全IP电视直播节目的即时回看技术

介绍了已运用于实践中的一种全IP电视直播节目即时回看技术,用户通过遥控器一键实现直播节目的即时回看,极大地提升了用户的观看体验。通过在临汾市智慧广电全IP融合业务网建设实践项目中的使用,技术的先进性和有效性得到实践验证。     

基于Bert预训练模型的虚假新闻文本检测

虚假新闻剥夺了人们获取真相的权利,也给社会和国家带来了许多危害。文中以虚假新闻文本为例,分析和验证了多种预训练语言模型在虚假新闻文本分类上的分类效果。经实验证明,相较于其他语言模型,Bert预训练语言模型取得了较好的结果,预测准确率为86.97%,召回率为88.12%,F1值为87.54%。     

基于深度EPG的智能导航平台建设

随着三网融合的大力推进以及双向互动业务的大力开展,运营商使用原有简单的EPG不足以支撑开展更多的增值业务,不能主动为用户推荐节目,不能为运营商带来新的收入机会。作为重要的创新能力支撑系统,介绍了江苏有线智能导航平台的设计与应用,为相关广电运营商提供一定的设计参考。     

面向二进制程序的导向性模糊测试方法

为了解决当前模糊测试技术中变异存在一定的盲目性以及变异生成的样本大多经过相同的高频路径的问题，提出并实现了一种基于轻量级程序分析技术的二进制程序模糊测试方法。首先对目标二进制程序进行静态分析来筛选在模糊测试过程中阻碍样本文件深入程序内部的比较指令；随后对目标文件进行插桩来获取比较指令中操作数的具体值，并根据该具体值为比较指令建立实时的比较进度信息，通过比较进度衡量样本的重要程度；然后基于模糊测试过程中实时的路径覆盖信息为经过稀有路径的样本增加其被挑选进行变异的概率；最后根据比较进度信息并结合启发式策略有针对性地对样本文件进行变异，通过变异引导提高模糊测试中生成能够绕过程序规约检查的有效样本的效率。实验结果表明，所提方法发现crash及发现新路径的能力均优于模糊测试工具AFL-Dyninst。     

非物质视域下现代珠绣装饰设计语言解读

基于非物质视角下分析珠绣装饰设计语言的整体特征,文章认为材料、图纹、工艺是促进其装饰设计语言变化的重要构成元素,同时以案例分析分别从视觉形态、符号指代、形式序列及思维界域等角度对珠绣装饰设计语言的演绎现状进行归纳。分析表明:人文意识的介入使珠绣装饰设计建立起全新的审美观念和创作模式,并基于艺术和技术的彼此糅合衍生出装饰设计语言的诸多表达形式,从而为现代珠绣的装饰创新运用提供灵感来源及设计思路。     

Arabic sign language intelligent translator

ABSTRACT

Arabic sign language (ArSL) is method of communication between deaf communities in Arab countries; therefore, the development of systemsthat can recognize the gestures provides a means for the Deaf to easily integrate into society. In this research we implemented a computational structurefor an intelligent interpreter that automatically recognizes the isolated dynamic gestures. The proposed system recognizes and translates gesturesperformed with one or both hands. It comprises five subsystems, building dataset, video processing, feature extraction, mapping between ArSL and Arabictext, and text generation. To apply the system, 100-signs of ArSL was used, which was applied on 1500 video files. It's were divided into five classes:alphabet, numbers, "prepositions, pronouns and question words", Arabic life expressions, and "nouns and verbs". The evaluation indicated that thesystem automatically recognizes and translates isolated dynamic ArSL gestures by highly accurate manner. The results showed that the system accuracy is 95.8%.     

Risk-constrained probabilistic optimal scheduling of FCPP-CHP based energy hub considering demand-side resources

Renewable energy sources (RES) with sharing a large percentage of future energy generation capacities play an essential role in the decarbonization of the future electricity and thermal networks as well as transportation sectors. However, the uncertainties in their outputs make some difficulties in making operational decisions. Hydrogen energy plays a considerable role in this concept. Besides, energy hubs (EHs) provide an efficient and reliable framework for gathering multi-type energy carriers.This paper optimally schedules the operating of the EH and decreases the emission cost, considering the electrical and thermal demand response (DR) program in a probabilistic environment. Besides plug-in electric vehicles (PEVs) and a complete model of hydrogen-based renewable energy sources are presented in the EH. Taking into account uncertainties of electrical/thermal energy markets real-time prices, customers' energy demand, and energy production of RESs into account, various scenarios are generated using the Monte Carlo simulation technique. Next, an efficient method is used to reduce the number of the scenario to make the optimization problem computable and fast. In order to reduce the risk of encountering high operating costs, the conditional value at risk (CVaR) technique is used to manage the associated risk. Simulation results show the efficiency of the proposed method in decreasing the operational cost and managing the risk of encountering unfavorable states.     

A knowledge construction methodology to automate case-based learning using clinical documents

The case-based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer-based domain knowledge to medical students for solving real-world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine-readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM-CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM-CD methodology, an interactive case-based learning system(iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.     

基于大数据的C语言程序设计

C语言作为一种程序设计语言,在大数据组成的物联网和云计算中应用广泛,具有强大的适应性和兼容性,是大数据时代下,在IT行业中起到关键作用的程序设计语言。文章分析了基于大数据的C语言程序设计的必然性和应用策略,为C语言在大数据上的应用方向提供了参考依据。以汉字的应用为例,提出了解决C语言应用问题的思路。