like-minded people. However, cross-cutting communications increase simultaneously with homogeneous ones when the activity level rises. Also, homogeneous communications significantly use more words related to perception, while cross-cutting ones have more words about cognition. We also unveiled the dark side of the crosscutting communications as they are generally more toxic and aggressive. The use of outside links in the tweets is rare for both cross-cutting and homogeneous communications. Nonetheless, the cross-cutting tweets embed more URLs while they direct to less diverse domains than the homogeneous ones. Left-leaning media sources with mixed to high factuality are linked as the outside source for mostly homogeneous interactions. Our work made contributions in the ways of providing the new methodology of subgraph mining to research about partisan sharing and rendering new insights to the research in online activism.Master of Science in Information Scienc