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关键词： Mutual information   Coherence   Training   Semantics   Telecommunications   Computer science   Visualization   Generative models   mutual information (MI)   neural topic modeling   text mining   unsupervised learning  

摘要： To automatically mine structured semantic topics from text, neural topic modeling has arisen and made some progress. However, most existing work focuses on designing a mechanism to enhance topic coherence but sacrificing the diversity of the extracted topics. To address this limitation, we propose the first neural-based topic modeling approach purely based on mutual information maximization, called the mutual information topic (MIT) model, in this article. The proposed MIT significantly improves topic diversity by maximizing the mutual information between word distribution and topic distribution. Meanwhile, MIT also utilizes Dirichlet prior in latent topic space to ensure the quality of mined topics. The experimental results on three publicly benchmark text corpora show that MIT could extract topics with higher coherence values (considering four topic coherence metrics) than competitive approaches and has a significant improvement on topic diversity metric. Besides, our experiments prove that the proposed MIT converges faster and more stable than adversarial-neural topic models.

关键词： TPACK   Scale development   Pre-service teacher   Science education   Programming  

摘要： Programming and computational thinking have been introduced into the curricula of several countries, also in relation to science and technology education. Preparing pre-service teachers for using programming in science education is therefore an important and relevant task. The purpose of this article is to describe what knowledge may be relevant for teachers who are to use programming in science and technology education and to propose a questionnaire to aid in assessing this knowledge. The proposed questionnaire can be used for tracking development over time and for identifying areas where teachers need more knowledge.

关键词： machine learning   food industry   agriculture sector   computer science   artificial neural networks   algorithms  

摘要： The current landscape of the food processing industry places a strong emphasis on improving food quality, nutritional value, and processing techniques. This focus arises from consumer demand for products that adhere to high standards of quality, sensory characteristics, and extended shelf life. The emergence of artificial intelligence (AI) and machine learning (ML) technologies is instrumental in addressing the challenges associated with variability in food processing. AI represents a promising interdisciplinary approach for enhancing performance across various sectors of the food industry. Significant advancements have been made to address challenges and facilitate growth within the food sector. This review highlights the applications of AI in agriculture and various sectors of the food industry, including bakery, beverage, dairy, food safety, fruit and vegetable industries, packaging and sorting, and the drying of fresh foods. Various strategies have been implemented across different food sectors to promote advancements in technology. Additionally, this article explores the potential for advancing 3D printing technology to enhance various aspects of the food industry, from manufacturing to service, while also outlining future perspectives.

关键词： Roads   Accuracy   Data augmentation   Uncertainty   Image segmentation   Deep learning   Real-time systems   Laser radar   Data models   Computer science   Road segmentation   evidence learning   RGB-D data augmentation  

摘要： Despite significant progress in RGB-D based road segmentation in recent years, the latest methods cannot achieve both state-of-the-art accuracy and real time due to the high-performance reliance on heavy structures. We argue that this reliance is due to unsuitable multimodal fusion. To be specific, RGB and depth data in road scenes are each sensitive to different regions, but current RGB-D based road segmentation methods generally combine features within sensitive regions which preserves false road representation from one of the data. Based on such findings, we design an Evidence-based Road Segmentation Method (Evi-RoadSeg), which incorporates prior knowledge of the modal-specific characteristics. Firstly, we abandon the cross-modal fusion operation commonly used in existing multimodal based methods. Instead, we collect the road evidence from RGB and depth inputs separately via two low-latency subnetworks, and fuse the road representation of the two subnetworks by taking both modalities' evidence as a measure of confidence. Secondly, we propose an RGB-D data augmentation scheme tailored to road scenes to enhance the unique properties of RGB and depth data. It facilitates learning by adding more sensitive regions to the samples. Finally, the proposed method is evaluated on the widely used KITTI-road, ORFD, and R2D datasets. Our method achieves state-of-the-art accuracy at over 70 FPS, 5 $\times$ faster than comparable RGB-D methods. Furthermore, extensive experiments illustrate that our method can be deployed on a Jetson Nano 2GB with a speed of 8 $+$ FPS. The code will be released in https://***/xuefeng-cvr/Evi-RoadSeg.

关键词： Advertising   Network topology   Heuristic algorithms   Vehicles   Trajectory   Topology   Computer science   Vehicle dynamics   Wireless networks   Vehicle-to-everything   Internet of Vehicles (IoV)   advertisements dissemination   influence maximization   DQN   dynamic seed replacement  

摘要： With the development of computing and communication technology, the popularity of Vehicle-to-Everything (V2X) has promoted the research of advertising among vehicles. The problem of vehicle advertising communication is similar to the problem of Influence Maximization (IM) considered in social networks as both prioritize a set of candidate nodes as the initial seed nodes for the initial information dissemination to maximize the influence of the information in the global network. As user nodes in vehicle networks, vehicles have high mobility, which leads to the highly complex and unstable topology of vehicle networks. Therefore, we propose a practical solution to solve the problem of advertising dissemination among Intelligent Networked Vehicles (INVs). In our strategy, advertisements are first sent to a group of selected seed INVs and then spread among INVs. To encourage INVs to disseminate advertising actively, we propose an incentive scheme that combines online and offline to maximize profit. We propose a dynamic seed INVs replacement algorithm using the Deep Q-Network (DQN) algorithm to decide the update cycle of the seed INVs set. Simulation results show that the proposed strategy is superior to the existing methods in terms of delivery ratio of advertisement dissemination and expected profit of merchant.

关键词： Feature extraction   Source coding   Natural languages   Visualization   Computer science   Codes   Software systems   Prototypes   Weaving   Vectors   Software design   software comprehension   natural language processing  

摘要： Complex software systems consist of multiple overlapping design structures, such as abstractions, features, crosscutting concerns, or patterns. This is similar to how a human body has multiple interacting subsystems, such as respiratory, digestive, or circulatory. Unlike in the medical domain, software designers do not have an effective way to distinguish, visualize, comprehend, and analyze these interleaving design structures. As a result, developers often struggle through the maze of source code. In this paper, we present an Automated Concept Explanation (ACE) framework that automatically extracts and categorizes major concepts from source code based on the roles that files play in design structures and their topic frequencies. Based on these categorized concepts, ACE recovers four categories of high-level design models using different algorithms and generates a natural language explanation for each. To assess if and how ACE can help developers better understand design structures, we conducted an empirical study where two groups of graduate students were assigned three design comprehension tasks: identifying feature-related files, identifying dependencies among features, and identifying design patterns used, in an open-source project. The results reveal that the students who used ACE can accomplish these tasks much faster and more accurately, and they acknowledged the usefulness of the categorized concepts and structures, multi-type high-level model visualization, and natural language explanations.

关键词： access   capacity   computer science education   equity   policymakers  

摘要： Access to computer science education (CSEd) has greatly expanded in recent years. Starting with the Computer Science for All Initiative, launched in 2016 under President Obama, CSEd has expanded across the U.S. with over half of high schools offering at least one computer science course. Although there has been growth in computer science course offerings, disparities exist when it comes to 'who' enrolls in CSEd courses, and 'where' those courses are being offered. These disparities highlight that while funding and support for CSEd has increased, little is known about the capacity of states to operationalize (and implement) policies to achieve equitable CSEd. Furthermore, how recent changes at the federal and state level(s) are directly impacting minoritized populations' access, participation, and engagement in CSEd. This commentary focuses on the state-level capacity to implement equity-focused CSEd policies that can lead to improvements in student opportunities and outcomes. We argue that state CSEd leaders engage at the intersection of four areas: (1) growing state investment in CSEd, (2) increasingly polarizing political environments, (3) securing human and fiscal resources to implement CSEd policy with fidelity, and (4) supporting Culturally Responsive Computing that sustains equitable teaching and learning practices in schools and classrooms. This commentary also serves as a Call to Action for STEM education researchers to listen closely and carefully to the communities served by CSEd research, including students, families, and educators.

关键词： Chatbots   Education   Computer science   Databases   Accuracy   Mentoring   Information technology   Information systems   Adaptation models   Vectors   higher education   large language model (LLM)   moodle   moodlebot  

摘要： Contribution: This research explores the benefits and challenges of developing, deploying, and evaluating a large language models (LLMs) chatbot, MoodleBot, in computer science classroom settings. It highlights the potential of integrating LLMs into LMSs like Moodle to support self-regulated learning (SRL) and help-seeking behavior. Background: Computer science educators face immense challenges incorporating novel tools into LMSs to create a supportive and engaging learning environment. MoodleBot addresses this challenge by offering an interactive platform for both students and teachers. Research Questions: Despite issues like bias, hallucinations, and teachers' and educators' resistance to embracing new (AI) technologies, this research investigates two questions: (RQ1) To what extent do students accept MoodleBot as a valuable tool for learning support? (RQ2) How accurately does MoodleBot churn out responses, and how congruent are these with the established course content? Methodology: This study reviews pedagogical literature on AI-driven chatbots and adopts the retrieval-augmented generation (RAG) approach for MoodleBot's design and data processing. The technology acceptance model (TAM) evaluates user acceptance through constructs like perceived usefulness (PU) and Ease of Use. Forty-six students participated, with 30 completing the TAM questionnaire. Findings: LLM-based chatbots like MoodleBot can significantly improve the teaching and learning process. This study revealed a high accuracy rate (88%) in providing course-related assistance. Positive responses from students attest to the efficacy and applicability of AI-driven educational tools. These findings indicate that educational chatbots are suitable for integration into courses to improve personalized learning and reduce teacher administrative burden, although improvements in automated fact-checking are needed.