大数据预测的伦理困境与出路

立足海量信息资源与尖端分析技术的大数据预测模型在有效指导商业决策、优化资源分配、降低管理成本并提升教育科研水平的同时,存在结果预判挑战自由、隐私披露挑战尊严、信息垄断挑战公平、固化标签挑战正义等伦理背反问题。亟待通过健全以尊重原则、橡皮原则、无害原则、可持续发展原则等为核心的后现代数据伦理体系,有效表达各方自由意志并平衡利益冲突、顺利传承良善伦理观念、实现大数据生态系统的良性循环、推动人类生存价值与自然永续发展的和谐共进。     

Improved sales time series predictions using deep neural networks with spatiotemporal dynamic pattern acquisition mechanism

The ability to predict product sales is invaluable for improving many of the routine decisions essential for the running of an enterprise. One significant challenge of sales prediction is that it is hard to dynamically capture changing dependent patterns along the sales time line, because sales are often influenced by complicated and changeable market environment. To address this issue, we model sales prediction as a task of multivariate time series (MTS) prediction, and propose a Spatiotemporal Dynamic Pattern Acquisition Mechanism (SDPA), which comprises four components, described below: (1) In the processing of input data: A Spatiotemporal Dynamic Kernel (SDK) component is designed for MTS to effectively capture different dependent correlation patterns during different time periods. (2) In terms of model design: A Simultaneous Regression (SR) component is proposed to dynamically detect stable correlations by using co-integration based dynamic programming over different time periods. (3) A novel Hierarchical Attention (HA) component is designed to incorporate SDK to detect spatiotemporal attention patterns from the captured dynamic correlations. (4) In the design of loss function, A Change Sensitive and Alignment component (DC) is proposed to provide more future information based on future trend correlations for better model training. The four components are incorporated into a unified framework by considering Homovariance Uncertainty (HU). This is referred to as SDPANet and contributes to model training and sales prediction. Extensive experiments were conducted on two real-world datasets: Galanz and Cainiao, and experimental results show that the proposed method achieves statistically significant improvements compared to the most state-of-the-art baselines, with average 41.5% reduction on RMAE, average 39.5% reduction on RRSE and average 46% improvement on CORR. Experiments are also conducted on two new datasets, which are Traffic and Exchange-Rate from other fields, to further verify the effectiveness of the proposed model. Case studies show that the model is capable of capturing dynamic changing patterns and of predicting future sales trends with greater accuracy.     

What the fake? Probing misinformation detection standing on the shoulder of novelty and emotion

One of the most time-critical challenges for the Natural Language Processing (NLP) community is to combat the spread of fake news and misinformation. Existing approaches for misinformation detection use neural network models, statistical methods, linguistic traits, fact-checking strategies, etc. However, the menace of fake news seems to grow more vigorous with the advent of humongous and unusually creative language models. Relevant literature reveals that one major characteristic of the virality of fake news is the presence of an element of surprise in the story, which attracts immediate attention and invokes strong emotional stimulus in the reader. In this work, we leverage this idea and propose textual novelty detection and emotion prediction as the two tasks relating to automatic misinformation detection. We re-purpose textual entailment for novelty detection and use the models trained on large-scale datasets of entailment and emotion to classify fake information. Our results correlate with the idea as we achieve state-of-the-art (SOTA) performance (7.92%, 1.54%, 17.31% and 8.13% improvement in terms of accuracy) on four large-scale misinformation datasets. We hope that our current probe will motivate the community to explore further research on misinformation detection along this line. The source code is available at the GitHub.²     

遥感图像椒盐噪声PDE扩散模型

文中首先对方向扩散模型和自蛇模型进行了分析和讨论,在此基础上提出一种去除遥感图像椒盐噪声的PDE扩散模型.该模型通过引入基于ENI的引导扩散函数,自适应的调整模型在遥感图像不同区域的平滑力度,克服了方向扩散模型容易模糊和丢失奇异点,自蛇模型去除椒盐噪声不彻底和容易丢失纹理细节的不足,在有效去除遥感图像椒盐噪声的同时,很好的保持了图像边缘和纹理细节信息.实验结果验证了所提模型的有效性和稳定性.     

女子自由泳全国纪录的灰色模型及其预测

本文针对运动成绩变化规律的模糊性、未知性等 ,运用灰色理论的模型方法建立了我国女子自由泳全国纪录 5个项目的灰色GM(1,1)预测模型 ,并进行了后验差检验和关联度分析 ,结果表明预测模型精度均为一级 ,模型可信度较高 ,具有一定的实用价值 ,可以用来揭示女子自由泳各项运动成绩的发展趋势。并对2 0 0 2年的运动成绩进行了预测。     

冲床的噪声及控制

该文从声音的本质出发，讨论了冲床工作时噪声的主要来源与传播途径，以及如何有效地控制冲床的噪声。     

赛场噪声对定量负荷时机体疲劳的影响

通过对8名体育学院男生分别在无、有赛场噪声暴露下进行定量负荷踏车运动后心率、反应时、运动时、握力、肌肉用力感以及睾酮/皮质醇等指标的测定,探讨赛场噪声对机体疲劳的影响。结果发现赛场噪声不仅加剧大强度运动时心血管的反应,而且诱发及加剧中枢神经机能下降,引起内分泌激素调节失调,从而加快疲劳的产生。     

十项全能运动员成绩多因素非均衡性预测模型的研究

以九运会和世锦赛男子十项全能比赛资料为依据，运用多元回归预测理论，建立十项全能多元回归预测模型。所建立的y模型预测值准确性高，具有一定的实用性和科学性，该模型不仅适用于我国十项全能运动员成绩预测，而且还对外国十项全能运动员适用。     

2008年奥运会我国田径竞争实力预测与分析

以历届全运会与奥运会的田径成绩、我国后备选手实力、我国田径主要战绩为研究对象,分别从全运会与奥运会田径成绩趋势预测、专家预测、全运会与奥运会田径成绩均值比较、优秀后备选手实力分析以及我国田径主要战绩分析等5个方面对2008年我国田径竞争实力做了预测与分析,运用这5个方面的预测信息对2008年奥运会我国各田径项目竞争实力做出综合评价。     

本钢薄板坯连铸机长漏斗形H2结晶器应用浅析

本钢薄板坯连铸生产线采用了组合式长漏斗形H2(高拉速、高质量)直结晶器.结晶器系统引进了意大利达涅利公司设计的 "漏钢预报"这一先进技术.在生产实践过程中,该系统在结晶器液位控制和在线调宽等功能上凸显了优越性,能够满足现场的实际使用要求.