ED-SWE: Event detection based on scoring and word embedding in online social networks for the internet of people

Online social media networks are gaining attention worldwide, with an increasing number of people relying on them to connect, communicate and share their daily pertinent event-related information. Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People. In this paper, a novel Event Detection model based on Scoring and Word Embedding (ED-SWE) is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets. The proposed ED-SWE model can distill high-quality tweets, reduce the negative impact of the advent of spam, and identify latent events in the data streams automatically. Moreover, a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data. In order to further improve the performance of the Expectation-Maximization (EM) iteration algorithm, a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely. Finally, a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event. Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.     

Multi-task learning for video anomaly detection

We propose a multi-task learning framework for video anomaly detection based on a novel pipeline. Our model contains two crossing streams, one stream employs the backbone of Attention-R2U-net for future frame prediction, while the other is designed based on an encoder–decoder network to reconstruct the input optical flow maps. In addition, the latent layers of the two streams are merged together and assigned with a Deep SVDD-based loss at each location individually. Through the combination of these three tasks, the two-stream-crossing pipeline can be trained end-to-end to provide a comprehensive evaluation for the anomaly targets. Experimental results on several popular benchmark datasets show that our model outperforms the state-of-the-art competing models, which can be applied to different types of anomalous targets and meanwhile achieves remarkable precision.     

Tuning of emission by effect of local symmetry in BaLaLiWO6:Dy3+/Eu3+ for WLEDs

The luminescence center energy transfer, crystal field strength, and covalency are limited by the crystal structure of the host and subsequently affect the luminescence efficiency, color, and intensity. Here, we report an excellent red phosphor BaLaLiWO₆:0.40Eu³⁺ and the dependence between symmetry and luminous performance. A model for changing symmetry is drawn by analyzing the Coulomb potential and structure for the application of a double-perovskite phosphor BLLWO: Dy³⁺, Eu³⁺ in white light LEDs. The addition of Dy³⁺/Eu³⁺ makes the W-O bond formed by the B-site and oxygen ion longer and the Li-O bond shorter, and the difference between the eight octahedral around the A-site is reduced, increasing the symmetry of the A-site. Local symmetry was successfully modulated by changing the Eu³⁺ concentration to control the Y/B ratio of Dy³⁺ and the R/O ratio of Eu³⁺ and smoothly achieved (0.382, 0.373) warm white light color coordinate. The phosphor has excellent thermal stability and still has 92.3% intensity at 475 K. The above results show that the wavelength composition of the luminescence is tunable by changing the symmetry of the environment in which the doped ions are located. It applies to single hosts for the regulation of white light emission.     

Rational design of thermally stable polymorphic layered cathode materials for next generation lithium rechargeable batteries

Classical layered transition metal oxides have remained the preferred cathode materials for commercial lithium-ion batteries. Variation in the transition metal composition and local ordering can greatly affect the structure stability. In classical layered cathodes, high concentrations of electrochemically inert Mn elements usually act as a pillar to stabilize the structure. When excess amount of Li and Mn are present in the layered structure, the capacity of the Li-rich layered oxide (molar ratio of lithium over transition metal is larger than one by design) can exceed that expected from transition metal redox. However, the over lithiation in the classical layered structure results in safety issues, which remains challenging for the commercialization of Li-rich layered oxides. To characterize the safety performance of a series of Li-rich layered cathodes, we utilize differential scanning calorimeter and thermal gravimetric analysis; this is coupled with local structural changes using in situ temperature dependent synchrotron X-ray diffraction and X-ray adsorption spectroscopy. These methods demonstrate that the gradual decrease of the Mn–M (M = Ni, Co, Mn and Li) coordination number directly reduces structural stability and accelerates oxygen release. For safety characterization tests in practice, we evaluate the thermal runaway process through accelerating rate calorimeter in 1.0 Ah pouch cells to confirm this trend. Using the insights obtained in this work, we design a polymorphic composition to improve the thermal stability of Li-rich layered cathode material, which outperforms Ni-rich layered oxides in terms of both electrochemical and safety performances.     

Regional consortia and transaction costs for sanitation services in Brazil

Studies on public cooperation usually focus on its impact on the service costs experienced by service providers. However, engagement in public cooperation in service provision often seeks other outcomes, like service coverage (measured by the population served) rather than simply minimizing service expenditure. Moreover, public cooperation by local governments, as either ‘providers’ or owners, generates transaction costs arising from negotiation, monitoring, agency costs and the enforcement of cooperative agreements. However, to date, little empirical effort has been directed at determining the impact of transaction costs on service provision owners and service outcomes. This paper addresses this gap in the empirical literature by examining the relationship between cooperative agreements between Brazilian municipalities for water and wastewater provision and service coverage in the light of transaction costs. We find that transaction costs are higher for cooperative providers due primarily to low service coverage levels, especially in sewage services that demand high investment in assets with high specificity. However, lower expenditures may occur after initial up-front investment.     

Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years. Coronary cardiovascular (CHD) is a kind of heart and blood vascular disease. Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders. Implementing Grid Search Optimization (GSO) machine training models is therefore a useful way to forecast the sickness as soon as possible. The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate. Three models with a cross-validation approach do the required task. Feature Selection based on the use of statistical and correlation matrices for multivariate analysis. For Random Search and Grid Search models, extensive comparison findings are produced utilizing retrieval, F1 score, and precision measurements. The models are evaluated using the metrics and kappa statistics that illustrate the three models’ comparability. The study effort focuses on optimizing function selection, tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification. Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.     

一种基于语义关联的查询优化方法

提出了一种综合词法关系和语义分析的查询优化方法。通过对查询关键字词法特性和本体实例之间语义关联强弱的分析,提高了查询关键字到本体概念映射的完整性和准确率,帮助搜索引擎对用户的意图做出有效推测,解决了传统搜索引擎不能很好理解用户意图的问题。设计实现了相应的查询优化系统,实验表明该系统可以有效提高搜索的准确率。     

煤矿搜救蛇形机器人MK-01控制系统设计

为用于煤矿搜救的蛇形机器人MK-01设计了一个控制系统。该系统能够利用机器人自身携带的仪器设备采集煤矿巷道内部的状态信息,通过无线网络将信息传输给地面系统,为营救人员施救提供依据。该系统对提高煤矿搜救的智能化、自动化水平,降低搜救成本、提高搜救效率具有一定的意义。     

廊坊市地籍信息系统的建立

介绍了廊坊市地籍信息系统的建立过程及系统的功能、组成、运行环境等，并就系统中地籍模型的建立、查询统计功能的实现等问题进行了讨论。     

计算机网络及Internet

就当前计算机网络的广泛应用，介绍了计算机网络、网络的分类及Internet的应用。