Diverse Motion Stylization for Multiple Style Domains via Spatial-Temporal Graph-Based Generative Model

This paper presents a novel deep learning-based framework for translating a motion into various styles within multiple domains. Our framework is a single set of generative adversarial networks that learns stylistic features from a collection of unpaired motion clips with style labels to support mapping between multiple style domains. We construct a spatio-temporal graph to model a motion sequence and employ the spatial-temporal graph convolution networks (ST-GCN) to extract stylistic properties along spatial and temporal dimensions. Through spatial-temporal modeling, our framework shows improved style translation results between significantly different actions and on a long motion sequence containing multiple actions. In addition, we first develop a mapping network for motion stylization that maps a random noise to style, which allows for generating diverse stylization results without using reference motions. Through various experiments, we demonstrate the ability of our method to generate improved results in terms of visual quality, stylistic diversity, and content preservation.

AS IN LIFE, SO IN DEATH: EMPLOYEE VALUES AND CONTENT PRESERVATION DURING PLATFORM CLOSURE

Despite dominant cultural narratives about platform vitality, whether their immense global penetration or companies’ overwhelming political and economic power, platform history is marked by shutdown and failure. Companies and sites shutter with an understated regularity. As they go, they often delete large swaths of user content, with consequences for the memory practices of both individuals and communities. In turn, this paper examines the ethical approaches that platform employees bring to the process of platform shutdown and user content deletion. This phenomenon is analyzed using 52 interviews with employees from now-shuttered platforms. Drawing on literature on values in technology, technological breakdown and decline, as well as from critical approaches to the study of platforms, this paper articulates the ways that platform employees understand the ethics of social media data deletion, and how these ethics come to shape what remains of these platforms after they close.

Reinforced NMT for Sentiment and Content Preservation in Low-resource Scenario

The preservation of domain knowledge from source to the target is crucial in any translation workflows. Hence, translation service providers that use machine translation (MT) in production could reasonably expect that the translation process should transfer both the underlying pragmatics and the semantics of the source-side sentences into the target language. However, recent studies suggest that the MT systems often fail to preserve such crucial information (e.g., sentiment, emotion, gender traits) embedded in the source text in the target. In this context, the raw automatic translations are often directly fed to other natural language processing (NLP) applications (e.g., sentiment classifier) in a cross-lingual platform. Hence, the loss of such crucial information during the translation could negatively affect the performance of such downstream NLP tasks that heavily rely on the output of the MT systems. In our current research, we carefully balance both the sides (i.e., sentiment and semantics) during translation, by controlling a global-attention-based neural MT (NMT), to generate translations that encode the underlying sentiment of a source sentence while preserving its non-opinionated semantic content. Toward this, we use a state-of-the-art reinforcement learning method, namely, actor-critic , that includes a novel reward combination module, to fine-tune the NMT system so that it learns to generate translations that are best suited for a downstream task, viz. sentiment classification while ensuring the source-side semantics is intact in the process. Experimental results for Hindi–English language pair show that our proposed method significantly improves the performance of the sentiment classifier and alongside results in an improved NMT system.

A Preliminary Study on the Management Strategy of University Personnel Files based on Artificial Intelligence Technology

In order to improve the management strategy for personnel files in colleges and universities, simplify the complex process of file management, and improve file management security and content preservation of the files. This paper elaborates on the application of Artificial Intelligence (AI) technology in university personnel file management through theoretical analysis based on the understanding of AI technology.

GTAE: Graph Transformer–Based Auto-Encoders for Linguistic-Constrained Text Style Transfer

Non-parallel text style transfer has attracted increasing research interests in recent years. Despite successes in transferring the style based on the encoder-decoder framework, current approaches still lack the ability to preserve the content and even logic of original sentences, mainly due to the large unconstrained model space or too simplified assumptions on latent embedding space. Since language itself is an intelligent product of humans with certain grammars and has a limited rule-based model space by its nature, relieving this problem requires reconciling the model capacity of deep neural networks with the intrinsic model constraints from human linguistic rules. To this end, we propose a method called Graph Transformer–based Auto-Encoder, which models a sentence as a linguistic graph and performs feature extraction and style transfer at the graph level, to maximally retain the content and the linguistic structure of original sentences. Quantitative experiment results on three non-parallel text style transfer tasks show that our model outperforms state-of-the-art methods in content preservation, while achieving comparable performance on transfer accuracy and sentence naturalness.

The problem of the absorption of national legal cultures by the emerging national legal anti-culture

The purpose of the research is to show the organizational and legal mechanism of new United States’ not powerful approach to the implementation and simultaneous convergence of its legal culture at the global level. General, general scientific and special methods were used in the research. Theoretical basis of the research are the basic laws of states, international universal and regional treaties, doctrinal works revealing the concept of legal culture, scientific articles of Russian and foreign scholars dealing with the content, preservation and transformation of legal cultures. The result of the research was a comparative presentation of the basic values of national legal cultures and global legal anti-culture; ways of “de-substantiation” of national legal cultures; ways of absorption of national legal cultures by global legal anti-culture. The novelty of the research lies in the fact that the article for the first time shows the organizational and legal mechanism of the not powerful way of replacing the global legal anti-culture with national legal cultures. For this purpose, traditional national legal cultures, as well as the global legal anti-culture, are considered in terms of their basic value content; shows ways to absorb global legal anti-culture through “de-substantiation” collective (public) and individual (personal) identities; highlights the organizational mechanism of global legal anti-culture takeover. This research contributes to the comprehension of the content, processes, and ways of forming a global legal anti-culture, as well as to the convergence of states in the vision of this problem and the search for answers to emerging challenges.

Development and Implementation of a Phased Framework for Collecting the COVID-19 Virus Material at a Medical University

As national leaders in infectious disease outbreaks, the University of Nebraska Medical Center (UNMC) and its hospital partner Nebraska Medicine monitored and treated COVID-19 patients, starting with evacuees from Wuhan, China in February 2020. To document UNMC’s institutional response to the COVID-19 pandemic, and future outbreaks or pandemics, UNMC’s McGoogan Health Sciences Library Special Collections and Archives (SCA) Department faculty quickly established an archival collecting strategy to create a broad documentation package with multiple collecting phases. The initial phase included a contemporaneous response for digital collecting, timeline creation, and community collecting, followed by later collecting of personal papers, artifacts, and oral histories from key players who are also front-line workers. The combined collecting approach ensured early digital content preservation, captured public momentum, and provided the structure for longer-term collecting, after time for healing and relationship-building with prospective donors.