Characteristics of Direct Distributors’ Consumption Behavior and How do they Influence on the Finance

Direct distributor s’ consumption behavior is important to direct distributors and direct selling enterprises, and it is different from general consumer behavior. But the research on it was relatively little. With questionnaire investigation, descriptive statistics and correlation analysis, this paper researched direct distributor’s consumer behavior characteristics, and how do they influence on the direct distributors’ financial situation. Characteristics of direct distributors’ consumption behavior included: most of direct distributors were loyal consumers; a great majority of the direct distributors consumed products which themselves sold; a great majority of the direct distributors consumed more than 50 percent of products which company sold; repurchase rate is high; the majority of direct distributors liked products; most of direct distributors were satisfied with the products quality; direct distributors’ consumption was with multiple operational motivations. The consumption characteristics influence on the direct distributor’s finance. Monthly consumption expenditure was notable, positively correlation with age, years of work, consumption/performance rate, repurchase rate, and experience products. Monthly income was notable, positively correlation with consumption/company products rate, and repurchase rate. Monthly income was notable, negative correlation with consumption/performance rate. The conclusions can provide references for direct sellers, selling enterprises and researchers

An Investigation into the Teaching Reform Strategy of Securities Investment Course under the Concept of OBE

In the context of educational development, teachers can promote the development of teaching through the organisation of teaching reform activities, giving students more opportunities for independent learning, so as to provide students with a good learning experience, which is conducive to achieving the purpose of improving teaching quality. Therefore, based on the OBE concept, teachers will carry out teaching reform throughout the teaching process of securities investment, based on the lack of the concept of keeping pace with the times and the poor comprehensive effect of course teaching, do a good job of strengthening the integration of course ideology and professional teaching, and strengthening the integration of other disciplines and professional teaching, etc., and make efforts to cultivate investment talents who meet the needs of the times for the society, so as to play an important role in teaching reform

Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction

Time series is a special type of sequence data, a set of observations collected at even time intervals and ordered chronologically. Existing deep learning techniques use generic sequence models (e.g., recurrent neural network, Transformer model, or temporal convolutional network) for time series analysis, which ignore some of its unique properties. In particular, three components characterize time series: trend, seasonality, and irregular components, and the former two components enable us to perform forecasting with reasonable accuracy. Other types of sequence data do not have such characteristics. Motivated by the above, in this paper, we propose a novel neural network architecture that conducts sample convolution and interaction for temporal modeling and apply it for the time series forecasting problem, namely \textbf{SCINet}. Compared to conventional dilated causal convolution architectures, the proposed downsample-convolve-interact architecture enables multi-resolution analysis besides expanding the receptive field of the convolution operation, which facilitates extracting temporal relation features with enhanced predictability. Experimental results show that SCINet achieves significant prediction accuracy improvement over existing solutions across various real-world time series forecasting datasets

Research on Blockchain-driven Agricultural Products E-commerce Supply Chain Innovation under Game Perspective

With the arrival of the big data era, digital technology has achieved an unprecedented development speed, and blockchain, as a representative of digital technology, solves the problem of mutual trust between multiple transaction subjects in different scenarios with the technological advantages of decentralisation, timestamping and non-tampering. Currently, in the agricultural products e-commerce supply chain scenario, there is the problem of credit risk of agricultural products producers and sellers. Aiming at the pain points of agricultural products e-commerce supply chain, this paper analyses from the perspective of game, through blockchain incentive mechanism, and puts forward blockchain-driven agricultural products e-commerce supply chain innovation suggestions

SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph Attention

3D visual grounding aims to automatically locate the 3D region of the specified object given the corresponding textual description. Existing works fail to distinguish similar objects especially when multiple referred objects are involved in the description. Experiments show that direct matching of language and visual modal has limited capacity to comprehend complex referential relationships in utterances. It is mainly due to the interference caused by redundant visual information in cross-modal alignment. To strengthen relation-orientated mapping between different modalities, we propose SeCG, a semantic-enhanced relational learning model based on a graph network with our designed memory graph attention layer. Our method replaces original language-independent encoding with cross-modal encoding in visual analysis. More text-related feature expressions are obtained through the guidance of global semantics and implicit relationships. Experimental results on ReferIt3D and ScanRefer benchmarks show that the proposed method outperforms the existing state-of-the-art methods, particularly improving the localization performance for the multi-relation challenges

Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation

Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multi-view stereo (MVS). However, existing methods rely on the assumption that the corresponding points among different views share the same color, which may not always be true in practice. This may lead to unreliable self-supervised signal and harm the final reconstruction performance. To address the issue, we propose a framework integrated with more reliable supervision guided by semantic co-segmentation and data-augmentation. Specially, we excavate mutual semantic from multi-view images to guide the semantic consistency. And we devise effective data-augmentation mechanism which ensures the transformation robustness by treating the prediction of regular samples as pseudo ground truth to regularize the prediction of augmented samples. Experimental results on DTU dataset show that our proposed methods achieve the state-of-the-art performance among unsupervised methods, and even compete on par with supervised methods. Furthermore, extensive experiments on Tanks&Temples dataset demonstrate the effective generalization ability of the proposed method.Comment: This paper is accepted by AAAI-21 with a Distinguished Paper Awar

Fully Homomorphic Encryption with k-bit Arithmetic Operations

We present a fully homomorphic encryption scheme continuing the line of works of Ducas and Micciancio (2015, [DM15]), Chillotti et al. (2016, [CGGI16a]; 2017, [CGGI17]; 2018, [CGGI18a]), and Gao (2018,[Gao18]). Ducas and Micciancio (2015) show that homomorphic computation of one bit operation on LWE ciphers can be done in less than a second, which is then reduced by Chillotti et al. (2016, 2017, 2018) to 13ms. According to Chillotti et al. (2018, [CGGI18b]), the cipher expansion for TFHE is still 8000. The ciphertext expansion problem was greatly reduced by Gao (2018) to 6 with private-key encryption and 20 for public key encryption. The bootstrapping in Gao (2018) is only done one bit at a time, and the bootstrapping design matches the previous two works in efficiency. Our contribution is to present a fully homomorphic encryption scheme based on these preceding schemes that generalizes the Gao (2018) scheme to perform operations on k-bit encrypted data and also removes the need for the Independence Heuristic of the Chillotti et al. papers. The amortized cost of computing k-bits at a time improves the efficiency. Operations supported include addition and multiplication modulo $2^k$, addition and multiplication in the integers as well as exponentiation, field inversion and the machine learning activation function RELU. The ciphertext expansion factor is also further improved, for $k = 4$ our scheme achieves a ciphertext expansion factor of 2.5 under secret key and 6.5 under public key. Asymptotically as k increases, our scheme achieves the optimal ciphertext expansion factor of 1 under private key encryption and 2 under public key encryption. We also introduces techniques for reducing the size of the bootstrapping key. Keywords. FHE, lattices, learning with errors (LWE), ring learning with errors (RLWE), TFHE, data security, RELU, machine learnin