Due to the limited sample size caused by preventive maintenance and the variable data distribution caused by environmental factors, the performance of well-trained laboratory models has decreased significantly when confronted with actual industrial bearing fault diagnosis. The existing domain adaptation methods using cross-domain labeled samples for classification make it difficult to resist the influence of outliers on the overall matching, which leads to a negative transfer problem. Given the above issues, a dynamic weight-optimized prototypical contrastive network is proposed. Primarily, domain adaptation is assisted by the discriminative information the classifiers convey during the prediction process. The model is prompted to find the source data that matches the distribution, removing the effect of distribution variability. Furthermore, the sample-aware weighting term evaluates the difficulty of the sample classification and suppresses the performance degeneration of domain adaptation. Subsequently, the in-domain prototypical contrastive learning evaluates dynamically the intensity of feature distribution around each prototype. The features from the same category are inspired to move closer to the prototype to enhance the consistency and discrimination of intra-class features. Meanwhile, cross-domain instance-prototype learning reduces the distribution inconsistency of the corresponding category data in the source and target domains to enable fine-grained inter-domain alignment and mitigate the negative transfer problem. Through numerous comparative experiments, this method shows superior effectiveness and engineering diagnostic feasibility under cross conditions with limited data resources. Note to Practitioners—This article addresses the cross-domain distribution variability and few-shot bearing limitation. Existing research on intelligent bearing fault diagnosis is based on the unattainable assumption that fault samples are sufficient and homogeneously distributed. Moreover, domain-adaptive methods using cross-domain labeled samples for classification are too coarse to resist the effect of the outlier of bearing operational data, leading to the negative transfer problem. In this article, a dynamic weight-optimized prototypical contrastive network is proposed. Primarily, a feature-level cross-domain alignment module pre-trains the feature extractor to generate unique features for disparate bearing fault signals. After pre-training, the model can find source data that matches the distribution, removing the effect of distribution variability. Among it, the sample-aware weighting term, which dynamically weights the samples according to their classification difficulty, prevents the model performance from degrading on the cross-domain task. Then, the prototypical contrastive learning module can construct prototypes, representing typical features of bearing faults. Each category of features is approached with similar prototypes, pushing away from disparate categories of prototypes. Relying on the prototypes to be aligned with disparate domain instances to weed out the effects of outliers. Not only does it achieve cross-domain alignment of underlying features, but it also aligns semantic structures in a shared cross-domain space. Through numerous experiments, it has been proved that the method can not only solve the cross-domain problem and the few-shot problem, but is also more applicable to the fault diagnosis of real industrial systems. Furthermore, its performance is much higher than the existing methods.

We introduce the concept of programmable actuated fidgeting, a type of fidgeting that involves devices integrated with actuators, sensors, and computing to enable a customizable interactive fidgeting experience. In particular, we explore the potential of a swarm of tabletop robots as an instance of programmable actuated fidgeting as robots are becoming increasingly available. Through ideation sessions among researchers and feedback from the participants, we formulate the design space for SwarmFidget, where swarm robots are used to facilitate programmable actuated fidgeting. To gather user impressions, we conducted an exploratory study where we introduced the concept of SwarmFidget to twelve participants and had them experience and provide feedback on six example fidgeting interactions. Our study demonstrates the potential of SwarmFidget for facilitating fidgeting interaction and provides insights and guidelines for designing effective and engaging fidgeting interactions with swarm robots. We believe our work can inspire future research in the area of programmable actuated fidgeting and open up new opportunities for designing novel swarm robot-based fidgeting systems.

In this case study, we tracked children's interest in math during a voluntary math learning program using a constrained version of Osmo's “Math Wizard Magical Workshop's Potions” game. This game targets addition and subtraction skills taught in first through third grade. Families with children six to eight years old (N = 75) volunteered to play 15-minutes daily for two weeks. The entire learning experience was conducted remotely. We administered six surveys to measure participants’ attitudes toward math at three time periods (Pre-, Mid-, and Post-experience). We then use regression to explore the relationship between interest and learning gain scores, and minutes of play. Results were mixed with mostly weak positive correlations across variables. Hand-coded responses revealed that the greatest increase of interest triggered from Mid- to Post-Experience was ‘Affect’ from parents. We discuss the implications of this study on future analyses with children during a pandemic.

In this paper, we study how a co-located robot affects human motivation and emotion. In particular, we examine the role of the co-located robot’s anthropomorphism, as well as the effects of the human’s personality and gender. To study this, we conducted an online experiment, where 182 participants completed a repetitive task, either easy or hard, in one of the four conditions: in the presence of a non-anthropomorphic robot, an anthropomorphic robot, another human, or alone. For each condition, we analyzed the number of repetitions and the total time users spent, which we treated as the proxy of their motivation, as well as their self-reported emotional states. The study results suggest that the presence of a non-anthropomorphic robot has the potential to lead to a higher level of motivation and a more desirable affective state for users than the presence of an anthropomorphic robot or another human, especially for introverts and female users during difficult tasks.

Prior research has shown that spatial abilities are crucial for STEM achievement and attainment. The connection between the digital and physical worlds provided by embodied interaction has been shown to enhance performance and engagement in educational contexts. Spatial reasoning is a domain that lends itself naturally to embodied, physical interaction; however, there is little understanding of how embodied interaction could be incorporated into educational technology designed to train spatial reasoning skills. We propose several guidelines for gestural interaction design in spatial reasoning education games based on an empirical study with students at a local afterschool program using a custom-built computer game for training spatial skills. We present a series of gesture sets derived from an iterative design approach that are easy for children to acquire, show sufficient congruency to specific spatial operations, and enable robust recognition from the system. We also compared children's behaviors when playing the game with our gestural interface and a traditional mouse-based interface and found that children take more time but fewer steps to complete game levels when using gestures.

Research has demonstrated that spatial visualization skills are crucial for success in Science, technology, engineering, and mathematics (STEM) disciplines. With an increasing number of students entering STEM disciplines, the question of how to effectively train students» spatial visualization skills has become very important. While a scalable existing solution is to implement online workshops for students, the problem of how to motivate students to participate in these online workshops remains unsolved. In this study, we studied gamification as a way to motivate first year engineering students to take part in an online workshop designed to train their spatial visualization skills. Our game contains eight modules, each designed to train a different component of spatial visualization. The game records players» in-game behavior with high granularity, which allows us to provide automated, scalable feedback on players» problem-solving strategies. Ten students with different levels of spatial ability played our game and expressed a strong interest in using the game to train their spatial visualization skills in the future. In addition, our analysis of players» in-game behaviors shows the potential benefits of implementing adaptive and personalized learning guidance.

Summary form only given, as follows. We first show that 3G wireless multimedia systems adopting scalable video encoding can achieve higher overall network utilization. Moreover, the problems of both packet loss and player rebuffering can degrade wireless streaming performance if the streaming video is non-scalable due to fading/retransmission and multi-user sharing. We demonstrate that these problems can be mitigated using a scalable video with rate control. The results indicate that MPEG-4 simple scalable visual profile is more suitable for wireless video applications than simple visual profile.

In this paper, we present a wavelet-based multigrid approach to solve the perturbation equation encountered in optical tomography. With this scheme, the unknown image, the data, as well as the weight matrix are all represented by wavelet expansions, thus yielding a multiresolution representation of the original perturbation equation in the wavelet domain. This transformed equation is then solved using a multigrid scheme, by which an increasing portion of wavelet coefficients of the unknown image are solved in successive approximations. One can also quickly identify regions of interest (ROI's) from a coarse level reconstruction and restrict the reconstruction in the following fine resolutions to those regions. At each resolution level a regularized least squares solution is obtained using the conjugate gradient descent method. This approach has been applied to continuous wave data calculated based on the diffusion approximation of several two-dimensional (2-D) test media. Compared to a previously reported one grid algorithm, the multigrid method requires substantially shorter computation time under the same reconstruction quality criterion.