OpenRaiser/Intern-Atlas huggingface dataset:
Separable Batch Normalization for Robust Facial Landmark Localization with Cross-protocol Network Training
Venue: arXiv cs.CV 2021
Year: 2021

A big, diverse and balanced training data is the key to the success of deep neural network training. However, existing publicly available datasets used in facial landmark localization are usually much smaller than those for other computer vision tasks. A small dataset without diverse and balanced training samples cannot support the training of a deep network effectively. To address the above issues, this paper presents a novel Separable Batch Normalization (SepBN) module with a Cross-protocol Network Training (CNT) strategy for robust facial landmark localization. Different from the standard BN layer that uses all the training data to calculate a single set of parameters, SepBN considers that the samples of a training dataset may belong to different sub-domains. Accordingly, the proposed SepBN module uses multiple sets of parameters, each corresponding to a specific sub-domain. However, the selection of an appropriate branch in the inference stage remains a challenging task because the sub-domain of a test sample is unknown. To mitigate this difficulty, we propose a novel attention mechanism that assigns different weights to each branch for automatic selection in an effective style. As a further innovation, the proposed CNT strategy trains a network using multiple datasets having different facial landmark annotation systems, boosting the performance and enhancing the generalization capacity of the trained network. The experimental results obtained on several well-known datasets demonstrate the effectiveness of the proposed method.
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions
Venue: arXiv cs.AI 2021
Year: 2021

Understanding the actions of both humans and artificial intelligence (AI) agents is important before modern AI systems can be fully integrated into our daily life. In this paper, we show that, despite their current huge success, deep learning based AI systems can be easily fooled by subtle adversarial noise to misinterpret the intention of an action in interaction scenarios. Based on a case study of skeleton-based human interactions, we propose a novel adversarial attack on interactions, and demonstrate how DNN-based interaction models can be tricked to predict the participants' reactions in unexpected ways. From a broader perspective, the scope of our proposed attack method is not confined to problems related to skeleton data but can also be extended to any type of problems involving sequential regressions. Our study highlights potential risks in the interaction loop with AI and humans, which need to be carefully addressed when deploying AI systems in safety-critical applications.
The BIVEE Project: an overview of methodology and tools
Venue: arXiv cs.CY 2021
Year: 2021

EU needs an effective exit strategy from the crisis, with a special attention to SMEs that represent the 99% of the enterprises active in the European production system. To this end, innovation appears to be a key factor to relaunch the EU industrial system. The BIVEE project proceeded for almost 4 years to develop a rich framework, i.e., a methodology and a cloud-based software environment, that includes business principles, models, and best practices, plus a number of advanced software services, to support and promote production improvement and business innovation in virtual enterprise environments (essentially, enterprise networks.)
Ten Simple Rules for Success with HPC, i.e. Responsibly BASHing that Linux Cluster
Venue: arXiv cs.DC 2021
Year: 2021

High-performance computing (HPC) clusters are widely used in-house at scientific and academic research institutions. For some users, the transition from running their analyses on a single workstation to running them on a complex, multi-tenanted cluster, usually employing some degree of parallelism, can be challenging, if not bewildering, especially for users whose role is not predominantly computational in nature. On the other hand, there are more experienced users, who can benefit from pointers on how to get the best from their use of HPC. This Ten Simple Rules guide is aimed at helping you identify ways to improve your utilisation of HPC, avoiding common pitfalls that can negatively impact other users and will also help ease the load (pun intended) on your HPC sysadmin. It is intended to provide technical advice common to the use of HPC platforms such as LSF, Slurm, PBS/Torque, SGE, LoadLeveler and YARN, the scheduler used with Hadoop/Spark platform.
Learning from pandemics: using extraordinary events can improve disease now-casting models
Venue: arXiv cs.LG 2021
Year: 2021

Online searches have been used to study different health-related behaviours, including monitoring disease outbreaks. An obvious caveat is that several reasons can motivate individuals to seek online information and models that are blind to people's motivations are of limited use and can even mislead. This is particularly true during extraordinary public health crisis, such as the ongoing pandemic, when fear, curiosity and many other reasons can lead individuals to search for health-related information, masking the disease-driven searches. However, health crisis can also offer an opportunity to disentangle between different drivers and learn about human behavior. Here, we focus on the two pandemics of the 21st century (2009-H1N1 flu and Covid-19) and propose a methodology to discriminate between search patterns linked to general information seeking (media driven) and search patterns possibly more associated with actual infection (disease driven). We show that by learning from such pandemic periods, with high anxiety and media hype, it is possible to select online searches and improve model performance both in pandemic and seasonal settings. Moreover, and despite the common claim that more data is always better, our results indicate that lower volume of the right data can be better than including large volumes of apparently similar data, especially in the long run. Our work provides a general framework that can be applied beyond specific events and diseases, and argues that algorithms can be improved simply by using less (better) data. This has important consequences, for example, to solve the accuracy-explainability trade-off in machine-learning.
Few Shot Dialogue State Tracking using Meta-learning
Venue: arXiv cs.CL 2021
Year: 2021

Dialogue State Tracking (DST) forms a core component of automated chatbot based systems designed for specific goals like hotel, taxi reservation, tourist information, etc. With the increasing need to deploy such systems in new domains, solving the problem of zero/few-shot DST has become necessary. There has been a rising trend for learning to transfer knowledge from resource-rich domains to unknown domains with minimal need for additional data. In this work, we explore the merits of meta-learning algorithms for this transfer and hence, propose a meta-learner D-REPTILE specific to the DST problem. With extensive experimentation, we provide clear evidence of benefits over conventional approaches across different domains, methods, base models, and datasets with significant (5-25%) improvement over the baseline in a low-data setting. Our proposed meta-learner is agnostic of the underlying model and hence any existing state-of-the-art DST system can improve its performance on unknown domains using our training strategy.
Demystifying Pythia: A Survey of ChainLink Oracles Usage on Ethereum
Venue: arXiv cs.DC 2021
Year: 2021

Smart contracts are dependent on oracle systems for their adoption and usability. We perform an empirical study of oracle systems' usage trends and adoption metrics to provide better insight into the health of the smart contract ecosystem. We collect ChainLink usage data on the Ethereum network using a modified Ethereum client and running a full node. We analyze the collected data and present our findings and insights surrounding the usage trends, adoption metrics, oracle pricing and service quality associated with ChainLink on the Ethereum network.
Chaotic-to-Fine Clustering for Unlabeled Plant Disease Images
Venue: arXiv cs.CV 2021
Year: 2021

Current annotation for plant disease images depends on manual sorting and handcrafted features by agricultural experts, which is time-consuming and labour-intensive. In this paper, we propose a self-supervised clustering framework for grouping plant disease images based on the vulnerability of Kernel K-means. The main idea is to establish a cross iterative under-clustering algorithm based on Kernel K-means to produce the pseudo-labeled training set and a chaotic cluster to be further classified by a deep learning module. In order to verify the effectiveness of our proposed framework, we conduct extensive experiments on three different plant disease datatsets with five plants and 17 plant diseases. The experimental results show the high superiority of our method to do image-based plant disease classification over balanced and unbalanced datasets by comparing with five state-of-the-art existing works in terms of different metrics.
GraphAttacker: A General Multi-Task GraphAttack Framework
Venue: arXiv cs.LG 2021
Year: 2021

Graph neural networks (GNNs) have been successfully exploited in graph analysis tasks in many real-world applications. The competition between attack and defense methods also enhances the robustness of GNNs. In this competition, the development of adversarial training methods put forward higher requirement for the diversity of attack examples. By contrast, most attack methods with specific attack strategies are difficult to satisfy such a requirement. To address this problem, we propose GraphAttacker, a novel generic graph attack framework that can flexibly adjust the structures and the attack strategies according to the graph analysis tasks. GraphAttacker generates adversarial examples through alternate training on three key components: the multi-strategy attack generator (MAG), the similarity discriminator (SD), and the attack discriminator (AD), based on the generative adversarial network (GAN). Furthermore, we introduce a novel similarity modification rate SMR to conduct a stealthier attack considering the change of node similarity distribution. Experiments on various benchmark datasets demonstrate that GraphAttacker can achieve state-of-the-art attack performance on graph analysis tasks of node classification, graph classification, and link prediction, no matter the adversarial training is conducted or not. Moreover, we also analyze the unique characteristics of each task and their specific response in the unified attack framework. The project code is available at https://github.com/honoluluuuu/GraphAttacker.
Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we introduce a non-parametric memory representation for spatio-temporal segmentation that captures the local space and time around an autonomous vehicle (AV). Our representation has three important properties: (i) it remembers what it has seen in the past, (ii) it reinforces and (iii) forgets its past beliefs based on new evidence. Reinforcing is important as the first time we see an element we might be uncertain, e.g, if the element is heavily occluded or at range. Forgetting is desirable, as otherwise false positives will make the self driving vehicle behave erratically. Our process is informed by 3D reasoning, as occlusion is key to distinguishing between the desire to forget and to remember. We show how our method can be used as an online component to complement static world representations such as HD maps by detecting and remembering changes that should be superimposed on top of this static view due to such events.
CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning methods for chest X-ray interpretation typically rely on pretrained models developed for ImageNet. This paradigm assumes that better ImageNet architectures perform better on chest X-ray tasks and that ImageNet-pretrained weights provide a performance boost over random initialization. In this work, we compare the transfer performance and parameter efficiency of 16 popular convolutional architectures on a large chest X-ray dataset (CheXpert) to investigate these assumptions. First, we find no relationship between ImageNet performance and CheXpert performance for both models without pretraining and models with pretraining. Second, we find that, for models without pretraining, the choice of model family influences performance more than size within a family for medical imaging tasks. Third, we observe that ImageNet pretraining yields a statistically significant boost in performance across architectures, with a higher boost for smaller architectures. Fourth, we examine whether ImageNet architectures are unnecessarily large for CheXpert by truncating final blocks from pretrained models, and find that we can make models 3.25x more parameter-efficient on average without a statistically significant drop in performance. Our work contributes new experimental evidence about the relation of ImageNet to chest x-ray interpretation performance.
Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements
Venue: arXiv cs.LG 2021
Year: 2021

Millimeter wave (mmWave) communication is a key component of 5G and beyond. Harvesting the gains of the large bandwidth and low latency at mmWave systems, however, is challenged by the sensitivity of mmWave signals to blockages; a sudden blockage in the line of sight (LOS) link leads to abrupt disconnection, which affects the reliability of the network. In addition, searching for an alternative base station to re-establish the link could result in needless latency overhead. In this paper, we address these challenges collectively by utilizing machine learning to anticipate dynamic blockages proactively. The proposed approach sees a machine learning algorithm learning to predict future blockages by observing what we refer to as the pre-blockage signature. To evaluate our proposed approach, we build a mmWave communication setup with a moving blockage and collect a dataset of received power sequences. Simulation results on a real dataset show that blockage occurrence could be predicted with more than 85% accuracy and the exact time instance of blockage occurrence can be obtained with low error. This highlights the potential of the proposed solution for dynamic blockage prediction and proactive hand-off, which enhances the reliability and latency of future wireless networks.
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

Training a multi-agent reinforcement learning (MARL) algorithm is more challenging than training a single-agent reinforcement learning algorithm, because the result of a multi-agent task strongly depends on the complex interactions among agents and their interactions with a stochastic and dynamic environment. We propose an algorithm that boosts MARL training using the biased action information of other agents based on a friend-or-foe concept. For a cooperative and competitive environment, there are generally two groups of agents: cooperative-agents and competitive-agents. In the proposed algorithm, each agent updates its value function using its own action and the biased action information of other agents in the two groups. The biased joint action of cooperative agents is computed as the sum of their actual joint action and the imaginary cooperative joint action, by assuming all the cooperative agents jointly maximize the target agent's value function. The biased joint action of competitive agents can be computed similarly. Each agent then updates its own value function using the biased action information, resulting in a biased value function and corresponding biased policy. Subsequently, the biased policy of each agent is inevitably subjected to recommend an action to cooperate and compete with other agents, thereby introducing more active interactions among agents and enhancing the MARL policy learning. We empirically demonstrate that our algorithm outperforms existing algorithms in various mixed cooperative-competitive environments. Furthermore, the introduced biases gradually decrease as the training proceeds and the correction based on the imaginary assumption vanishes.
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning models are the state-of-the-art methods for semantic point cloud segmentation, the success of which relies on the availability of large-scale annotated datasets. However, it can be extremely time-consuming and prohibitively expensive to compile such datasets. In this work, we propose an active learning approach to maximize model performance given limited annotation budgets. We investigate the appropriate sample granularity for active selection under realistic annotation cost measurement (clicks), and demonstrate that super-point based selection allows for more efficient usage of the limited budget compared to point-level and instance-level selection. We further exploit local consistency constraints to boost the performance of the super-point based approach. We evaluate our methods on two benchmarking datasets (ShapeNet and S3DIS) and the results demonstrate that active learning is an effective strategy to address the high annotation costs in semantic point cloud segmentation.
Learning DNN networks using un-rectifying ReLU with compressed sensing application
Venue: arXiv cs.LG 2021
Year: 2021

The un-rectifying technique expresses a non-linear point-wise activation function as a data-dependent variable, which means that the activation variable along with its input and output can all be employed in optimization. The ReLU network in this study was un-rectified means that the activation functions could be replaced with data-dependent activation variables in the form of equations and constraints. The discrete nature of activation variables associated with un-rectifying ReLUs allows the reformulation of deep learning problems as problems of combinatorial optimization. However, we demonstrate that the optimal solution to a combinatorial optimization problem can be preserved by relaxing the discrete domains of activation variables to closed intervals. This makes it easier to learn a network using methods developed for real-domain constrained optimization. We also demonstrate that by introducing data-dependent slack variables as constraints, it is possible to optimize a network based on the augmented Lagrangian approach. This means that our method could theoretically achieve global convergence and all limit points are critical points of the learning problem. In experiments, our novel approach to solving the compressed sensing recovery problem achieved state-of-the-art performance when applied to the MNIST database and natural images.
Time-Efficient and High-Quality Graph Partitioning for Graph Dynamic Scaling
Venue: arXiv cs.DC 2021
Year: 2021

The dynamic scaling of distributed computations plays an important role in the utilization of elastic computational resources, such as the cloud. It enables the provisioning and de-provisioning of resources to match dynamic resource availability and demands. In the case of distributed graph processing, changing the number of the graph partitions while maintaining high partitioning quality imposes serious computational overheads as typically a time-consuming graph partitioning algorithm needs to execute each time repartitioning is required. In this paper, we propose a dynamic scaling method that can efficiently change the number of graph partitions while keeping its quality high. Our idea is based on two techniques: preprocessing and very fast edge partitioning, called graph edge ordering and chunk-based edge partitioning, respectively. The former converts the graph data into an ordered edge list in such a way that edges with high locality are closer to each other. The latter immediately divides the ordered edge list into an arbitrary number of high-quality partitions. The evaluation with the real-world billion-scale graphs demonstrates that our proposed approach significantly reduces the repartitioning time, while the partitioning quality it achieves is on par with that of the best existing static method.
Reversible Computation in Petri Nets
Venue: arXiv cs.LO 2021
Year: 2021

Reversible computation is an unconventional form of computing that extends the standard forward-only mode of computation with the ability to execute a sequence of operations in reverse at any point during computation. As such, in this thesis we propose a reversible approach to Petri nets by introducing machinery and associated operational semantics to tackle the challenges of the main forms of reversibility. Our proposal concerns a variation of cyclic Petri nets, called Reversing Petri Nets (RPNs) where tokens are persistent and distinguished from each other by an identity. An immediate extension of the original model includes allowing multiple tokens of the same base/type to occur in a model. Specifically, we explore the individual token interpretation where one distinguishes different tokens residing in the same place by keeping track of where they come from. We also propose the collective token interpretation, as the opposite approach to token ambiguity, which considers all tokens of a certain type to be identical, disregarding their history during execution. Both of the proposed models of RPNs (with single or multi tokens) implement the notion of uncontrolled reversibility, meaning that it specifies how to reverse an execution and allows to do so freely, yet it places no restrictions as to when and whether to prefer backward execution over forward execution or vice versa. In this respect, a further aim is to control reversibility by extending our formal semantics where transitions are associated with conditions whose satisfaction allows the execution of transitions in the forward/reversed direction.
SD-Regular Transducer Expressions for Aperiodic Transformations
Venue: arXiv cs.FL 2021
Year: 2021

FO transductions, aperiodic deterministic two-way transducers, as well as aperiodic streaming string transducers are all equivalent models for first order definable functions. In this paper, we solve the long standing open problem of expressions capturing first order definable functions, thereby generalizing the seminal SF=AP (star free expressions = aperiodic languages) result of Schützenberger. Our result also generalizes a lesser known characterization by Schützenberger of aperiodic languages by SD-regular expressions (SD=AP). We show that every first order definable function over finite words captured by an aperiodic deterministic two-way transducer can be described with an SD-regular transducer expression (SDRTE). An SDRTE is a regular expression where Kleene stars are used in a restricted way: they can appear only on aperiodic languages which are prefix codes of bounded synchronization delay. SDRTEs are constructed from simple functions using the combinators unambiguous sum (deterministic choice), Hadamard product, and unambiguous versions of the Cauchy product and the k-chained Kleene-star, where the star is restricted as mentioned. In order to construct an SDRTE associated with an aperiodic deterministic two-way transducer, (i) we concretize Schützenberger's SD=AP result, by proving that aperiodic languages are captured by SD-regular expressions which are unambiguous and stabilising; (ii) by structural induction on the unambiguous, stabilising SD-regular expressions describing the domain of the transducer, we construct SDRTEs. Finally, we also look at various formalisms equivalent to SDRTEs which use the function composition, allowing to trade the k-chained star for a 1-star.