Judicial Determination of Confidentiality in Trade Secrets

Trade secrets as one of the core competitiveness of enterprises, related to the survival of enterprises. In the commercial competition, trade secrets is undoubtedly the right person’s wealth code, how the right person to take effective measures to protect trade secrets from being stolen is a matter of concern. In recent years, the practice of trade secret disputes have increased year by year, in the protection of trade secrets dispute cases, the right and the defendant on the identification of trade secrets, especially on the identification of confidentiality has always been the focus of controversy. Even though more and more enterprises on trade secret protection awareness is increasing. But from the many relevant judicial cases, can be seen: trade secrets in practice for trade secrets protection measures still have great loopholes, specifically manifested as: the right to protect the meaning of trade secrets is not clear, the protection of the object is not specific, the duty of confidentiality can not be confidential subject to know or limitations; confidentiality measures and the value of trade secrets are not adaptive; confidentiality measures can not be recognized and so on. This paper combines the recent judicial practice cases, to explore in practice to achieve the “corresponding confidentiality measures” of the core conditions, and in this way for the enterprise to establish trade secret protection system to provide reference

Proposed neutron interferometry test of Berry's phase for a circulating planar spin

The energy eigenstates of a spin$-\frac{1}{2}$ particle in a magnetic field confined to a plane, define a planar spin. If the particle moves adiabatically around a loop in this plane, it picks up a topological Berry phase that can only be an integer multiple of $\pi$. We propose a neutron interferometry test of the Berry phase for a circulating planar spin induced by a magnetic field caused by a very long current-carrying straight wire perpendicular to the plane. This Berry phase causes destructive interference in the direction of the incoming beam of thermal neutrons moving through a triple-Laue interferometer

Understanding User Engagement in Online Communities during COVID-19 Pandemic: Evidence from Sentiment and Semantic Analysis on YouTube

Since the outbreak of COVID-19, the pandemic has changed the lives of many people and brought dramatic motional experiences. Among many social media platforms, YouTube saw the most significant growth of any social media app among American users during the pandemic, according to the Pew Research Center on 7th April 2021. Exposure to COVID-19 related news can have a significant impact on user engagement on social networks. Different news may trigger different emotions (i.e., anger, anticipation, disgust, fear, joy, sadness, surprise, or trust), and a user may engage differently in response to the news. On YouTube, user engagement is manifested through actions such as liking, disliking, commenting, or sharing videos. During the pandemic, many users provide constructive comments that are encouraging, respectful, and informative to support each other. We applied sentiment analysis in the study to investigate different emotions and applied semantic analysis to investigate positive appraisal (i.e., encouraging, respectful, and informative) to identify salient factors that can motivate user engagement. The findings of the work shed light on how social network platforms could encourage constructive comments to help people provide emotional support to each other during pandemics through using positive appraisal in online news comments. The first research objective is to study the impact of sentiment valence of different emotions on people’s liking of news comments. News about COVID-19 on social networks may provide valuable information but also bring about public panic. In response to this COVID-19 related news, reviewers expressed their feelings by clicking the like, dislike buttons to the video and comments, or writing some comments under the video on YouTube. Some positive news was followed by comments expressing their anticipation, joy, and trust, while negative news might trigger sadness, fear, disgust, or anger. Our research focuses on sentiment analysis of news titles and the comments following each video. News title provides important information about the video, showing the summary of the video and allowing people to get a first glimpse of the content of the video. Through sentiment analysis of title and comments, correlations could be found between title/comments sentiment and user engagement. The second research objective is to investigate the impact of comments’ positive appraisal (i.e., encouraging, respectful, and informative content) on user engagement. The informative comments under the negative news have significant implications for the audience. They can be considered as a complement or judgment of the video content. Encouraging and respectful comments also help people build good conversations online. Our research focuses on semantic analysis of news titles and comments based on the three dimensions of positive appraisal and analyzes their impacts on user engagement to like the corresponding comment. We discuss the correlation between video title sentiment and the positive appraisal followed in the comments of the video to provide good conversations on the platform. A group of 38,085 online comments was collected from more than 400 different publishers from January 1st to January 30th, 2021, on YouTube. The dataset contains the most-viewed videos that were related to at least one of the following search queries: coronavirus, COVID-19, pandemic, or vaccine. NRC lexicon is adopted in the sentiment analysis to identify different emotions in titles and comments of the video. We adopt the topic modeling method and build a classifier from the Yahoo News Annotated Comments Corpus to identify constructive online comments for specific topics. We also measure inter-annotator agreements and compare the reliability of manual annotation and the classifier. We find that longer titles and sad emotions can obtain more likes on the comments of COVID-19 related news. During the pandemic, people tend to show their support when they find others are quite sad. We also expect to see correlations between some positive appraisals and user engagement

Demand Response Method Considering Multiple Types of Flexible Loads in Industrial Parks

With the rapid development of the energy internet, the proportion of flexible loads in smart grid is getting much higher than before. It is highly important to model flexible loads based on demand response. Therefore, a new demand response method considering multiple flexible loads is proposed in this paper to character the integrated demand response (IDR) resources. Firstly, a physical process analytical deduction (PPAD) model is proposed to improve the classification of flexible loads in industrial parks. Scenario generation, data point augmentation, and smooth curves under various operating conditions are considered to enhance the applicability of the model. Secondly, in view of the strong volatility and poor modeling effect of Wasserstein-generative adversarial networks (WGAN), an improved WGAN-gradient penalty (IWGAN-GP) model is developed to get a faster convergence speed than traditional WGAN and generate a higher quality samples. Finally, the PPAD and IWGAN-GP models are jointly implemented to reveal the degree of correlation between flexible loads. Meanwhile, an intelligent offline database is built to deal with the impact of nonlinear factors in different response scenarios. Numerical examples have been performed with the results proving that the proposed method is significantly better than the existing technologies in reducing load modeling deviation and improving the responsiveness of park loads.Comment: Submitted to Expert Systems with Application

AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations

Multi-task learning (MTL) aims at enhancing the performance and efficiency of machine learning models by training them on multiple tasks simultaneously. However, MTL research faces two challenges: 1) modeling the relationships between tasks to effectively share knowledge between them, and 2) jointly learning task-specific and shared knowledge. In this paper, we present a novel model Adaptive Task-to-Task Fusion Network (AdaTT) to address both challenges. AdaTT is a deep fusion network built with task specific and optional shared fusion units at multiple levels. By leveraging a residual mechanism and gating mechanism for task-to-task fusion, these units adaptively learn shared knowledge and task specific knowledge. To evaluate the performance of AdaTT, we conduct experiments on a public benchmark and an industrial recommendation dataset using various task groups. Results demonstrate AdaTT can significantly outperform existing state-of-the-art baselines

Research on key architecture and model of coal mine water hazard intelligent early warning system

In order to ensure the safe production of mine threatened by water hazard, speed up the intelligent process of mine water hazard prediction and early warning technology, and improve the effect of mine water hazard prediction and early warning, based on the research status of water hazard mechanism and monitoring and early warning at home and abroad, four types of key technical issues for constructing water hazard monitoring and intelligent early warning systems are analyzed. The complexity of early warning requirements and data access standards, the classification and spatio-temporal matching of multi-source heterogeneous big data information, the intelligent processing and analysis of water hazard big data information, and the timeliness of early warning and intelligent decision information release are discussed in detail. From the perspective of early warning system resource integration and data drive, water hazard warning resources are divided into information collection resources and computing resources, water hazard warning big data information is divided into static source information and dynamic monitoring information, and data processing is divided into basic geological model data processing, numerical processing and Computational simulation and information fusion data processing divide coal mine disaster early warning into primary monitoring parameter early warning, intermediate index grading early warning, and advanced intelligent model early warning. The key technical architecture of an intelligent warning system for coal mine water hazards is proposed and analyzed. A software service architecture that meets the technical requirements is proposed, including infrastructure layer, data resource layer, application support layer, business application layer, and user presentation layer. Based on the water hazard warning construction process, a Gated Recurrent Unit algorithm warning model for water hazard monitoring data is proposed, and the network structure of the warning model is given. The forward calculation, backward propagation calculation, and weight gradient calculation methods of the warning model are studied. The classification of different types of perception data access, storage, encoding, models, construction and testing of intelligent deep learning models, and technical paths for warning information release are analyzed. It provides a reference for the intelligent construction of coal mine water hazard early warning