Reviewerly/exHarmony huggingface dataset:
High-Fidelity 3D Digital Human Creation from RGB-D Selfies

We present a fully automatic system that can produce high-fidelity, photo-realistic 3D digital human characters with a consumer RGB-D selfie camera. The system only needs the user to take a short selfie RGB-D video while rotating his/her head, and can produce a high quality reconstruction in less than 30 seconds. Our main contribution is a new facial geometry modeling and reflectance synthesis procedure that significantly improves the state-of-the-art. Specifically, given the input video a two-stage frame selection algorithm is first employed to select a few high-quality frames for reconstruction. A novel, differentiable renderer based 3D Morphable Model (3DMM) fitting method is then applied to recover facial geometries from multiview RGB-D data, which takes advantages of extensive data generation and perturbation. Our 3DMM has much larger expressive capacities than conventional 3DMM, allowing us to recover more accurate facial geometry using merely linear bases. For reflectance synthesis, we present a hybrid approach that combines parametric fitting and CNNs to synthesize high-resolution albedo/normal maps with realistic hair/pore/wrinkle details. Results show that our system can produce faithful 3D characters with extremely realistic details. Code and the constructed 3DMM is publicly available.
Tail: An Automated and Lightweight Gradient Compression Framework for Distributed Deep Learning

Existing gradient compression schemes fail to automatically determine the compression ratio or are accompanied by high compression overhead. To address this, we present Tail, an automated and lightweight gradient compression framework stacked by three modules, quantization, sparsification, and encoding. Without any hand-tuned effort, quantization module automatically adjusts the compression ratio along training iterations to retain accuracy first. Then, sparsification and encoding modules are successively applied to the quantized gradient to further improve compression ratio. Moreover, Tail reduces the compression overhead by approximate computing in the automated decision-making process. Experiments validate that Tail can reduce communication traffic by an order of magnitude while retaining or even improving model accuracy.
Multi-hop Inference for Question-driven Summarization

Question-driven summarization has been recently studied as an effective approach to summarizing the source document to produce concise but informative answers for non-factoid questions. In this work, we propose a novel question-driven abstractive summarization method, Multi-hop Selective Generator (MSG), to incorporate multi-hop reasoning into question-driven summarization and, meanwhile, provide justifications for the generated summaries. Specifically, we jointly model the relevance to the question and the interrelation among different sentences via a human-like multi-hop inference module, which captures important sentences for justifying the summarized answer. A gated selective pointer generator network with a multi-view coverage mechanism is designed to integrate diverse information from different perspectives. Experimental results show that the proposed method consistently outperforms state-of-the-art methods on two non-factoid QA datasets, namely WikiHow and PubMedQA.
On Stabilizing Generative Adversarial Training with Noise

We present a novel method and analysis to train generative adversarial networks (GAN) in a stable manner. As shown in recent analysis, training is often undermined by the probability distribution of the data being zero on neighborhoods of the data space. We notice that the distributions of real and generated data should match even when they undergo the same filtering. Therefore, to address the limited support problem we propose to train GANs by using different filtered versions of the real and generated data distributions. In this way, filtering does not prevent the exact matching of the data distribution, while helping training by extending the support of both distributions. As filtering we consider adding samples from an arbitrary distribution to the data, which corresponds to a convolution of the data distribution with the arbitrary one. We also propose to learn the generation of these samples so as to challenge the discriminator in the adversarial training. We show that our approach results in a stable and well-behaved training of even the original minimax GAN formulation. Moreover, our technique can be incorporated in most modern GAN formulations and leads to a consistent improvement on several common datasets.
Performance Evaluation of T-Shirt Image Generation System Considering User’s Preference

In this study, we propose a system to generate the T-shirt design images that match a user’s preference based on generative adversarial networks (GAN) and a Kansei agent. The generator of GAN can be used to generate lifelike images similar to the learned data. The Kansei agent is a model that learns the user’s Kansei evaluation and retrieves images that the user likes. The Kansei agent is constructed using two neural networks. The first network extracts features from given T-shirt images. The second one estimates the user’s evaluation of T-shirt design using the extracted features. We examined the performance of the proposed system according to the results of the evaluation experiment. The results of the experiment showed that the proposed system finally presented most users T-shirt design images that were highly evaluated.
Keep CALM and Explore: Language Models for Action Generation in Text-based Games

Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces. In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state. Our key insight is to train language models on human gameplay, where people demonstrate linguistic priors and a general game sense for promising actions conditioned on game history. We combine CALM with a reinforcement learning agent which re-ranks the generated action candidates to maximize in-game rewards. We evaluate our approach using the Jericho benchmark, on games unseen by CALM during training. Our method obtains a 69% relative improvement in average game score over the previous state-of-the-art model. Surprisingly, on half of these games, CALM is competitive with or better than other models that have access to ground truth admissible actions. Code and data are available at https://github.com/princeton-nlp/calm-textgame.
Factors Affecting Sleep Hygiene Training of Nursing Students

Sleep, which is one of the basic needs, is a major variable of health affecting individuals' quality of life.Sleep is effective in maintaining normal thermoregulation, strengthening the immune system, and helping brain development as well as somatic growth and development (1,2).Regular and highquality sleep increases the ability of reasoning; thus, the ability to make decisions and learn new information (3).Many factors affect the quality and quantity of sleep, which is one of the requirements of a healthy life.Many studies demonstrate that patients have low sleep quality and experience a higher number of sleep disorders (4-7).Sleep disorders in patients cause adverse conditions such as delayed healing, pain, anxiety, increased risk of falling, and physical-mental health problems (4,8).Due to such problems, it is necessary to provide a suitable environment for the hospitalized patients to sleep comfortably. Öz
Multilevel Approach to Professionally Oriented Foreign Language Teaching at Non-Linguistic Higher School

The paper justifies the necessity to introduce integrated thematically organized textbooks and proves efficiency of the synthetic multilevel and competence-based approach to foreign language teaching at non-linguistic higher school. The article examines problems of organizing professionally oriented foreign language teaching on the basis of the multilevel approach. Scientific originality of the study includes development and approbation of the methodology based on the synthesis of multilevel and competence-based approaches to teaching professional foreign language taking into account not only the academic aspect, but also psychologically comfortable educational environment. The obtained results indicate that the methodology based on the synthesis of multilevel and competence-based approaches to teaching professional English language to non-linguistic students significantly saves time and intensifies processes such as perception, assimilation and motivation.
The Use of ARCore Technology for Online Control Simulations

The paper describes an educational mobile application that controls the 3D model of towercopter using augmented reality for smartphones.It is developed using the ARCore technology that allows insertion of 3D objects into a real space via smartphone or tablet.The application serves as a simple guide for a real device which is placed in laboratory and enables to create simulations based on user input data.The application interface is connected with Scilab API simulation module that provides data for 3D model animations.Users can set their own controller parameters into the predefined control structures.Application is a part of virtual laboratory and can help students with understanding of problems connected with education process.
E-Stitchup: Data Augmentation for Pre-Trained Embeddings

In this work, we propose data augmentation methods for embeddings from pre-trained deep learning models that take a weighted combination of a pair of input embeddings, as inspired by Mixup, and combine such augmentation with extra label softening. These methods are shown to significantly increase classification accuracy, reduce training time, and improve confidence calibration of a downstream model that is trained with them. As a result of such improved confidence calibration, the model output can be more intuitively interpreted and used to accurately identify out-of-distribution data by applying an appropriate confidence threshold to model predictions. The identified out-of-distribution data can then be prioritized for labeling, thus focusing labeling effort on data that is more likely to boost model performance. These findings, we believe, lay a solid foundation for improving the classification performance and calibration of models that use pre-trained embeddings as input and provide several benefits that prove extremely useful in a production-level deep learning system.
Duff: A Dataset-Distance-Based Utility Function Family for the Exponential Mechanism

We propose and analyze a general-purpose dataset-distance-based utility function family, Duff, for differential privacy's exponential mechanism. Given a particular dataset and a statistic (e.g., median, mode), this function family assigns utility to a possible output o based on the number of individuals whose data would have to be added to or removed from the dataset in order for the statistic to take on value o. We show that the exponential mechanism based on Duff often offers provably higher fidelity to the statistic's true value compared to existing differential privacy mechanisms based on smooth sensitivity. In particular, Duff is an affirmative answer to the open question of whether it is possible to have a noise distribution whose variance is proportional to smooth sensitivity and whose tails decay at a faster-than-polynomial rate. We conclude our paper with an empirical evaluation of the practical advantages of Duff for the task of computing medians.
TRANSFORMATION OF HIGHER EDUCATION IN THE CONTEXT OF INDUSTRY 4.0

The article looks into key trends of higher education development analysed against the backdrop of the changing developmental paradigm and the changing goals of universities that are adapting to the Industry 4.0. An analysis of international and domestic experience of the higher education adaptation to the digital transformation context has been performed on two levels, the macro- and micro ones, as the authors proceed from the assumption that the macro-level being an external context, synergizes the factors of the international and national levels that in the context of internationalization have common system-building parameters. Aim of the article is to present concrete recommendations for universities in different areas of their development, to facilitate their effective and proactive adaptation to the Industry 4.0 context based on the undertaken comparative analysis. The research was performed using the following methods: content analysis, comparative analysis, analysis of international reports and publications, method of expert assessment, of referentometry and direct observation. The novelty is in the examination of the object of research on two levels - the macro- and micro-levels proceeding from the presupposition about the fractal character of changes generated by the digital revolution on the international/global and national level. Such approach has resulted in systemic conclusions and goals relevant for the university sector to succeed in new conditions. The results of the research can be used to underpin future research studies, pre-service and in-service study programs for different target groups within the higher education sector, as well as institutional decisions and policy-making.
A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions

Recent models achieve promising results in visually grounded dialogues. However, existing datasets often contain undesirable biases and lack sophisticated linguistic analyses, which make it difficult to understand how well current models recognize their precise linguistic structures. To address this problem, we make two design choices: first, we focus on OneCommon Corpus \citep{udagawa2019natural,udagawa2020annotated}, a simple yet challenging common grounding dataset which contains minimal bias by design. Second, we analyze their linguistic structures based on \textit{spatial expressions} and provide comprehensive and reliable annotation for 600 dialogues. We show that our annotation captures important linguistic structures including predicate-argument structure, modification and ellipsis. In our experiments, we assess the model's understanding of these structures through reference resolution. We demonstrate that our annotation can reveal both the strengths and weaknesses of baseline models in essential levels of detail. Overall, we propose a novel framework and resource for investigating fine-grained language understanding in visually grounded dialogues.
Fractal photonic bandgap fibers

Photonic bandgap fibers have a critical constraint determined by wavelength. The principle of scale invariance requires that features remain unchanged even as the scale of an object changes. This paper introduces a new concept for fractal photonic crystal fibers integrating these two. Our simulation confirmed single-mode transmission is possible for a fiber whose core diameter exceeds 35 times the wavelength.
Enhancing the Numeracy of Word Embeddings: A Linear Algebraic Perspective

To reason over the embeddings of numbers, they should capture numeracy information. In this work, we consider the magnitude aspect of numeracy information. We could find a vector in a high dimensional space and a subspace of original space. After projecting the original embeddings of numbers onto that vector or subspace, the magnitude information could be significantly enhanced. Therefore, this paper proposes a new angle to study numeracy of word embeddings, which is interpretable and has nice mathematical formulations.
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack

Adversarial attacks against natural language processing systems, which perform seemingly innocuous modifications to inputs, can induce arbitrary mistakes to the target models. Though raised great concerns, such adversarial attacks can be leveraged to estimate the robustness of NLP models. Compared with the adversarial example generation in continuous data domain (e.g., image), generating adversarial text that preserves the original meaning is challenging since the text space is discrete and non-differentiable. To handle these challenges, we propose a target-controllable adversarial attack framework T3, which is applicable to a range of NLP tasks. In particular, we propose a tree-based autoencoder to embed the discrete text data into a continuous representation space, upon which we optimize the adversarial perturbation. A novel tree-based decoder is then applied to regularize the syntactic correctness of the generated text and manipulate it on either sentence (T3(Sent)) or word (T3(Word)) level. We consider two most representative NLP tasks: sentiment analysis and question answering (QA). Extensive experimental results and human studies show that T3 generated adversarial texts can successfully manipulate the NLP models to output the targeted incorrect answer without misleading the human. Moreover, we show that the generated adversarial texts have high transferability which enables the black-box attacks in practice. Our work sheds light on an effective and general way to examine the robustness of NLP models. Our code is publicly available at https://github.com/AI-secure/T3/.
Capturing the Evolution of Service-oriented Systems with Architectural Decisions

Software evolution is becoming ever more important.SOA is nowadays a well-established and popular software technology.Because of its properties, such as loose-coupling between services and their reconfigurable composition, SOA is an architecture that is particularly suitable for rapidly evolving systems.However, the research on the evolution methodologies for SOA systems is rather scarce.We present a model called MAD4SOA, developed in order to support and capture the evolution of service-oriented systems.Architectural decisions are the first class entities that represent the evolution of a serviceoriented system.They are accompanied by a set of relations between model entities and formal integrity constraints.The suitability of the MAD4SOA model has been validated using the real-world example of a system operated in a clearing house company.
Towards Developing a Comprehensive Tag Set for the Arabic Language

Abstract This paper presents a comprehensive Tag set as a fundamental component for developing an automated Word Class/Part-of-Speech ( PoS ) tagging system for the Arabic language. The aim is to develop a standard and comprehensive PoS tag set that based upon PoS classes and Arabic inflectional morphology useful for Linguistics and Natural Language Processing (NLP) developers to extract more linguistic information from it. The tag names in the developed tag set uses terminology from Arabic tradition grammar rather than English grammar. The usability of the presented Tag set has been tested in manual tagging and built up a set of tagged text to serve as a goal corpus used to compare it with the results obtained from the tagger. The tagger has achieved an average accuracy of 90% using the developed detailed tag set.
Database Annotation with Few Examples: An Atlas-Based Framework Using Diffeomorphic Registration of 3D Trees

Automatic annotation of anatomical structures can help simplify workflow during interventions in numerous clinical applications but usually involves a large amount of annotated data. The complexity of the labeling task, together with the lack of representative data, slows down the development of robust solutions. In this paper, we propose a solution requiring very few annotated cases to label 3D pelvic arterial trees of patients with benign prostatic hyperplasia. We take advantage of Large Deformation Diffeomorphic Metric Mapping (LDDMM) to perform registration based on meaningful deformations from which we build an atlas. Branch pairing is then computed from the atlas to new cases using optimal transport to ensure one-to-one correspondence during the labeling process. To tackle topological variations in the tree, which usually degrades the performance of atlas-based techniques, we propose a simple bottom-up label assignment adapted to the pelvic anatomy. The proposed method achieves 97.6% labeling precision with only 5 cases for training, while in comparison learning-based methods only reach 82.2% on such small training sets.
The Interactive Development of Outdoor Sports and Water Resources Industry from the Perspective of Geographical Environment Integration

Wang, G.P. and Chen, W., 2020. The interactive development of outdoor sports and water resources industry from the perspective of geographical environment integration. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 656–659. Coconut Creek (Florida), ISSN 0749-0208.The 18th National Congress was held to put the construction of ecological civilization at the top of economic and social development, and the subsequent 19th National Congress further emphasized the importance of building ecological civilization. The top-level design emphasizes “ecological civilization” and changes the concept of environmental protection and governance, which can be learned that the state in the process of environmental protection, follow the unique natural laws of the ecological environment at the same time, further emphasize the construction of ecological civilization in the social psychology and behavioral aspects of the construction. The bottleneck of the interaction between, outdoor sports industry and water resources industry is mainly the lack of basic conditions of ecological environment, “leisure, green water, health, outdoor sports lifestyle conditions do not have, the two need to complement each other, in order to promote each other, to achieve a win-win effect. China is a large agricultural country and has an important base for agricultural production and energy. However, under the influence of different geography, climate, soil and water, and energy environment, the impact on the ecological environment is not only the same, but also the supply and demand of water resources, the distribution of water and sand and the degree of environmental pollution. This paper combines the analysis of the different geographical distribution of environmental water resources, combined with the real situation of water pollution, and deeply studies the development strategy analysis of the interaction of water resources industry.
Analysis of fractal dimension allows identification of malignancies in breast tissue histopathological images

Background: Histopathological analysis remains as the gold standard diagnosis of breast cancer (BC) and benign tissue alterations. Nevertheless, despite training and standardization, it is considered operator-dependent and subject to errors. Recent works have shown that image processing algorithms are useful in identifying either benign or malignant alterations on tissues. Fractal dimension analysis is a computational image processing technique that allows assessing the degree of complexity in patterns. Therefore, it may represent a powerful tool in the investigation of alterations in biological systems. This technique has successfully differentiated normal and pathological images in histo- and cytopathology; radiology; and melanomas. In the present study, we have evaluated this tool in the histopathological diagnosis of BC.