OpenRaiser/Intern-Atlas huggingface dataset:
Intrinsic Bias Identification on Medical Image Datasets
Venue: arXiv cs.CV 2022
Year: 2022

Machine learning based medical image analysis highly depends on datasets. Biases in the dataset can be learned by the model and degrade the generalizability of the applications. There are studies on debiased models. However, scientists and practitioners are difficult to identify implicit biases in the datasets, which causes lack of reliable unbias test datasets to valid models. To tackle this issue, we first define the data intrinsic bias attribute, and then propose a novel bias identification framework for medical image datasets. The framework contains two major components, KlotskiNet and Bias Discriminant Direction Analysis(bdda), where KlostkiNet is to build the mapping which makes backgrounds to distinguish positive and negative samples and bdda provides a theoretical solution on determining bias attributes. Experimental results on three datasets show the effectiveness of the bias attributes discovered by the framework.
Weakly-Supervised End-to-End CAD Retrieval to Scan Objects
Venue: arXiv cs.CV 2022
Year: 2022

CAD model retrieval to real-world scene observations has shown strong promise as a basis for 3D perception of objects and a clean, lightweight mesh-based scene representation; however, current approaches to retrieve CAD models to a query scan rely on expensive manual annotations of 1:1 associations of CAD-scan objects, which typically contain strong lower-level geometric differences. We thus propose a new weakly-supervised approach to retrieve semantically and structurally similar CAD models to a query 3D scanned scene without requiring any CAD-scan associations, and only object detection information as oriented bounding boxes. Our approach leverages a fully-differentiable top-$k$ retrieval layer, enabling end-to-end training guided by geometric and perceptual similarity of the top retrieved CAD models to the scan queries. We demonstrate that our weakly-supervised approach can outperform fully-supervised retrieval methods on challenging real-world ScanNet scans, and maintain robustness for unseen class categories, achieving significantly improved performance over fully-supervised state of the art in zero-shot CAD retrieval.
An Ensemble Approach for Facial Expression Analysis in Video
Venue: arXiv cs.CV 2022
Year: 2022

Human emotions recognization contributes to the development of human-computer interaction. The machines understanding human emotions in the real world will significantly contribute to life in the future. This paper will introduce the Affective Behavior Analysis in-the-wild (ABAW3) 2022 challenge. The paper focuses on solving the problem of the valence-arousal estimation and action unit detection. For valence-arousal estimation, we conducted two stages: creating new features from multimodel and temporal learning to predict valence-arousal. First, we make new features; the Gated Recurrent Unit (GRU) and Transformer are combined using a Regular Networks (RegNet) feature, which is extracted from the image. The next step is the GRU combined with Local Attention to predict valence-arousal. The Concordance Correlation Coefficient (CCC) was used to evaluate the model.
Improved Communication Complexity of Fault-Tolerant Consensus
Venue: arXiv cs.DC 2022
Year: 2022

Consensus is one of the most thoroughly studied problems in distributed computing, yet there are still complexity gaps that have not been bridged for decades. In particular, in the classical message-passing setting with processes' crashes, since the seminal works of Bar-Joseph and Ben-Or [1998] \cite{Bar-JosephB98} and Aspnes and Waarts [1996, 1998] \cite{AspnesW-SICOMP-96,Aspnes-JACM-98} in the previous century, there is still a fundamental unresolved question about communication complexity of fast randomized Consensus against a (strong) adaptive adversary crashing processes arbitrarily online. The best known upper bound on the number of communication bits is $Θ(\frac{n^{3/2}}{\sqrt{\log{n}}})$ per process, while the best lower bound is $Ω(1)$. This is in contrast to randomized Consensus against a (weak) oblivious adversary, for which time-almost-optimal algorithms guarantee amortized $O(1)$ communication bits per process \cite{GK-SODA-10}. We design an algorithm against adaptive adversary that reduces the communication gap by nearly linear factor to $O(\sqrt{n}\cdot\text{polylog } n)$ bits per process, while keeping almost-optimal (up to factor $O(\log^3 n)$) time complexity $O(\sqrt{n}\cdot\log^{5/2} n)$. More surprisingly, we show this complexity indeed can be lowered further, but at the expense of increasing time complexity, i.e., there is a {\em trade-off} between communication complexity and time complexity. More specifically, our main Consensus algorithm allows to reduce communication complexity per process to any value from $\text{polylog } n$ to $O(\sqrt{n}\cdot\text{polylog } n)$, as long as Time $\times$ Communication $= O(n\cdot \text{polylog } n)$. Similarly, reducing time complexity requires more random bits per process, i.e., Time $\times$ Randomness $=O(n\cdot \text{polylog } n)$.
NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

Deep learning has recently been widely applied to many applications across different domains, e.g., image classification and audio recognition. However, the quality of Deep Neural Networks (DNNs) still raises concerns in the practical operational environment, which calls for systematic testing, especially in safety-critical scenarios. Inspired by software testing, a number of structural coverage criteria are designed and proposed to measure the test adequacy of DNNs. However, due to the blackbox nature of DNN, the existing structural coverage criteria are difficult to interpret, making it hard to understand the underlying principles of these criteria. The relationship between the structural coverage and the decision logic of DNNs is unknown. Moreover, recent studies have further revealed the non-existence of correlation between the structural coverage and DNN defect detection, which further posts concerns on what a suitable DNN testing criterion should be. In this paper, we propose the interpretable coverage criteria through constructing the decision structure of a DNN. Mirroring the control flow graph of the traditional program, we first extract a decision graph from a DNN based on its interpretation, where a path of the decision graph represents a decision logic of the DNN. Based on the control flow and data flow of the decision graph, we propose two variants of path coverage to measure the adequacy of the test cases in exercising the decision logic. The higher the path coverage, the more diverse decision logic the DNN is expected to be explored. Our large-scale evaluation results demonstrate that: the path in the decision graph is effective in characterizing the decision of the DNN, and the proposed coverage criteria are also sensitive with errors including natural errors and adversarial examples, and strongly correlated with the output impartiality.
Extended critical regimes of deep neural networks
Venue: arXiv cs.LG 2022
Year: 2022

Deep neural networks (DNNs) have been successfully applied to many real-world problems, but a complete understanding of their dynamical and computational principles is still lacking. Conventional theoretical frameworks for analysing DNNs often assume random networks with coupling weights obeying Gaussian statistics. However, non-Gaussian, heavy-tailed coupling is a ubiquitous phenomenon in DNNs. Here, by weaving together theories of heavy-tailed random matrices and non-equilibrium statistical physics, we develop a new type of mean field theory for DNNs which predicts that heavy-tailed weights enable the emergence of an extended critical regime without fine-tuning parameters. In this extended critical regime, DNNs exhibit rich and complex propagation dynamics across layers. We further elucidate that the extended criticality endows DNNs with profound computational advantages: balancing the contraction as well as expansion of internal neural representations and speeding up training processes, hence providing a theoretical guide for the design of efficient neural architectures.
MERLIN -- Malware Evasion with Reinforcement LearnINg
Venue: arXiv cs.CR 2022
Year: 2022

In addition to signature-based and heuristics-based detection techniques, machine learning (ML) is widely used to generalize to new, never-before-seen malicious software (malware). However, it has been demonstrated that ML models can be fooled by tricking the classifier into returning the incorrect label. These studies, for instance, usually rely on a prediction score that is fragile to gradient-based attacks. In the context of a more realistic situation where an attacker has very little information about the outputs of a malware detection engine, modest evasion rates are achieved. In this paper, we propose a method using reinforcement learning with DQN and REINFORCE algorithms to challenge two state-of-the-art ML-based detection engines (MalConv \& EMBER) and a commercial AV classified by Gartner as a leader AV. Our method combines several actions, modifying a Windows portable execution (PE) file without breaking its functionalities. Our method also identifies which actions perform better and compiles a detailed vulnerability report to help mitigate the evasion. We demonstrate that REINFORCE achieves very good evasion rates even on a commercial AV with limited available information.
Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems
Venue: arXiv cs.HC 2022
Year: 2022

Conversational recommender systems (CRSs) imitate human advisors to assist users in finding items through conversations and have recently gained increasing attention in domains such as media and e-commerce. Like in human communication, building trust in human-agent communication is essential given its significant influence on user behavior. However, inspiring user trust in CRSs with a "one-size-fits-all" design is difficult, as individual users may have their own expectations for conversational interactions (e.g., who, user or system, takes the initiative), which are potentially related to their personal characteristics. In this study, we investigated the impacts of three personal characteristics, namely personality traits, trust propensity, and domain knowledge, on user trust in two types of text-based CRSs, i.e., user-initiative and mixed-initiative. Our between-subjects user study (N=148) revealed that users' trust propensity and domain knowledge positively influenced their trust in CRSs, and that users with high conscientiousness tended to trust the mixed-initiative system.
Millimeter-wave Foresight Sensing for Safety and Resilience in Autonomous Operations
Venue: arXiv cs.RO 2022
Year: 2022

Robotic platforms are highly programmable, scalable and versatile to complete several tasks including Inspection, Maintenance and Repair (IMR). Mobile robotics offer reduced restrictions in operating environments, resulting in greater flexibility; operation at height, dangerous areas and repetitive tasks. Cyber physical infrastructures have been identified by the UK Robotics Growth Partnership as a key enabler in how we utilize and interact with sensors and machines via the virtual and physical worlds. Cyber Physical Systems (CPS) allow for robotics and artificial intelligence to adapt and repurpose at pace, allowing for the addressment of new challenges in CPS. A challenge exists within robotics to secure an effective partnership in a wide range of areas which include shared workspaces and Beyond Visual Line of Sight (BVLOS). Robotic manipulation abilities have improved a robots accessibility via the ability to open doorways, however, challenges exist in how a robot decides if it is safe to move into a new workspace. Current sensing methods are limited to line of sight and are unable to capture data beyond doorways or walls, therefore, a robot is unable to sense if it is safe to open a door. Another limitation exists as robots are unable to detect if a human is within a shared workspace. Therefore, if a human is detected, extended safety precautions can be taken to ensure the safe autonomous operation of a robot. These challenges are represented as safety, trust and resilience, inhibiting the successful advancement of CPS. This paper evaluates the use of frequency modulated continuous wave radar sensing for human detection and through-wall detection to increase situational awareness. The results validate the use of the sensor to detect the difference between a person and infrastructure, and increased situational awareness for navigation via foresight monitoring through walls.
GX-Plug: a Middleware for Plugging Accelerators to Distributed Graph Processing
Venue: arXiv cs.DC 2022
Year: 2022

Recently, research communities highlight the necessity of formulating a scalability continuum for large-scale graph processing, which gains the scale-out benefits from distributed graph systems, and the scale-up benefits from high-performance accelerators. To this end, we propose a middleware, called the GX-plug, for the ease of integrating the merits of both. As a middleware, the GX-plug is versatile in supporting different runtime environments, computation models, and programming models. More, for improving the middleware performance, we study a series of techniques, including pipeline shuffle, synchronization caching and skipping, and workload balancing, for intra-, inter-, and beyond-iteration optimizations, respectively. Experiments show that our middleware efficiently plugs accelerators to representative distributed graph systems, e.g., GraphX and Powergraph, with up-to 20x acceleration ratio.
Multi-modal Emotion Estimation for in-the-wild Videos
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we briefly introduce our submission to the Valence-Arousal Estimation Challenge of the 3rd Affective Behavior Analysis in-the-wild (ABAW) competition. Our method utilizes the multi-modal information, i.e., the visual and audio information, and employs a temporal encoder to model the temporal context in the videos. Besides, a smooth processor is applied to get more reasonable predictions, and a model ensemble strategy is used to improve the performance of our proposed method. The experiment results show that our method achieves 65.55% ccc for valence and 70.88% ccc for arousal on the validation set of the Aff-Wild2 dataset, which prove the effectiveness of our proposed method.
Behavior Trees in Robot Control Systems
Venue: arXiv cs.RO 2022
Year: 2022

In this paper we will give a control theoretic perspective on the research area of behavior trees in robotics. The key idea underlying behavior trees is to make use of modularity, hierarchies and feedback, in order to handle the complexity of a versatile robot control system. Modularity is a well-known tool to handle software complexity by enabling development, debugging and extension of separate modules without having detailed knowledge of the entire system. A hierarchy of such modules is natural, since robot tasks can often be decomposed into a hierarchy of sub-tasks. Finally, feedback control is a fundamental tool for handling uncertainties and disturbances in any low level control system, but in order to enable feedback control on the higher level, where one module decides what submodule to execute, information regarding progress and applicability of each submodule needs to be shared in the module interfaces. We will describe how these three concepts come to use in theoretical analysis, practical design, as well as extensions and combinations with other ideas from control theory and robotics.
Approximate Parametric Computation of Minimum-Cost Flows with Convex Costs
Venue: arXiv cs.DS 2022
Year: 2022

This paper studies a variant of the minimum-cost flow problem in a graph with convex cost function where the demands at the vertices are functions depending on a one-dimensional parameter $λ$. We devise two algorithmic approaches for the approximate computation of parametric solutions for this problem. The first approach transforms an instance of the parametric problem into an instance with piecewise quadratic cost functions by interpolating the marginal cost functions. The new instance can be solved exactly with an algorithm we developed in prior work. In the second approach, we compute a fixed number of non-parametric solutions and interpolate the resulting flows yielding an approximate solution for the original, parametric problem. For both methods we formulate explicit bounds on the step sizes used in the respective interpolations that guarantee relative and absolute error margins. Finally, we test our approaches on real-world traffic and gas instances in an empirical study.
Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their ability to capture well-scaled representations with respect to the values, and (b) the prescaled attention maps, for carrying value-independent global contextual information. We depict the perks of each distilling strategy on two image recognition benchmarks (CIFAR100 and ImageNet-32) -- while (a) leads to a better overall accuracy, (b) helps enhance the rigidity by maintaining competitive performances. Furthermore, we identify the limitation imposed by the symmetric nature of regularization losses. To alleviate this, we propose an asymmetric variant and apply it to the pooled output distillation (POD) loss adapted for ViTs. Our experiments confirm that introducing asymmetry to POD boosts its plasticity while retaining stability across (a) and (b). Moreover, we acknowledge low forgetting measures for all the compared methods, indicating that ViTs might be naturally inclined continual learner
Novel Metric based on Walsh Coefficients for measuring problem difficulty in Estimation of Distribution Algorithms
Venue: arXiv cs.NE 2022
Year: 2022

Estimation of distribution algorithms are evolutionary algorithms that use extracted information from the population instead of traditional genetic operators to generate new solutions. This information is represented as a probabilistic model and the effectiveness of these algorithms is dependent on the quality of these models. However, some studies have shown that even multivariate EDAs fail to build a proper model in some problems. Usually, in these problems, there is intrinsic pairwise independence between variables. In the literature, there are few studies that investigate the difficulty and the nature of problems that can not be solved by EDAs easily. This paper proposes a new metric for measuring problem difficulty by examining the properties of model-building mechanisms in EDAs. For this purpose, we use the estimated Walsh coefficients of dependent and independent variables. The proposed metric is used to evaluate the difficulty of some well-known benchmark problems in EDAs. Different metrics like Fitness Distance Correlation (FDC) are used to compare how well the proposed metric measures problem difficulty for EDAs. Results indicate that the proposed metric can accurately predict the EDA's performance in different problems.
Supervised Training of Siamese Spiking Neural Networks with Earth Mover's Distance
Venue: arXiv cs.NE 2022
Year: 2022

This study adapts the highly-versatile siamese neural network model to the event data domain. We introduce a supervised training framework for optimizing Earth Mover's Distance (EMD) between spike trains with spiking neural networks (SNN). We train this model on images of the MNIST dataset converted into spiking domain with novel conversion schemes. The quality of the siamese embeddings of input images was evaluated by measuring the classifier performance for different dataset coding types. The models achieved performance similar to existing SNN-based approaches (F1-score of up to 0.9386) while using only about 15% of hidden layer neurons to classify each example. Furthermore, models which did not employ a sparse neural code were about 45% slower than their sparse counterparts. These properties make the model suitable for low energy consumption and low prediction latency applications.
Neural Neighbor Style Transfer
Venue: arXiv cs.CV 2022
Year: 2022

We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer. Our approach is based on explicitly replacing neural features extracted from the content input (to be stylized) with those from a style exemplar, then synthesizing the final output based on these rearranged features. While the spirit of our approach is similar to prior work, we show that our design decisions dramatically improve the final visual quality.
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training
Venue: arXiv cs.CV 2022
Year: 2022

Multiple datasets and open challenges for object detection have been introduced in recent years. To build more general and powerful object detection systems, in this paper, we construct a new large-scale benchmark termed BigDetection. Our goal is to simply leverage the training data from existing datasets (LVIS, OpenImages and Object365) with carefully designed principles, and curate a larger dataset for improved detector pre-training. Specifically, we generate a new taxonomy which unifies the heterogeneous label spaces from different sources. Our BigDetection dataset has 600 object categories and contains over 3.4M training images with 36M bounding boxes. It is much larger in multiple dimensions than previous benchmarks, which offers both opportunities and challenges. Extensive experiments demonstrate its validity as a new benchmark for evaluating different object detection methods, and its effectiveness as a pre-training dataset.
Multi-platform Process Flow Models and Algorithms for Extraction and Documentation of Digital Forensic Evidence from Mobile Devices
Venue: arXiv cs.CR 2022
Year: 2022

The increasing need for the examination of evidence from mobile and portable gadgets increases the essential need to establish dependable measures for the investigation of these gadgets. Many differences exist while detailing the requirement for the examination of each gadget, to help detectives and examiners in guaranteeing that of any kind piece of evidence extracted/ collected from any mobile devices is well documented and the outcomes can be repeatable, a reliable and well-documented investigation process must be implemented if the results of the examination are to be repeatable and defensible in courts of law. In this paper we developed a generic process flow model for the extraction of digital evidence in mobile devices running on android, Windows, iOs and Blackberry operating system. The research adopted survey approach and extensive literature review a s means to collect data. The models developed were validate through expert opinion. Results of this work can guide solution developers in ensuring standardization of evidence extraction tools for mobile devices.