OpenRaiser/Intern-Atlas huggingface dataset:
Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis
Venue: arXiv cs.LG 2021
Year: 2021

Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving the final model performance without acknowledging the computational cost of the proposed approaches, in terms of execution time and environmental impact. This paper proposes a simple yet effective KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches, while producing competitive performance. We highlight three technical innovations: full batch learning via relational matrices, closed-form Orthogonal Procrustes Analysis for KGEs, and non-negative-sampling training. In addition, as the first KGE method whose entity embeddings also store full relation information, our trained models encode rich semantics and are highly interpretable. Comprehensive experiments and ablation studies involving 13 strong baselines and two standard datasets verify the effectiveness and efficiency of our algorithm.
An Extended Epidemic Model on Interconnected Networks for COVID-19 to Explore the Epidemic Dynamics
Venue: arXiv cs.CY 2021
Year: 2021

COVID-19 has resulted in a public health global crisis. The pandemic control necessitates epidemic models that capture the trends and impacts on infectious individuals. Many exciting models can implement this but they lack practical interpretability. This study combines the epidemiological and network theories and proposes a framework with causal interpretability in response to this issue. This framework consists of an extended epidemic model in interconnected networks and a dynamic structure that has major human mobility. The networked causal analysis focuses on the stochastic processing mechanism. It highlights the social infectivity as the intervention estimator between the observable effect (the number of daily new cases) and unobservable causes (the number of infectious persons). According to an experiment on the dataset for Tokyo metropolitan areas, the computational results indicate the propagation features of the symptomatic and asymptomatic infectious persons. These new spatiotemporal findings can be beneficial for policy decision making.
Practical Two-party Privacy-preserving Neural Network Based on Secret Sharing
Venue: arXiv cs.CR 2021
Year: 2021

Neural networks, with the capability to provide efficient predictive models, have been widely used in medical, financial, and other fields, bringing great convenience to our lives. However, the high accuracy of the model requires a large amount of data from multiple parties, raising public concerns about privacy. Privacy-preserving neural network based on multi-party computation is one of the current methods used to provide model training and inference under the premise of solving data privacy. In this study, we propose a new two-party privacy-preserving neural network training and inference framework in which privacy data is distributed to two non-colluding servers. We construct a preprocessing protocol for mask generation, support and realize secret sharing comparison on 2PC, and propose a new method to further reduce the communication rounds. Based on the comparison protocol, we construct building blocks such as division and exponential, and realize the process of training and inference that no longer needs to convert between different types of secret sharings and is entirely based on arithmetic secret sharing. Compared with the previous works, our work obtains higher accuracy, which is very close to that of plaintext training. While the accuracy has been improved, the runtime is reduced, considering the online phase, our work is 5x faster than SecureML, 4.32-5.75x faster than SecureNN, and is very close to the current optimal 3PC implementation, FALCON. For secure inference, as far as known knowledge is concerned, we should be the current optimal 2PC implementation, which is 4-358x faster than other works.
Do as we do: Multiple Person Video-To-Video Transfer
Venue: arXiv cs.CV 2021
Year: 2021

Our goal is to transfer the motion of real people from a source video to a target video with realistic results. While recent advances significantly improved image-to-image translations, only few works account for body motions and temporal consistency. However, those focus only on video re-targeting for a single actor/ for single actors. In this work, we propose a marker-less approach for multiple-person video-to-video transfer using pose as an intermediate representation. Given a source video with multiple persons dancing or working out, our method transfers the body motion of all actors to a new set of actors in a different video. Differently from recent "do as I do" methods, we focus specifically on transferring multiple person at the same time and tackle the related identity switch problem. Our method is able to convincingly transfer body motion to the target video, while preserving specific features of the target video, such as feet touching the floor and relative position of the actors. The evaluation is performed with visual quality and appearance metrics using publicly available videos with the permission of their owners.
Preprocessing Methods of Lane Detection and Tracking for Autonomous Driving
Venue: arXiv cs.CV 2021
Year: 2021

In the past few years, researches on advanced driver assistance systems (ADASs) have been carried out and deployed in intelligent vehicles. Systems that have been developed can perform different tasks, such as lane keeping assistance (LKA), lane departure warning (LDW), lane change warning (LCW) and adaptive cruise control (ACC). Real time lane detection and tracking (LDT) is one of the most consequential parts to performing the above tasks. Images which are extracted from the video, contain noise and other unwanted factors such as variation in lightening, shadow from nearby objects and etc. that requires robust preprocessing methods for lane marking detection and tracking. Preprocessing is critical for the subsequent steps and real time performance because its main function is to remove the irrelevant image parts and enhance the feature of interest. In this paper, we survey preprocessing methods for detecting lane marking as well as tracking lane boundaries in real time focusing on vision-based system.
Adversarially-Trained Nonnegative Matrix Factorization
Venue: arXiv cs.LG 2021
Year: 2021

We consider an adversarially-trained version of the nonnegative matrix factorization, a popular latent dimensionality reduction technique. In our formulation, an attacker adds an arbitrary matrix of bounded norm to the given data matrix. We design efficient algorithms inspired by adversarial training to optimize for dictionary and coefficient matrices with enhanced generalization abilities. Extensive simulations on synthetic and benchmark datasets demonstrate the superior predictive performance on matrix completion tasks of our proposed method compared to state-of-the-art competitors, including other variants of adversarial nonnegative matrix factorization.
Beyond Pointwise Submodularity: Non-Monotone Adaptive Submodular Maximization subject to Knapsack and $k$-System Constraints
Venue: arXiv cs.DS 2021
Year: 2021

In this paper, we study the non-monotone adaptive submodular maximization problem subject to a knapsack and a $k$-system constraints. The input of our problem is a set of items, where each item has a particular state drawn from a known prior distribution. However, the state of an item is initially unknown, one must select an item in order to reveal the state of that item. There is a utility function which is defined over items and states. Our objective is to sequentially select a group of items to maximize the expected utility. Although the cardinality-constrained non-monotone adaptive submodular maximization has been well studied in the literature, whether there exists a constant approximation solution for the knapsack-constrained or $k$-system constrained adaptive submodular maximization problem remains an open problem. It fact, it has only been settled given the additional assumption of pointwise submodularity. In this paper, we remove the common assumption on pointwise submodularity and propose the first constant approximation solutions for both cases. Inspired by two recent studies on non-monotone adaptive submodular maximization, we develop a sampling-based randomized algorithm that achieves a $\frac{1}{10}$ approximation for the case of a knapsack constraint and that achieves a $\frac{1}{2k+4}$ approximation ratio for the case of a $k$-system constraint.
Assessing and Supplying the Health of Videos Games via Formal Semantics
Venue: arXiv cs.SE 2021
Year: 2021

Video games, just like any other media have both explicit and implicit messages, and they can have impact on physical and mental health of the users. These impacts can be positive or negative. The impacts, the implications and the meanings which exist in a game can be very widespread, multilayered and complicated. To investigate and guarantee the health of these video games, it is necessary to be able to estimate, assess and determine the implications of video games (from different perspectives). A common approach for studying complicated and multilayered phenomenon is formal semantics. Formal and rigorous methods can help in assessment and supplying the health of video games. In this article, an organizing for this assessment is proposed which is based on formal and rigorous methods and it considers various beneficiaries concerns. Moreover, a technological solution is presented which is based on system compliance to meanings, model checking methods and logical solution. The proposed organizing has several features such as: agility, flexibility, scalability, repeatability of reviews, transparency, adaptation, available details for reviews, assessing various layers and implicit and explicit implications of system of the game, avoiding subjectivity or individual skills, relying on rules and regulations, ability to plan for beneficiaries because of its transparency and employment for specialists.
Sublinear Time Nearest Neighbor Search over Generalized Weighted Manhattan Distance
Venue: arXiv cs.DB 2021
Year: 2021

Nearest Neighbor Search (NNS) over generalized weighted distances is fundamental to a wide range of applications. The problem of NNS over the generalized weighted square Euclidean distance has been studied in previous work. However, numerous studies have shown that the Manhattan distance could be more effective than the Euclidean distance for high-dimensional NNS, which indicates that the generalized weighted Manhattan distance is possibly more practical than the generalized weighted square Euclidean distance in high dimensions. To the best of our knowledge, no prior work solves the problem of NNS over the generalized weighted Manhattan distance in sublinear time. This paper achieves the goal by proposing two novel hashing schemes ($d_w^{l_1},l_2$)-ALSH and ($d_w^{l_1},θ$)-ALSH.
Visualization Improvisation
Venue: arXiv cs.HC 2021
Year: 2021

Teaching visualization design involve making students familiar and make them work with visualization models, framework and perspectives. Visualization research accommodates a plethora of perspectives emerging from researchers of varied backgrounds. These diverse range of perspectives give rise to multiples models, frameworks and perspectives to teach visualization design. In this paper, we look at an approach to visualization teaching by using improvisational techniques. The basic idea is to design a visualization without using an existing predefined model. Since improvisation, by definition, is not a model or a framework, this work presents a reflection on how improvisation can be a way of teaching visualization design.
Velocity Skinning for Real-time Stylized Skeletal Animation
Venue: arXiv cs.GR 2021
Year: 2021

Secondary animation effects are essential for liveliness. We propose a simple, real-time solution for adding them on top of standard skinning, enabling artist-driven stylization of skeletal motion. Our method takes a standard skeleton animation as input, along with a skin mesh and rig weights. It then derives per-vertex deformations from the different linear and angular velocities along the skeletal hierarchy. We highlight two specific applications of this general framework, namely the cartoonlike "squashy" and "floppy" effects, achieved from specific combinations of velocity terms. As our results show, combining these effects enables to mimic, enhance and stylize physical-looking behaviours within a standard animation pipeline, for arbitrary skinned characters. Interactive on CPU, our method allows for GPU implementation, yielding real-time performances even on large meshes. Animator control is supported through a simple interface toolkit, enabling to refine the desired type and magnitude of deformation at relevant vertices by simply painting weights. The resulting rigged character automatically responds to new skeletal animation, without further input.
A Graph Convolutional Neural Network based Framework for Estimating Future Citations Count of Research Articles
Venue: arXiv cs.DL 2021
Year: 2021

Scientific publications play a vital role in the career of a researcher. However, some articles become more popular than others among the research community and subsequently drive future research directions. One of the indicative signs of popular articles is the number of citations an article receives. The citation count, which is also the basis with various other metrics, such as the journal impact factor score, the $h$-index, is an essential measure for assessing a scientific paper's quality. In this work, we proposed a Graph Convolutional Network (GCN) based framework for estimating future research publication citations for both the short-term (1-year) and long-term (for 5-years and 10-years) duration. We have tested our proposed approach over the AMiner dataset, specifically on research articles from the computer science domain, consisting of more than 0.8 million articles.
A tight negative example for MMS fair allocations
Venue: arXiv cs.GT 2021
Year: 2021

We consider the problem of allocating indivisible goods to agents with additive valuation functions. Kurokawa, Procaccia and Wang {[JACM, 2018]} present instances for which every allocation gives some agent less than her maximin share. We present such examples with larger gaps. For three agents and nine items, we design an instance in which at least one agent does not get more than a $\frac{39}{40}$ fraction of her maximin share. {Moreover, we show that there is no negative example in which the difference between the number of items and the number of agents is smaller than six, and that the gap (of $\frac{1}{40}$) of our example is worst possible among all instances with nine items.} For $n \ge 4$ agents, we show examples in which at least one agent does not get more than a $1 - \frac{1}{n^4}$ fraction of her maximin share. {In the instances designed by Kurokawa, Procaccia and Wang, the gap is exponentially small in $n$.} Our proof techniques extend to allocation of chores (items of negative value), though the quantitative bounds for chores are different from those for goods. For three agents and nine chores, we design an instance in which the MMS gap is $\frac{1}{43}$.
Instagram Filter Removal on Fashionable Images
Venue: arXiv cs.CV 2021
Year: 2021

Social media images are generally transformed by filtering to obtain aesthetically more pleasing appearances. However, CNNs generally fail to interpret both the image and its filtered version as the same in the visual analysis of social media images. We introduce Instagram Filter Removal Network (IFRNet) to mitigate the effects of image filters for social media analysis applications. To achieve this, we assume any filter applied to an image substantially injects a piece of additional style information to it, and we consider this problem as a reverse style transfer problem. The visual effects of filtering can be directly removed by adaptively normalizing external style information in each level of the encoder. Experiments demonstrate that IFRNet outperforms all compared methods in quantitative and qualitative comparisons, and has the ability to remove the visual effects to a great extent. Additionally, we present the filter classification performance of our proposed model, and analyze the dominant color estimation on the images unfiltered by all compared methods.
Event-based Timestamp Image Encoding Network for Human Action Recognition and Anticipation
Venue: arXiv cs.CV 2021
Year: 2021

Event camera is an asynchronous, high frequency vision sensor with low power consumption, which is suitable for human action understanding task. It is vital to encode the spatial-temporal information of event data properly and use standard computer vision tool to learn from the data. In this work, we propose a timestamp image encoding 2D network, which takes the encoded spatial-temporal images with polarity information of the event data as input and output the action label. In addition, we propose a future timestamp image generator to generate futureaction information to aid the model to anticipate the human action when the action is not completed. Experiment results show that our method can achieve the same level of performance as those RGB-based benchmarks on real world action recognition,and also achieve the state of the art (SOTA) result on gesture recognition. Our future timestamp image generating model can effectively improve the prediction accuracy when the action is not completed. We also provide insight discussion on the importance of motion and appearance information in action recognition and anticipation.
Object-Centric Representation Learning for Video Question Answering
Venue: arXiv cs.CV 2021
Year: 2021

Video question answering (Video QA) presents a powerful testbed for human-like intelligent behaviors. The task demands new capabilities to integrate video processing, language understanding, binding abstract linguistic concepts to concrete visual artifacts, and deliberative reasoning over spacetime. Neural networks offer a promising approach to reach this potential through learning from examples rather than handcrafting features and rules. However, neural networks are predominantly feature-based - they map data to unstructured vectorial representation and thus can fall into the trap of exploiting shortcuts through surface statistics instead of true systematic reasoning seen in symbolic systems. To tackle this issue, we advocate for object-centric representation as a basis for constructing spatio-temporal structures from videos, essentially bridging the semantic gap between low-level pattern recognition and high-level symbolic algebra. To this end, we propose a new query-guided representation framework to turn a video into an evolving relational graph of objects, whose features and interactions are dynamically and conditionally inferred. The object lives are then summarized into resumes, lending naturally for deliberative relational reasoning that produces an answer to the query. The framework is evaluated on major Video QA datasets, demonstrating clear benefits of the object-centric approach to video reasoning.
SuperSim: a test set for word similarity and relatedness in Swedish
Venue: arXiv cs.CL 2021
Year: 2021

Language models are notoriously difficult to evaluate. We release SuperSim, a large-scale similarity and relatedness test set for Swedish built with expert human judgments. The test set is composed of 1,360 word-pairs independently judged for both relatedness and similarity by five annotators. We evaluate three different models (Word2Vec, fastText, and GloVe) trained on two separate Swedish datasets, namely the Swedish Gigaword corpus and a Swedish Wikipedia dump, to provide a baseline for future comparison. We release the fully annotated test set, code, baseline models, and data.
Distributed Learning Systems with First-order Methods
Venue: arXiv cs.LG 2021
Year: 2021

Scalable and efficient distributed learning is one of the main driving forces behind the recent rapid advancement of machine learning and artificial intelligence. One prominent feature of this topic is that recent progresses have been made by researchers in two communities: (1) the system community such as database, data management, and distributed systems, and (2) the machine learning and mathematical optimization community. The interaction and knowledge sharing between these two communities has led to the rapid development of new distributed learning systems and theory. In this work, we hope to provide a brief introduction of some distributed learning techniques that have recently been developed, namely lossy communication compression (e.g., quantization and sparsification), asynchronous communication, and decentralized communication. One special focus in this work is on making sure that it can be easily understood by researchers in both communities -- On the system side, we rely on a simplified system model hiding many system details that are not necessary for the intuition behind the system speedups; while, on the theory side, we rely on minimal assumptions and significantly simplify the proof of some recent work to achieve comparable results.
Ambient awareness for agricultural robotic vehicles
Venue: arXiv cs.RO 2021
Year: 2021

In the last few years, robotic technology has been increasingly employed in agriculture to develop intelligent vehicles that can improve productivity and competitiveness. Accurate and robust environmental perception is a critical requirement to address unsolved issues including safe interaction with field workers and animals, obstacle detection in controlled traffic applications, crop row guidance, surveying for variable rate applications, and situation awareness, in general, towards increased process automation. Given the variety of conditions thatmay be encountered in the field, no single sensor exists that can guarantee reliable results in every scenario. The development of a multi-sensory perception systemto increase the ambient awareness of an agricultural vehicle operating in crop fields is the objective of the Ambient Awareness for Autonomous Agricultural Vehicles (QUAD-AV) project. Different onboard sensor technologies, namely stereovision, LIDAR, radar, and thermography, are considered. Novel methods for their combination are proposed to automatically detect obstacles and discern traversable from non-traversable areas. Experimental results, obtained in agricultural contexts, are presented showing the effectiveness of the proposed methods.
Approach for modeling single branches of meadow orchard trees with 3D point clouds
Venue: arXiv cs.CV 2021
Year: 2021

The cultivation of orchard meadows provides an ecological benefit for biodiversity, which is significantly higher than in intensively cultivated orchards. The goal of this research is to create a tree model to automatically determine possible pruning points for stand-alone trees within meadows. The algorithm which is presented here is capable of building a skeleton model based on a pre-segmented photogrammetric 3D point cloud. Good results were achieved in assigning the points to their leading branches and building a virtual tree model, reaching an overall accuracy of 95.19 %. This model provided the necessary information about the geometry of the tree for automated pruning.
Voting-based probabilistic consensuses and their applications in distributed ledgers
Venue: arXiv cs.DC 2021
Year: 2021

We review probabilistic models known as majority dynamics (also known as threshold Voter Models) and discuss their possible applications for achieving consensus in cryptocurrency systems. In particular, we show that using this approach straightforwardly for practical consensus in Byzantine setting can be problematic and requires extensive further research. We then discuss the FPC consensus protocol which circumvents the problems mentioned above by using external randomness.