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关键词： Attendance management system   Embedded computer   Face recognition   Haar cascade classification   Machine learning  

摘要： Haar Cascade is a cost-effective and user-friendly machine learning-based algorithm for detecting objects in images and videos. Unlike Deep Learning algorithms, which typically require significant resources and expensive computing costs, it uses simple image processing techniques like edge detection and Haar features that are easy to comprehend and implement. By combining Haar Cascade with OpenCV2 on an embedded computer like the NVIDIA Jetson Nano, this system can accurately detect and match faces in a database for attendance tracking. This system aims to achieve several specific objectives that set it apart from existing solutions. It leverages Haar Cascade, enriched with carefully selected Haar features, such as Haar-like wavelets, and employs advanced edge detection techniques. These techniques enable precise face detection and matching in both images and videos, contributing to high accuracy and robust performance. By doing so, it minimizes manual intervention and reduces errors, thereby strengthening accountability. Additionally, the integration of OpenCV2 and the NVIDIA Jetson Nano optimizes processing efficiency, making it suitable for resource-constrained environments. This system caters to a diverse range of educational institutions, including schools, colleges, vocational training centers, and various workplace settings such as small businesses, offices, and factories. Its adaptability to distinct organizational requirements ensures its relevance and effectiveness across a broad spectrum of users. One of the distinguishing features of this system is its robust integration with databases. It enables efficient storage of attendance records and supports customizable report generation. This comprehensive data management capability ensures that attendance data is readily accessible for monitoring and analysis purposes, contributing to improved decision-making processes. Implementing this Haar Cascade-based attendance management system offers several significan

摘要： To aid in understanding the current climate of diversity and inclusion in the Computer Science Department at our university, we developed a survey to identify and evaluate factors related to these topics. The survey was administered before the third week of participating Spring 2024 courses, which ranged from introductory classes open to any student from any major, to advanced classes taken as part of the computer science major course sequence. Our findings show that students expressed feeling more confident in their potential for success in class versus as a future professional in the field. They reported more positive interactions with faculty than with peers. Students also indicated a desire for more structured opportunities to connect with peers, and commented positively on current departmental efforts to make our common areas more welcoming. The survey results provide a baseline that we can use in the future to evaluate the impact of curricular interventions on student perceptions of inclusion and belonging. We also plan to evaluate how enhanced support for student well-being in the department and within student groups impacts perceptions and retention of underrepresented groups.

关键词： 农民专业合作社   水产养殖   湘阴县   条件优越   繁育技术   绿色食品认证   商标   鲫  

摘要： 湘阴县李公湖三面环江、背靠哑河、水系发达,水产养殖条件优越。湘阴县地方品种黑背鲫以其脊背深黑、肉嫩、汤鲜而闻名。黑背鲫于2024年4月获得农业农村部所属中国绿色食品发展中心的绿色食品认证,市场售价高达120元/千克。2021年,湘阴县李公湖黑背鲫鱼养殖农民专业合作社成功申报“李公湖”商标,并入选湘阴县地方名优产品。现将黑背鲫繁育技术总结如下。

摘要： Once upon a time, Birmingham-Southern College (BSC), a small Liberal Arts school in Birmingham, Alabama, was looking to increase the number of underrepresented groups in Computer Science (CS), which led to participating in a National Center for Women and Information Technology (NCWIT) Learning Circle[21]. It was the Learning Circle that enabled BSC to learn more about the students and how to better support them throughout their studies. To better understand why students were taking the introductory classes and why students remained in the major through graduation, students were given entry and exit surveys. The entry survey results demonstrate that students predominantly enrolled in the introductory course out of curiosity or an interest in computing. The exit survey results show that students were most impacted by supportive faculty, interesting coursework, their own future career goals, and engaging assignments.

摘要： Transferring from a community college to a university computer science program presents many barriers to the student. Many students face personal barriers balancing education with family and work responsibilities. Some of these students wait before continuing on and face challenges in the transition back into the academic environment after many years.

关键词： Predictive models   Data models   Accuracy   Deep learning   Real-time systems   Process mining   Computer science   predictive analysis   process discovery   activity suffix   event logs  

摘要： Process mining, a burgeoning discipline within data science, demonstrates a significant contribution to the software development lifecycle of diverse real-time consumer-centric projects. This paper underscores the prominence of integrating predictive business process monitoring into organizational process models, as it can substantially impact profits and efficiency in any possible business domain along with improving services to consumers. The paper proposes a novel deep learning-based business process prediction model consisting of multiple layers with fine-tuning hyperparameters. The proposed model leverages input embeddings to represent each of the activities, and based on the training of the proposed model, the accuracy of the next activity is calculated. To assess the efficacy of the proposed model, it has been compared with the existing benchmark models. Our proposed model has shown a significant gain over the existing approaches. The results show that the proposed model outperforms these approaches by achieving an accuracy of 76% on the consumer helpdesk dataset along with an accuracy of 78% on the benchmark BPI dataset.

关键词： Training   Pipelines   Vectors   Task analysis   Semantics   Computer science   Iterative methods   Multiple instance learning   whole slide image   weakly-supervised learning   deep learning  

摘要： Multiple Instance Learning (MIL) has demonstrated promise in Whole Slide Image (WSI) classification. However, a major challenge persists due to the high computational cost associated with processing these gigapixel images. Existing methods generally adopt a two-stage approach, comprising a non-learnable feature embedding stage and a classifier training stage. Though it can greatly reduce memory consumption by using a fixed feature embedder pre-trained on other domains, such a scheme also results in a disparity between the two stages, leading to suboptimal classification accuracy. To address this issue, we propose that a bag-level classifier can be a good instance-level teacher. Based on this idea, we design Iteratively Coupled Multiple Instance Learning (ICMIL) to couple the embedder and the bag classifier at a low cost. ICMIL initially fixes the patch embedder to train the bag classifier, followed by fixing the bag classifier to fine-tune the patch embedder. The refined embedder can then generate better representations in return, leading to a more accurate classifier for the next iteration. To realize more flexible and more effective embedder fine-tuning, we also introduce a teacher-student framework to efficiently distill the category knowledge in the bag classifier to help the instance-level embedder fine-tuning. Intensive experiments were conducted on four distinct datasets to validate the effectiveness of ICMIL. The experimental results consistently demonstrated that our method significantly improves the performance of existing MIL backbones, achieving state-of-the-art results. The code and the organized datasets can be accessed by: https://***/Dootmaan/ICMIL/tree/confidence-based.

关键词： Computer science   Gradient boosting   Decision tree   Gated recurrent network   Regional economy  

摘要： This study evaluates the catalytic effects of advanced manufacturing industry clusters and unified large markets on regional economic development from a computer science perspective, revealing their underlying mechanisms. It employs a Gated Recurrent Network (GRN) model optimized with Gradient Boosting Decision Tree (GBDT) technology to conduct empirical analysis through comprehensive data collection and analysis. The primary objectives are to assess these catalytic effects, highlight the importance of innovation and environmental indicators, determine the contribution levels of various factors, and test the computational fit and predictive accuracy of the model. Key findings indicate that the GBDT-GRN model demonstrates a significant improvement in data computation accuracy, ranging from 20% to 52%, and an increase in response time by 23% to 52%. The model achieves a computational fit of 92% to 99% when analyzing regional economic development. The proposed GBDT-GRN model is highly accurate and reliable in evaluating catalytic effects, providing strong support for policy-making and business decision-making. Innovation and environmental indicators play a crucial role, with varying contributions from different factors. This study offers an effective solution for sequence data prediction problems, supports policy-making and business decisions, and points to promising directions for future research.

关键词： Video surveillance   Brain modeling   Consumer products   Consumer electronics   Mathematical models   Computer science   Performance evaluation   Consumer products detection   video surveillance   feature fusion   spider monkey optimization   deep learning  

摘要： Video surveillance systems have been instrumental in the consumer electronics industry, providing advanced functionalities such as product detection. This system utilizes sophisticated techniques to analyze real-time video feeds, enabling us to detect and track different objects, like products. By incorporating product detection abilities into video surveillance technology, consumers may improve security measures while gaining meaningful information in personalized advertising, inventory management, and retail analytics. This convergence of product detection and video surveillance opens new possibilities for businesses to optimize customer experiences, operations, and security. By leveraging deep learning (DL) for product detection, companies can provide enhanced consumer experiences, improve efficiency, and streamline operations. The incorporation of DL into video surveillance systems represented a breakthrough in the intersection of technology and security within the consumer electronics field. This study develops a new feature fusion technique with deep learning-based next-generation consumer products detection (FFDL-NGCPD) on video surveillance monitoring systems. The main aim of the FFDL-NGCPD system is to detect and classify consumer products. In the FFDL-NGCPD technique, a feature fusion process comprises two DL models: ShuffleNet and MobileNet. To improve the performance of the FFDL-NGCPD technique, the spider monkey optimization (SMO) algorithm is applied for the hyperparameter selection process. Finally, an extreme learning machine (ELM) classifier recognizes the various consumer products. The performance evaluation of the FFDL-NGCPD approach is verified utilizing a benchmark dataset. An extensive comprehensive study underlined the enhanced detection results of the FFDL-NGCPD technique.

关键词： Task analysis   Trajectory   Costs   Crowdsourcing   Computer science   Planning   Industries   Spatial crowdsourcing   task assignment   task coalition   trajectory  

摘要： With the popularity of GPS-equipped smart devices, spatial crowdsourcing (SC) techniques have attracted growing attention in both academia and industry. A fundamental problem in SC is assigning location-based tasks to workers under spatial-temporal constraints. In many real-life applications, workers choose tasks on the basis of their preferred trajectories. However, by existing trajectory-aware task assignment approaches, tasks assigned to a worker may be far apart from each other, resulting in a higher detour cost as the worker needs to deviate from the original trajectory more often than necessary. Motivated by the above observations, we investigate a trajectory-aware task coalition assignment (TCA) problem and prove it to be NP-hard. The goal is to maximize the number of assigned tasks by assigning task coalitions to workers based on their preferred trajectories. For tackling the TCA problem, we develop a batch-based three-stage framework consisting of task grouping, planning, and assignment. First, we design greedy and spanning grouping approaches to generate task coalitions. Second, to gain candidate task coalitions for each worker efficiently, we design task-based and trajectory-based pruning strategies to reduce the search space. Furthermore, a 2-approximate algorithm, termed MST-Euler, is proposed to obtain a route among each worker and task coalition with a minimal detour cost. Third, the MST-Euler Greedy (MEG) algorithm is presented to compute an assignment that results in the maximal number of tasks assigned and a parallel strategy is introduced to boost its efficiency. Extensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed algorithms.