OpenRaiser/Intern-Atlas huggingface dataset:
Towards Similarity-Aware Time-Series Classification
Venue: arXiv cs.LG 2022
Year: 2022

We study time-series classification (TSC), a fundamental task of time-series data mining. Prior work has approached TSC from two major directions: (1) similarity-based methods that classify time-series based on the nearest neighbors, and (2) deep learning models that directly learn the representations for classification in a data-driven manner. Motivated by the different working mechanisms within these two research lines, we aim to connect them in such a way as to jointly model time-series similarities and learn the representations. This is a challenging task because it is unclear how we should efficiently leverage similarity information. To tackle the challenge, we propose Similarity-Aware Time-Series Classification (SimTSC), a conceptually simple and general framework that models similarity information with graph neural networks (GNNs). Specifically, we formulate TSC as a node classification problem in graphs, where the nodes correspond to time-series, and the links correspond to pair-wise similarities. We further design a graph construction strategy and a batch training algorithm with negative sampling to improve training efficiency. We instantiate SimTSC with ResNet as the backbone and Dynamic Time Warping (DTW) as the similarity measure. Extensive experiments on the full UCR datasets and several multivariate datasets demonstrate the effectiveness of incorporating similarity information into deep learning models in both supervised and semi-supervised settings. Our code is available at https://github.com/daochenzha/SimTSC
Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms
Venue: arXiv cs.DC 2022
Year: 2022

High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of \textit{ab initio} molecular dynamics in terms of spatial and temporal scales on modern supercomputers. Previous state-of-the-art can achieve $1-2$ nanoseconds molecular dynamics simulation per day for 100-million atoms on the entire Summit supercomputer. In this paper, we have significantly reduced the memory footprint and computational time by a comprehensive approach with both algorithmic and system innovations. The neural network model is compressed by model tabulation, kernel fusion, and redundancy removal. Then optimizations such as acceleration of customized kernel, tabulation of activation function, MPI+OpenMP parallelization are implemented on GPU and ARM architectures. Testing results of the copper system show that the optimized code can scale up to the entire machine of both Fugaku and Summit, and the corresponding system size can be extended by a factor of $134$ to an unprecedented $17$ billion atoms. The strong scaling of a $13.5$-million atom copper system shows that the time-to-solution can be 7 times faster, reaching $11.2$ nanoseconds per day. This work opens the door for unprecedentedly large-scale molecular dynamics simulations based on {\it ab initio} accuracy and can be potentially utilized in studying more realistic applications such as mechanical properties of metals, semiconductor devices, batteries, etc. The optimization techniques detailed in this paper also provide insight for relevant high-performance computing applications.
Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence
Venue: arXiv cs.AI 2022
Year: 2022

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.
Gradient-based Bit Encoding Optimization for Noise-Robust Binary Memristive Crossbar
Venue: arXiv cs.NE 2022
Year: 2022

Binary memristive crossbars have gained huge attention as an energy-efficient deep learning hardware accelerator. Nonetheless, they suffer from various noises due to the analog nature of the crossbars. To overcome such limitations, most previous works train weight parameters with noise data obtained from a crossbar. These methods are, however, ineffective because it is difficult to collect noise data in large-volume manufacturing environment where each crossbar has a large device/circuit level variation. Moreover, we argue that there is still room for improvement even though these methods somewhat improve accuracy. This paper explores a new perspective on mitigating crossbar noise in a more generalized way by manipulating input binary bit encoding rather than training the weight of networks with respect to noise data. We first mathematically show that the noise decreases as the number of binary bit encoding pulses increases when representing the same amount of information. In addition, we propose Gradient-based Bit Encoding Optimization (GBO) which optimizes a different number of pulses at each layer, based on our in-depth analysis that each layer has a different level of noise sensitivity. The proposed heterogeneous layer-wise bit encoding scheme achieves high noise robustness with low computational cost. Our experimental results on public benchmark datasets show that GBO improves the classification accuracy by ~5-40% in severe noise scenarios.
Harmonics Virtual Lights : fast projection of luminance field on spherical harmonics for efficient rendering
Venue: arXiv cs.GR 2022
Year: 2022

In this paper, we introduce Harmonics Virtual Lights (HVL), to model indirect light sources for interactive global illumination of dynamic 3D scenes. Virtual Point Lights (VPL) are an efficient approach to define indirect light sources and to evaluate the resulting indirect lighting. Nonetheless, VPL suffer from disturbing artifacts, especially with high frequency materials. Virtual Spherical Lights (VSL) avoid these artifacts by considering spheres instead of points but estimates the lighting integral using Monte Carlo which results to noise in the final image. We define HVL as an extension of VSL in a Spherical Harmonics (SH) framework, defining a closed form of the lighting integral evaluation. We propose an efficient SH projection of spherical lights contribution faster than existing methods. Computing the outgoing luminance requires $\mathcal{O}(n)$ operations when using materials with circular symmetric lobes, and $\mathcal{O}(n^2)$ operations for the general case, where $n$ is the number of SH bands. HVL can be used with either parametric or measured BRDF without extra cost and offers control over rendering time and image quality, by either decreasing or increasing the band limit used for SH projection. Our approach is particularly well designed to render medium-frequency one-bounce global illumination with arbitrary BRDF in interactive time.
Reinforcing Cybersecurity Hands-on Training With Adaptive Learning
Venue: arXiv cs.CR 2022
Year: 2022

This paper presents how learning experience influences students' capability to learn and their motivation for learning. Although each student is different, standard instruction methods do not adapt to individuals. Adaptive learning reverses this practice and attempts to improve the student experience. While adaptive learning is well-established in programming, it is rarely used in cybersecurity education. This paper is one of the first works investigating adaptive learning in security training. First, we analyze the performance of 95 students in 12 training sessions to understand the limitations of the current training practice. Less than half of the students completed the training without displaying a solution, and only in two sessions, all students completed all phases. Then, we simulate how students would proceed in one of the past training sessions if it would offer more paths of various difficulty. Based on this simulation, we propose a novel tutor model for adaptive training, which considers students' proficiency before and during an ongoing training session. The proficiency is assessed using a pre-training questionnaire and various in-training metrics. Finally, we conduct a study with 24 students and new training using the proposed tutor model and adaptive training format. The results show that the adaptive training does not overwhelm students as the original static training. Adaptive training enables students to enter several alternative training phases with lower difficulty than the original training. The proposed format is not restricted to a particular training. Therefore, it can be applied to practicing any security topic or even in related fields, such as networking or operating systems. Our study indicates that adaptive learning is a promising approach for improving the student experience in security education. We also highlight implications for educational practice.
Adaptive Online Incremental Learning for Evolving Data Streams
Venue: arXiv cs.LG 2022
Year: 2022

Recent years have witnessed growing interests in online incremental learning. However, there are three major challenges in this area. The first major difficulty is concept drift, that is, the probability distribution in the streaming data would change as the data arrives. The second major difficulty is catastrophic forgetting, that is, forgetting what we have learned before when learning new knowledge. The last one we often ignore is the learning of the latent representation. Only good latent representation can improve the prediction accuracy of the model. Our research builds on this observation and attempts to overcome these difficulties. To this end, we propose an Adaptive Online Incremental Learning for evolving data streams (AOIL). We use auto-encoder with the memory module, on the one hand, we obtained the latent features of the input, on the other hand, according to the reconstruction loss of the auto-encoder with memory module, we could successfully detect the existence of concept drift and trigger the update mechanism, adjust the model parameters in time. In addition, we divide features, which are derived from the activation of the hidden layers, into two parts, which are used to extract the common and private features respectively. By means of this approach, the model could learn the private features of the new coming instances, but do not forget what we have learned in the past (shared features), which reduces the occurrence of catastrophic forgetting. At the same time, to get the fusion feature vector we use the self-attention mechanism to effectively fuse the extracted features, which further improved the latent representation learning.
Comparison of biomedical relationship extraction methods and models for knowledge graph creation
Venue: arXiv cs.AI 2022
Year: 2022

Biomedical research is growing at such an exponential pace that scientists, researchers, and practitioners are no more able to cope with the amount of published literature in the domain. The knowledge presented in the literature needs to be systematized in such a way that claims and hypotheses can be easily found, accessed, and validated. Knowledge graphs can provide such a framework for semantic knowledge representation from literature. However, in order to build a knowledge graph, it is necessary to extract knowledge as relationships between biomedical entities and normalize both entities and relationship types. In this paper, we present and compare few rule-based and machine learning-based (Naive Bayes, Random Forests as examples of traditional machine learning methods and DistilBERT, PubMedBERT, T5 and SciFive-based models as examples of modern deep learning transformers) methods for scalable relationship extraction from biomedical literature, and for the integration into the knowledge graphs. We examine how resilient are these various methods to unbalanced and fairly small datasets. Our experiments show that transformer-based models handle well both small (due to pre-training on a large dataset) and unbalanced datasets. The best performing model was the PubMedBERT-based model fine-tuned on balanced data, with a reported F1-score of 0.92. DistilBERT-based model followed with F1-score of 0.89, performing faster and with lower resource requirements. BERT-based models performed better then T5-based generative models.
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations
Venue: arXiv cs.LG 2022
Year: 2022

Self-supervision is recently surging at its new frontier of graph learning. It facilitates graph representations beneficial to downstream tasks; but its success could hinge on domain knowledge for handcraft or the often expensive trials and errors. Even its state-of-the-art representative, graph contrastive learning (GraphCL), is not completely free of those needs as GraphCL uses a prefabricated prior reflected by the ad-hoc manual selection of graph data augmentations. Our work aims at advancing GraphCL by answering the following questions: How to represent the space of graph augmented views? What principle can be relied upon to learn a prior in that space? And what framework can be constructed to learn the prior in tandem with contrastive learning? Accordingly, we have extended the prefabricated discrete prior in the augmentation set, to a learnable continuous prior in the parameter space of graph generators, assuming that graph priors per se, similar to the concept of image manifolds, can be learned by data generation. Furthermore, to form contrastive views without collapsing to trivial solutions due to the prior learnability, we have leveraged both principles of information minimization (InfoMin) and information bottleneck (InfoBN) to regularize the learned priors. Eventually, contrastive learning, InfoMin, and InfoBN are incorporated organically into one framework of bi-level optimization. Our principled and automated approach has proven to be competitive against the state-of-the-art graph self-supervision methods, including GraphCL, on benchmarks of small graphs; and shown even better generalizability on large-scale graphs, without resorting to human expertise or downstream validation. Our code is publicly released at https://github.com/Shen-Lab/GraphCL_Automated.
Knowledge Informed Machine Learning using a Weibull-based Loss Function
Venue: arXiv cs.LG 2022
Year: 2022

Machine learning can be enhanced through the integration of external knowledge. This method, called knowledge informed machine learning, is also applicable within the field of Prognostics and Health Management (PHM). In this paper, the various methods of knowledge informed machine learning, from a PHM context, are reviewed with the goal of helping the reader understand the domain. In addition, a knowledge informed machine learning technique is demonstrated, using the common IMS and PRONOSTIA bearing data sets, for remaining useful life (RUL) prediction. Specifically, knowledge is garnered from the field of reliability engineering which is represented through the Weibull distribution. The knowledge is then integrated into a neural network through a novel Weibull-based loss function. A thorough statistical analysis of the Weibull-based loss function is conducted, demonstrating the effectiveness of the method on the PRONOSTIA data set. However, the Weibull-based loss function is less effective on the IMS data set. The results, shortcomings, and benefits of the approach are discussed in length. Finally, all the code is publicly available for the benefit of other researchers.
Privacy-Friendly Peer-to-Peer Energy Trading: A Game Theoretical Approach
Venue: arXiv cs.GT 2022
Year: 2022

In this paper, we propose a decentralized, privacy-friendly energy trading platform (PFET) based on game theoretical approach - specifically Stackelberg competition. Unlike existing trading schemes, PFET provides a competitive market in which prices and demands are determined based on competition, and computations are performed in a decentralized manner which does not rely on trusted third parties. It uses homomorphic encryption cryptosystem to encrypt sensitive information of buyers and sellers such as sellers$'$ prices and buyers$'$ demands. Buyers calculate total demand on particular seller using an encrypted data and sensitive buyer profile data is hidden from sellers. Hence, privacy of both sellers and buyers is preserved. Through privacy analysis and performance evaluation, we show that PFET preserves users$'$ privacy in an efficient manner.
Sparsifying Disk Intersection Graphs for Reliable Connectivity
Venue: arXiv cs.CG 2022
Year: 2022

The intersection graph induced by a set $\Disks$ of $n$ disks can be dense. It is thus natural to try and sparsify it, while preserving connectivity. Unfortunately, sparse graphs can always be made disconnected by removing a small number of vertices. In this work, we present a sparsification algorithm that maintains connectivity between two disks in the computed graph, if the original graph remains ``well-connected'' even after removing an arbitrary ``attack'' set $\BSet \subseteq \Disks$ from both graphs. Thus, the new sparse graph has similar reliability to the original disk graph, and can withstand catastrophic failure of nodes while still providing a connectivity guarantee for the remaining graph. The new graphs has near linear complexity, and can be constructed in near linear time. The algorithm extends to any collection of shapes in the plane, such that their union complexity is near linear.
Multi-Grid Redundant Bounding Box Annotation for Accurate Object Detection
Venue: arXiv cs.CV 2022
Year: 2022

Modern leading object detectors are either two-stage or one-stage networks repurposed from a deep CNN-based backbone classifier network. YOLOv3 is one such very-well known state-of-the-art one-shot detector that takes in an input image and divides it into an equal-sized grid matrix. The grid cell having the center of an object is the one responsible for detecting the particular object. This paper presents a new mathematical approach that assigns multiple grids per object for accurately tight-fit bounding box prediction. We also propose an effective offline copy-paste data augmentation for object detection. Our proposed method significantly outperforms some current state-of-the-art object detectors with a prospect for further better performance.
Towards realistic symmetry-based completion of previously unseen point clouds
Venue: arXiv cs.CV 2022
Year: 2022

3D scanning is a complex multistage process that generates a point cloud of an object typically containing damaged parts due to occlusions, reflections, shadows, scanner motion, specific properties of the object surface, imperfect reconstruction algorithms, etc. Point cloud completion is specifically designed to fill in the missing parts of the object and obtain its high-quality 3D representation. The existing completion approaches perform well on the academic datasets with a predefined set of object classes and very specific types of defects; however, their performance drops significantly in the real-world settings and degrades even further on previously unseen object classes. We propose a novel framework that performs well on symmetric objects, which are ubiquitous in man-made environments. Unlike learning-based approaches, the proposed framework does not require training data and is capable of completing non-critical damages occurring in customer 3D scanning process using e.g. Kinect, time-of-flight, or structured light scanners. With thorough experiments, we demonstrate that the proposed framework achieves state-of-the-art efficiency in point cloud completion of real-world customer scans. We benchmark the framework performance on two types of datasets: properly augmented existing academic dataset and the actual 3D scans of various objects.
Bayesian Optimization Based Trustworthiness Model for Multi-robot Bounding Overwatch
Venue: arXiv cs.RO 2022
Year: 2022

In multi-robot system (MRS) bounding overwatch, it is crucial to determine which point to choose for overwatch at each step and whether the robots' positions are trustworthy so that the overwatch can be performed effectively. In this paper, we develop a Bayesian optimization based computational trustworthiness model (CTM) for the MRS to select overwatch points. The CTM can provide real-time trustworthiness evaluation for the MRS on the overwatch points by referring to the robots' situational awareness information, such as traversability and line of sight. The evaluation can quantify each robot's trustworthiness in protecting its robot team members during the bounding overwatch. The trustworthiness evaluation can generate a dynamic cost map for each robot in the workspace and help obtain the most trustworthy bounding overwatch path. Our proposed Bayesian based CTM and motion planning can reduce the number of explorations for the workspace in data collection and improve the CTM learning efficiency. It also enables the MRS to deal with the dynamic and uncertain environments for the multi-robot bounding overwatch task. A robot simulation is implemented in ROS Gazebo to demonstrate the effectiveness of the proposed framework.
Stay in Touch! Shape and Shadow Influence Surface Contact in XR Displays
Venue: arXiv cs.HC 2022
Year: 2022

The information provided to a person's visual system by extended reality (XR) displays is not a veridical match to the information provided by the real world. Due in part to graphical limitations in XR head-mounted displays (HMDs), which vary by device, our perception of space may be altered. However, we do not yet know which properties of virtual objects rendered by HMDs -- particularly augmented reality displays -- influence our ability to understand space. In the current research, we evaluate how immersive graphics affect spatial perception across three unique XR displays: virtual reality (VR), video see-through augmented reality (VST AR), and optical see-through augmented reality (OST AR). We manipulated the geometry of the presented objects as well as the shading techniques for objects' cast shadows. Shape and shadow were selected for evaluation as they play an important role in determining where an object is in space by providing points of contact between an object and its environment -- be it real or virtual. Our results suggest that a non-photorealistic (NPR) shading technique, in this case for cast shadows, may be used to improve depth perception by enhancing perceived surface contact in XR. Further, the benefit of NPR graphics is more pronounced in AR than in VR displays. One's perception of ground contact is influenced by an object's shape, as well. However, the relationship between shape and surface contact perception is more complicated.