User Roles in Virtual Community of Crowdsourcing for Innovation: A Case Study of Xiaomi MIUI in China

Crowdsourcing innovation, as a new innovation pattern, helps companies reduce the risks and costs of innovation, which has received widespread attention and practical application. What is critical for improving crowdsourcing innovation performance is to understand the heterogeneity of participating users deeply, guide and motivate users to participate actively. Based on the typical characteristics of crowdsourcing innovation communities, this paper proposes a model integrating social network analysis (SNA) & K-means clustering algorithm to identify participants’ roles and conducting empirical research with Xiaomi MIUI community. The result indicates that users can be divided into nine categories: active user, positive user, negative user, bystander, creative contributor, faithful supporter, tourist, and new participant. In order to provide decision support for enterprises to govern crowdsourcing innovation virtual community effectively and improve innovation performance, this paper analyses the behavioural characteristics of each user role from two dimensions: interaction behaviour and contribution behaviour

Preparation of an antitumor and antivirus agent: chemical modification of α-MMC and MAP30 from Momordica Charantia L. with covalent conjugation of polyethyelene glycol

Background: Alpha-momorcharin (α-MMC) and momordica anti-HIV protein (MAP30) derived from Momordica charantia L. have been confirmed to possess antitumor and antivirus activities due to their RNA-N-glycosidase activity. However, strong immunogenicity and short plasma half-life limit their clinical application. To solve this problem, the two proteins were modified with (mPEG)[subscript 2]-Lys-NHS (20 kDa). Methodology/principal findings: In this article, a novel purification strategy for the two main type I ribosome-inactivating proteins (RIPs), α-MMC and MAP30, was successfully developed for laboratory-scale preparation. Using this dramatic method, 200 mg of α-MMC and about 120 mg of MAP30 was obtained in only one purification process from 200 g of Momordica charantia seeds. The homogeneity and some other properties of the two proteins were assessed by gradient SDS-PAGE, electrospray ionization quadruple mass spectrometry, and N-terminal sequence analysis as well as Western blot. Two polyethylene glycol (PEG)ylated proteins were synthesized and purified. Homogeneous mono-, di-, or tri-PEGylated proteins were characterized by matrix-assisted laser desorption ionization-time of flight mass spectrometry. The analysis of antitumor and antivirus activities indicated that the serial PEGylated RIPs preserved moderate activities on JAR choriocarcinoma cells and herpes simplex virus-1. Furthermore, both PEGylated proteins showed about 60%–70% antitumor and antivirus activities, and at the same time decreased 50%–70% immunogenicity when compared with their unmodified counterparts. Conclusion/significance: α-MMC and MAP30 obtained from this novel purification strategy can meet the requirement of a large amount of samples for research. Their chemical modification can solve the problem of strong immunogenicity and meanwhile preserve moderate activities. All these findings suggest the potential application of PEGylated α-MMC and PEGylated MAP30 as antitumor and antivirus agents. According to these results, PEGylated RIPs can be constructed with nanomaterials to be a targeting drug that can further decrease immunogenicity and side effects. Through nanotechnology we can make them low-release drugs, which can further prolong their half-life period in the human body

Unsupervised multiple choices question answering via universal corpus

Unsupervised question answering is a promising yet challenging task, which alleviates the burden of building large-scale annotated data in a new domain. It motivates us to study the unsupervised multiple-choice question answering (MCQA) problem. In this paper, we propose a novel framework designed to generate synthetic MCQA data barely based on contexts from the universal domain without relying on any form of manual annotation. Possible answers are extracted and used to produce related questions, then we leverage both named entities (NE) and knowledge graphs to discover plausible distractors to form complete synthetic samples. Experiments on multiple MCQA datasets demonstrate the effectiveness of our method.Comment: 5 pages, 1 figures, published to ICASSP 202

Unsupervised Extractive Summarization with Learnable Length Control Strategies

Unsupervised extractive summarization is an important technique in information extraction and retrieval. Compared with supervised method, it does not require high-quality human-labelled summaries for training and thus can be easily applied for documents with different types, domains or languages. Most of existing unsupervised methods including TextRank and PACSUM rely on graph-based ranking on sentence centrality. However, this scorer can not be directly applied in end-to-end training, and the positional-related prior assumption is often needed for achieving good summaries. In addition, less attention is paid to length-controllable extractor, where users can decide to summarize texts under particular length constraint. This paper introduces an unsupervised extractive summarization model based on a siamese network, for which we develop a trainable bidirectional prediction objective between the selected summary and the original document. Different from the centrality-based ranking methods, our extractive scorer can be trained in an end-to-end manner, with no other requirement of positional assumption. In addition, we introduce a differentiable length control module by approximating 0-1 knapsack solver for end-to-end length-controllable extracting. Experiments show that our unsupervised method largely outperforms the centrality-based baseline using a same sentence encoder. In terms of length control ability, via our trainable knapsack module, the performance consistently outperforms the strong baseline without utilizing end-to-end training. Human evaluation further evidences that our method performs the best among baselines in terms of relevance and consistency.Comment: accepted by AAAI202

Когнитивно-креативное назначение режима «диалога» в работе с художественным текстом на уроках русского языка как иностранного (на примере рассказа А. П. Чехова «Тоска»)

In this paper, a stochastic model predictive control (SMPC) approach to integrated energy (load and generation) management is proposed for a microgrid with the penetration of renewable energy sources (RES). The considered microgrid consists of RES, controllable generators (CGs), energy storages and various loads (e.g., curtailable loads, shiftable loads). Firstly, the forecasting uncertainties of load demand, wind and photovoltaic generation in the microgrid as well as the electricity prices are represented by typical scenarios reduced from a large number of primary scenarios via a two-stage scenario reduction technique. Secondly, a finite horizon stochastic mixed integer quadratic programming model is developed to minimize the microgrid operation cost and to reduce the spinning reserve based on the selected typical scenarios. Finally, A SMPC based control framework is proposed to take into account newly updated information to reduce the negative impacts introduced by forecast uncertainties. Through a comprehensive comparison study, simulation results show that our proposed SMPC method outperforms other state of the art approaches that it could achieve the lowest operation cost

Fine structure and distribution of antennal sensilla of stink bug Arma chinensis (Heteroptera: Pentatomidae)

Scanning electron microscopy was used to examine the morphology, ultrastructure, and distribution of antennal sensilla of the stink bug Arma chinensis. Two types of sensilla trichodea (ST1–2), four types of sensilla basiconica (SB 1– 4), one type of sensilla chaetica (SCH), one type of sensilla cavity (SCA) and one type of sensilla coeloconica (SCO) were distinguished on the antennae in both sexes. ST1 and ST2 were absent from the scape and pedicel. SB1 were absent from the scape. SB2 were distributed throughout the antennae. SB3 were located on the second pedicel and the two flagellomeres. SB4 were absent from the second flagellomere. SCH was observed on the second pedicel and the two flagellomeres. SCA and SCO occurred only on the second flagellomere. SB1 clusters occurred on the distal part of the second flagellomere. We compared the morphology and structure of these sensilla to other Heteroptera and discuss their possible functions

Enhancing Coherence of Extractive Summarization with Multitask Learning

This study proposes a multitask learning architecture for extractive summarization with coherence boosting. The architecture contains an extractive summarizer and coherent discriminator module. The coherent discriminator is trained online on the sentence vectors of the augmented textual input, thus improving its general ability of judging whether the input sentences are coherent. Meanwhile, we maximize the coherent scores from the coherent discriminator by updating the parameters of the summarizer. To make the extractive sentences trainable in a differentiable manner, we introduce two strategies, including pre-trained converting model (model-based) and converting matrix (MAT-based) that merge sentence representations. Experiments show that our proposed method significantly improves the proportion of consecutive sentences in the extracted summaries based on their positions in the original article (i.e., automatic sentence-level coherence metric), while the goodness in terms of other automatic metrics (i.e., Rouge scores and BertScores) are preserved. Human evaluation also evidences the improvement of coherence and consistency of the extracted summaries given by our method.Comment: 11 pages, 4 figure

Genome-wide identification, phylogeny and expressional profiles of mitogen activated protein kinase kinase kinase (MAPKKK) gene family in bread wheat (Triticum aestivum L.)

FPKM values of the wheat MAPKKK gene in 5 tissues (grain, root, stem, leaf and spike) and under 4 abiotic stresses (drought, salt, heat and cold). (XLSX 34 kb