He is a celebrity in the commercial dubbing industry, with a mysterious main business and a lack of social interaction. Due to an accident, he began to introduce her to delicious food every night, describing each dish's preparation in an enticing voice, and also wiped her dry one by one. One day, he asked her, "Do you miss me?" "I want to..." "Do you want to see me often?" "Um... but you're busy and there's nothing you can do." "Do you want to find me anytime?" Of course I want to "Do you want to sleep in our bed even if I'm not here at night, waiting for me to come back at dawn?" Does his meaning mean His voice softened and he had a somewhat hoarse and seductive texture. "Do you want to hear my voice every day... no matter how late it is, I will make you fall asleep?" "I want to..." She finally surrendered. He smiled and said, "Just think about it." So, this is considered "So, from now on, it will only be you and me, and I will only cook for my wife." This is clearly the most obvious sound temptation, just like when the story goes back to the beginning, she seduced him with her voice, and he also used his voice to make her no longer see anyone else in her eyes
Spanning four centuries, from 221 B.C. to A.D. 220, the Qin and Han dynasties were pivotal to Chinese history, establishing the social and cultural underpinnings of China as we know it today. Age of Empires: Art of the Qin and Han Dynasties is a revelatory study of the dawn of China’s imperial age, delving into more than 160 objects that attest to the artistic and cultural flowering that occurred under Qin and Han rule. Before this time, China consisted of seven independent states. They were brought together by Qin Shihuangdi, the self-proclaimed First Emperor of the newly unified realm. Under him, the earliest foundations of the Great Wall were laid, and the Qin army made spectacular advances in the arts of war—an achievement best expressed in the magnificent army of lifesize terracotta warriors and horses that stood before his tomb, seven of which are reproduced here. The Han built on the successes of the Qin, the increasing wealth and refinement of the empire reflected in dazzling bronze and lacquer vessels, ingeniously engineered lamps, and sparkling ornaments of jade and gold from elite Han tombs. But of all the achievements of the Qin-Han era, the most significant is, no doubt, the emergence of a national identity, for it was during this time of unprecedented change that people across the empire began to see themselves as one, with China as their common homeland. p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Verdana} With its engaging, authoritative essays and evocative illustrations, Age of Empires provides an invaluable record of a unique epoch in Chinese history, one whose historic and artistic impact continues to resonate into the modern age.
This fascinating book examines the artistic exchange between the nomadic peoples of what is now Inner Mongolia and their settled Chinese neighbors during the first millennium B.C.
This book reports in detail the newly developed Communicative Listening Comprehension Test (CLCT) for the National College English Test (CET) of China. Following the principles of communicative testing in general and test construction approach proposed by Bachman and Palmer (1996) in particular, the project develops CLCT for CET-4 and CET-6. The research begins with the construction of frameworks of listening task characteristics and communicative listening ability. Subsequently, based on a survey of Chinese college students' English listening needs and an analysis of listening tasks in influential English listening course books and public tests, CLCT-4 and CLCT-6 test specifications are developed. Finally, sample papers are produced and a series of posteriori studies are conducted to examine the difficulty and usefulness of the newly developed notes-completion task type in two CLCT tests. As an example of successful integration of communicative testing theories and test construction practice, this research provides valuable insights into listening test development for other large-scale tests.
This book provides a comprehensive overview of security vulnerabilities and state-of-the-art countermeasures using explainable artificial intelligence (AI). Specifically, it describes how explainable AI can be effectively used for detection and mitigation of hardware vulnerabilities (e.g., hardware Trojans) as well as software attacks (e.g., malware and ransomware). It provides insights into the security threats towards machine learning models and presents effective countermeasures. It also explores hardware acceleration of explainable AI algorithms. The reader will be able to comprehend a complete picture of cybersecurity challenges and how to detect them using explainable AI. This book serves as a single source of reference for students, researchers, engineers, and practitioners for designing secure and trustworthy systems.
In both the database and machine learning communities, data quality has become a serious issue which cannot be ignored. In this context, we refer to data with quality problems as “dirty data.” Clearly, for a given data mining or machine learning task, dirty data in both training and test datasets can affect the accuracy of results. Accordingly, this book analyzes the impacts of dirty data and explores effective methods for dirty data processing. Although existing data cleaning methods improve data quality dramatically, the cleaning costs are still high. If we knew how dirty data affected the accuracy of machine learning models, we could clean data selectively according to the accuracy requirements instead of cleaning all dirty data, which entails substantial costs. However, no book to date has studied the impacts of dirty data on machine learning models in terms of data quality. Filling precisely this gap, the book is intended for a broad audience ranging from researchers in the database and machine learning communities to industry practitioners. Readers will find valuable takeaway suggestions on: model selection and data cleaning; incomplete data classification with view-based decision trees; density-based clustering for incomplete data; the feature selection method, which reduces the time costs and guarantees the accuracy of machine learning models; and cost-sensitive decision tree induction approaches under different scenarios. Further, the book opens many promising avenues for the further study of dirty data processing, such as data cleaning on demand, constructing a model to predict dirty-data impacts, and integrating data quality issues into other machine learning models. Readers will be introduced to state-of-the-art dirty data processing techniques, and the latest research advances, while also finding new inspirations in this field.
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