OpenRaiser/Intern-Atlas huggingface dataset:
Dance In the Wild: Monocular Human Animation with Neural Dynamic Appearance Synthesis
Venue: arXiv cs.CV 2021
Year: 2021

Synthesizing dynamic appearances of humans in motion plays a central role in applications such as AR/VR and video editing. While many recent methods have been proposed to tackle this problem, handling loose garments with complex textures and high dynamic motion still remains challenging. In this paper, we propose a video based appearance synthesis method that tackles such challenges and demonstrates high quality results for in-the-wild videos that have not been shown before. Specifically, we adopt a StyleGAN based architecture to the task of person specific video based motion retargeting. We introduce a novel motion signature that is used to modulate the generator weights to capture dynamic appearance changes as well as regularizing the single frame based pose estimates to improve temporal coherency. We evaluate our method on a set of challenging videos and show that our approach achieves state-of-the art performance both qualitatively and quantitatively.
Recent Advances in Automated Question Answering In Biomedical Domain
Venue: arXiv cs.AI 2021
Year: 2021

The objective of automated Question Answering (QA) systems is to provide answers to user queries in a time efficient manner. The answers are usually found in either databases (or knowledge bases) or a collection of documents commonly referred to as the corpus. In the past few decades there has been a proliferation of acquisition of knowledge and consequently there has been an exponential growth in new scientific articles in the field of biomedicine. Therefore, it has become difficult to keep track of all the information in the domain, even for domain experts. With the improvements in commercial search engines, users can type in their queries and get a small set of documents most relevant for answering their query, as well as relevant snippets from the documents in some cases. However, it may be still tedious and time consuming to manually look for the required information or answers. This has necessitated the development of efficient QA systems which aim to find exact and precise answers to user provided natural language questions in the domain of biomedicine. In this paper, we introduce the basic methodologies used for developing general domain QA systems, followed by a thorough investigation of different aspects of biomedical QA systems, including benchmark datasets and several proposed approaches, both using structured databases and collection of texts. We also explore the limitations of current systems and explore potential avenues for further advancement.
Classification of the Chess Endgame problem using Logistic Regression, Decision Trees, and Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

In this study we worked on the classification of the Chess Endgame problem using different algorithms like logistic regression, decision trees and neural networks. Our experiments indicates that the Neural Networks provides the best accuracy (85%) then the decision trees (79%). We did these experiments using Microsoft Azure Machine Learning as a case-study on using Visual Programming in classification. Our experiments demonstrates that this tool is powerful and save a lot of time, also it could be improved with more features that increase the usability and reduce the learning curve. We also developed an application for dataset visualization using a new programming language called Ring, our experiments demonstrates that this language have simple design like Python while integrates RAD tools like Visual Basic which is good for GUI development in the open-source world
Traffic4cast -- Large-scale Traffic Prediction using 3DResNet and Sparse-UNet
Venue: arXiv cs.CV 2021
Year: 2021

The IARAI competition Traffic4cast 2021 aims to predict short-term city-wide high-resolution traffic states given the static and dynamic traffic information obtained previously. The aim is to build a machine learning model for predicting the normalized average traffic speed and flow of the subregions of multiple large-scale cities using historical data points. The model is supposed to be generic, in a way that it can be applied to new cities. By considering spatiotemporal feature learning and modeling efficiency, we explore 3DResNet and Sparse-UNet approaches for the tasks in this competition. The 3DResNet based models use 3D convolution to learn the spatiotemporal features and apply sequential convolutional layers to enhance the temporal relationship of the outputs. The Sparse-UNet model uses sparse convolutions as the backbone for spatiotemporal feature learning. Since the latter algorithm mainly focuses on non-zero data points of the inputs, it dramatically reduces the computation time, while maintaining a competitive accuracy. Our results show that both of the proposed models achieve much better performance than the baseline algorithms. The codes and pretrained models are available at https://github.com/resuly/Traffic4Cast-2021.
FINO: Flow-based Joint Image and Noise Model
Venue: arXiv cs.CV 2021
Year: 2021

One of the fundamental challenges in image restoration is denoising, where the objective is to estimate the clean image from its noisy measurements. To tackle such an ill-posed inverse problem, the existing denoising approaches generally focus on exploiting effective natural image priors. The utilization and analysis of the noise model are often ignored, although the noise model can provide complementary information to the denoising algorithms. In this paper, we propose a novel Flow-based joint Image and NOise model (FINO) that distinctly decouples the image and noise in the latent space and losslessly reconstructs them via a series of invertible transformations. We further present a variable swapping strategy to align structural information in images and a noise correlation matrix to constrain the noise based on spatially minimized correlation information. Experimental results demonstrate FINO's capacity to remove both synthetic additive white Gaussian noise (AWGN) and real noise. Furthermore, the generalization of FINO to the removal of spatially variant noise and noise with inaccurate estimation surpasses that of the popular and state-of-the-art methods by large margins.
Music Score Expansion with Variable-Length Infilling
Venue: arXiv cs.SD 2021
Year: 2021

In this paper, we investigate using the variable-length infilling (VLI) model, which is originally proposed to infill missing segments, to "prolong" existing musical segments at musical boundaries. Specifically, as a case study, we expand 20 musical segments from 12 bars to 16 bars, and examine the degree to which the VLI model preserves musical boundaries in the expanded results using a few objective metrics, including the Register Histogram Similarity we newly propose. The results show that the VLI model has the potential to address the expansion task.
Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity
Venue: arXiv cs.LG 2021
Year: 2021

Developing shopping experiences that delight the customer requires businesses to understand customer taste. This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity. Shopping activity is represented as a bipartite graph. This graph is refined by applying sparsity-based statistical learning methods. These methods are interpretable and reveal insights about customers' preferences as well as products driving revenue to the store.
On Novel Peer Review System for Academic Journals: Experimental Study Based on Social Computing
Venue: arXiv cs.DL 2021
Year: 2021

For improving the performance and effectiveness of peer review, a novel review system is proposed, based on analysis of peer review process for academic journals under a parallel model built via Monte Carlo method. The model can simulate the review, application and acceptance activities of the review systems, in a distributed manner. According to simulation experiments on two distinct review systems respectively, significant advantages manifest for the novel one.
Implementation of Ethically Aligned Design with Ethical User stories in SMART terminal Digitalization project: Use case Passenger Flow
Venue: arXiv cs.CY 2021
Year: 2021

Digitalization and Smart systems are part of our everyday lives today. So far the development has been rapid and all the implications that comes after the deployment has not been able to foresee or even assess during the development, especially when ethics or trustworthiness is concerned. Artificial Intelligence (AI) and Autonomous Systems (AS) are the direction that software systems are taking today. It is witnessed in banks, stores, internet and it is proceeding to transportation as well as on traveling. Autonomous maritime industry has also taking this direction when taking under development in digitalization on fairway and port terminals. AI ethics has advanced profoundly since the machine learning develop during the last decade and is now being implemented in AI development and workflow of software engineers. It is not an easy task and tools are needed to make the ethical assessment easier. This paper will review a research in an industrial setting, where Ethically Aligned Design practice, Ethical User Stories are used to transfer ethical requirements to ethical user stories to form practical solutions for project use. This project is in the field of maritime industry and concentrates on digitalization of port terminals and this particular paper focuses on the passenger flow. Results are positive towards the practice of Ethical User Stories, drawn from a large empirical data set.
From words to connections: Word use similarity as an honest signal conducive to employees' digital communication
Venue: arXiv cs.SI 2021
Year: 2021

Bringing together considerations from three research trends (honest signals of collaboration, socio-semantic networks and homophily theory), we hypothesise that word use similarity and having similar social network positions are linked with the level of employees' digital interaction. To verify our hypothesis, we analyse the communication of close to 1600 employees, interacting on the intranet communication forum of a large company. We study their social dynamics and the 'honest signals' that, in past research, proved to be conducive to employees' engagement and collaboration. We find that word use similarity is the main driver of interaction, much more than other language characteristics or similarity in network position. Our results suggest carefully choosing the language according to the target audience and have practical implications for both company managers and online community administrators. Understanding how to better use language could, for example, support the development of knowledge sharing practices or internal communication campaigns.
Clicking Matters:Towards Interactive Human Parsing
Venue: arXiv cs.CV 2021
Year: 2021

In this work, we focus on Interactive Human Parsing (IHP), which aims to segment a human image into multiple human body parts with guidance from users' interactions. This new task inherits the class-aware property of human parsing, which cannot be well solved by traditional interactive image segmentation approaches that are generally class-agnostic. To tackle this new task, we first exploit user clicks to identify different human parts in the given image. These clicks are subsequently transformed into semantic-aware localization maps, which are concatenated with the RGB image to form the input of the segmentation network and generate the initial parsing result. To enable the network to better perceive user's purpose during the correction process, we investigate several principal ways for the refinement, and reveal that random-sampling-based click augmentation is the best way for promoting the correction effectiveness. Furthermore, we also propose a semantic-perceiving loss (SP-loss) to augment the training, which can effectively exploit the semantic relationships of clicks for better optimization. To the best knowledge, this work is the first attempt to tackle the human parsing task under the interactive setting. Our IHP solution achieves 85\% mIoU on the benchmark LIP, 80\% mIoU on PASCAL-Person-Part and CIHP, 75\% mIoU on Helen with only 1.95, 3.02, 2.84 and 1.09 clicks per class respectively. These results demonstrate that we can simply acquire high-quality human parsing masks with only a few human effort. We hope this work can motivate more researchers to develop data-efficient solutions to IHP in the future.
Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities
Venue: arXiv cs.CE 2021
Year: 2021

Recent advances in machine learning, coupled with low-cost computation, availability of cheap streaming sensors, data storage and cloud technologies, has led to widespread multi-disciplinary research activity with significant interest and investment from commercial stakeholders. Mechanistic models, based on physical equations, and purely data-driven statistical approaches represent two ends of the modelling spectrum. New hybrid, data-centric engineering approaches, leveraging the best of both worlds and integrating both simulations and data, are emerging as a powerful tool with a transformative impact on the physical disciplines. We review the key research trends and application scenarios in the emerging field of integrating simulations, machine learning, and statistics. We highlight the opportunities that such an integrated vision can unlock and outline the key challenges holding back its realisation. We also discuss the bottlenecks in the translational aspects of the field and the long-term upskilling requirements of the existing workforce and future university graduates.
Towards Live Video Analytics with On-Drone Deeper-yet-Compatible Compression
Venue: arXiv cs.NI 2021
Year: 2021

In this work, we present DCC(Deeper-yet-Compatible Compression), one enabling technique for real-time drone-sourced edge-assisted video analytics built on top of the existing codec. DCC tackles an important technical problem to compress streamed video from the drone to the edge without scarifying accuracy and timeliness of video analytical tasks performed at the edge. DCC is inspired by the fact that not every bit in streamed video is equally valuable to video analytics, which opens new compression room over the conventional analytics-oblivious video codec technology. We exploit drone-specific context and intermediate hints from object detection to pursue adaptive fidelity needed to retain analytical quality. We have prototyped DCC in one showcase application of vehicle detection and validated its efficiency in representative scenarios. DCC has reduced transmission volume by 9.5-fold over the baseline approach and 19-683% over the state-of-the-art with comparable detection accuracy.
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping
Venue: arXiv cs.CV 2021
Year: 2021

Real-time robotic grasping, supporting a subsequent precise object-in-hand operation task, is a priority target towards highly advanced autonomous systems. However, such an algorithm which can perform sufficiently-accurate grasping with time efficiency is yet to be found. This paper proposes a novel method with a 2-stage approach that combines a fast 2D object recognition using a deep neural network and a subsequent accurate and fast 6D pose estimation based on Point Pair Feature framework to form a real-time 3D object recognition and grasping solution capable of multi-object class scenes. The proposed solution has a potential to perform robustly on real-time applications, requiring both efficiency and accuracy. In order to validate our method, we conducted extensive and thorough experiments involving laborious preparation of our own dataset. The experiment results show that the proposed method scores 97.37% accuracy in 5cm5deg metric and 99.37% in Average Distance metric. Experiment results have shown an overall 62% relative improvement (5cm5deg metric) and 52.48% (Average Distance metric) by using the proposed method. Moreover, the pose estimation execution also showed an average improvement of 47.6% in running time. Finally, to illustrate the overall efficiency of the system in real-time operations, a pick-and-place robotic experiment is conducted and has shown a convincing success rate with 90% of accuracy. This experiment video is available at https://sites.google.com/view/dl-ppf6dpose/.
On Nash-solvability of finite $n$-person deterministic graphical games; Catch 22
Venue: arXiv cs.GT 2021
Year: 2021

We consider finite $n$-person deterministic graphical (DG) games. These games are modelled by finite directed graphs (digraphs) $G$ which may have directed cycles and, hence, infinite plays. Yet, it is assumed that all these plays are equivalent and form a single outcome $c$, while the terminal vertices $V_T = \{a_1, \ldots, a_p\}$ form $p$ remaining outcomes. We study the existence of Nash equilibria (NE) in pure stationary strategies. It is known that NE exist when $n=2$ and may fail to exist when $n > 2$. Yet, the question becomes open for $n > 2$ under the following extra condition: (C) For each of $n$ players, $c$ is worse than each of $p$ terminal outcomes. In other words, all players are interested in terminating the play, which is a natural assumption. Moreover, Nash-solvability remains open even if we replace (C) by a weaker condition: (C22) There exist no two players for whom $c$ is better than (at least) two terminal outcomes. We conjecture that such two players exist in each NE-free DG game, or in other words, that (C22) implies Nash-solvability, for all $n$. Recently, the DG games were extended to a wider class of the DG multi-stage (DGMS) games, whose outcomes are the strongly connected components (SCC) of digraph $G$. Merging all outcomes of a DGMS game that correspond to its non-terminal SCCs we obtain a DG game. Clearly, this operation respects Nash-solvability (NS). Basic conditions and conjectures related to NS can be extended from the DG to DGMS games: in both cases NE exist if $n=2$ and may fail to exist when $n > 2$; furthermore, we modify conditions (C) and (C22) to adapt them for the DGMS games. Keywords: $n$-person deterministic graphical (multi-stage) games, Nash equilibrium, Nash-solvability, pure stationary strategy, digraph, directed cycle, strongly connected component.
Automatically identifying a mobile phone user's position within a vehicle
Venue: arXiv cs.CV 2021
Year: 2021

Traffic-related injuries and fatalities are major health risks in the United States. Mobile phone use while driving quadruples the risk for a motor vehicle crash. This work demonstrates the feasibility of using the mobile phone camera to passively detect the location of the phone's user within a vehicle. In a large, varied dataset we were able correctly identify if the user was in the driver's seat or one of the passenger seats with 94.9% accuracy. This model could be used by application developers to selectively change or lock functionality while a user is driving, but not if the user is a passenger in a moving vehicle.
An Online Multi-Index Approach to Human Ergonomics Assessment in the Workplace
Venue: arXiv cs.RO 2021
Year: 2021

Work-related musculoskeletal disorders (WMSDs) remain one of the major occupational safety and health problems in the European Union nowadays. Thus, continuous tracking of workers' exposure to the factors that may contribute to their development is paramount. This paper introduces an online approach to monitor kinematic and dynamic quantities on the workers, providing on the spot an estimate of the physical load required in their daily jobs. A set of ergonomic indexes is defined to account for multiple potential contributors to WMSDs, also giving importance to the subject-specific requirements of the workers. To evaluate the proposed framework, a thorough experimental analysis was conducted on twelve human subjects considering tasks that represent typical working activities in the manufacturing sector. For each task, the ergonomic indexes that better explain the underlying physical load were identified, following a statistical analysis, and supported by the outcome of a surface electromyography (sEMG) analysis. A comparison was also made with a well-recognised and standard tool to evaluate human ergonomics in the workplace, to highlight the benefits introduced by the proposed framework. Results demonstrate the high potential of the proposed framework in identifying the physical risk factors, and therefore to adopt preventive measures. Another equally important contribution of this study is the creation of a comprehensive database on human kinodynamic measurements, which hosts multiple sensory data of healthy subjects performing typical industrial tasks.
Towards an Efficient Voice Identification Using Wav2Vec2.0 and HuBERT Based on the Quran Reciters Dataset
Venue: arXiv cs.SD 2021
Year: 2021

Current authentication and trusted systems depend on classical and biometric methods to recognize or authorize users. Such methods include audio speech recognitions, eye, and finger signatures. Recent tools utilize deep learning and transformers to achieve better results. In this paper, we develop a deep learning constructed model for Arabic speakers identification by using Wav2Vec2.0 and HuBERT audio representation learning tools. The end-to-end Wav2Vec2.0 paradigm acquires contextualized speech representations learnings by randomly masking a set of feature vectors, and then applies a transformer neural network. We employ an MLP classifier that is able to differentiate between invariant labeled classes. We show several experimental results that safeguard the high accuracy of the proposed model. The experiments ensure that an arbitrary wave signal for a certain speaker can be identified with 98% and 97.1% accuracies in the cases of Wav2Vec2.0 and HuBERT, respectively.
Supporting and Controlling Complex Concurrency in Fault- Tolerant Distributed Systems
Venue: arXiv cs.DC 2021
Year: 2021

Distributed computing often gives rise to complex concurrent and interacting activities. In some cases several concurrent activities may be working together, i.e. cooperating, to solve a given problem; in other cases, the activities may be independent but needing to share common system resources for which they must compete. Many difficulties and limitations occur in the widely advocated objects and (trans)actions model when it is supposed to support cooperating activities. We have introduced previously the concept of coordinated atomic (CA) actions [Xu et al. 1995]; this paper analyzes and examines the derived objects and CA actions model for constructing fault-tolerant distributed systems and providing unified support for both cooperative and competitive concurrency. Our investigation reveals and clarifies several significant problems that have not previously been studied extensively, including the problem of ensuring consistent access to shared objects from a joint action as opposed to a set of independent actions. Conceptual and implementation-related solutions are proposed and illustrated.