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检索条件"丛书名=Communications in Computer and Information Science"

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Pair Programming and Underrepresented Groups in information Technology 53rd

Pair Programming and Underrepresented Groups in Information ...

引用

53rd Southern African computer Lecturers Association Meeting

作者： Liebenberg, Janet North West Univ Sch Comp Sci & Informat Syst Potchefstroom South Africa

ISBN: (纸本)9783031824777;9783031824784

It's concerning that in a country where the majority of the population comprises Black South Africans, they constitute the minority within the IT industry. Additionally, the issue of gender inequality persists in IT, with females remaining underrepresented both in IT classes and in the workplace. The aim of this study was to examine the impact that pair programming has on shaping the experiences and perceptions of underrepresented minority students in information Technology (IT) in South Africa. The study included a total of 284 first-year students taking a User Interface programming module. The students were given the opportunity to pick a desired programming partner and for the duration of the semester, they programmed in pairs. At the end of the semester, the students completed a questionnaire. The findings suggested that the group of students had a positive experience with pair programming. However, the underrepresented minority students had an even more positive learning experience with pair programming. The female students (White and Black) and male Black students perceived pair programming in an exceedingly positive light, as it improved their overall programming skills and teamwork skills. It can be recommended that with timely training and adequate implementation of the pair programming principles, all students, but indeed more so the underrepresented minority students in a programming course can reap the benefits of the use of pair programming. This approach may attract and increase the retention rate of underrepresented groups in IT and encourage them to further pursue IT careers in South Africa.

关键词： Pair Programming information Technology Underrepresentation Minorities

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Exploring the Impact of Foot-Based Haptic Feedback on User Experience in Virtual Reality Navigation 15th

Exploring the Impact of Foot-Based Haptic Feedback on User E...

引用

15th Indian Conference on Human-computer Interaction Design and Research

作者： Borah, Nayan Mungi, Pradnya Sneha, R. Sachdeva, Pragya Borah, Pranjal Protim Tezpur Univ Tezpur Assam India Indian Inst Technol IIT Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9783031808289;9783031808296

Haptic feedback in Virtual Reality (VR) navigation transforms the digital experience into an immersive tangible experience. Haptic feedback, particularly on the legs and feet, augments VR experiences by imitating real-world actions, such as stepping on or encountering objects. It is beneficial for training, simulations, therapeutic interventions, and gaming. Providing haptic feedback on feet can not only enhance user immersion and presence but also enable more natural and emotionally involved user experiences. Although the use of haptics is prevalent in VR devices like handheld controllers, its influence on user experience and workload during VR navigation, especially accompanied by vibrotactile feedback on feet, needs further investigation. In this two-phase study, we investigated vibrotactile feedback on feet during VR navigation using a mixed-method approach. This work's results indicate that foot-based haptic feedback enhances participants' perception, spatial awareness, collision information, and path guidance without solely depending on visual feedback while reducing mental and temporal demands and effort.

关键词： Human-computer Interaction (HCI) Virtual Reality (VR) Multisensory User Experience Haptic Feedback on Feet

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Multi-objective Waterborne Trash Tracking Based on D-StrongSORT 4th

Multi-objective Waterborne Trash Tracking Based on D-StrongS...

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Hou, Yongqi Gao, Li Ma, Long Wu, Baijing Lanzhou Jiaotong Univ Coll Elect & Informat Engn Lanzhou Gansu Peoples R China

ISBN: (纸本)9789819603534;9789819603541

Addressing the challenge of effectively tracking floating litter in water bodies, which undergoes significant changes in appearance due to buoyancy and mobility, compounded by its small size, this paper proposes the D-StrongSORT algorithm. This algorithm designs a cost association model comprising motion and appearance models. The motion model incorporates ECC camera compensation and NSA Kalman filter, while the appearance model employs the ResNeSt50 feature extractor and EMA feature update strategy. The motion model compensates for camera noise through ECC compensation, predicts target positions using the Kalman filter, and calculates motion association costs by correlating with detection results. Optimizing the association matching with the DIOU algorithm accurately captures the motion trajectories and appearance features of floating litter. Experimental results demonstrate that compared to StrongSORT, D-StrongSORT reduces the number of identity switches from 19 to 15 and increases the number of multiple tracked targets from 74 to 79, with other metrics showing varying degrees of improvement, reflecting the algorithm's enhanced ability to sustain tracking of the same target over time.

关键词： Floating Debris DIOU MOT StrongSort

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Sybil-Proof Mechanism for information Propagation with Budgets 1st

Sybil-Proof Mechanism for Information Propagation with Budge...

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1st International Workshop on Mechanism Design in Social Networks

作者： Zheng, Junjie Ge, Xu Li, Bin Zhao, Dengji ShanghaiTech Univ Shanghai Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9789819602131;9789819602148

This research investigates the problem of reward distribution in social networks to increase the effectiveness of crowdsourcing tasks for sponsors. Our goal is to design reward mechanisms that encourage early adopters to actively recruit more contributors, thereby streamlining task completion. However, participants may resort to strategic manoeuvres such as withholding invitations, exaggerating their task skills, or fabricating identities (Sybil attacks) to maximize personal gains. To address this dilemma, we introduce a novel mechanism, called the Propagation Reward Distribution Mechanism (PRDM), which effectively motivates agents to exert maximum effort, disseminate tasks throughout their social networks, and discourages Sybil attacks.

关键词： Social Network Reward Mechanism Sybil Attack

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Prediction Model of Energy Consumption Using Autoregressive Models and Differential Evolution 4th

Prediction Model of Energy Consumption Using Autoregressive ...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Rosero Vasquez, Shendry Bone Andrade, Miguel Zambrano Abad, Julio Univ Estatal Peninsula Santa Elena Km 5 Via Santa Elena La Libertad La Libertad Ecuador Ecuador Univ Tecn Luis Vargas Torres Fac Ingn Esmeraldas Ecuador Univ Politecn Salesiana Calle Turuhuayco 3-69 & Calle Vieja Cuenca Ecuador Univ Politecn Salesiana Grp Invest Interacc Robot & Automat Calle Turuhuayco 3-69 & Calle Vieja Cuenca Ecuador

ISBN: (纸本)9783031832093;9783031832109

This study presents an innovative approach for predicting energy consumption by combining autoregressive (AR) models and differential evolution (DE) algorithms. The research addresses the growing need for accurate energy consumption prediction due to increasing demand and environmental concerns. AR models are known for their ability to capture temporal dependencies in time series, while DE algorithms optimize these models by finding the optimal parameters. Residential energy consumption data from an electrical feeder were used, comprising 100 observations for estimation and 30 for validation, with predictions made for a horizon of 24 samples. The process focused on data analysis and visualization to identify trends and patterns, without applying differencing to ensure stationarity. Five variants of the DE algorithm were evaluated, with the DE/best/1/bin strategy standing out for its rapid convergence and accuracy in minimizing the root mean square error (RMSE). The results show that combining AR models with DE significantly improves prediction accuracy, providing a robust framework for optimizing predictive models in the energy sector. This approach enables better planning and management of energy resources, reducing costs and enhancing sustainability. Additionally, the potential integration of hybrid techniques for future research is highlighted, which could offer further improvements in prediction accuracy and stability.

关键词： Energy consumption prediction Autoregressive models (AR) Differential evolution (DE) Time series Optimization Residential energy data Prediction accuracy Root mean square error (RMSE) Data analysis Hybrid techniques

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The Influence of Board Characteristics on Environmental Sustainability in Northern Europe 4th

The Influence of Board Characteristics on Environmental Sust...

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4th International Conference on Society 5.0

作者： Geerawo, Thakoor Sharma Mahadew, Bhavna Univ Technol Port Louis Mauritius

ISBN: (纸本)9783031714115;9783031714122

This paper empirically analyses the influence of board characteristics on the environmental sustainability of listed companies in the United Kingdom, France and Germany. This quantitative study spans from 2013 to 2023, utilising a dataset comprising 2,726 firm-year observations. Employing robust panel data regression with multiple fixed effects, the analysis reveals that board gender diversity, board cultural diversity, the presence of a corporate governance committee and larger firms tend to exhibit higher environmental sustainability performance. The findings of this paper advance the understanding of two key theories: the agency theory and the stakeholder theory. This study thus contributes to the growing literature on corporate governance and sustainability by providing empirical evidence of the role of board characteristics in shaping environmental sustainability outcomes.

关键词： Environmental Sustainability Gender Diversity Cultural Diversity Corporate Governance Firm Size

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Average Merging and Prompting Training for Policy Generalization 5th

Average Merging and Prompting Training for Policy Generaliza...

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5th China Intelligent Robotics Academic Conference

作者： Zhao, Dongdong Yao, Song Yan, Shi Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Recent research has demonstrated that the flexible creation of generic policies can be achieved by merging multiple task-specific, individually trained policies, although this approach has been limited to tasks within the distribution. The present study investigates whether a prompt-based transformer architecture can enable merged multi-task models to generalize to unseen tasks. In this study, multi-task models are obtained without the requirement for centralized training by averaging and merging the attention layer parameters of single models trained on three different tasks. These models are then fine-tuned based on the prompting trajectories of the test tasks. Additionally, a language pretraining model is introduced as an initialization during the individual model training phase. Experiments conducted in four environments of Mujoco and Meta-World demonstrate that the merge-fine-tuning model performs at least as well as the single-task model in unseen test tasks while significantly reducing the number of convergence steps. This work facilitates the extension of generic models to out-of-distribution environments and provides a novel approach for building generic strategies capable of generalizing to unseen tasks.

关键词： policy generalization average merging prompt-based training

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Competence of Teachers and Ethical Aspects of Implementing AI Technologies in Education

Competence of Teachers and Ethical Aspects of Implementing A...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Iryna, Vorotnykova Borys Grinchenko Kyiv Metropolitan Univ Kyiv Ukraine

ISBN: (纸本)9783031834318;9783031834325

The integration of artificial intelligence (AI) technologies into education presents both significant opportunities and challenges for educators. For AI to be effectively and ethically implemented in the educational environment, teachers must possess the necessary competencies and understanding of the ethical implications associated with these technologies. The article examines critical aspects of teacher competence and ethical aspects that must be paid attention to for the successful implementation of AI in education. Using data from a survey conducted among 102 participants, we analyse the distribution of teachers' qualification levels and their views on ethical challenges, advantages and disadvantages arising from the use of AI in education and teachers' readiness to use the new technology (opinions on how AI will improve their work, how easy the use of AI, what is the general attitude and intention to use technology in the future and what is the organisational support and individual characteristics of the teachers themselves). The results show that the majority of teachers have the highest qualifications an average level of digital competence and a low level of AI literacy, which indicates the need to improve the skills of teachers in the implementation of AI, the self-assessment by teachers of the ethics of using AI is higher than the assessment of understanding, knowledge and consideration in the practice of ethical norms It is important to plan and develop critical competencies for the effective use of AI in education, ensuring its safe and ethical implementation based on technological, pedagogical training and the formation of ethical literacy.

关键词： Artificial Intelligence Teacher Competencies AI Competence Digital Competence Ethics Of Using AI

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Image Segmentation Network Based on Convolutional Layers, a Clustering Layer and Cluster Classification 37th

Image Segmentation Network Based on Convolutional Layers, a ...

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37th International Conference on computer Applications in Industry and Engineering

作者： Rosas-Romero, Roberto Univ Amer Puebla San Andres Cholula Puebl Mexico

ISBN: (纸本)9783031762727;9783031762734

This work contributes to the detection of regions of interest on images and their corresponding classification in medical imaging applications by introducing an image segmentation network that consists of four stages. In the first stage, multi-resolution processing is applied to outline regions where further segmentation and classification are to be conducted. Subsequently, a quad-tree division stage followed by a clustering stage deliver an image that is divided into unlabeled clusters. The output stage assigns each cluster to one class. This architecture offers flexibility in the input and output stages since this network can be fed with images of any size and the output stage can be implemented with any traditional classification model such as k-nearest neighbors, multi-layer perceptron, and support vector machine. Another contribution of this work is that this network does not rely on a very large number of annotated images for its training.

关键词： Image segmentation Convolutional Neural Networks Multi-Resolution Analysis Clustering

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The Application of Imaging Methods and Machine Learning in the Agroindustry Sector at Production Activity 4th

The Application of Imaging Methods and Machine Learning in t...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Vardasca, Ricardo Pratas, Antonio Tereso, Marco Bento, Fernando ISLA Santarem Santarem Portugal Univ Porto LAETA INEGI Porto Portugal Univ Evora VISTA Lab ALGORITMI Evora Portugal ISCTE IUL ISTAR Lisbon Portugal

ISBN: (纸本)9783031832093;9783031832109

The agricultural production sector is of great interest to national economies, their GDP and the population's food chain. During digitalization and modernization, imaging techniques and machine learning are important driving factors. In this research, a narrative critical literature review was conducted on the most used imaging modalities and their use with machine learning methods, which are examined along with their applications in the agricultural industry in production activities, as well as their limitations and existing challenges. This research is intended to support a further development of a technological framework for intelligent precision agroindustry production. It was found that the most used imaging methods are hyperspectral and multispectral imaging, infrared thermal imaging, magnetic resonance imaging, X-ray imaging, scanning electron microscopy and ultraviolet imaging. The majority of employments of machine learning along with imaging was using supervised learning algorithms, there were a few applications using unsupervised and reinforcement learning algorithms. From the results and analysis, it can be concluded that the use of imaging techniques enables an increase in quality and profit maximization of agro-industrial products and their characterization data, which can be better analyzed with the help of machine learning methods and lead to a more sustainable and innovative agro-industrial productive sector.

关键词： Agroindustry Imaging Machine Learning Production activity

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