Determinants that influence users’ behavioral intentions in adopting non-immersive virtual reality training systems remains unclear from the perspective of the interaction experience. Based on the stimulus-biological-response framework, a hypothetical model was constructed by integrating, refining, and dismantling three related theories to investigate the behavioral intentions of users adopting wearable devices for non-immersive virtual reality upper limb training. Structural equation modeling and necessary condition analysis (NCA) were used to analyze and interpret 182 data points. Perceived interactivity and spatial presence affected experience dimensions significantly. Arousal had no significant effect on focused attention. Experience was influenced by perceived interactivity for males and spatial presence for females. NCA results verified the influence of each dimension on behavioral intention versus the actual threshold. Stakeholders should consider the positive effects of perceived interactivity and spatial presence on the different experiential dimensions. The effects of different experiential dimensions on behavioral intentions should also not be ignored.

As the exorbitant expense of labeling autopilot datasets and the growing trend of utilizing unlabeled data, semi-supervised segmentation on point clouds becomes increasingly imperative. Intuitively, finding out more ``unspoken words'' (i.e., latent instance information) beyond the label itself should be helpful to improve performance. In this paper, we discover two types of latent labels behind the displayed label embedded in LiDAR and image data. First, in the LiDAR Branch, we propose a novel augmentation, Cylinder-Mix, which is able to augment more yet reliable samples for training. Second, in the Image Branch, we propose the Instance Position-scale Learning (IPSL) Module to learn and fuse the information of instance position and scale, which is from a 2D pre-trained detector and a type of latent label obtained from 3D to 2D projection. Finally, the two latent labels are embedded into the multi-modal panoptic segmentation network. The ablation of the IPSL module demonstrates its robust adaptability, and the experiments evaluated on SemanticKITTI and nuScenes demonstrate that our model outperforms the state-of-the-art method, LaserMix.

Network embedding is a process of encoding nodes into latent vectors by preserving network structure and content information. It is used in various applications, especially in recommender systems. In a social network setting, when recommending new friends to a user, the similarity between the user's embedding and the target friend will be examined. Traditional methods generate user node embedding without considering the recommendation target. No matter which target is to be recommended, the same embedding vector is generated for that particular user. This approach has its limitations. For example, a user can be both a computer scientist and a musician. When recommending music friends with potentially the same taste to him, we are interested in getting his representation that is useful in recommending music friends rather than computer scientists. His corresponding embedding should consider the user's musical features rather than those associated with computer science with the awareness that the recommendation targets are music friends. In order to address this issue, we propose a new framework which we name it as Recommendation Target-Aware Network embedding method (RTANet). Herein, the embedding of each user is no longer fixed to a constant vector, but it can vary according to their specific recommendation target. Concretely, RTANet assigns different attention weights to each neighbour node, allowing us to obtain the user's context information aggregated from its neighbours before transforming this context into its embedding. Different from other graph attention approaches, the attention weights in our work measure the similarity between each user's neighbour node and the target node, which in return generates the target-aware embedding. To demonstrate the effectiveness of our method, we compared RTANet with several state-of-the-art network embedding methods on four real-world datasets and showed that RTANet outperforms other comparative methods in the recommendation tasks.

Prompt-based techniques have demostrated great potential for improving the few-shot generalization of pretrained language models.However, their performance heavily relies on the manual design of prompts and thus requires a lot of human efforts.In this paper, we introduce Genetic Prompt Search (GPS) to improve few-shot learning with prompts, which utilizes a genetic algorithm to automatically search for high-performing prompts.GPS is gradient-free and requires no update of model parameters but only a small validation set.Experiments on diverse datasets proved the effectiveness of GPS, which outperforms manual prompts by a large margin of 2.6 points.Our method is also better than other parameter-efficient tuning methods such as prompt tuning.

The task of rationalization aims to extract pieces of input text as rationales to justify neural network predictions on text classification tasks. By definition, rationales represent key text pieces used for prediction and thus should have similar classification feature distribution compared to the original input text. However, previous methods mainly focused on maximizing the mutual information between rationales and labels while neglecting the relationship between rationales and input text. To address this issue, we propose a novel rationalization method that matches the distributions of rationales and input text in both the feature space and output space. Empirically, the proposed distribution matching approach consistently outperforms previous methods by a large margin. Our data and code are available.

In large-scale online service systems, incidents occur frequently due to a variety of causes, from updates of software and hardware to changes in operation environment. These incidents could significantly degrade system’s availability and customers’ satisfaction. Some incidents are linked because they are duplicate or inter-related. The linked incidents can greatly help on-call engineers find mitigation solutions and identify the root causes. In this work, we investigate the incidents and their links in a representative real-world incident management (IcM) system. Based on the identified indicators of linked incidents, we further propose LiDAR (Linked Incident identification with DAta-driven Representation), a deep learning based approach to incident linking. More specifically, we incorporate the textual description of incidents and structural information extracted from historical linked incidents to identify possible links among a large number of incidents. To show the effectiveness of our method, we apply our method to a real-world IcM system and find that our method outperforms other state-of-the-art methods.

In this paper, we study the problem of recommending the appropriate datasets for authors, which is implemented to infer the proximity between authors and datasets by leveraging the information from a three-layered network, composed by authors, papers and datasets. To link author-dataset semantically by taking advantage of the rich content information of papers in the intermediate layer, we design an attentive and aggregate multi-layer network learning model. The aggregation is for integrating the intra-layer information of paper content and citations, while the attention is used for coordinating authors at the top-layer and datasets at the bottom-layer in the semantic space learned from papers in the intermediate layer. The experimental study demonstrates the superiority of our method compared with the solutions that extend existing models to our problem.

With the rapid growth of cloud service systems and their increasing complexity, service failures become unavoidable. Outages, which are critical service failures, could dramatically degrade system availability and impact user experience. To minimize service downtime and ensure high system availability, we develop an intelligent outage management approach, called AirAlert, which can forecast the occurrence of outages before they actually happen and diagnose the root cause after they indeed occur. AirAlert works as a global watcher for the entire cloud system, which collects all alerting signals, detects dependency among signals and proactively predicts outages that may happen anywhere in the whole cloud system. We analyze the relationships between outages and alerting signals by leveraging Bayesian network and predict outages using a robust gradient boosting tree based classification method. The proposed outage management approach is evaluated using the outage dataset collected from a Microsoft cloud system and the results confirm the effectiveness of the proposed approach.

A metropolis consists of zones segmented by major roads. People travel between zones to conduct social activities. To analyze the characteristics of the entire city, we can explore regions' features and find region-wise latent relationships. In this paper, we propose a semantic associated zone embedding (SAZE) method using distributed representation learning. SAZE can generate zone embeddings which extract more comprehensive characteristics of each zone and fit for many urban computing tasks, rather than the task-oriented methods. To feed our SAZE, we not only consider the connections between zones via trajectory, but also embed its intrinsic properties. Furthermore, we apply SAZE to two tasks, zone classification and zone clustering visualization, respectively. For each task, we compare SAZE with other state-of-the-art baseline methods and the results have demonstrated the advantage of our model over the several methods.

Network embedding, also known as graph representation, is a classical topic in data mining. It has been widely used in real-world network applications such as node classification and community detection. However, it remains open to find a method that is scalable and preserves both structure and content information. Based on generative adversarial networks, we propose an unsupervised network embedding framework NEGAN, which is featured by combining graph topology and node content. In NEGAN, network nodes are mapped to the target space in a highly flexible non-linear way, guided by the content of the nodes. This mapping is learned from the generator of the generative adversarial networks, and node adjacency in the input network is preserved. Experiments on real datasets show that NEGAN outperforms all the existing methods on many scenarios including node classification, visualization and community detection tasks.

We introduce a non-uniform composition that integrates multiple rendering styles in a picture driven by an importance map. This map, either issued from saliency estimation or designed by a user, is introduced both in the creation of the multiple styles and in the final composition. Our approach accommodates a variety of stylization techniques, such as color desaturation, line drawing, blurring, edge-preserving smoothing and enhancement. We illustrate the versatility of the proposed approach and the variety of rendering styles on different applications such as images, videos, 3D scenes and even mixed reality. We also demonstrate that such an approach may help in directing user attention.