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关键词： digital game-based training   intellectual disability   life skill training   motivational model  

摘要： Background For learners with intellectual disability, it is a challenge and crucial issue to foster their life skills. These learners generally encounter several problems during the learning process, such as difficulties paying and maintaining attention, difficulties with memory and application and slow learning ability. Life skills training can help them facilitate their physical functions, maintain cognitive memory ability and strengthen their ability to play a greater role in society. Objective Conventional training for learners with intellectual disabilities is generally taught with traditional lectures and demonstrations, which could lack interaction and immediate feedback. To address this issue, by referring to the attention, relevance confidence and satisfaction motivational model, a digital game-based training approach is proposed. Method To confirm the effectiveness of the approach, this study experimented with a life skills training course for learners with intellectual disabilities using a pre- and post-test design for 6 months. One class of learners comprised the experimental group trained with the digital game, while the other class was the control group that learned with conventional training. The learners' learning achievement, anxiety level, learning attitude, problem-solving tendency, learning satisfaction and learning perceptions were assessed. Results and Conclusions The findings indicate that digital game-based training enhanced the learning achievement, learning attitudes, problem-solving tendency and learning satisfaction of students with intellectual disabilities, while also reducing their anxiety levels.

关键词： Federated learning   Feature extraction   Big Data   Collaboration   Graph convolutional networks   Computer science   Accuracy   Feature fusion   graph convolutional networks   online distillation   sequential recommendation  

摘要： Sequential recommendation seeks to understand user preferences based on their past actions and predict future interactions with items. Recently, several techniques for sequential recommendation have emerged, primarily leveraging graph convolutional networks (GCNs) for their ability to model relationships effectively. However, real-world scenarios often involve sparse interactions, where early and recent short-term preferences play distinct roles in the recommendation process. Consequently, vanilla GCNs struggle to effectively capture the explicit correlations between these early and recent short-term preferences. To address these challenges, we introduce a novel approach termed Graph Convolutional Networks with Collaborative Feature Fusion (COFF). Specifically, our method addresses the issue by initially dividing each user interaction sequence into two segments. We then construct two separate graphs for these segments, aiming to capture the user's early and recent short-term preferences independently. To obtain robust prediction, we employ multiple GCNs in a collaborative distillation manner, incorporating a feature fusion module to establish connections between the early and recent short-term preferences. This approach enables a more precise representation of user preferences. Experimental evaluations conducted on five popular sequential recommendation datasets demonstrate that our COFF model outperforms recent state-of-the-art methods in terms of recommendation accuracy.

关键词： Computer science   Engineering  

摘要： Completing complex tasks in unpredictable settings challenges robotic systems, requiring a step change in machine intelligence. Sensorimotor abilities are considered integral to human intelligence. Thus, biologically inspired machine intelligence might usefully combine artificial intelligence with robotic sensorimotor capabilities. Here we report an embodied large-language-model-enabled robot (ELLMER) framework, utilizing GPT-4 and a retrieval-augmented generation infrastructure, to enable robots to complete long-horizon tasks in unpredictable settings. The method extracts contextually relevant examples from a knowledge base, producing action plans that incorporate force and visual feedback and enabling adaptation to changing conditions. We tested ELLMER on a robot tasked with coffee making and plate decoration;these tasks consist of a sequence of sub-tasks from drawer opening to pouring, each benefiting from distinct feedback types and methods. We show that the ELLMER framework allows the robot to complete the tasks. This demonstration marks progress towards scalable, efficient and 'intelligent robots' able to complete complex tasks in uncertain environments.

关键词： Resource management   Spread spectrum communication   Multi-agent systems   Robots   Robot kinematics   Time factors   Collaboration   Computer science   Optimization   Deep reinforcement learning   Task allocation   deep reinforcement learning   task exploration   network connectivity  

摘要： For a limited scale self-organized multi-agent system operating in environments with unknown task distributions, one challenge is to reduce the task response time via efficiently combining task exploration and allocation, another challenge is to improve the task completion rate via unlocking the potential of network cooperation in task allocation. However, in the existing studies, task allocation is generally regarded as an independent issue for known task distribution environments, rarely combined with task exploration, also hardly solving the conflict between the multi-hop network cooperation and mobility flexibility of agents. In view of this, this paper proposes a network connectivity-aware deep reinforcement learning method for task exploration and allocation in limited scale multi-agent systems (NCADRL4TEA). This method divides the task environment into regions and integrates task exploration with task allocation via two policies: a leaving policy to guide global task exploration among regions according to the distribution of agents and tasks, and a stay policy to guide local task allocation within each region according to the multi-hop network cooperation performance between agents. Further, in the stay policy, a network connectivity-aware task allocation optimization model is provided, which leads agents in the same region to cooperate with each other via multi-hop intermittent network connectivity and flexibly adjust their locations until the optimal multi-hop network cooperation performance is achieved. The experimental results verify that NCADRL4TEA can reduce the task response time in combination of task exploration and allocation, and improve the task completion rate in network cooperation.

关键词： Cognitive diagnostic modeling   Programming education   Information technology   Computer science   Educational assessment  

摘要： The global shortage of skilled programmers remains a persistent challenge. High dropout rates in introductory programming courses pose a significant obstacle to graduation. Previous studies highlighted learning difficulties in programming students, but their specific weaknesses remained unclear. This gap exists due to the predominant focus on the overall academic performance evaluation. To address this gap, this study employed cognitive diagnostic modeling (CDM) to profile the skill mastery of programming students. An empirical analysis was conducted to select the most appropriate model for the data, and the linear logistic model (LLM) was determined to be the best fit. Final examination results from 308 information technology (IT) and 279 computer science (CS) students were analyzed using the LLM. Unfortunately, findings revealed that programming students exhibited proficiency primarily in code tracing and language proficiency but displayed deficits in theoretical understanding, logical reasoning, and algorithmic thinking. From a practical standpoint, this deficiency in fundamental skills sheds light on the factors contributing to academic failures and potentially eventual dropout in programming education. When comparing the student population by academic program, CS students demonstrated superior mastery compared to their IT counterparts, although both groups exhibited a lack of mastery in code tracing. These deviations underscore the pressing need for tailored educational strategies that address the unique strengths and weaknesses of each student group. Overall, this study offers valuable insights into programming education literature and contributes to the expanding application of CDM in educational research.

关键词： Face recognition   Faces   Convolution   Image restoration   Generative adversarial networks   Accuracy   COVID-19   Semantics   Robustness   Computer science   Face de-occlusion   masked face dataset   face recognition   structure-guided   generative adversarial network  

摘要： During the COVID-19 coronavirus epidemic, almost everyone wears a facial mask, which poses a huge challenge for face recognition. Therefore, it is urgent to improve the performance of face de-occlusion of masked faces. However, previous inpainting approaches are limited as they require the knowledge of a given mask, and in the past there was also a lack of real-world masked face datasets suitable for the face de-occlusion task. To tackle above issues, we pioneer a real-world masked face de-occlusion dataset (RMFDD) with accurate mask labels. Further, we propose an occlusion aware and structure-guided network (OASG-Net) for face de-occlusion, consisting of mask prediction subnet, structure prediction subnet, and face de-occlusion subnet. In particular, due to the mask prediction subnet, OASG-Net achieves face de-occlusion without a given external mask. We also use face structure to guide OASG-Net, which makes the recovered face topology more natural and realistic. Besides, we design the mask aware layer to avoid the hard 0-1 mask updating of partial convolution in the face de-occlusion subnet. Extensive results on both face de-occlusion and face recognition tasks demonstrate the superiority of our OASG-Net over the state-of-the-art competitors. Code is available at https://***/WHUfreeway/OASG-Net-Occlusion-Aware-and-Structure-Guided-Network-for-Face-De-Occlusion.

关键词： Computer science   Digital badges   Computational thinking (CT) skills   CT self-efficacy   Learning motivation   Primary education  

摘要： Computational thinking (CT) is regarded as the conceptual basis required for identifying and solving real-world problems through algorithmic approaches to achieve transferable solutions. Developing students' CT in primary school stages is essential and has received considerable attention. Meanwhile, researchers incorporated digital badges into classrooms to improve students' learning motivation in computer science (CS). However, few solid empirical studies analyze the effects of the digital badges approach in CS education to develop students' CT. Therefore, to bridge this gap, this study designed and implemented a block-based programming course in a primary school and examined the impact of the digital badges approach on pupils' CT skills and self-efficacy and learning motivation in CS. A quasi-experimental design was conducted among 99 third-grade students across three classes. The results revealed that the adapted digital badges in primary school (1) significantly improved the students' CT skills, (2) had no significant positive impact on their CT self-efficacy or learning motivation in CS, and (3) showed no mediating effect of learning motivation on the relationship between CT skills and CT selfefficacy. In addition, we found that whether students could see only their own digital badges or also those of others had no impact on CT skills, self-efficacy, or learning motivation in CS. These findings have implications for the design of digital badge educational interventions intended to promote students' CT and learning motivation.

关键词： Adaptation models   Data models   Electronic mail   Vectors   Data visualization   Heuristic algorithms   Computer science   Training   Tensors   Periodic structures   Confident graph   graph learning   incomplete multiview clustering (IMVC)   MVC  

摘要： Exploring the structure information is crucial for data clustering task, particularly for the sceneries of incomplete multiview clustering (IMVC) when some views are missing. However, almost all of the existing graph-based IMVC methods either introduce the Laplacian constraint with fixed graphs or simply fuse the graphs of all views, which are vulnerable to the quality of the constructed graphs. To address this issue, we propose a new graph-based method, called confident local structure-aware incomplete multiview spectral clustering. Different from existing works, our method seeks to adaptively uncover the inherent similarity structure among the available instances in each view and learn the optimal consensus graph within a unified learning framework. Moreover, to mitigate the adverse effects of imbalance information across incomplete views and improve the quality of consensus graph, we further impose some adaptive weights on the consensus graph learning model w.r.t. each view and introduce some confident structure graphs to explore the most confident similarity information in the model. In contrast to existing works, our approach simultaneously takes into account the pairwise similarity information and neighbor group-based confident structure information. This dual consideration makes our method more effective in achieving the optimal consensus graph and delivering superior IMVC performance. Experimental results on several datasets demonstrate that our method effectively learns a high-quality and clustering-friendly graph from incomplete multiview data, and it outperforms many state-of-the-art IMVC methods in terms of clustering performance.

关键词： Frequency estimation   Accuracy   Source address validation   Routing protocols   Information processing   Filtering   Time-frequency analysis   Distributed databases   Computer science   Wide area networks   Data streams   global top- K   top- K -fairness   sketch  

摘要： Finding top-K frequent items has been a hot topic in data stream processing with wide-ranging applications. However, most existing sketch algorithms focus on finding local top-K in a single data stream. In this paper, we tackle finding global top-K across multiple data streams. We find that using prior sketch algorithms directly is often unfair in global scenarios, degrading global top-K accuracy. We define top-K-fairness and show its importance for finding global top-K. To achieve this, we propose the Double-Anonymous (DA) sketch, where double-anonymity ensures fairness. We also propose two techniques, hot-filtering and early-freezing, to improve accuracy further. We theoretically prove that the DA sketch achieves top-K-fairness while maintaining high accuracy. Extensive experiments verify top-K-fairness in disjoint data streams, showing that the DA sketch's error is up to 129 times (60 times on average) smaller than the state-of-the-art. To enhance the applicability and technical depth, we also investigate how to extend the DA sketch to general distributed data stream scenarios and how to provide a fairer and more accurate global ranking for top-K items. The experimental results show that the extended version of the DA sketch can indeed compute better rankings and still has significant advantages in general data streams.

关键词： Crowdsourcing   Social networking (online)   Mobile computing   Recruitment   Computer science   Spatiotemporal phenomena   Predictive models   Faces   Sensors   Prediction algorithms   Mobile crowdsourcing   cold start problem   social networks   user retention  

摘要： Mobile crowdsourcing (MCS) has attracted widespread attention by recruiting users with mobile devices to collect crowdsourcing data. Existing research on MCS assumes that the platform has sufficient users. However, platforms in their early stages of development face the cold start problem, which can lead to their inability to grow or even result in bankruptcy. While some studies try to solve it by recruiting users through social networks to participate in crowdsourcing tasks, they only focus on how to recruit more users without addressing the issue of user retention. This can lead to an increasing proportion of users losing interest in the platform and dropping out and thus it fails to solve the cold start problem truly. In light of this, we present a task recommendation-based method to recruit new users via the social network and keep registered users active on the platform. Specifically, we first use an extended independent cascade model to describe the recruitment of users through social networks. Secondly, we use a task acceptance model to describe user decisions. Finally, we utilize a fuzzy control system that incorporates spatiotemporal crowdsourcing information to predict user behaviour and recommend tasks to users most likely to complete them. Extensive experiments on large-scale real datasets were conducted to evaluate the proposed solution. The results indicate that compared to existing methods such as SocialRecruiter, our solution reduces the 30-day average user churn rate by 23.90% while significantly boosting user retention and task completion rates by up to 23.73% and 48.7%, respectively.