Designing a talents training model for cross-border e-commerce: a mixed approach of problem-based learning with social media

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Cross-border e-commerce has developed rapidly integrating the global economy. Research has presented some solutions for the challenges and barriers in cross-border e-commerce from the perspective of the enterprise. However, little is known about the requirements of cross-border e-commerce talents and how to train them. In this paper, we firstly conducted semi-structured interviews to acquire the requirements of cross-border e-commerce talents. Business and market knowledge, technical skills, analytical ability and business practical ability were found to be the four core requirements. Then, we integrated problem-based learning and social media to design a talents training model for cross-border e-commerce and did a program to evaluate effectiveness of the model. Finally, its effectiveness was evaluated from the four evaluation dimensions of attitude, perceived enjoyment, concentration and work intention. The talents training model was improved according to the suggestions

Robust object representation by boosting-like deep learning architecture

This paper presents a new deep learning architecture for robust object representation, aiming at efficiently combining the proposed synchronized multi-stage feature (SMF) and a boosting-like algorithm. The SMF structure can capture a variety of characteristics from the inputting object based on the fusion of the handcraft features and deep learned features. With the proposed boosting-like algorithm, we can obtain more convergence stability on training multi-layer network by using the boosted samples. We show the generalization of our object representation architecture by applying it to undertake various tasks, i.e. pedestrian detection and action recognition. Our approach achieves 15.89% and 3.85% reduction in the average miss rate compared with ACF and JointDeep on the largest Caltech dataset, and acquires competitive results on the MSRAction3D dataset

Synthesis and Formation Mechanism of CuInS\u3csub\u3e2\u3c/sub\u3e Nanocrystals with a Tunable Phase

Chalcopyrite CuInS2 (CIS) hierarchical structures composed of nanoflakes with a thickness of about 5 nm were synthesized by a facial solvothermal method. The thermodynamically metastable wurtzite phase CIS would be obtained by using InCl3 instead of In(NO3)3 as In precursor. The effects of the In precursor and the volume of concentrated HCl aqueous solution on the phases and morphologies of CIS nanocrystals have been systematically investigated. Experimental results indicated that the obtained phases of CIS nanocrystals were predominantly determined by precursor-induced intermediate products. The photocatalytic properties of chalcopyrite and wurtzite CIS in visible-light-driven degradation of organic dye were also compared

Chemokines and chemokine receptors in Behçet’s disease

Behçet’s disease (BD), a chronic vascular inflammatory disease, is characterized by the symptoms of ocular lesions, recurrent genital and oral ulcers, skin symptoms and arthritis in addition to neurological, intestinal and vascular involvement. The pathogenesis of BD is poorly understood, and there are no effective laboratory markers for the diagnosis of BD. In addition, BD is presently incurable. Chemokines, a family of small secreted chemotactic cytokines, interact with chemokine receptors and mediate the migration, localization and cellular interactions of inflammatory cells. Several studies have suggested that chemokines and their receptors play an important role in the occurrence and development of BD and that these chemokines along with their receptors can be utilized as biomarkers and therapeutic targets. In the present review, chemokines and chemokine receptors involved in BD and their potential application in diagnosis and therapy have been discussed

Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection

This paper presents our Facial Action Units (AUs) recognition submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition

Dataset: Global seamless tidal simulation using a 3D unstructured-grid model

Dataset: We present a new 3D unstructured-grid global ocean model to study both tidal and non-tidal processes, with a focus on the total water elevation. Unlike existing global ocean models, the new model resolves estuaries and rivers down to ~8m without the need for grid nesting. The model is validated with both satellite and in-situ observations for elevation, temperature and salinity. Tidal elevation solutions have a mean complex RMSE of 4.2 cm for M2 and 5.4 cm for all 5 major constituents in the deep ocean (the RMSEs for the other 4 constituents (S2, N2, K1, O1) are respectively: 2.05cm, 0.93cm, 2.08cm, 1.34cm). The non-tidal residual assessed by a tide gauge dataset (GESLA) has a mean RMSE of 7 cm. For the first time ever, we demonstrate the potential for seamless simulation, on a single mesh, from the global ocean into several estuaries along the US west coast. The model is able to accurately capture the total elevation, even at some upstream stations. The model can therefore potentially serve as the backbone in a global tide-surge and compound flooding forecasting framework

Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping

Adversarial examples generated by a surrogate model typically exhibit limited transferability to unknown target systems. To address this problem, many transferability enhancement approaches (e.g., input transformation and model augmentation) have been proposed. However, they show poor performances in attacking systems having different model genera from the surrogate model. In this paper, we propose a novel and generic attacking strategy, called Deformation-Constrained Warping Attack (DeCoWA), that can be effectively applied to cross model genus attack. Specifically, DeCoWA firstly augments input examples via an elastic deformation, namely Deformation-Constrained Warping (DeCoW), to obtain rich local details of the augmented input. To avoid severe distortion of global semantics led by random deformation, DeCoW further constrains the strength and direction of the warping transformation by a novel adaptive control strategy. Extensive experiments demonstrate that the transferable examples crafted by our DeCoWA on CNN surrogates can significantly hinder the performance of Transformers (and vice versa) on various tasks, including image classification, video action recognition, and audio recognition. Code is made available at https://github.com/LinQinLiang/DeCoWA.Comment: AAAI 202

Development and Application of a Simplified Model for the Design of a Super-Tall Mega-Braced Frame-Core Tube Building

This article discusses the development and application of a simplified nonlinear model to compare two design schemes of a super-tall mega-braced frame-core tube building

Finding causality and responsibility for probabilistic reverse skyline query non-answers

NSF