Generating rich and controllable motion is a pivotal challenge in video synthesis. We propose Boximator, a new approach for fine-grained motion control. Boximator introduces two constraint types: hard box and soft box. Users select objects in the conditional frame using hard boxes and then use either type of boxes to roughly or rigorously define the object’s position, shape, or motion path in future frames. Boximator functions as a plug-in for existing video diffusion models. Its training process preserves the base model’s knowledge by freezing the original weights and training only the control module. To address training challenges, we introduce a novel self-tracking technique that greatly simplifies the learning of box-object correlations. Empirically, Boximator achieves state-of-the-art video quality (FVD) scores, improving on two base models, and further enhanced after incorporating box constraints. Its robust motion controllability is validated by drastic increases in the bounding box alignment metric. Human evaluation also shows that users favor Boximator generation results over the base model.

State-of-the-art grammatical error correction (GEC) systems rely on parallel training data (ungrammatical sentences and their manually corrected counterparts), which are expensive to construct.In this paper, we employ the Break-It-Fix-It (BIFI) method to build an unsupervised GEC system.The BIFI framework generates parallel data from unlabeled text using a fixer to transform ungrammatical sentences into grammatical ones, and a critic to predict sentence grammaticality.We present an unsupervised approach to build the fixer and the critic, and an algorithm that allows them to iteratively improve each other.We evaluate our unsupervised GEC system on English and Chinese GEC.Empirical results show that our GEC system outperforms previous unsupervised GEC systems, and achieves performance comparable to supervised GEC systems without ensemble.Furthermore, when combined with labeled training data, our system achieves new state-of-the-art results on the CoNLL-2014 and NLPCC-2018 test sets. 1

Extreme Learning Machine (ELM) is widely popular for its advantages such as fast training speed and good generalization performance. However, the randomness of hidden layer parameters in ELM leads to unstable prediction performance of the model. We propose a novel two-hidden-layer extreme learning machine (TELM) for complex data classification. Firstly, the idea of weighting is introduced into TELM, and a weighted two-hidden-layer extreme learning machine (WTELM) model is proposed to improve the classification accuracy of the model. Secondly, the convergence factor and position update formula in gray wolf optimization algorithm (GWO) are adjusted to enhance the optimization algorithm’s ability to search for optimal parameters. Finally, an improved gray wolf optimization (IGWO) is utilized to search for the optimal parameters of the WTELM model. The impact of different intelligent optimization algorithms on the model’s classification results is compared. The experimental results demonstrate that the classification accuracy of the WTELM model has been improved by about 11–18% compared to traditional ELM models. Moreover, compared to the WTELM model, the classification accuracy of the IGWO-WTELM model has improved by about 1–19%. This indicates that the method proposed in this paper is significantly superior to traditional ELM methods and their variants, improving the stability, training speed and classification accuracy of the model.

Deep neural networks are vulnerable to adversarial attacks, where a small perturbation to an input alters the model prediction.In many cases, malicious inputs intentionally crafted for one model can fool another model.In this paper, we present the first study to systematically investigate the transferability of adversarial examples for text classification models and explore how various factors, including network architecture, tokenization scheme, word embedding, and model capacity, affect the transferability of adversarial examples.Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models.Such adversarial examples reflect the defects of the learning process and the data bias in the training set.Finally, we derive word replacement rules that can be used for model diagnostics from these adversarial examples. A.2 Transferability among Different Neural ModelsWe show in Figure 4 the transferability rate among all neural models in the model pool.The column and row headers indicate the IDs of source and target models respectively.The mapping of IDs and the corresponding models is shown in Figure 8.We generate adversarial examples by attacking a source model, and report the transferability rates on a target (or victim) model. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 .71.50.68.66.21.30.50.39.48.48.22.27.51.49.57.50.21.29 .44 .38 2 .55.63.58.59.21.33 .46.47.46.48.20 .31.49.59.52 .50.18.30.45.47 3 .68.53.70.70.22.28.49.41.50.48.21.26.52 .50.58.55.20 .27.44 .41 4 .

Remanufacturing helps to protect the environment and reduce the manufacturing cost by making full use of end-of-life products, and it has been paid much attention nowadays. Disassembly, which is the key step of remanufacturing, is always manually executed due to the uncertainties faced in disassembly process. Recently, robotic disassembly is the research hotspot of remanufacturing research field and it helps to promote the automatic and intelligent developments of disassembly process. Cloud manufacturing helps to improve manufacturing efficiency by encapsulating manufacturing resources into services. Combined with cloud manufacturing and robotic disassembly, the framework and enabling technologies of cloud robotic disassembly (CRD) is proposed in this paper. The enabling technologies of CRD are introduced from the perspective of perception methods, modeling and data storage, planning and controlling services, service scheduling and interaction methods. This paper could provide guidelines to build the CRD.