Reviewerly/exHarmony huggingface dataset:
Quo Vadis, Skeleton Action Recognition?

In this paper, we study current and upcoming frontiers across the landscape of skeleton-based human action recognition. To study skeleton-action recognition in the wild, we introduce Skeletics-152, a curated and 3-D pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale action dataset. We extend our study to include out-of-context actions by introducing Skeleton-Mimetics, a dataset derived from the recently introduced Mimetics dataset. We also introduce Metaphorics, a dataset with caption-style annotated YouTube videos of the popular social game Dumb Charades and interpretative dance performances. We benchmark state-of-the-art models on the NTU-120 dataset and provide multi-layered assessment of the results. The results from benchmarking the top performers of NTU-120 on the newly introduced datasets reveal the challenges and domain gap induced by actions in the wild. Overall, our work characterizes the strengths and limitations of existing approaches and datasets. Via the introduced datasets, our work enables new frontiers for human action recognition.
Navigating the GAN Parameter Space for Semantic Image Editing

Generative Adversarial Networks (GANs) are currently an indispensable tool for visual editing, being a standard component of image-to-image translation and image restoration pipelines. Furthermore, GANs are especially advantageous for controllable generation since their latent spaces contain a wide range of interpretable directions, well suited for semantic editing operations. By gradually changing latent codes along these directions, one can produce impressive visual effects, unattainable without GANs.In this paper, we significantly expand the range of visual effects achievable with the state-of-the-art models, like StyleGAN2. In contrast to existing works, which mostly operate by latent codes, we discover interpretable directions in the space of the generator parameters. By several simple methods, we explore this space and demonstrate that it also contains a plethora of interpretable directions, which are an excellent source of non-trivial semantic manipulations. The discovered manipulations cannot be achieved by transforming the latent codes and can be used to edit both synthetic and real images. We release our code and models and hope they will serve as a handy tool for further efforts on GAN-based image editing.
Generalized phase unwrapping method that avoids jump errors for fringe projection profilometry

Jump errors easily occur on the discontinuity of the wrapped phase because of the misalignment between wrapped phase and fringe order in fringe projection profilometry (FPP). In this paper, a phase unwrapping method that avoids jump errors is proposed for FPP. By building two other staggered wrapped phases from the original wrapped phase and dividing each period of fringe order into three parts, the proposed generalized tripartite phase unwrapping (Tri-PU) method can be used to avoid rather than compensatorily correct jump errors. It is suitable for the phase unwrapping method assisted by fringe order with a basic wrapped phase and fringe order, no matter which method is used to recover them. The experimental results demonstrate the effectiveness and generality of the proposed method, which is simple to implement and superior to measure complex objects with sharp edges.
Specific Emitter Identification With Limited Samples: A Model-Agnostic Meta-Learning Approach

It is necessary but difficult to obtain a large number of labeled samples to train the classification model in many real scenes. This letter proposes an approach for specific emitter identification(SEI) by introducing model-agnostic meta-learning, which can achieve high accuracy in the case of a limited number of labeled training samples. Specially, we improve the approach to make it suitable for the classification of electromagnetic signals of multiple types of equipments, without spending a lot of time and data to retrain the model structure. The data collected from ZigBee devices and UAVs are used to verify the proposed approach. The simulation results shows that the accuracy of proposed approach can reach more than 90% even though the training task and testing task are two types of devices.
Mobile Collaborative Secrecy Performance Prediction for Artificial IoT Networks

The integration of artificial intelligence and Internet of Things (IoT) has promoted the rapid development of artificial IoT (AIoT) networks. A wide range of AIoT applications have generated a great deal of data. The fifth-generation (5G) mobile communication has powerful data processing capabilities, and it is a key technology to enable AIoT big data processing. The explosive growth of the 5G users has made information security in AIoT networks a significant issue. Real-time security evaluation in AIoT networks is difficult due to user mobility and dynamic wireless environments. Thus, the evaluation and prediction of secrecy performance is a very critical research. In this article, new expressions for the nonzero secrecy capacity probability (NSCP) are derived to evaluate the mobile collaborative secrecy performance. An improved convolutional neural network (CNN) model, named as SI-CNN in this article, is proposed to predict the NSCP performance. The SI-CNN model combines the SqueezeNet and InceptionNet, and it has four convolution layers, which all adopt the same convolution model. For the first two layers, they employ a 2 × 1 convolution and a three-branch convolution, which not only increase the number of channels but also extract more features. For the last two layers, they employ the same structure, but different convolution kernels. The proposed SI-CNN prediction algorithm is shown to provide better NSCP performance prediction than other state-of-the-art methods. In particular, compared with wavelet neural network, the prediction precision of SI-CNN is improved by 26.8%.
Education Exchange Storage Protocol: Transformation Into Decentralized Learning Platform

The current micro-teaching process is readily online, and it is functional anywhere and anytime ubiquitously. All or most teaching and learning activities are accessible in centralized storage. However, centralized storage has inherent problems, such as a single point of failure with many possible data breaches, much duplication of data stored repeatedly in one location, and the lack of trust in third parties for data stored in centralized storage. Further issues include the high cost and low performance of the online systems that hinder the quality of the education process. In this paper, we propose a new framework Education Exchange Storage Protocol (EESP). EESP aims to improve the efficiency of the decentralized storage ecosystem in micro-teaching, coupled with blockchain technology acting as a control layer. Blockchain empowers the decentralized system by bringing together the most incompatible unstructured entities and integrate them. The decentralized storage system is armed with a blockchain smart contract that acts as a control layer, featuring impregnable security, immutability, trace-ability, and transparency. The EESP framework aims to elevate teaching and learning through blockchain decentralized storage systems in a transformational way, including but not limited to things like micro-credential, massive open online courses, and gamification, all in a single immersive learning platform. Finally, we tested and evaluated this framework using the truffle simulator, and the results demonstrate that the EESP model significantly improves performance.
CCHZ‐DISO: A Timely New Assessment System for Data Quality or Model Performance From Da Dao Zhi Jian

Abstract With the rapid development of big data, assessment of data quality or model performance has become a hot scientific question. However, most existing lots of metrics focus on specific aspects of the assessment, and comprehensive assessment is rare. Therefore, it is very necessary to develop new assessment system. To address this problem, a new assessment system is constructed which is named after Chen, Chen, Hu, and Zhou (CCHZ)‐distance between indices of simulation and observation (DISO) according to the contributions of Xi Chen, Deliang Chen, Zengyun Hu, and Qiming Zhou. CCHZ‐DISO system builds on the Euclidean Distance and flexible determination of statistical metrics and their numbers. Due to its simplicity and flexibility, CCHZ‐DISO can be readily and widely applied to any subject of science. Therefore, it follows the principle of the Chinese philosopher Lao Zi's Da Dao Zhi Jian which means that the most basic truth is very simple.
EfficientNeRF - Efficient Neural Radiance Fields

Neural Radiance Fields (NeRF) has been wildly applied to various tasks for its high-quality representation of 3D scenes. It takes long per-scene training time and per-image testing time. In this paper, we present EfficientNeRF as an efficient NeRF-based method to represent 3D scene and synthesize novel-view images. Although several ways exist to accelerate the training or testing process, it is still difficult to much reduce time for both phases simultaneously. We analyze the density and weight distribution of the sampled points then propose valid and pivotal sampling at the coarse and fine stage, respectively, to significantly improve sampling efficiency. In addition, we design a novel data structure to cache the whole scene during testing to accelerate the rendering speed. Overall, our method can reduce over 88% of training time, reach rendering speed of over 200 FPS, while still achieving competitive accuracy. Experiments prove that our method promotes the practicality of NeRF in the real world and enables many applications. The code is available in https://github.com/dvlabresearch/EfficientNeRF.
Examining the effects of altered avatars on perception-action in virtual reality.

In virtual reality (VR), avatars are graphical representations of people. Previous research highlights benefits of having a self-avatar when perceiving-acting while embedded in a virtual environment. We studied the effect that an altered avatar had on the perception of one's action capabilities. In Experiment 1, some participants acted with a normal, or faithful, avatar whereas another group of participants used an avatar with an extended arm, all in virtual reality. Experiment 2 utilized the same methodology and procedure as Experiment 1, except that only a calibration phase occurred in VR, whereas other phases were completed in the real world. All participants performed reaches to various distances presented visually. Results showed that calibration to altered dimensions of avatars is possible after receiving feedback while acting with the altered avatar. Calibration occurred more quickly when feedback was used to transition from a normal avatar to an altered avatar than when transitioning from the altered avatar back to the normal avatar without feedback. The implications of these findings for training in virtual reality simulations and for transfer to the real world are discussed, along with the implications for the concept of an embodied action schema. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
An Accelerated Linearly Convergent Stochastic L-BFGS Algorithm

The limited memory version of the Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm is the most popular quasi-Newton algorithm in machine learning and optimization. Recently, it was shown that the stochastic L-BFGS (sL-BFGS) algorithm with the variance-reduced stochastic gradient converges linearly. In this paper, we propose a new sL-BFGS algorithm by importing a proper momentum. We prove an accelerated linear convergence rate under mild conditions. The experimental results on different data sets also verify this acceleration advantage.
When will useful quantum computers be constructed? Not in the foreseeable future, this physicist argues. Here's why: The case against: Quantum computing

Quantum Computing IS ALL THE RAGE. It seems like hardly a day goes by without some news outlet describing the extraordinary things this technology promises. Most commentators forget, or just gloss over, the fact that people have been working on quantum computing for decades-and without any practical results to show for it. t IBM notes that quantum computers could "provide breakthroughs in many disciplines, including materials and drug discovery, the optimization of complex systems, and artificial intelligence." Microsoft assures us that quantum computers will "forever alter our economic, industrial, academic, and societal landscape." And journalists repeatedly warn that quantum computers may soon break the encryption that protects the Internet. It has gotten to the point where many researchers in various fields of physics feel obliged to justify whatever work they are doing by claiming that it has some relevance to quantum computing.
A Secured Proxy-Based Data Sharing Module in IoT Environments Using Blockchain

Access and utilization of data are central to the cloud computing paradigm. With the advent of the Internet of Things (IoT), the tendency of data sharing on the cloud has seen enormous growth. With data sharing comes numerous security and privacy issues. In the process of ensuring data confidentiality and fine-grained access control to data in the cloud, several studies have proposed Attribute-Based Encryption (ABE) schemes, with Key Policy-ABE (KP-ABE) being the prominent one. Recent works have however suggested that the confidentiality of data is violated through collusion attacks between a revoked user and the cloud server. We present a secured and efficient Proxy Re-Encryption (PRE) scheme that incorporates an Inner-Product Encryption (IPE) scheme in which decryption of data is possible if the inner product of the private key, associated with a set of attributes specified by the data owner, and the associated ciphertext is equal to zero 0 . We utilize a blockchain network whose processing node acts as the proxy server and performs re-encryption on the data. In ensuring data confidentiality and preventing collusion attacks, the data are divided into two, with one part stored on the blockchain network and the other part stored on the cloud. Our approach also achieves fine-grained access control.
IMHO Fine-Tuning Improves Claim Detection

Tuhin Chakrabarty, Christopher Hidey, Kathy McKeown. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Online Teacher-Students Interactions Using WhatsApp in a Law Course

Aim/Purpose: The purpose of this study was to analyze the online teacher- students’ interactions using WhatsApp, an instant messaging tool, and to identify the students’ view towards the use of that tool in a law course from a higher education institution in Colombia. Background: WhatsApp is a trending tool that is ultimately being used in academic contexts. However, little research is known on the types of interactions that occur when teachers are involved in student conversation groups. Methodology: This is a mixed- method study. Participants completed an opinion survey in order to establish students’ satisfaction towards the use of WhatsApp to complement face-to- face classes, a focus group to explore in depth the students´ opinions and acceptance of the WhatsApp tool for academic purposes and a chat conversation register to analyze the different types of interactions. The sample included 166 Law students. Contribution: Our contribution is to enrich the current literature on the interactions between teachers and students in a virtual environment where teachers can monitor the different academic tasks, coordinate in real time and analyze the students’ interactions that impact on the students’ ´learning process. Findings: The findings found in this research reveal that the different interactions between students and teachers in order to facilitate learning should be valued not only the relationships of knowledge construction, but also the social and interdependence presences due to the fact that in traditional learning processes they are not usually taken into account. Recommendations for Practitioners: The results of our research give evidence of how students in each subgroup (Plaintiffs, Defendant or Judges) diversify the use of the WhatsApp tool. Whether it is to organize, coordinate meetings, plan work, make quick inquiries, clear doubts, share messages and especially be able to communicate in real time and directly with the teacher, thus facilitating the learning process in the classroom. Recommendation for Researchers: This study identified that law university students appear to have a special preference for the WhatsApp tool, thanks to the immediacy of being able to coordinate tasks and communicate with the teacher, in comparison to using other technological means such as email. We recommend continuing to explore the use of WhatsApp in other different disciplines in order to compare the teacher-student interactions. Impact on Society: The analysis of academic interactions through WhatsApp may lead to further exploration of innovative forms of communication of teachers with their millennial students and new teacher roles to design constructive learning environments. Future Research: Future studies are suggested with regard to this topic and it would be interesting to carry out research work that deeply analyzes the role the instructor plays when participating in a WhatsApp chat group with academic purposes and how it may condition the way students interact.
Ablate, Variate, and Contemplate: Visual Analytics for Discovering Neural Architectures

The performance of deep learning models is dependent on the precise configuration of many layers and parameters. However, there are currently few systematic guidelines for how to configure a successful model. This means model builders often have to experiment with different configurations by manually programming different architectures (which is tedious and time consuming) or rely on purely automated approaches to generate and train the architectures (which is expensive). In this paper, we present Rapid Exploration of Model Architectures and Parameters, or REMAP, a visual analytics tool that allows a model builder to discover a deep learning model quickly via exploration and rapid experimentation of neural network architectures. In REMAP, the user explores the large and complex parameter space for neural network architectures using a combination of global inspection and local experimentation. Through a visual overview of a set of models, the user identifies interesting clusters of architectures. Based on their findings, the user can run ablation and variation experiments to identify the effects of adding, removing, or replacing layers in a given architecture and generate new models accordingly. They can also handcraft new models using a simple graphical interface. As a result, a model builder can build deep learning models quickly, efficiently, and without manual programming. We inform the design of REMAP through a design study with four deep learning model builders. Through a use case, we demonstrate that REMAP allows users to discover performant neural network architectures efficiently using visual exploration and user-defined semi-automated searches through the model space.
Developing the Path Signature Methodology and Its Application to Landmark- Based Human Action Recognition

Landmark-based human action recognition in videos is a challenging task in computer vision. One key step is to design a generic approach that generates discriminative features for the spatial structure and temporal dynamics. To this end, we regard the evolving landmark data as a high-dimensional path and apply path signature techniques to provide an expressive, robust, non-linear, and interpretable representation for the sequential events. We do not extract signature features from the raw path, rather we propose path disintegrations and path transformations as preprocessing steps. Path disintegrations turn a high-dimensional path linearly into a collection of lower-dimensional paths; some of these paths are in pose space while others are defined over a multi-scale collection of temporal intervals. Path transformations decorate the paths with additional coordinates in standard ways to allow the truncated signatures of transformed paths to expose additional features. For spatial representation, we apply the non-linear signature transform to vectorize the paths that arise out of pose disintegration, and for temporal representation, we apply it again to describe this evolving vectorization. Finally, all the features are joined together to constitute the input vector of a linear single-hidden-layer fully-connected network for classification. Experimental results on four diverse datasets demonstrated that the proposed feature set with only a linear shallow network is effective and achieves comparable state-of-the-art results to the advanced deep networks, and meanwhile, is capable of interpretation.
DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer

Positron emission tomography (PET) imaging is an imaging modality for diagnosing a number of neurological diseases. In contrast to Magnetic Resonance Imaging (MRI), PET is costly and involves injecting a radioactive substance into the patient. Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data. We derive new flow-based generative models which we show perform well in this small sample size regime (much smaller than dataset sizes available in standard vision tasks). Our formulation, DUAL-GLOW, is based on two invertible networks and a relation network that maps the latent spaces to each other. We discuss how given the prior distribution, learning the conditional distribution of PET given the MRI image reduces to obtaining the conditional distribution between the two latent codes w.r.t. the two image types. We also extend our framework to leverage ``side'' information (or attributes) when available. By controlling the PET generation through ``conditioning'' on age, our model is also able to capture brain FDG-PET (hypometabolism) changes, as a function of age. We present experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with 826 subjects, and obtain good performance in PET image synthesis, qualitatively and quantitatively better than recent works.
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

Bayesian neural networks (BNNs) demonstrate promising success in improving the robustness and uncertainty quantification of modern deep learning. However, they generally struggle with underfitting at scale and parameter efficiency. On the other hand, deep ensembles have emerged as alternatives for uncertainty quantification that, while outperforming BNNs on certain problems, also suffer from efficiency issues. It remains unclear how to combine the strengths of these two approaches and remediate their common issues. To tackle this challenge, we propose a rank-1 parameterization of BNNs, where each weight matrix involves only a distribution on a rank-1 subspace. We also revisit the use of mixture approximate posteriors to capture multiple modes, where unlike typical mixtures, this approach admits a significantly smaller memory increase (e.g., only a 0.4% increase for a ResNet-50 mixture of size 10). We perform a systematic empirical study on the choices of prior, variational posterior, and methods to improve training. For ResNet-50 on ImageNet, Wide ResNet 28-10 on CIFAR-10/100, and an RNN on MIMIC-III, rank-1 BNNs achieve state-of-the-art performance across log-likelihood, accuracy, and calibration on the test sets and out-of-distribution variants.