OpenRaiser/Intern-Atlas huggingface dataset:
Jointly Learnable Data Augmentations for Self-Supervised GNNs
Venue: arXiv cs.LG 2021
Year: 2021

Self-supervised Learning (SSL) aims at learning representations of objects without relying on manual labeling. Recently, a number of SSL methods for graph representation learning have achieved performance comparable to SOTA semi-supervised GNNs. A Siamese network, which relies on data augmentation, is the popular architecture used in these methods. However, these methods rely on heuristically crafted data augmentation techniques. Furthermore, they use either contrastive terms or other tricks (e.g., asymmetry) to avoid trivial solutions that can occur in Siamese networks. In this study, we propose, GraphSurgeon, a novel SSL method for GNNs with the following features. First, instead of heuristics we propose a learnable data augmentation method that is jointly learned with the embeddings by leveraging the inherent signal encoded in the graph. In addition, we take advantage of the flexibility of the learnable data augmentation and introduce a new strategy that augments in the embedding space, called post augmentation. This strategy has a significantly lower memory overhead and run-time cost. Second, as it is difficult to sample truly contrastive terms, we avoid explicit negative sampling. Third, instead of relying on engineering tricks, we use a scalable constrained optimization objective motivated by Laplacian Eigenmaps to avoid trivial solutions. To validate the practical use of GraphSurgeon, we perform empirical evaluation using 14 public datasets across a number of domains and ranging from small to large scale graphs with hundreds of millions of edges. Our finding shows that GraphSurgeon is comparable to six SOTA semi-supervised and on par with five SOTA self-supervised baselines in node classification tasks. The source code is available at https://github.com/zekarias-tilahun/graph-surgeon.
CoverTheFace: face covering monitoring and demonstrating using deep learning and statistical shape analysis
Venue: arXiv cs.CV 2021
Year: 2021

Wearing a mask is a strong protection against the COVID-19 pandemic, even though the vaccine has been successfully developed and is widely available. However, many people wear them incorrectly. This observation prompts us to devise an automated approach to monitor the condition of people wearing masks. Unlike previous studies, our work goes beyond mask detection; it focuses on generating a personalized demonstration on proper mask-wearing, which helps people use masks better through visual demonstration rather than text explanation. The pipeline starts from the detection of face covering. For images where faces are improperly covered, our mask overlay module incorporates statistical shape analysis (SSA) and dense landmark alignment to approximate the geometry of a face and generates corresponding face-covering examples. Our results show that the proposed system successfully identifies images with faces covered properly. Our ablation study on mask overlay suggests that the SSA model helps to address variations in face shapes, orientations, and scales. The final face-covering examples, especially half profile face images, surpass previous arts by a noticeable margin.
Infinite Choice and Probability Distributions. An Open Problem: The Real Hotel
Venue: arXiv cs.LO 2021
Year: 2021

We sketch a process algebra with data and probability distributions. This allows to combine two very powerful abstraction mechanisms namely non-deterministic choice and probabilities. However, it is not clear how to define an appropriate semantics for the generalised choice over data in combination with probability density functions. The real hotel is a puzzle that exemplifies the core of the problem.
Maximum Likelihood Estimation for Multimodal Learning with Missing Modality
Venue: arXiv cs.LG 2021
Year: 2021

Multimodal learning has achieved great successes in many scenarios. Compared with unimodal learning, it can effectively combine the information from different modalities to improve the performance of learning tasks. In reality, the multimodal data may have missing modalities due to various reasons, such as sensor failure and data transmission error. In previous works, the information of the modality-missing data has not been well exploited. To address this problem, we propose an efficient approach based on maximum likelihood estimation to incorporate the knowledge in the modality-missing data. Specifically, we design a likelihood function to characterize the conditional distribution of the modality-complete data and the modality-missing data, which is theoretically optimal. Moreover, we develop a generalized form of the softmax function to effectively implement maximum likelihood estimation in an end-to-end manner. Such training strategy guarantees the computability of our algorithm capably. Finally, we conduct a series of experiments on real-world multimodal datasets. Our results demonstrate the effectiveness of the proposed approach, even when 95% of the training data has missing modality.
ARShoe: Real-Time Augmented Reality Shoe Try-on System on Smartphones
Venue: arXiv cs.CV 2021
Year: 2021

Virtual try-on technology enables users to try various fashion items using augmented reality and provides a convenient online shopping experience. However, most previous works focus on the virtual try-on for clothes while neglecting that for shoes, which is also a promising task. To this concern, this work proposes a real-time augmented reality virtual shoe try-on system for smartphones, namely ARShoe. Specifically, ARShoe adopts a novel multi-branch network to realize pose estimation and segmentation simultaneously. A solution to generate realistic 3D shoe model occlusion during the try-on process is presented. To achieve a smooth and stable try-on effect, this work further develop a novel stabilization method. Moreover, for training and evaluation, we construct the very first large-scale foot benchmark with multiple virtual shoe try-on task-related labels annotated. Exhaustive experiments on our newly constructed benchmark demonstrate the satisfying performance of ARShoe. Practical tests on common smartphones validate the real-time performance and stabilization of the proposed approach.
Authentication and Access Control in 5G Device-to-Device Communication
Venue: arXiv cs.CR 2021
Year: 2021

Device-to-device (D2D) communication is one of the most recent advancements in wireless communication technology. It was introduced in cellular communication technology by the 3rd Generation Partnership Project (3GPP) to lay a foundation for the evolving 5G architecture. It has now emerged as a promising technology for proximate devices. It enables proximate devices to communicate directly without the involvement of a third party network infrastructure. Researchers are analysing various methods to facilitate the smooth integration of D2D communication technology into the existing network system architecture. This paper lists all the different possible modes of operation in D2D communication based on the varying use-case scenarios and highlights the security and privacy requirements for D2D communication. Some of the recent authentication proposals for D2D communication technology are further reviewed, and their security and privacy capabilities are analysed. Apart from authentication, we also reviewed some recent proposals of access control in D2D and highlighted the security issues addressed. We then identified the open issues that prevail in implementing D2D technology in a real-world scenario for future researchers, emphasising the existing authentication and access control techniques in D2D communication.
Learning Spatiotemporal Occupancy Grid Maps for Lifelong Navigation in Dynamic Scenes
Venue: arXiv cs.RO 2021
Year: 2021

We present a novel method for generating, predicting, and using Spatiotemporal Occupancy Grid Maps (SOGM), which embed future information of dynamic scenes. Our automated generation process creates groundtruth SOGMs from previous navigation data. We build on prior work to annotate lidar points based on their dynamic properties, which are then projected on time-stamped 2D grids: SOGMs. We design a 3D-2D feedforward architecture, trained to predict the future time steps of SOGMs, given 3D lidar frames as input. Our pipeline is entirely self-supervised, thus enabling lifelong learning for robots. The network is composed of a 3D back-end that extracts rich features and enables the semantic segmentation of the lidar frames, and a 2D front-end that predicts the future information embedded in the SOGMs within planning. We also design a navigation pipeline that uses these predicted SOGMs. We provide both quantitative and qualitative insights into the predictions and validate our choices of network design with a comparison to the state of the art and ablation studies.
Communication-hiding pipelined BiCGSafe methods for solving large linear systems
Venue: arXiv cs.DC 2021
Year: 2021

Recently, a new variant of the BiCGStab method, known as the pipeline BiCGStab, has been proposed. This method can achieve a higher degree of scalability and speed-up rates through a mechanism in which the communication phase for the computation of the inner product can be overlapped with the computation of the matrix-vector product. On the other hand, there exist several generalized iteration methods with better convergence behavior than BiCGStab such as ssBiCGSafe, BiCGSafe, GPBi-CG. Of these methods, ssBiCGSafe, which requires a single phase of computing inner products per one iteration, is best suited for high-performance computing systems. In this paper, inspired by the success of the pipelined BiCGStab method, we propose variations of the ssBiCGSafe method, in which only one phase of inner product computation per iteration is required and this inner product computation phase can be overlapped with the matrix-vector computation. Through numerical experiments, we show that the proposed methods lead to improvements in convergence behavior and execution time compared to the pipelined BiCGStab and ssBiCGSafe methods.
Morality-based Assertion and Homophily on Social Media: A Cultural Comparison between English and Japanese Languages
Venue: arXiv cs.CL 2021
Year: 2021

Moral psychology is a domain that deals with moral identity, appraisals and emotions. Previous work has primarily focused on moral development and the associated role of culture. Knowing that language is an inherent element of a culture, we used the social media platform Twitter to compare moral behaviors of Japanese tweets with English tweets. The five basic moral foundations, i.e., Care, Fairness, Ingroup, Authority and Purity, along with the associated emotional valence were compared between English and Japanese tweets. The tweets from Japanese users depicted relatively higher Fairness, Ingroup, and Purity, whereas English tweets expressed more positive emotions for all moral dimensions. Considering moral similarities in connecting users on social media, we quantified homophily concerning different moral dimensions using our proposed method. The moral dimensions Care, Authority and Purity for English and Ingroup, Authority and Purity for Japanese depicted homophily on Twitter. Overall, our study uncovers the underlying cultural differences with respect to moral behavior in English- and Japanese-speaking users.
On the Effectiveness of Genetic Operations in Symbolic Regression
Venue: arXiv cs.LG 2021
Year: 2021

This paper describes a methodology for analyzing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the fitness variation from parent to offspring. We introduce a new subtree tracing approach for identifying the origins of genes in the structure of individuals, and we show that only a small fraction of ancestor individuals are responsible for the evolvement of the best solutions in the population.
Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning methods have been adopted in the literature as contenders to conventional methods to solve the energy time series forecasting (TSF) problems. Recently, deep learning methods have been emerged in the artificial intelligence field attaining astonishing performance in a wide range of applications. Yet, the evidence about their performance in to solve the energy TSF problems, in terms of accuracy and computational requirements, is scanty. Most of the review articles that handle the energy TSF problem are systematic reviews, however, a qualitative and quantitative study for the energy TSF problem is not yet available in the literature. The purpose of this paper is twofold, first it provides a comprehensive analytical assessment for conventional,machine learning, and deep learning methods that can be utilized to solve various energy TSF problems. Second, the paper carries out an empirical assessment for many selected methods through three real-world datasets. These datasets related to electrical energy consumption problem, natural gas problem, and electric power consumption of an individual household problem.The first two problems are univariate TSF and the third problem is a multivariate TSF. Com-pared to both conventional and machine learning contenders, the deep learning methods attain a significant improvement in terms of accuracy and forecasting horizons examined. In the mean-time, their computational requirements are notably greater than other contenders. Eventually,the paper identifies a number of challenges, potential research directions, and recommendations to the research community may serve as a basis for further research in the energy forecasting domain.
Generalized Optimal Linear Orders
Venue: arXiv cs.CL 2021
Year: 2021

The sequential structure of language, and the order of words in a sentence specifically, plays a central role in human language processing. Consequently, in designing computational models of language, the de facto approach is to present sentences to machines with the words ordered in the same order as in the original human-authored sentence. The very essence of this work is to question the implicit assumption that this is desirable and inject theoretical soundness into the consideration of word order in natural language processing. In this thesis, we begin by uniting the disparate treatments of word order in cognitive science, psycholinguistics, computational linguistics, and natural language processing under a flexible algorithmic framework. We proceed to use this heterogeneous theoretical foundation as the basis for exploring new word orders with an undercurrent of psycholinguistic optimality. In particular, we focus on notions of dependency length minimization given the difficulties in human and computational language processing in handling long-distance dependencies. We then discuss algorithms for finding optimal word orders efficiently in spite of the combinatorial space of possibilities. We conclude by addressing the implications of these word orders on human language and their downstream impacts when integrated in computational models.
Does Adversarial Oversampling Help us?
Venue: arXiv cs.LG 2021
Year: 2021

Traditional oversampling methods are generally employed to handle class imbalance in datasets. This oversampling approach is independent of the classifier; thus, it does not offer an end-to-end solution. To overcome this, we propose a three-player adversarial game-based end-to-end method, where a domain-constraints mixture of generators, a discriminator, and a multi-class classifier are used. Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach. In AO, the generator updates by fooling both the classifier and discriminator, however, in DO, it updates by favoring the classifier and fooling the discriminator. While updating the classifier, it considers both the real and synthetically generated samples in AO. But, in DO, it favors the real samples and fools the subset class-specific generated samples. To mitigate the biases of a classifier towards the majority class, minority samples are over-sampled at a fractional rate. Such implementation is shown to provide more robust classification boundaries. The effectiveness of our proposed method has been validated with high-dimensional, highly imbalanced and large-scale multi-class tabular datasets. The results as measured by average class specific accuracy (ACSA) clearly indicate that the proposed method provides better classification accuracy (improvement in the range of 0.7% to 49.27%) as compared to the baseline classifier.
Federated Learning for UAV Swarms Under Class Imbalance and Power Consumption Constraints
Venue: arXiv cs.LG 2021
Year: 2021

The usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches. Despite the abundance of such advantages, it is imperative to investigate the performance of UAV utilization while considering their design limitations. This paper investigates the deployment of UAV swarms when each UAV carries a machine learning classification task. To avoid data exchange with ground-based processing nodes, a federated learning approach is adopted between a UAV leader and the swarm members to improve the local learning model while avoiding excessive air-to-ground and ground-to-air communications. Moreover, the proposed deployment framework considers the stringent energy constraints of UAVs and the problem of class imbalance, where we show that considering these design parameters significantly improves the performances of the UAV swarm in terms of classification accuracy, energy consumption and availability of UAVs when compared with several baseline algorithms.
Transient Execution of Non-Canonical Accesses
Venue: arXiv cs.CR 2021
Year: 2021

Recent years have brought microarchitectural security intothe spotlight, proving that modern CPUs are vulnerable toseveral classes of microarchitectural attacks. These attacksbypass the basic isolation primitives provided by the CPUs:process isolation, memory permissions, access checks, andso on. Nevertheless, most of the research was focused on In-tel CPUs, with only a few exceptions. As a result, few vulner-abilities have been found in other CPUs, leading to specula-tions about their immunity to certain types of microarchi-tectural attacks. In this paper, we provide a black-box anal-ysis of one of these under-explored areas. Namely, we inves-tigate the flaw of AMD CPUs which may lead to a transientexecution hijacking attack. Contrary to nominal immunity,we discover that AMD Zen family CPUs exhibit transient ex-ecution patterns similar for Meltdown/MDS. Our analysisof exploitation possibilities shows that AMDs design deci-sions indeed limit the exploitability scope comparing to In-tel CPUs, yet it may be possible to use them to amplify othermicroarchitectural attacks.
Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System
Venue: arXiv cs.AI 2021
Year: 2021

Respiratory diseases, including asthma, bronchitis, pneumonia, and upper respiratory tract infection (RTI), are among the most common diseases in clinics. The similarities among the symptoms of these diseases precludes prompt diagnosis upon the patients' arrival. In pediatrics, the patients' limited ability in expressing their situation makes precise diagnosis even harder. This becomes worse in primary hospitals, where the lack of medical imaging devices and the doctors' limited experience further increase the difficulty of distinguishing among similar diseases. In this paper, a pediatric fine-grained diagnosis-assistant system is proposed to provide prompt and precise diagnosis using solely clinical notes upon admission, which would assist clinicians without changing the diagnostic process. The proposed system consists of two stages: a test result structuralization stage and a disease identification stage. The first stage structuralizes test results by extracting relevant numerical values from clinical notes, and the disease identification stage provides a diagnosis based on text-form clinical notes and the structured data obtained from the first stage. A novel deep learning algorithm was developed for the disease identification stage, where techniques including adaptive feature infusion and multi-modal attentive fusion were introduced to fuse structured and text data together. Clinical notes from over 12000 patients with respiratory diseases were used to train a deep learning model, and clinical notes from a non-overlapping set of about 1800 patients were used to evaluate the performance of the trained model. The average precisions (AP) for pneumonia, RTI, bronchitis and asthma are 0.878, 0.857, 0.714, and 0.825, respectively, achieving a mean AP (mAP) of 0.819.
Towards Formalising Schutz' Axioms for Minkowski Spacetime in Isabelle/HOL
Venue: arXiv cs.LO 2021
Year: 2021

Special Relativity is a cornerstone of modern physical theory. While a standard coordinate model is well-known and widely taught today, several alternative systems of axioms exist. This paper reports on the formalisation of one such system which is closer in spirit to Hilbert's axiomatic approach to Euclidean geometry than to the vector space approach employed by Minkowski. We present a mechanisation in Isabelle/HOL of the system of axioms as well as theorems relating to temporal order. Proofs and excerpts of Isabelle/Isar scripts are discussed, particularly where the formal work required additional steps, alternative approaches, or corrections to Schutz' prose.
Hybrid Multisource Feature Fusion for the Text Clustering
Venue: arXiv cs.CL 2021
Year: 2021

The text clustering technique is an unsupervised text mining method which are used to partition a huge amount of text documents into groups. It has been reported that text clustering algorithms are hard to achieve better performance than supervised methods and their clustering performance is highly dependent on the picked text features. Currently, there are many different types of text feature generation algorithms, each of which extracts text features from some specific aspects, such as VSM and distributed word embedding, thus seeking a new way of obtaining features as complete as possible from the corpus is the key to enhance the clustering effects. In this paper, we present a hybrid multisource feature fusion (HMFF) framework comprising three components, feature representation of multimodel, mutual similarity matrices and feature fusion, in which we construct mutual similarity matrices for each feature source and fuse discriminative features from mutual similarity matrices by reducing dimensionality to generate HMFF features, then k-means clustering algorithm could be configured to partition input samples into groups. The experimental tests show our HMFF framework outperforms other recently published algorithms on 7 of 11 public benchmark datasets and has the leading performance on the rest 4 benchmark datasets as well. At last, we compare HMFF framework with those competitors on a COVID-19 dataset from the wild with the unknown cluster count, which shows the clusters generated by HMFF framework partition those similar samples much closer.