OpenRaiser/Intern-Atlas huggingface dataset:
YOLOX: Exceeding YOLO Series in 2021
Venue: arXiv cs.CV 2021
Year: 2021

In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX. We switch the YOLO detector to an anchor-free manner and conduct other advanced detection techniques, i.e., a decoupled head and the leading label assignment strategy SimOTA to achieve state-of-the-art results across a large scale range of models: For YOLO-Nano with only 0.91M parameters and 1.08G FLOPs, we get 25.3% AP on COCO, surpassing NanoDet by 1.8% AP; for YOLOv3, one of the most widely used detectors in industry, we boost it to 47.3% AP on COCO, outperforming the current best practice by 3.0% AP; for YOLOX-L with roughly the same amount of parameters as YOLOv4-CSP, YOLOv5-L, we achieve 50.0% AP on COCO at a speed of 68.9 FPS on Tesla V100, exceeding YOLOv5-L by 1.8% AP. Further, we won the 1st Place on Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021) using a single YOLOX-L model. We hope this report can provide useful experience for developers and researchers in practical scenes, and we also provide deploy versions with ONNX, TensorRT, NCNN, and Openvino supported. Source code is at https://github.com/Megvii-BaseDetection/YOLOX.
Accessing United States Bulk Patent Data with patentpy and patentr
Venue: arXiv cs.DL 2021
Year: 2021

The United States Patent and Trademark Office (USPTO) provides publicly accessible bulk data files containing information for all patents from 1976 onward. However, the format of these files changes over time and is memory-inefficient, which can pose issues for individual researchers. Here, we introduce the patentpy and patentr packages for the Python and R programming languages. They allow users to programmatically fetch bulk data from the USPTO website and access it locally in a cleaned, rectangular format. Research depending on United States patent data would benefit from the use of patentpy and patentr. We describe package implementation, quality control mechanisms, and present use cases highlighting simple, yet effective, applications of this software.
System-Wide Security for Offline Payment Terminals
Venue: arXiv cs.DC 2021
Year: 2021

Most self-service payment terminals require network connectivity for processing electronic payments. The necessity to maintain network connectivity increases costs, introduces cybersecurity risks, and significantly limits the number of places where the terminals can be installed. Leading payment service providers have proposed offline payment solutions that rely on algorithmically generated payment tokens. Existing payment token solutions, however, require complex mechanisms for authentication, transaction management, and most importantly, security risk management. In this paper, we present VolgaPay, a blockchain-based system that allows merchants to deploy secure offline payment terminal infrastructure that does not require collection and storage of any sensitive data. We design a novel payment protocol which mitigates security threats for all the participants of VolgaPay, such that the maximum loss from gaining full access to any component by an adversary incurs only a limited scope of harm. We achieve significant enhancements in security, operation efficiency, and cost reduction via a combination of polynomial multi-hash chain micropayment channels and blockchain grafting for off-chain channel state transition. We implement the VolgaPay payment system, and with thorough evaluation and security analysis, we demonstrate that VolgaPay is capable of delivering a fast, secure, and cost-efficient solution for offline payment terminals.
Decentralized federated learning of deep neural networks on non-iid data
Venue: arXiv cs.LG 2021
Year: 2021

We tackle the non-convex problem of learning a personalized deep learning model in a decentralized setting. More specifically, we study decentralized federated learning, a peer-to-peer setting where data is distributed among many clients and where there is no central server to orchestrate the training. In real world scenarios, the data distributions are often heterogeneous between clients. Therefore, in this work we study the problem of how to efficiently learn a model in a peer-to-peer system with non-iid client data. We propose a method named Performance-Based Neighbor Selection (PENS) where clients with similar data distributions detect each other and cooperate by evaluating their training losses on each other's data to learn a model suitable for the local data distribution. Our experiments on benchmark datasets show that our proposed method is able to achieve higher accuracies as compared to strong baselines.
Effective GPU Sharing Under Compiler Guidance
Venue: arXiv cs.DC 2021
Year: 2021

Modern computing platforms tend to deploy multiple GPUs (2, 4, or more) on a single node to boost system performance, with each GPU having a large capacity of global memory and streaming multiprocessors (SMs). GPUs are an expensive resource, and boosting utilization of GPUs without causing performance degradation of individual workloads is an important and challenging problem. Although services like MPS support simultaneous execution of multiple co-operative kernels on a single device, they do not solve the above problem for uncooperative kernels, MPS being oblivious to the resource needs of each kernel. We propose a fully automated compiler-assisted scheduling framework. The compiler constructs GPU tasks by identifying kernel launches and their related GPU operations (e.g. memory allocations). For each GPU task, a probe is instrumented in the host-side code right before its launch point. At runtime, the probe conveys the information about the task's resource requirements (e.g. memory and compute cores) to a scheduler, such that the scheduler can place the task on an appropriate device based on the task's resource requirements and devices' load in a memory-safe, resource-aware manner. To demonstrate its advantages, we prototyped a throughput-oriented scheduler based on the framework, and evaluated it with the Rodinia benchmark suite and the Darknet neural network framework on NVIDIA GPUs. The results show that the proposed solution outperforms existing state-of-the-art solutions by leveraging its knowledge about applications' multiple resource requirements, which include memory as well as SMs. It improves throughput by up to 2.5x for Rodinia benchmarks, and up to 2.7x for Darknet neural networks. In addition, it improves job turnaround time by up to 4.9x, and limits individual kernel performance degradation to at most 2.5%.
UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Action recognition based on skeleton data has recently witnessed increasing attention and progress. State-of-the-art approaches adopting Graph Convolutional networks (GCNs) can effectively extract features on human skeletons relying on the pre-defined human topology. Despite associated progress, GCN-based methods have difficulties to generalize across domains, especially with different human topological structures. In this context, we introduce UNIK, a novel skeleton-based action recognition method that is not only effective to learn spatio-temporal features on human skeleton sequences but also able to generalize across datasets. This is achieved by learning an optimal dependency matrix from the uniform distribution based on a multi-head attention mechanism. Subsequently, to study the cross-domain generalizability of skeleton-based action recognition in real-world videos, we re-evaluate state-of-the-art approaches as well as the proposed UNIK in light of a novel Posetics dataset. This dataset is created from Kinetics-400 videos by estimating, refining and filtering poses. We provide an analysis on how much performance improves on smaller benchmark datasets after pre-training on Posetics for the action classification task. Experimental results show that the proposed UNIK, with pre-training on Posetics, generalizes well and outperforms state-of-the-art when transferred onto four target action classification datasets: Toyota Smarthome, Penn Action, NTU-RGB+D 60 and NTU-RGB+D 120.
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision
Venue: arXiv cs.CL 2021
Year: 2021

Despite recent success in machine reading comprehension (MRC), learning high-quality MRC models still requires large-scale labeled training data, even using strong pre-trained language models (PLMs). The pre-training tasks for PLMs are not question-answering or MRC-based tasks, making existing PLMs unable to be directly used for unsupervised MRC. Specifically, MRC aims to spot an accurate answer span from the given document, but PLMs focus on token filling in sentences. In this paper, we propose a new framework for unsupervised MRC. Firstly, we propose to learn to spot answer spans in documents via self-supervised learning, by designing a self-supervision pretext task for MRC - Spotting-MLM. Solving this task requires capturing deep interactions between sentences in documents. Secondly, we apply a simple sentence rewriting strategy in the inference stage to alleviate the expression mismatch between questions and documents. Experiments show that our method achieves a new state-of-the-art performance for unsupervised MRC.
EvilModel: Hiding Malware Inside of Neural Network Models
Venue: arXiv cs.CR 2021
Year: 2021

Delivering malware covertly and evasively is critical to advanced malware campaigns. In this paper, we present a new method to covertly and evasively deliver malware through a neural network model. Neural network models are poorly explainable and have a good generalization ability. By embedding malware in neurons, the malware can be delivered covertly, with minor or no impact on the performance of neural network. Meanwhile, because the structure of the neural network model remains unchanged, it can pass the security scan of antivirus engines. Experiments show that 36.9MB of malware can be embedded in a 178MB-AlexNet model within 1% accuracy loss, and no suspicion is raised by anti-virus engines in VirusTotal, which verifies the feasibility of this method. With the widespread application of artificial intelligence, utilizing neural networks for attacks becomes a forwarding trend. We hope this work can provide a reference scenario for the defense on neural network-assisted attacks.
Compound Figure Separation of Biomedical Images with Side Loss
Venue: arXiv cs.CV 2021
Year: 2021

Unsupervised learning algorithms (e.g., self-supervised learning, auto-encoder, contrastive learning) allow deep learning models to learn effective image representations from large-scale unlabeled data. In medical image analysis, even unannotated data can be difficult to obtain for individual labs. Fortunately, national-level efforts have been made to provide efficient access to obtain biomedical image data from previous scientific publications. For instance, NIH has launched the Open-i search engine that provides a large-scale image database with free access. However, the images in scientific publications consist of a considerable amount of compound figures with subplots. To extract and curate individual subplots, many different compound figure separation approaches have been developed, especially with the recent advances in deep learning. However, previous approaches typically required resource extensive bounding box annotation to train detection models. In this paper, we propose a simple compound figure separation (SimCFS) framework that uses weak classification annotations from individual images. Our technical contribution is three-fold: (1) we introduce a new side loss that is designed for compound figure separation; (2) we introduce an intra-class image augmentation method to simulate hard cases; (3) the proposed framework enables an efficient deployment to new classes of images, without requiring resource extensive bounding box annotations. From the results, the SimCFS achieved a new state-of-the-art performance on the ImageCLEF 2016 Compound Figure Separation Database. The source code of SimCFS is made publicly available at https://github.com/hrlblab/ImageSeperation.
Several methods of analysis for cardinality constrained bin packing
Venue: arXiv cs.DS 2021
Year: 2021

We consider a known variant of bin packing called {\it cardinality constrained bin packing}, also called {\it bin packing with cardinality constraints} (BPCC). In this problem, there is a parameter k\geq 2, and items of rational sizes in [0,1] are to be packed into bins, such that no bin has more than k items or total size larger than 1. The goal is to minimize the number of bins. A recently introduced concept, called the price of clustering, deals with inputs that are presented in a way that they are split into clusters. Thus, an item has two attributes which are its size and its cluster. The goal is to measure the relation between an optimal solution that cannot combine items of different clusters into bins, and an optimal solution that can combine items of different clusters arbitrarily. Usually the number of clusters may be large, while clusters are relatively small, though not trivially small. Such problems are related to greedy bin packing algorithms, and to batched bin packing, which is similar to the price of clustering, but there is a constant number of large clusters. We analyze the price of clustering for BPCC, including the parametric case with bounded item sizes. We discuss several greedy algorithms for this problem that were not studied in the past, and comment on batched bin packing.
E-PDDL: A Standardized Way of Defining Epistemic Planning Problems
Venue: arXiv cs.AI 2021
Year: 2021

Epistemic Planning (EP) refers to an automated planning setting where the agent reasons in the space of knowledge states and tries to find a plan to reach a desirable state from the current state. Its general form, the Multi-agent Epistemic Planning (MEP) problem involves multiple agents who need to reason about both the state of the world and the information flow between agents. In a MEP problem, multiple approaches have been developed recently with varying restrictions, such as considering only the concept of knowledge while not allowing the idea of belief, or not allowing for ``complex" modal operators such as those needed to handle dynamic common knowledge. While the diversity of approaches has led to a deeper understanding of the problem space, the lack of a standardized way to specify MEP problems independently of solution approaches has created difficulties in comparing performance of planners, identifying promising techniques, exploring new strategies like ensemble methods, and making it easy for new researchers to contribute to this research area. To address the situation, we propose a unified way of specifying EP problems - the Epistemic Planning Domain Definition Language, E-PDDL. We show that E-PPDL can be supported by leading MEP planners and provide corresponding parser code that translates EP problems specified in E-PDDL into (M)EP problems that can be handled by several planners. This work is also useful in building more general epistemic planning environments where we envision a meta-cognitive module that takes a planning problem in E-PDDL, identifies and assesses some of its features, and autonomously decides which planner is the best one to solve it.
Joint Dermatological Lesion Classification and Confidence Modeling with Uncertainty Estimation
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning has played a major role in the interpretation of dermoscopic images for detecting skin defects and abnormalities. However, current deep learning solutions for dermatological lesion analysis are typically limited in providing probabilistic predictions which highlights the importance of concerning uncertainties. This concept of uncertainty can provide a confidence level for each feature which prevents overconfident predictions with poor generalization on unseen data. In this paper, we propose an overall framework that jointly considers dermatological classification and uncertainty estimation together. The estimated confidence of each feature to avoid uncertain feature and undesirable shift, which are caused by environmental difference of input image, in the latent space is pooled from confidence network. Our qualitative results show that modeling uncertainties not only helps to quantify model confidence for each prediction but also helps classification layers to focus on confident features, therefore, improving the accuracy for dermatological lesion classification. We demonstrate the potential of the proposed approach in two state-of-the-art dermoscopic datasets (ISIC 2018 and ISIC 2019).
How does a robot's social credibility relate to its perceived trustworthiness?
Venue: arXiv cs.RO 2021
Year: 2021

This position paper aims to highlight and discuss the role of a robot's social credibility in interaction with humans. In particular, I want to explore a potential relation between social credibility and a robot's acceptability and ultimately its trustworthiness. I thereby also review and expand the notion of social credibility as a measure of how well the robot obeys social norms during interaction with the concept of conscious acknowledgement.
MARC: Mining Association Rules from datasets by using Clustering models
Venue: arXiv cs.DB 2021
Year: 2021

Association rules are useful to discover relationships, which are mostly hidden, between the different items in large datasets. Symbolic models are the principal tools to extract association rules. This basic technique is time-consuming, and it generates a big number of associated rules. To overcome this drawback, we suggest a new method, called MARC, to extract the more important association rules of two important levels: Type I, and Type II. This approach relies on a multi-topographic unsupervised neural network model as well as clustering quality measures that evaluate the success of a given numerical classification model to behave as a natural symbolic model.
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data
Venue: arXiv cs.LG 2021
Year: 2021

Recently, neural network compression schemes like channel pruning have been widely used to reduce the model size and computational complexity of deep neural network (DNN) for applications in power-constrained scenarios such as embedded systems. Reinforcement learning (RL)-based auto-pruning has been further proposed to automate the DNN pruning process to avoid expensive hand-crafted work. However, the RL-based pruner involves a time-consuming training process and the high expense of each sample further exacerbates this problem. These impediments have greatly restricted the real-world application of RL-based auto-pruning. Thus, in this paper, we propose an efficient auto-pruning framework which solves this problem by taking advantage of the historical data from the previous auto-pruning process. In our framework, we first boost the convergence of the RL-pruner by transfer learning. Then, an augmented transfer learning scheme is proposed to further speed up the training process by improving the transferability. Finally, an assistant learning process is proposed to improve the sample efficiency of the RL agent. The experiments have shown that our framework can accelerate the auto-pruning process by 1.5-2.5 times for ResNet20, and 1.81-2.375 times for other neural networks like ResNet56, ResNet18, and MobileNet v1.
Proximity in face-to-face interaction is associated with mobile phone communication
Venue: arXiv cs.SI 2021
Year: 2021

The frequency of mobile communication is often used as an indicator of the strength of a tie between two individuals, but how mobile communication relates to other forms of behaving close in social relationships is poorly understood. We used a unique multi-channel 10-month dataset from 510 participants to examine how the frequency of mobile communication was related to the frequency of face-to-face interaction, as measured by Bluetooth scans between the participants mobile phones. The number of phone calls between a dyad was significantly related to the number of face-to-face interactions. Physical proximity during face-to-face interactions was the single strongest predictor of the number of phone calls. Overall, 36 percent of variance in phone calls could be explained by face-to-face interactions and the control variables. Our results suggest that the amount of mobile communication between a dyad is a useful but noisy measure of tie strength with some significant limitations.
Towards synthesizing grasps for 3D deformable objects with physics-based simulation
Venue: arXiv cs.RO 2021
Year: 2021

Grasping deformable objects is not well researched due to the complexity in modelling and simulating the dynamic behavior of such objects. However, with the rapid development of physics-based simulators that support soft bodies, the research gap between rigid and deformable objects is getting smaller. To leverage the capability of such simulators and to challenge the assumption that has guided robotic grasping research so far, i.e., object rigidity, we proposed a deep-learning based approach that generates stiffness-dependent grasps. Our network is trained on purely synthetic data generated from a physics-based simulator. The same simulator is also used to evaluate the trained network. The results show improvement in terms of grasp ranking and grasp success rate. Furthermore, our network can adapt the grasps based on the stiffness. We are currently validating the proposed approach on a larger test dataset in simulation and on a physical robot.
Test-optional Policies: Overcoming Strategic Behavior and Informational Gaps
Venue: arXiv cs.GT 2021
Year: 2021

Due to the Covid-19 pandemic, more than 500 US-based colleges and universities went "test-optional" for admissions and promised that they would not penalize applicants for not submitting test scores, part of a longer trend to rethink the role of testing in college admissions. However, it remains unclear how (and whether) a college can simultaneously use test scores for those who submit them, while not penalizing those who do not--and what that promise even means. We formalize these questions, and study how a college can overcome two challenges with optional testing: $\textit{strategic applicants}$ (when those with low test scores can pretend to not have taken the test), and $\textit{informational gaps}$ (it has more information on those who submit a test score than those who do not). We find that colleges can indeed do so, if and only if they are able to use information on who has test access and are willing to randomize admissions.
Introducing a Family of Synthetic Datasets for Research on Bias in Machine Learning
Venue: arXiv cs.LG 2021
Year: 2021

A significant impediment to progress in research on bias in machine learning (ML) is the availability of relevant datasets. This situation is unlikely to change much given the sensitivity of such data. For this reason, there is a role for synthetic data in this research. In this short paper, we present one such family of synthetic data sets. We provide an overview of the data, describe how the level of bias can be varied, and present a simple example of an experiment on the data.