OpenRaiser/Intern-Atlas huggingface dataset:
Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning
Venue: arXiv cs.AI 2022
Year: 2022

Electric vehicle (EV) charging stations represent a substantial load with significant flexibility. The exploitation of that flexibility in demand response (DR) algorithms becomes increasingly important to manage and balance demand and supply in power grids. Model-free DR based on reinforcement learning (RL) is an attractive approach to balance such EV charging load. We build on previous research on RL, based on a Markov decision process (MDP) to simultaneously coordinate multiple charging stations. However, we note that the computationally expensive cost function adopted in the previous research leads to large training times, which limits the feasibility and practicality of the approach. We, therefore, propose an improved cost function that essentially forces the learned control policy to always fulfill any charging demand that does not offer any flexibility. We rigorously compare the newly proposed batch RL fitted Q-iteration implementation with the original (costly) one, using real-world data. Specifically, for the case of load flattening, we compare the two approaches in terms of (i) the processing time to learn the RL-based charging policy, as well as (ii) the overall performance of the policy decisions in terms of meeting the target load for unseen test data. The performance is analyzed for different training periods and varying training sample sizes. In addition to both RL policies performance results, we provide performance bounds in terms of both (i) an optimal all-knowing strategy, and (ii) a simple heuristic spreading individual EV charging uniformly over time
$β$-DARTS: Beta-Decay Regularization for Differentiable Architecture Search
Venue: arXiv cs.LG 2022
Year: 2022

Neural Architecture Search~(NAS) has attracted increasingly more attention in recent years because of its capability to design deep neural networks automatically. Among them, differential NAS approaches such as DARTS, have gained popularity for the search efficiency. However, they suffer from two main issues, the weak robustness to the performance collapse and the poor generalization ability of the searched architectures. To solve these two problems, a simple-but-efficient regularization method, termed as Beta-Decay, is proposed to regularize the DARTS-based NAS searching process. Specifically, Beta-Decay regularization can impose constraints to keep the value and variance of activated architecture parameters from too large. Furthermore, we provide in-depth theoretical analysis on how it works and why it works. Experimental results on NAS-Bench-201 show that our proposed method can help to stabilize the searching process and makes the searched network more transferable across different datasets. In addition, our search scheme shows an outstanding property of being less dependent on training time and data. Comprehensive experiments on a variety of search spaces and datasets validate the effectiveness of the proposed method.
Anomaly Detection in Big Data
Venue: arXiv cs.LG 2022
Year: 2022

Anomaly is defined as a state of the system that do not conform to the normal behavior. For example, the emission of neutrons in a nuclear reactor channel above the specified threshold is an anomaly. Big data refers to the data set that is \emph{high volume, streaming, heterogeneous, distributed} and often \emph{sparse}. Big data is not uncommon these days. For example, as per Internet live stats, the number of tweets posted per day has gone above 500 millions. Due to data explosion in data laden domains, traditional anomaly detection techniques developed for small data sets scale poorly on large-scale data sets. Therefore, we take an alternative approach to tackle anomaly detection in big data. Essentially, there are two ways to scale anomaly detection in big data. The first is based on the \emph{online} learning and the second is based on the \emph{distributed} learning. Our aim in the thesis is to tackle big data problems while detecting anomaly efficiently. To that end, we first take \emph{streaming} issue of the big data and propose Passive-Aggressive GMEAN (PAGMEAN) algorithms. Although, online learning algorithm can scale well over large number of data points and dimensions, they can not process data when it is distributed at multiple locations; which is quite common these days. Therefore, we propose anomaly detection algorithm which is inherently distributed using ADMM. Finally, we present a case study on anomaly detection in nuclear power plant data.
SaPHyRa: A Learning Theory Approach to Ranking Nodes in Large Networks
Venue: arXiv cs.SI 2022
Year: 2022

Ranking nodes based on their centrality stands a fundamental, yet, challenging problem in large-scale networks. Approximate methods can quickly estimate nodes' centrality and identify the most central nodes, but the ranking for the majority of remaining nodes may be meaningless. For example, ranking for less-known websites in search queries is known to be noisy and unstable. To this end, we investigate a new node ranking problem with two important distinctions: a) ranking quality, rather than the centrality estimation quality, as the primary objective; and b) ranking only nodes of interest, e.g., websites that matched search criteria. We propose Sample space Partitioning Hypothesis Ranking, or SaPHyRa, that transforms node ranking into a hypothesis ranking in machine learning. This transformation maps nodes' centrality to the expected risks of hypotheses, opening doors for theoretical machine learning (ML) tools. The key of SaPHyRa is to partition the sample space into exact and approximate subspaces. The exact subspace contains samples related to the nodes of interest, increasing both estimation and ranking qualities. The approximate space can be efficiently sampled with ML-based techniques to provide theoretical guarantees on the estimation error. Lastly, we present SaPHyRa_bc, an illustration of SaPHyRa on ranking nodes' betweenness centrality (BC). By combining a novel bi-component sampling, a 2-hop sample partitioning, and improved bounds on the Vapnik-Chervonenkis dimension, SaPHyRa_bc can effectively rank any node subset in BC. Its performance is up to 200x faster than state-of-the-art methods in approximating BC, while its rank correlation to the ground truth is improved by multifold.
On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical Efficiency
Venue: arXiv cs.LG 2022
Year: 2022

This thesis rigorously studies fundamental reinforcement learning (RL) methods in modern practical considerations, including robust RL, distributional RL, and offline RL with neural function approximation. The thesis first prepares the readers with an overall overview of RL and key technical background in statistics and optimization. In each of the settings, the thesis motivates the problems to be studied, reviews the current literature, provides computationally efficient algorithms with provable efficiency guarantees, and concludes with future research directions. The thesis makes fundamental contributions to the three settings above, both algorithmically, theoretically, and empirically, while staying relevant to practical considerations.
Generative Modeling for Low Dimensional Speech Attributes with Neural Spline Flows
Venue: arXiv cs.SD 2022
Year: 2022

Despite recent advances in generative modeling for text-to-speech synthesis, these models do not yet have the same fine-grained adjustability of pitch-conditioned deterministic models such as FastPitch and FastSpeech2. Pitch information is not only low-dimensional, but also discontinuous, making it particularly difficult to model in a generative setting. Our work explores several techniques for handling the aforementioned issues in the context of Normalizing Flow models. We also find this problem to be very well suited for Neural Spline flows, which is a highly expressive alternative to the more common affine-coupling mechanism in Normalizing Flows.
Intensity Image-based LiDAR Fiducial Marker System
Venue: arXiv cs.RO 2022
Year: 2022

The fiducial marker system for LiDAR is crucial for the robotic application but it is still rare to date. In this paper, an Intensity Image-based LiDAR Fiducial Marker (IILFM) system is developed. This system only requires an unstructured point cloud with intensity as the input and it has no restriction on marker placement and shape. A marker detection method that locates the predefined 3D fiducials in the point cloud through the intensity image is introduced. Then, an approach that utilizes the detected 3D fiducials to estimate the LiDAR 6-DOF pose that describes the transmission from the world coordinate system to the LiDAR coordinate system is developed. Moreover, all these processes run in real-time (approx 40 Hz on Livox Mid-40 and approx 143 Hz on VLP-16). Qualitative and quantitative experiments are conducted to demonstrate that the proposed system has similar convenience and accuracy as the conventional visual fiducial marker system. The codes and results are available at: https://github.com/York-SDCNLab/IILFM.
Implicit Polarized F: local type inference for impredicativity
Venue: arXiv cs.PL 2022
Year: 2022

System F, the polymorphic lambda calculus, features the principle of impredicativity: polymorphic types may be (explicitly) instantiated at other types, enabling many powerful idioms such as Church encoding and data abstraction. Unfortunately, type applications need to be implicit for a language to be human-usable, and the problem of inferring all type applications in System F is undecidable. As a result, language designers have historically avoided impredicative type inference. We reformulate System F in terms of call-by-push-value, and study type inference for it. Surprisingly, this new perspective yields a novel type inference algorithm which is extremely simple to implement (not even requiring unification), infers many types, and has a simple declarative specification. Furthermore, our approach offers type theoretic explanations of how many of the heuristics used in existing algorithms for impredicative polymorphism arise.
An Adaptive Human Driver Model for Realistic Race Car Simulations
Venue: arXiv cs.LG 2022
Year: 2022

Engineering a high-performance race car requires a direct consideration of the human driver using real-world tests or Human-Driver-in-the-Loop simulations. Apart from that, offline simulations with human-like race driver models could make this vehicle development process more effective and efficient but are hard to obtain due to various challenges. With this work, we intend to provide a better understanding of race driver behavior and introduce an adaptive human race driver model based on imitation learning. Using existing findings and an interview with a professional race engineer, we identify fundamental adaptation mechanisms and how drivers learn to optimize lap time on a new track. Subsequently, we use these insights to develop generalization and adaptation techniques for a recently presented probabilistic driver modeling approach and evaluate it using data from professional race drivers and a state-of-the-art race car simulator. We show that our framework can create realistic driving line distributions on unseen race tracks with almost human-like performance. Moreover, our driver model optimizes its driving lap by lap, correcting driving errors from previous laps while achieving faster lap times. This work contributes to a better understanding and modeling of the human driver, aiming to expedite simulation methods in the modern vehicle development process and potentially supporting automated driving and racing technologies.
Fast Neural Architecture Search for Lightweight Dense Prediction Networks
Venue: arXiv cs.CV 2022
Year: 2022

We present LDP, a lightweight dense prediction neural architecture search (NAS) framework. Starting from a pre-defined generic backbone, LDP applies the novel Assisted Tabu Search for efficient architecture exploration. LDP is fast and suitable for various dense estimation problems, unlike previous NAS methods that are either computational demanding or deployed only for a single subtask. The performance of LPD is evaluated on monocular depth estimation, semantic segmentation, and image super-resolution tasks on diverse datasets, including NYU-Depth-v2, KITTI, Cityscapes, COCO-stuff, DIV2K, Set5, Set14, BSD100, Urban100. Experiments show that the proposed framework yields consistent improvements on all tested dense prediction tasks, while being $5\%-315\%$ more compact in terms of the number of model parameters than prior arts.
Can Authoritative Governments Abuse the Right to Access?
Venue: arXiv cs.CR 2022
Year: 2022

The right to access is a great tool provided by the GDPR to empower data subjects with their data. However, it needs to be implemented properly otherwise it could turn subject access requests against the subjects privacy. Indeed, recent works have shown that it is possible to abuse the right to access using impersonation attacks. We propose to extend those impersonation attacks by considering that the adversary has an access to governmental resources. In this case, the adversary can forge official documents or exploit copy of them. Our attack affects more people than one may expect. To defeat the attacks from this kind of adversary, several solutions are available like multi-factors or proof of aliveness. Our attacks highlight the need for strong procedures to authenticate subject access requests.
Dynamic Backdoors with Global Average Pooling
Venue: arXiv cs.CR 2022
Year: 2022

Outsourced training and machine learning as a service have resulted in novel attack vectors like backdoor attacks. Such attacks embed a secret functionality in a neural network activated when the trigger is added to its input. In most works in the literature, the trigger is static, both in terms of location and pattern. The effectiveness of various detection mechanisms depends on this property. It was recently shown that countermeasures in image classification, like Neural Cleanse and ABS, could be bypassed with dynamic triggers that are effective regardless of their pattern and location. Still, such backdoors are demanding as they require a large percentage of poisoned training data. In this work, we are the first to show that dynamic backdoor attacks could happen due to a global average pooling layer without increasing the percentage of the poisoned training data. Nevertheless, our experiments in sound classification, text sentiment analysis, and image classification show this to be very difficult in practice.
WPNAS: Neural Architecture Search by jointly using Weight Sharing and Predictor
Venue: arXiv cs.LG 2022
Year: 2022

Weight sharing based and predictor based methods are two major types of fast neural architecture search methods. In this paper, we propose to jointly use weight sharing and predictor in a unified framework. First, we construct a SuperNet in a weight-sharing way and probabilisticly sample architectures from the SuperNet. To increase the correctness of the evaluation of architectures, besides direct evaluation using the inherited weights, we further apply a few-shot predictor to assess the architecture on the other hand. The final evaluation of the architecture is the combination of direct evaluation, the prediction from the predictor and the cost of the architecture. We regard the evaluation as a reward and apply a self-critical policy gradient approach to update the architecture probabilities. To further reduce the side effects of weight sharing, we propose a weakly weight sharing method by introducing another HyperNet. We conduct experiments on datasets including CIFAR-10, CIFAR-100 and ImageNet under NATS-Bench, DARTS and MobileNet search space. The proposed WPNAS method achieves state-of-the-art performance on these datasets.
Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores
Venue: arXiv cs.LG 2022
Year: 2022

The availability of wide-ranging third-party intellectual property (3PIP) cores enables integrated circuit (IC) designers to focus on designing high-level features in ASICs/SoCs. The massive proliferation of ICs brings with it an increased number of bad actors seeking to exploit those circuits for various nefarious reasons. This is not surprising as integrated circuits affect every aspect of society. Thus, malicious logic (Hardware Trojans, HT) being surreptitiously injected by untrusted vendors into 3PIP cores used in IC design is an ever present threat. In this paper, we explore methods for identification of trigger-based HT in designs containing synthesizable IP cores without a golden model. Specifically, we develop methods to detect hardware trojans by detecting triggers embedded in ICs purely based on netlists acquired from the vendor. We propose GATE-Net, a deep learning model based on graph-convolutional networks (GCN) trained using supervised contrastive learning, for flagging designs containing randomly-inserted triggers using only the corresponding netlist. Our proposed architecture achieves significant improvements over state-of-the-art learning models yielding an average 46.99% improvement in detection performance for combinatorial triggers and 21.91% improvement for sequential triggers across a variety of circuit types. Through rigorous experimentation, qualitative and quantitative performance evaluations, we demonstrate effectiveness of GATE-Net and the supervised contrastive training of GATE-Net for HT detection.
Detecting Offensive Language on Social Networks: An End-to-end Detection Method based on Graph Attention Networks
Venue: arXiv cs.SI 2022
Year: 2022

The pervasiveness of offensive language on the social network has caused adverse effects on society, such as abusive behavior online. It is urgent to detect offensive language and curb its spread. Existing research shows that methods with community structure features effectively improve the performance of offensive language detection. However, the existing models deal with community structure independently, which seriously affects the effectiveness of detection models. In this paper, we propose an end-to-end method based on community structure and text features for offensive language detection (CT-OLD). Specifically, the community structure features are directly captured by the graph attention network layer, and the text embeddings are taken from the last hidden layer of BERT. Attention mechanisms and position encoding are used to fuse these features. Meanwhile, we add user opinion to the community structure for representing user features. The user opinion is represented by user historical behavior information, which outperforms that represented by text information. Besides the above point, the distribution of users and tweets is unbalanced in the popular datasets, which limits the generalization ability of the model. To address this issue, we construct and release a dataset with reasonable user distribution. Our method outperforms baselines with the F1 score of 89.94%. The results show that the end-to-end model effectively learns the potential information of community structure and text, and user historical behavior information is more suitable for user opinion representation.
PatchMVSNet: Patch-wise Unsupervised Multi-View Stereo for Weakly-Textured Surface Reconstruction
Venue: arXiv cs.CV 2022
Year: 2022

Learning-based multi-view stereo (MVS) has gained fine reconstructions on popular datasets. However, supervised learning methods require ground truth for training, which is hard to be collected, especially for the large-scale datasets. Though nowadays unsupervised learning methods have been proposed and have gotten gratifying results, those methods still fail to reconstruct intact results in challenging scenes, such as weakly-textured surfaces, as those methods primarily depend on pixel-wise photometric consistency which is subjected to various illuminations. To alleviate matching ambiguity in those challenging scenes, this paper proposes robust loss functions leveraging constraints beneath multi-view images: 1) Patch-wise photometric consistency loss, which expands the receptive field of the features in multi-view similarity measuring, 2) Robust twoview geometric consistency, which includes a cross-view depth consistency checking with the minimum occlusion. Our unsupervised strategy can be implemented with arbitrary depth estimation frameworks and can be trained with arbitrary large-scale MVS datasets. Experiments show that our method can decrease the matching ambiguity and particularly improve the completeness of weakly-textured reconstruction. Moreover, our method reaches the performance of the state-of-the-art methods on popular benchmarks, like DTU, Tanks and Temples and ETH3D. The code will be released soon.
Beyond Plain Toxic: Detection of Inappropriate Statements on Flammable Topics for the Russian Language
Venue: arXiv cs.CL 2022
Year: 2022

Toxicity on the Internet, such as hate speech, offenses towards particular users or groups of people, or the use of obscene words, is an acknowledged problem. However, there also exist other types of inappropriate messages which are usually not viewed as toxic, e.g. as they do not contain explicit offences. Such messages can contain covered toxicity or generalizations, incite harmful actions (crime, suicide, drug use), provoke "heated" discussions. Such messages are often related to particular sensitive topics, e.g. on politics, sexual minorities, social injustice which more often than other topics, e.g. cars or computing, yield toxic emotional reactions. At the same time, clearly not all messages within such flammable topics are inappropriate. Towards this end, in this work, we present two text collections labelled according to binary notion of inapropriateness and a multinomial notion of sensitive topic. Assuming that the notion of inappropriateness is common among people of the same culture, we base our approach on human intuitive understanding of what is not acceptable and harmful. To objectivise the notion of inappropriateness, we define it in a data-driven way though crowdsourcing. Namely we run a large-scale annotation study asking workers if a given chatbot textual statement could harm reputation of a company created it. Acceptably high values of inter-annotator agreement suggest that the notion of inappropriateness exists and can be uniformly understood by different people. To define the notion of sensitive topics in an objective way we use on guidelines suggested commonly by specialists of legal and PR department of a large public company as potentially harmful.
AutoDIME: Automatic Design of Interesting Multi-Agent Environments
Venue: arXiv cs.LG 2022
Year: 2022

Designing a distribution of environments in which RL agents can learn interesting and useful skills is a challenging and poorly understood task, for multi-agent environments the difficulties are only exacerbated. One approach is to train a second RL agent, called a teacher, who samples environments that are conducive for the learning of student agents. However, most previous proposals for teacher rewards do not generalize straightforwardly to the multi-agent setting. We examine a set of intrinsic teacher rewards derived from prediction problems that can be applied in multi-agent settings and evaluate them in Mujoco tasks such as multi-agent Hide and Seek as well as a diagnostic single-agent maze task. Of the intrinsic rewards considered we found value disagreement to be most consistent across tasks, leading to faster and more reliable emergence of advanced skills in Hide and Seek and the maze task. Another candidate intrinsic reward considered, value prediction error, also worked well in Hide and Seek but was susceptible to noisy-TV style distractions in stochastic environments. Policy disagreement performed well in the maze task but did not speed up learning in Hide and Seek. Our results suggest that intrinsic teacher rewards, and in particular value disagreement, are a promising approach for automating both single and multi-agent environment design.