Reviewerly/exHarmony huggingface dataset:
Overcoming the Liability of Outsidership Through Different Partnerships

This study investigates how emerging-market multinationals (EMNEs) overcome the liability of outsidership (LOO). We first argue that the LOO that EMNEs face has two sources: the lack of access to a local network and the lack of access to their preferred position in the global network. We then propose that EMNEs suffer more from LOO than do developed-country multinationals (DMNEs). Next, we argue that allying with different types of partners helps EMNEs overcome different sources of the LOO. We test these arguments with a database consisting of mobile money services provided by EMNEs, DMNEs and local firms in 93 countries in 2001–2015, finding support for most of our hypotheses.
THE SOCIOLINGUISTIC INFLUENCE ON THE LEARNERS AS THE READERS (A Case Study in the Fourth Semester of English Study Program At Islamic University of Indragiri Tembilahan )

As learners of the English language, they were good readers is one of the main aims. To realize this one is not an easy act. There are many influences; one of them is sociolinguistic. This research purposed to describe the influence of sociolinguistic on the learners as the reader. This is a Descriptive Research. The research participants were the Fourth Semester of English Study Program students at the Islamic University of Indragiri Tembilahan. The data was collected from spreading the questionnaire. After analyzing the data, the researcher found a very significant influence than the other very high influence was motivation, which motivation is imperative for the learner as the reader. The mean score of all indicators was 77.93; it was indicated that the influence of sociolinguistics on the learner as the reader was great.
THE STUDENTS’ READING INTEREST IN MEDIA “LINE WEBTOON” AT ELEVENTH-GRADE OF SMA NEGERI 2 TEMBILAHAN

This study analyzed the students’ reading interest in media LINE Webtoon. It purposes to know the students' interest in reading toward media LINE Webtoon and some factors influencing their reading interest. The research design is descriptive research that combined qualitative and quantitative approaches. It was conducted at SMA Negeri 2 Tembilahan. The researchers used the purposive sampling technique in choosing the research samples. They distributed a questionnaire to fifty eleventh-grade students. The questionnaires were distributed to know their reading interest in media LINE Webtoon to answer the first research question. In addition, to answer the second research question, the writer used an open-ended questionnaire to know the factors influencing their interest. After collecting the data, the results were transcribed. The results of this research showed that they had a high interest in reading about media LINE Webtoon. The result of the questionnaire also supported it. The percentage was 77,84%. Furthermore, the students explained that some factors that influenced their reading interest in media LINE Webtoon were: 1) lots of good and exciting stories, 2) the students liked the picture of the story of the LINE Webtoon, 3) add students English vocabulary, 4) more motivate students to learn English, 5) helps students to understand the culture and habits of American people, 6) students can learn some slang, 7) students can learn some synonyms of a word, and 8) suggested by their friend. Based on the research results, the researchers concluded that the media LINE Webtoon has a substantial positive impact on the students.
Advances for Digital Humans in VFX Production at Goodbye Kansas Studios

Digital humans and digital doubles are core products at Goodbye Kansas Studios and in order to further improve their quality and related workflows, a number of new tools have been developed and integrated into our existing pipeline.
Accelerating facial motion capture with video-driven animation transfer

We describe a hybrid pipeline that leverages: 1) video-driven animation transfer [Moser et al. 2021] for regressing high-quality animation under partially-controlled conditions from a single input image, and 2) a marker-based tracking approach [Moser et al. 2017] that, while more complex and slower, is capable of handling the most challenging scenarios seen in the capture set. By applying the most suited approach to each shot, we have an overall pipeline that, without loss of quality, is faster and has less user intervention. We also improve the prior work [Moser et al. 2021] with augmentations during training to make it more robust for the Head Mounted Camera (HMC) scenario. The new pipeline is currently being integrated into our offline and real-time workflows.
Many Heads but One Brain: an Overview of Fusion Brain Challenge on AI  Journey 2021

Supporting the current trend in the AI community, we propose the AI Journey 2021 Challenge called Fusion Brain which is targeted to make the universal architecture process different modalities (namely, images, texts, and code) and to solve multiple tasks for vision and language. The Fusion Brain Challenge https://github.com/sberbank-ai/fusion_brain_aij2021 combines the following specific tasks: Code2code Translation, Handwritten Text recognition, Zero-shot Object Detection, and Visual Question Answering. We have created datasets for each task to test the participants' submissions on it. Moreover, we have opened a new handwritten dataset in both Russian and English, which consists of 94,130 pairs of images and texts. The Russian part of the dataset is the largest Russian handwritten dataset in the world. We also propose the baseline solution and corresponding task-specific solutions as well as overall metrics.
What Do We Mean by Generalization in Federated Learning?

Federated learning data is drawn from a distribution of distributions: clients are drawn from a meta-distribution, and their data are drawn from local data distributions. Thus generalization studies in federated learning should separate performance gaps from unseen client data (out-of-sample gap) from performance gaps from unseen client distributions (participation gap). In this work, we propose a framework for disentangling these performance gaps. Using this framework, we observe and explain differences in behavior across natural and synthetic federated datasets, indicating that dataset synthesis strategy can be important for realistic simulations of generalization in federated learning. We propose a semantic synthesis strategy that enables realistic simulation without naturally-partitioned data. Informed by our findings, we call out community suggestions for future federated learning works.
Self-Supervised Video Representation Learning by Video Incoherence Detection

This article introduces a novel self-supervised method that leverages incoherence detection for video representation learning. It stems from the observation that the visual system of human beings can easily identify video incoherence based on their comprehensive understanding of videos. Specifically, we construct the incoherent clip by multiple subclips hierarchically sampled from the same raw video with various lengths of incoherence. The network is trained to learn the high-level representation by predicting the location and length of incoherence given the incoherent clip as input. Additionally, we introduce intravideo contrastive learning to maximize the mutual information between incoherent clips from the same raw video. We evaluate our proposed method through extensive experiments on action recognition and video retrieval using various backbone networks. Experiments show that our proposed method achieves remarkable performance across different backbone networks and different datasets compared to previous coherence-based methods.
Image Captioning for Effective Use of Language Models in Knowledge-Based  Visual Question Answering

Integrating outside knowledge for reasoning in visio-linguistic tasks such as visual question answering (VQA) is an open problem. Given that pretrained language models have been shown to include world knowledge, we propose to use a unimodal (text-only) train and inference procedure based on automatic off-the-shelf captioning of images and pretrained language models. Our results on a visual question answering task which requires external knowledge (OK-VQA) show that our text-only model outperforms pretrained multimodal (image-text) models of comparable number of parameters. In contrast, our model is less effective in a standard VQA task (VQA 2.0) confirming that our text-only method is specially effective for tasks requiring external knowledge. In addition, we show that increasing the language model's size improves notably its performance, yielding results comparable to the state-of-the-art with our largest model, significantly outperforming current multimodal systems, even though augmented with external knowledge. Our qualitative analysis on OK-VQA reveals that automatic captions often fail to capture relevant information in the images, which seems to be balanced by the better inference ability of the text-only language models. Our work opens up possibilities to further improve inference in visio-linguistic tasks
Are Missing Links Predictable? An Inferential Benchmark for Knowledge  Graph Completion

We present InferWiki, a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns. First, each testing sample is predictable with supportive data in the training set. To ensure it, we propose to utilize rule-guided train/test generation, instead of conventional random split. Second, InferWiki initiates the evaluation following the open-world assumption and improves the inferential difficulty of the closed-world assumption, by providing manually annotated negative and unknown triples. Third, we include various inference patterns (e.g., reasoning path length and types) for comprehensive evaluation. In experiments, we curate two settings of InferWiki varying in sizes and structures, and apply the construction process on CoDEx as comparative datasets. The results and empirical analyses demonstrate the necessity and high-quality of InferWiki. Nevertheless, the performance gap among various inferential assumptions and patterns presents the difficulty and inspires future research direction. Our datasets can be found in https://github.com/TaoMiner/inferwiki
대학 성인학습자의 개인배경, 평생학습에 대한 이해･가치･태도가 평생학습 행복에 미치는 영향

This study was conducted to find out how the individual background, understanding, value, and participation attitude of adult learners participating in the university lifelong curriculum affect perceived happiness through lifelong education. The perceived happiness of adult learners, the dependent variable of this study, was measured mainly in four areas: improving self-esteem, satisfaction of internal growth, decreasing negative effect, and improving the searching for meaning. To achieve the purpose of the study, a self-report online survey was conducted on adult learners participating in the lifelong education degree course at C University in Chungcheong Province, and 189 people who agreed to participate in the survey and responded without missing data were used for analysis. As for the analysis method, descriptive statistics, correlation analysis, and multiple regression analysis were applied. As a result of the analysis, first, intrinsic values, individual satisfaction-oriented attitudes, and social activity participation-oriented attitudes of adult learners influenced 'improving self-esteem'. Second, the attitude toward self-satisfaction and the attitude toward leisure influenced 'satisfaction with internal growth'. Third, grade, intrinsic value, social activity participation-oriented attitude, and leisure-oriented attitude influenced 'reduction of negative emotions'. Fourth, intrinsic value, extrinsic value, and leisure-oriented attitude had a statistically significant effect on 'improving the pursuit of meaning'. Based on the results of this study, implications that can enhance perceived happiness as an educational outcome in the affective aspect of adult learners participating in lifelong education were derived.
Doctors and Nurses Social Media Ads Reduced Holiday Travel and COVID-19 infections: A cluster randomized controlled trial in 13 States.

During the COVID-19 epidemic, many health professionals started using mass communication on social media to relay critical information and persuade individuals to adopt preventative health behaviors. Our group of clinicians and nurses developed and recorded short video messages to encourage viewers to stay home for the Thanksgiving and Christmas Holidays. We then conducted a two-stage clustered randomized controlled trial in 820 counties (covering 13 States) in the United States of a large-scale Facebook ad campaign disseminating these messages. In the first level of randomization, we randomly divided the counties into two groups: high intensity and low intensity. In the second level, we randomly assigned zip codes to either treatment or control such that 75% of zip codes in high intensity counties received the treatment, while 25% of zip codes in low intensity counties received the treatment. In each treated zip code, we sent the ad to as many Facebook subscribers as possible (11,954,109 users received at least one ad at Thanksgiving and 23,302,290 users received at least one ad at Christmas). The first primary outcome was aggregate holiday travel, measured using mobile phone location data, available at the county level: we find that average distance travelled in high-intensity counties decreased by -0.993 percentage points (95% CI -1.616, -0.371, p-value 0.002) the three days before each holiday. The second primary outcome was COVID-19 infection at the zip-code level: COVID-19 infections recorded in the two-week period starting five days post-holiday declined by 3.5 percent (adjusted 95% CI [-6.2 percent, -0.7 percent], p-value 0.013) in intervention zip codes compared to control zip codes.
Many Agent Reinforcement Learning Under Partial Observability

Recent renewed interest in multi-agent reinforcement learning (MARL) has generated an impressive array of techniques that leverage deep reinforcement learning, primarily actor-critic architectures, and can be applied to a limited range of settings in terms of observability and communication. However, a continuing limitation of much of this work is the curse of dimensionality when it comes to representations based on joint actions, which grow exponentially with the number of agents. In this paper, we squarely focus on this challenge of scalability. We apply the key insight of action anonymity, which leads to permutation invariance of joint actions, to two recently presented deep MARL algorithms, MADDPG and IA2C, and compare these instantiations to another recent technique that leverages action anonymity, viz., mean-field MARL. We show that our instantiations can learn the optimal behavior in a broader class of agent networks than the mean-field method, using a recently introduced pragmatic domain.
Relational Analysis of Sensor Attacks on Cyber-Physical Systems

Cyber-physical systems, such as self-driving cars or autonomous aircraft, must defend against attacks that target sensor hardware. Analyzing system design can help engineers understand how a compromised sensor could impact the system's behavior; however, designing security analyses for cyber-physical systems is difficult due to their combination of discrete dynamics, continuous dynamics, and nondeterminism. This paper contributes a framework for modeling and analyzing sensor attacks on cyber-physical systems, using the formalism of hybrid programs. We formalize and analyze two relational properties of a system's robustness. These relational properties respectively express (1) whether a system's safety property can be influenced by sensor attacks, and (2) whether a system's high-integrity state can be affected by sensor attacks. We characterize these relational properties by defining an equivalence relation between a system under attack and the original unattacked system. That is, the system satisfies the robustness properties if executions of the attacked system are appropriately related to executions of the unattacked system. We present two techniques for reasoning about the equivalence relation and thus proving the relational properties for a system. One proof technique decomposes large proof obligations to smaller proof obligations. The other proof technique adapts the self-composition technique from the literature on secure information-flow, allowing us to reduce reasoning about the equivalence of two systems to reasoning about properties of a single system. This technique allows us to reuse existing tools for reasoning about properties of hybrid programs, but is challenging due to the combination of discrete dynamics, continuous dynamics, and nondeterminism. To evaluate, we present three case studies motivated by real design flaws in existing cyber-physical systems.
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic  Distillation

We propose Joint-DetNAS, a unified NAS framework for object detection, which integrates 3 key components: Neural Architecture Search, pruning, and Knowledge Distillation. Instead of naively pipelining these techniques, our Joint-DetNAS optimizes them jointly. The algorithm consists of two core processes: student morphism optimizes the student's architecture and removes the redundant parameters, while dynamic distillation aims to find the optimal matching teacher. For student morphism, weight inheritance strategy is adopted, allowing the student to flexibly update its architecture while fully utilize the predecessor's weights, which considerably accelerates the search; To facilitate dynamic distillation, an elastic teacher pool is trained via integrated progressive shrinking strategy, from which teacher detectors can be sampled without additional cost in subsequent searches. Given a base detector as the input, our algorithm directly outputs the derived student detector with high performance without additional training. Experiments demonstrate that our Joint-DetNAS outperforms the naive pipelining approach by a great margin. Given a classic R101-FPN as the base detector, Joint-DetNAS is able to boost its mAP from 41.4 to 43.9 on MS COCO and reduce the latency by 47%, which is on par with the SOTA EfficientDet while requiring less search cost. We hope our proposed method can provide the community with a new way of jointly optimizing NAS, KD and pruning.
Smile Like You Mean It: Driving Animatronic Robotic Face with Learned  Models

Ability to generate intelligent and generalizable facial expressions is essential for building human-like social robots. At present, progress in this field is hindered by the fact that each facial expression needs to be programmed by humans. In order to adapt robot behavior in real time to different situations that arise when interacting with human subjects, robots need to be able to train themselves without requiring human labels, as well as make fast action decisions and generalize the acquired knowledge to diverse and new contexts. We addressed this challenge by designing a physical animatronic robotic face with soft skin and by developing a vision-based self-supervised learning framework for facial mimicry. Our algorithm does not require any knowledge of the robot's kinematic model, camera calibration or predefined expression set. By decomposing the learning process into a generative model and an inverse model, our framework can be trained using a single motor babbling dataset. Comprehensive evaluations show that our method enables accurate and diverse face mimicry across diverse human subjects. The project website is at http://www.cs.columbia.edu/~bchen/aiface/
Divergence Optimization for Noisy Universal Domain Adaptation

Universal domain adaptation (UniDA) has been proposed to transfer knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the label sets. In practice, however, it is difficult to obtain a large amount of perfectly clean labeled data in a source domain with limited resources. Existing UniDA methods rely on source samples with correct annotations, which greatly limits their application in the real world. Hence, we consider a new realistic setting called Noisy UniDA, in which classifiers are trained with noisy labeled data from the source domain and unlabeled data with an unknown class distribution from the target domain. This paper introduces a two-head convolutional neural network framework to solve all problems simultaneously. Our network consists of one common feature generator and two classifiers with different decision boundaries. By optimizing the divergence between the two classifiers' outputs, we can detect noisy source samples, find "unknown" classes in the target domain, and align the distribution of the source and target domains. In an extensive evaluation of different domain adaptation settings, the proposed method outperformed existing methods by a large margin in most settings.
Deep Image Harmonization by Bridging the Reality Gap

Image harmonization has been significantly advanced with large-scale harmonization dataset. However, the current way to build dataset is still labor-intensive, which adversely affects the extendability of dataset. To address this problem, we propose to construct a large-scale rendered harmonization dataset RHHarmony with fewer human efforts to augment the existing real-world dataset. To leverage both real-world images and rendered images, we propose a cross-domain harmonization network CharmNet to bridge the domain gap between two domains. Moreover, we also employ well-designed style classifiers and losses to facilitate cross-domain knowledge transfer. Extensive experiments demonstrate the potential of using rendered images for image harmonization and the effectiveness of our proposed network. Our dataset and code are available at https://github.com/bcmi/Rendered_Image_Harmonization_Datasets.
A Pose-only Solution to Visual Reconstruction and Navigation

Visual navigation and three-dimensional (3D) scene reconstruction are essential for robotics to interact with the surrounding environment. Large-scale scenes and critical camera motions are great challenges facing the research community to achieve this goal. We raised a pose-only imaging geometry framework and algorithms that can help solve these challenges. The representation is a linear function of camera global translations, which allows for efficient and robust camera motion estimation. As a result, the spatial feature coordinates can be analytically reconstructed and do not require nonlinear optimization. Experiments demonstrate that the computational efficiency of recovering the scene and associated camera poses is significantly improved by 2-4 orders of magnitude. This solution might be promising to unlock real-time 3D visual computing in many forefront applications.
Using contrastive learning to improve the performance of steganalysis  schemes

To improve the detection accuracy and generalization of steganalysis, this paper proposes the Steganalysis Contrastive Framework (SCF) based on contrastive learning. The SCF improves the feature representation of steganalysis by maximizing the distance between features of samples of different categories and minimizing the distance between features of samples of the same category. To decrease the computing complexity of the contrastive loss in supervised learning, we design a novel Steganalysis Contrastive Loss (StegCL) based on the equivalence and transitivity of similarity. The StegCL eliminates the redundant computing in the existing contrastive loss. The experimental results show that the SCF improves the generalization and detection accuracy of existing steganalysis DNNs, and the maximum promotion is 2% and 3% respectively. Without decreasing the detection accuracy, the training time of using the StegCL is 10% of that of using the contrastive loss in supervised learning.