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关键词： Design   Film studies   Computer science  

摘要： In animated film, costumes are an important part of visual storytelling. Costumes can communicate the details of a story's setting and reveal key aspects of a character's personality to the audience. Furthermore, directors and costume designers use clothing to accentuate cinematic performance and artistic direction. However, it is challenging to produce digital costumes that not only maintain the motion and detail of real garments, but also embody the visual style of an animated film. This thesis suggests the use of digital costumes to tell the biographical story of Frances Glessner Lee (1878-1962). In the 1940s, Lee used her skills in the traditionally feminine art of miniature-making to revolutionize the field of homicide investigation with The Nutshell Studies of Unexplained Death, a series of meticulously detailed and visually enchanting miniature crime scenes used to train police to observe evidence thoroughly and without bias. Along with her contributions to forensic science, Lee's narrative highlights the shifting cultural backdrop of the late nineteenth and twentieth centuries in the United States. In homage to her legacy, this thesis presents CG animated costumes for a hypothetical film about Lee's life and work. Demonstrated is an approach using stylized costume design, pattern-based garment construction techniques, and industry-standard cloth simulation practices to create production-quality costumes which delicately balance physicality and artistic direction.

关键词： Social psychology   Cognitive psychology   Computer science  

摘要： At first sight of a new person, people quickly form impressions on how attractive, trustworthy, and warm the person looks. Despite the dubious accuracy of these first impressions, people rely on them to navigate social interactions regarding interpersonal relationships, political voting decisions, and financial choices. As we progress into a digital era with frequent social media usage and cultural exchanges, it is crucial to understand the universal principles and cultural idiosyncrasies of impression formation from face images. In this thesis, we examine social impressions of faces through the lens of computational modeling, psychological experiments, and cultural comparisons. First, we develop a model that automatically predicts human social impression judgments using neural networks. Building on the predictive model, second, we build a generative model that can change faces holistically to augment or decrease specific characteristics, such as attractiveness and aggressiveness of a face. Third, we examine how specific physical attributes, such as hair color, affect impressions of faces, using a GAN model, and psychological experiments. Finally, we conduct a large-scale cross-cultural study, using 18 traits related to approachability, youthful-attractiveness, and competence evaluation, with Caucasian and Asian participants rating Caucasian and Asian faces. We investigate the mediating factors behind impression formation and estimate how high-level facial features such as age, eyeglasses, and smiles are judged similarly and differently by people from two cultures. Overall, our work provides a computational framework to predict and modify faces, which is practically useful in real-life scenarios regarding optimal self-image presentation. Our model lays the foundation for the quantitative study of first impressions. Our psychological and cross-cultural studies reveal the universality and culture-specific judgments mediated by high-level features that affect first imp

关键词： Protocols   Authentication   Vehicular ad hoc networks   Privacy   Computer science   Computational modeling   Authentication   anonymous   privacy preseving   proverif   vehicular ad-hoc network (VANET)  

摘要： With the emergence of the concept of smart city and the increase of vehicles, the vehicular ad-hoc network (VANET) is widely accepted for the communication of vehicles to get information including road condition, traffic congestion description, speed, and location of vehicles. Naturally, the security of the data transmission in VANET has become one of the urgent tasks of researchers. Many privacy-preserving authentication protocols for VANETs have been presented. However, heavy computations and security flaws exist in them. Thus, we put forward a lightweight authentication protocol in a suitable communication model for VANET that meets the privacy protection needs, with only hash functions and exclusive-OR operations. Formal security analysis using BAN logic shows that our protocol achieves the security goals. Proverif is used to verify the security of the protocol and the results show that the privacy can be guaranteed under the simulated attacker. Moreover, through security properties analysis, our protocol is robust enough to get rid of common attacks and keep the communication data secretly. The performance comparison results show that our protocol is lightweight and efficient. Furthermore, we simulate the network with SUMO and NS-3, and all show that our protocol is efficient and practical for VANETs.

关键词： Computer Science   Fingerprinting   Skills  

摘要： The popularity of smart speakers with virtual personal assistants such as Amazon Alexa and Google Assistant has been growing. However, the security and privacy of users using these devices have not been thoroughly examined which can lead to severe security and privacy concerns. In this thesis, we perform fingerprinting attack of skills on Amazon Echo by analyzing the encrypted network traffic and show the potential privacy leakage of users using skills on smart speakers. Skills are third-party applications that can be accessed through Amazon Alexa. Amazon Echo, the most dominant smart speaker in the current market provides the feature of Skills & Games which can be accessed through the smart speaker. For example, by enabling the BBC skill, you can hear the live streaming news from BBC on your smart speaker. Skills increase the diversity of using Amazon smart speaker by providing a variety of applications that can be used from a single platform just with the use of voice commands without accessing phone or a computer. In this fingerprinting attack, an adversary can eavesdrop on the network traffic of the smart speaker and predict the skill that is used by the user without decrypting the network traffic. For this attack, we collect the dataset consisting of 10,000 traces of network traffic related to 100 popular skills. We perform feature selection to investigate the top features that contribute towards the users privacy leakage. We implement the attack by leveraging popular machine learning models which have been used in previous network traffic analysis works. Among the different machine learning models we implement, Random Forest Classifier performs the best by achieving an accuracy of 69.85%. The experimental results show that there is a need for developers to focus on the privacy leakages of users. Our results can also be used to detect malicious attacks on smart speakers and can contribute to encrypted traffic analysis and privacy preserving at large.

关键词： Receivers   Encryption   Media   Public key   Computer science   Tools   Attribute-based encryption   deniable encryption   identity-based encryption  

摘要： Attribute-based encryption (ABE) is a useful tool for sharing an encrypted data to a target group. In a ciphertext-policy ABE (CP-ABE) scheme, a ciphertext includes a policy to indicate its receivers and only those receivers can correctly decrypt the ciphertext. Since this design leaks the receiver identity, it may raise a new security issue about user privacy. Some hidden-policy ABE schemes, where the policy is secretly protected, are proposed to keep user privacy. However, these hidden-policy ABE schemes rely on the user trying all possibilities to decide if it belongs to the wanted receiver group. The decryption costs too much and every potential receiver will run the decryption process in vain since it does not know the policy. In this work, we apply the deniability concept to solve this problem. The encryption scheme allows the sender to claim the ciphertext is for some receiver group while actually it is for another receiver group. Both receiver groups can correctly decrypt the ciphertext except that the real group can get the real message and the cover group will get the cover message. While coercion, the sender can definitely claim the ciphertext is for the cover group and the real group is kept confidential.

关键词： Data structures   Wireless fidelity   Spatial databases   Euclidean distance   Spatial indexes   Computer science   Bitmap   compact data structure   k NN   spatial keyword query   spatio-textual data  

摘要： A vast amount of geo-referenced data is being generated by mobile devices and other sensors increasing the importance of spatio-textual analyses on such data. Due to the large volume of data, the use of indexes to speed up the queries that facilitate such analyses is imperative. Many disk resident indexes have been proposed for different types of spatial keyword queries, but their efficiency is harmed by their high I/O costs. In this work, we propose , the first spatio-textual index that uses compact data structures to reduce the size of the structure, hence facilitating its usage in main memory. Our experimental evaluation, shows that this approach needs half the space and is more than one order of magnitude faster than a disk resident state-of-the-art index. Also, we show that our approach is competitive even in a scenario where the disk resident data structure is warmed-up to fit in main memory.

关键词： Computer science  

摘要： Network embedding learning is important for analyzing many real-world applications and systems, i.e., social networks, citation networks and communication networks. It targets at learning low-dimensional vector representations of nodes with preserved graph structure (e.g., link relations) and content (e.g., texts) information. The derived node representations can be directly applied in many downstream applications, including node classification, clustering and visualization. In addition to the complex network structures, nodes may have rich non-structure information such as labels and contents. Therefore, structure, label and content constitute different aspects of the entire network system that reflect node similarities from multiple complementary facets. This thesis focuses on multifaceted network embedding learning, which aims to efficiently incorporate distinct aspects of information such as node labels and node contents for cooperative low-dimensional representation learning together with node topology. Existing network embedding methods are mainly specialized in networks where each node on the network only has one single label and network nodes follow a balanced label (or class) distribution, which may fail to handle multi-label and class-imbalanced networks for effective representation learning. In addition, the strong assumption that network topology and node content have alignment similarities to explain relationships between nodes could also degrade the performance of existing methods, especially in situations where nodes have many irrelevant and/or missing links on the network. We propose to take above problem settings into account, aiming to achieve effective and robust embedding learning with preserved multifaceted node information. Furthermore, the proposed network embedding techniques are applied in more realistic Web service network systems by preserving multiple aspects of information such as service tags, text descriptions and link structures, demo

关键词： Linguistics   Artificial intelligence   Computer science  

摘要： Languages, specially gestural and sign languages, are best learned in immersive environments with rich feedback. Computer-Aided Language Learning (CALL) solu- tions for spoken languages have successfully incorporated some feedback mechanisms, but no such solution exists for signed languages. Computer Aided Sign Language Learning (CASLL) is a recent and promising field of research which is made feasible by advances in Computer Vision and Sign Language Recognition(SLR). Leveraging existing SLR systems for feedback based learning is not feasible because their decision processes are not human interpretable and do not facilitate conceptual feedback to learners. Thus, fundamental research is needed towards designing systems that are modular and explainable. The explanations from these systems can then be used to produce feedback to aid in the learning process. In this work, I present novel approaches for the recognition of location, movement and handshape that are components of American Sign Language (ASL) using both wrist-worn sensors as well as webcams. Finally, I present Learn2Sign(L2S), a chat- bot based AI tutor that can provide fine-grained conceptual feedback to learners of ASL using the modular recognition approaches. L2S is designed to provide feedback directly relating to the fundamental concepts of ASL using an explainable AI. I present the system performance results in terms of Precision, Recall and F-1 scores as well as validation results towards the learning outcomes of users. Both retention and execution tests for 26 participants for 14 different ASL words learned using learn2sign is presented. Finally, I also present the results of a post-usage usability survey for all the participants. In this work, I found that learners who received live feedback on their executions improved their execution as well as retention performances. The average increase in execution performance was 28% points and that for retention was 4% points.

摘要： This project uncovers the causes and consequences of China’s death penalty reform in the 21st century. China is the world’s leading executioner state. Yet in recent years China has also become a leading death penalty reformer. This reform took place through the courts. In 2007 the Supreme People’s Court (SPC) reinstituted a process of central review and approval of death sentences that reportedly led to a significant decline in death sentences nationwide. Why did a one-party authoritarian state empower the judiciary to restrain state punishment? And what were the political effects of this shift? I adopt an explicitly comparative method to answer these questions. Throughout, I consider China’s court-focused death penalty reform in light of another country that also used the judiciary to regulate capital punishment: the United States. My findings rest on a diverse body of evidence including case verdicts, statutes and Chinese-language scholarship. The centerpiece of my materials is an original data set of more than 70 interviews I conducted with death penalty stakeholders in China. The first part of the project explains the causes of reform in China. In Chapters One and Two I examine the politics and functions of capital punishment administration in late Imperial China and the People’s Republic of China (PRC). I show that in late Imperial China the administration of capital punishment was tightly controlled through an extensive process of hierarchical review with relatively few executions. I argue that this process served a crucial and under-recognized auditing function in helping the state regulate hazards from its own local bureaucracy. In Chapter Two I show that by contrast the political environment of the early PRC produced a heavy reliance on capital punishment with little central oversight. I contend that the implementation of death penalty review in the 21st century was not driven by concerns over the scale of capital punishment, but by a need to reassert centr

关键词： Computer science  

摘要： The impact of technology on small and medium businesses (SMBs) is undeniable. The expansion of the Internet of Things (IoT), and the need to remain a viable, and competitive business in today’s work, necessitates SMBs to embrace the advantages of the Internet. To successfully navigate the current business environment, owners of such businesses must appreciate and undertake the necessary risk management practices to protect themselves from the multitude of cyber risks and threats that have propelled SMBs into the forefront of cybercriminals. This research has demonstrated that the threats posed to SMBs are increasing, not only through malicious viruses and hacking, but through social media, phishing, and third-party vendors. Findings revealed that SMBs are hampered in their efforts to combat cybercrime with a notable lack of resources, trained personnel, inadequate security budgets, and an overall lack of information pertaining to the overall risk exposure of cyber threats. This research has also demonstrated that there are effective measures available to confront cyber threats, if not preventing, at least mitigating exposure, and effective response measures in the event such incidents occur. Research has also revealed that SMBs comprise the vast majority of all US businesses, and their viability following a cyberattack is tenuous, with a significant percentage failing to recover.