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关键词： control process   biodiesel   optimization   sensorless technique  

摘要： This paper demonstrates that biodiesel production processes can be optimized through implementing a controller based on fuzzy logic and neural networks. The system dynamics are identified utilizing convolutional neural networks, enabling tests of the reactor temperature response under different control law proposals. In addition, a sensorless technique using a convolutional neural network to replace the sensor/transmitter signal in case of failure is implemented. Two optimization functions are proposed utilizing a metaheuristic algorithm based on differential evolution, where the aim is to minimize the use of cooling for the control of the reactor temperature. Finally, the control system proposals are compared, and the results show that a neuro-fuzzy controller without optimization restrictions generated unviable ITAE (1.9597x107) and TVU (22.3993) performance metrics, while the restriction proposed in this work managed to minimize these metrics, improving both the ITAE (3.3928x106) and TVU (17.9132). These results show that combining the sensorless technique and our optimization method for the cooling stage enables energy saving in the temperature control processes required for biodiesel production.

关键词： Machine learning   Arts   Computer science  

摘要： Generative artificial-intelligence illustrations can be helpful, but fall short as scientific records.

关键词： Online learning   Learning engagement   Influencing factors   Computer science major  

摘要： Digital technology is reshaping the landscape of higher education, especially in the field of computer science. As digital platforms become central to the learning process, understanding student engagement in these continuously evolving environments is increasingly vital. This study explores the current state of online learning among undergraduate computer science students, investigates factors influencing their engagement, and proposes strategies to enhance online education. The research framework is grounded in the TPACK model, behaviorist learning theory, learning engagement theory, and situated cognition learning theory, encompassing student characteristics, multidimensional online learning engagement, and key influencing factors. Data on participants' basic attributes, levels of engagement, and the major determinants of these engagement levels were collected via a questionnaire survey. Analyses using SPSS 25.0-employing t-tests, ANOVA, and Pearson correlation-revealed significant trends and relationships. Findings show a notable positive correlation between the duration of online learning and overall engagement, whereas gender, home location, and academic major exerted relatively limited influence. Subjective intention, attitude, and motivation emerged as crucial determinants, and interactions with instructors and Peers-reinforced by teaching approaches and feedback-played an essential role in fostering emotional involvement. Building on these insights, the study recommends initiatives to strengthen self-motivation, nurture meaningful online interactions, enhance technical support systems, and reinforce mechanisms for assessing learning outcomes. This work provides empirical evidence for a deeper understanding of online education while indicating directions for ongoing improvement.

关键词： Adversarial machine learning  

摘要： When executed well, project-based learning (PBL) engages students’ intrinsic motivation, encourages students to learn far beyond a course’s limited curriculum, and prepares students to think critically and maturely about the skills and tools at their disposal. However, educators experience mixed results when using PBL in their classrooms: some students thrive with minimal guidance and others flounder. Early evaluation of project proposals could help educators determine which students need more support, yet evaluating project proposals and student aptitude is time-consuming and difficult to scale. In this work, we design, implement, and conduct an initial user study (n = 36) for a software system that collects project proposals and aptitude information to support educators in determining whether a student is ready to engage with PBL. We find that (1) users perceived the system as helpful for writing project proposals and identifying tools and technologies to learn more about, (2) educator ratings indicate that users with less technical experience in the project topic tend to write lower-quality project proposals, and (3) GPT-4o’s ratings show agreement with educator ratings. While the prospect of using LLMs to rate the quality of students’ project proposals is promising, its long-term effectiveness strongly hinges on future efforts at characterizing indicators that reliably predict students’ success and motivation to learn. © 2025, CC BY.

关键词： computer science   digital literacy   web tracking   content moderation   biography   philosophy  

摘要： It is the article's overall aim to elucidate the contingency and volatility inherent to academic biographies. In order to derive these, but also underpin them, the article begins by outlining the argument that Mareike Lisker makes in her Master's thesis, which explores the nexus between the disciplines of computer science and philosophy. There, she posits that the demand for more digital literacy places an onerous responsibility on individual users when it comes to the control of their own data. She argues that the individual users are structurally ill-equipped to meet that responsibility in face of all-encompassing tracking infrastructures. In accordance with the aim of the article, the thesis' topic will be situated within Lisker's scientific career path. This path will then be traced up until her current PhD project, which focusses on content moderation on decentralized platforms.

摘要： This forum focuses on the conditions and futures of the labor underpinning technology production and maintenance. We welcome standalone articles as well as interviews and conversations about all tech labor within the global supply chain of digital technologies. - - Seyram Avle and Sarah Fox, Editors. © 2025 Association for Computing Machinery. All rights reserved.

关键词： Transformers   Adaptation models   Accuracy   Encoding   Contrastive learning   Motion pictures   Computer science   Electronic mail   Statistical analysis   Social networking (online)   Conformity   popularity bias   sequential recommendation (SR)  

摘要： A significant problem in sequential recommendation (SR) is the over-recommendation of popular items, leading to popularity bias, as users often follow these items due to conformity. Existing methods measure users' conformity factors to reduce the impact of popularity bias. However, these methods do not consider the differences in users' conformity behavior in the long-term and short-term. To address this, we propose LSDRec, a novel debiased SR method structured around three key tasks: degree-centrality conformity awareness, dual-scale interest encoding, and adaptive conformity information fusing. The degree-centrality conformity awareness task constructs a multiuser interaction graph, employs a graph convolutional network (GCN) to obtain global user conformity representations, and uses the degree centrality algorithm to compute users' long-term and short-term conformity factors. The dual-scale interest encoding task models users' long-term and short-term interests separately, obtaining corresponding interest representations and further enhancing them through the adaptive conformity information fusing task. The adaptive conformity information fusing task contrasts global conformity representations with long-term and short-term interest representations, adaptively integrating conformity factors and dynamically adjusting the degree of conformity information transfer. Together, these three tasks effectively mitigate popularity bias and improve the accuracy of user interest modeling. Our extensive evaluations of four diverse datasets demonstrate LSDRec's superior performance over current state-of-the-art methods.

关键词： STEM   Black girls   Creativity   Computer science  

摘要： In the USA, Black women face significant underrepresentation in the computer science field. By diversifying the field with women of color, the technology industry can mitigate prevailing unconscious and conscious biases. The study presented in this article addresses a gap in creativity research by examining Black creativity, which is often overlooked by scholars. Through a 9-week afterschool program, Black girls learned to code virtual chatbots through authentic computer science activities, such as coding with Python. Through utilizing quantitative ethnography methods and epistemic network analysis, this study explores the extent in which Black girls engage in creative problem-solving as they learn to code. The epistemic networks revealed nuanced insights into the students' creative problem-solving processes as they engaged in coding. Epistemic network analysis has been demonstrated to be an innovative and culturally responsive approach to examining creativity, a concept historically measured through surveys. This study introduces innovative methods to involve Black girls in a STEM-related field where they are often marginalized while also capturing the intricate ways they engage in creative problem-solving.