OpenRaiser/Intern-Atlas huggingface dataset:
A Security-aware and LUT-based CAD Flow for the Physical Synthesis of eASICs
Venue: arXiv cs.CR 2022
Year: 2022

Numerous threats are associated with the globalized integrated circuit (IC) supply chain, such as piracy, reverse engineering, overproduction, and malicious logic insertion. Many obfuscation approaches have been proposed to mitigate these threats by preventing an adversary from fully understanding the IC (or parts of it). The use of reconfigurable elements inside an IC is a known obfuscation technique, either as a coarse grain reconfigurable block (i.e., eFPGA) or as a fine grain element (i.e., FPGA-like look-up tables). This paper presents a security-aware CAD flow that is LUT-based yet still compatible with the standard cell based physical synthesis flow. More precisely, our CAD flow explores the FPGA-ASIC design space and produces heavily obfuscated designs where only small portions of the logic resemble an ASIC. Therefore, we term this specialized solution an "embedded ASIC" (eASIC). Nevertheless, even for heavily LUT-dominated designs, our proposed decomposition and pin swapping algorithms allow for performance gains that enable performance levels that only ASICs would otherwise achieve. On the security side, we have developed novel template-based attacks and also applied existing attacks, both oracle-free and oracle-based. Our security analysis revealed that the obfuscation rate for an SHA-256 study case should be at least 45% for withstanding traditional attacks and at least 80% for withstanding template-based attacks. When the 80\% obfuscated SHA-256 design is physically implemented, it achieves a remarkable frequency of 368MHz in a 65nm commercial technology, whereas its FPGA implementation (in a superior technology) achieves only 77MHz.
A Baseline for Detecting Out-of-Distribution Examples in Image Captioning
Venue: arXiv cs.CV 2022
Year: 2022

Image captioning research achieved breakthroughs in recent years by developing neural models that can generate diverse and high-quality descriptions for images drawn from the same distribution as training images. However, when facing out-of-distribution (OOD) images, such as corrupted images, or images containing unknown objects, the models fail in generating relevant captions. In this paper, we consider the problem of OOD detection in image captioning. We formulate the problem and suggest an evaluation setup for assessing the model's performance on the task. Then, we analyze and show the effectiveness of the caption's likelihood score at detecting and rejecting OOD images, which implies that the relatedness between the input image and the generated caption is encapsulated within the score.
Learning Diverse Tone Styles for Image Retouching
Venue: arXiv cs.CV 2022
Year: 2022

Image retouching, aiming to regenerate the visually pleasing renditions of given images, is a subjective task where the users are with different aesthetic sensations. Most existing methods deploy a deterministic model to learn the retouching style from a specific expert, making it less flexible to meet diverse subjective preferences. Besides, the intrinsic diversity of an expert due to the targeted processing on different images is also deficiently described. To circumvent such issues, we propose to learn diverse image retouching with normalizing flow-based architectures. Unlike current flow-based methods which directly generate the output image, we argue that learning in a style domain could (i) disentangle the retouching styles from the image content, (ii) lead to a stable style presentation form, and (iii) avoid the spatial disharmony effects. For obtaining meaningful image tone style representations, a joint-training pipeline is delicately designed, which is composed of a style encoder, a conditional RetouchNet, and the image tone style normalizing flow (TSFlow) module. In particular, the style encoder predicts the target style representation of an input image, which serves as the conditional information in the RetouchNet for retouching, while the TSFlow maps the style representation vector into a Gaussian distribution in the forward pass. After training, the TSFlow can generate diverse image tone style vectors by sampling from the Gaussian distribution. Extensive experiments on MIT-Adobe FiveK and PPR10K datasets show that our proposed method performs favorably against state-of-the-art methods and is effective in generating diverse results to satisfy different human aesthetic preferences. Source code and pre-trained models are publicly available at https://github.com/SSRHeart/TSFlow.
A Generative deep learning approach for shape recognition of arbitrary objects from phaseless acoustic scattering data
Venue: arXiv cs.SD 2022
Year: 2022

We propose and demonstrate a generative deep learning approach for the shape recognition of an arbitrary object from its acoustic scattering properties. The strategy exploits deep neural networks to learn the mapping between the latent space of a two-dimensional acoustic object and the far-field scattering amplitudes. A neural network is designed as an Adversarial autoencoder and trained via unsupervised learning to determine the latent space of the acoustic object. Important structural features of the object are embedded in lower-dimensional latent space which supports the modeling of a shape generator and accelerates the learning in the inverse design process.The proposed inverse design uses the variational inference approach with encoder and decoder-like architecture where the decoder is composed of two pretrained neural networks, the generator and the forward model. The data-driven framework finds an accurate solution to the ill-posed inverse scattering problem, where non-unique solution space is overcome by the multifrequency phaseless far-field patterns. This inverse method is a powerful design tool that does not require complex analytical calculation and opens up new avenues for practical realization, automatic recognition of arbitrary shaped submarines or large fish, and other underwater applications.
Markov Decision Process For Automatic Cyber Defense
Venue: arXiv cs.CR 2022
Year: 2022

It is challenging for a security analyst to detect or defend against cyber-attacks. Moreover, traditional defense deployment methods require the security analyst to manually enforce the defenses in the presence of uncertainties about the defense to deploy. As a result, it is essential to develop an automated and resilient defense deployment mechanism to thwart the new generation of attacks. In this paper, we propose a framework based on Markov Decision Process (MDP) and Q-learning to automatically generate optimal defense solutions for networked system states. The framework consists of four phases namely; the model initialization phase, model generation phase, Q-learning phase, and the conclusion phase. The proposed model collects real network information as inputs and then builds them into structural data. We implement a Q-learning process in the model to learn the quality of a defense action in a particular state. To investigate the feasibility of the proposed model, we perform simulation experiments and the result reveals that the model can reduce the risk of network systems from cyber attacks. Furthermore, the experiment shows that the model has shown a certain level of flexibility when different parameters are used for Q-learning.
Affinity Classification Problem by Stochastic Cellular Automata
Venue: arXiv cs.FL 2022
Year: 2022

This work introduces a new problem, named as, affinity classification problem which is a generalization of the density classification problem. To solve this problem, we introduce temporally stochastic cellular automata where two rules are stochastically applied in each step on all cells of the automata. Our model is defined on 2-dimensional grid having affection capability. We show that this model can be used in several applications like modeling self-healing systems.
Adversarial Robustness Assessment of NeuroEvolution Approaches
Venue: arXiv cs.NE 2022
Year: 2022

NeuroEvolution automates the generation of Artificial Neural Networks through the application of techniques from Evolutionary Computation. The main goal of these approaches is to build models that maximize predictive performance, sometimes with an additional objective of minimizing computational complexity. Although the evolved models achieve competitive results performance-wise, their robustness to adversarial examples, which becomes a concern in security-critical scenarios, has received limited attention. In this paper, we evaluate the adversarial robustness of models found by two prominent NeuroEvolution approaches on the CIFAR-10 image classification task: DENSER and NSGA-Net. Since the models are publicly available, we consider white-box untargeted attacks, where the perturbations are bounded by either the L2 or the Linfinity-norm. Similarly to manually-designed networks, our results show that when the evolved models are attacked with iterative methods, their accuracy usually drops to, or close to, zero under both distance metrics. The DENSER model is an exception to this trend, showing some resistance under the L2 threat model, where its accuracy only drops from 93.70% to 18.10% even with iterative attacks. Additionally, we analyzed the impact of pre-processing applied to the data before the first layer of the network. Our observations suggest that some of these techniques can exacerbate the perturbations added to the original inputs, potentially harming robustness. Thus, this choice should not be neglected when automatically designing networks for applications where adversarial attacks are prone to occur.
Bitcoin: a new Proof-of-Work system with reduced variance
Venue: arXiv cs.DC 2022
Year: 2022

Proof-of-Work (PoW) is a popular consensus protocol used by Bitcoin since its inception. PoW has the well-known flaw of assigning all the reward to the single miner (or pool) that inserts the new block. This has the consequence of making the variance of the reward and thus the mining enterprise risk extremely high. To address this problem, Shi in 2016 proposed a theoretical algorithm that would substantially reduce the issue. We introduce a variant of Proof-of-Work that improves on Shi's idea and can be easily implemented in practice. In order to insert a block, the network must not find a single nonce, but must find a few of them. This small change allows for a fairer distribution of rewards and at the same time has the effect of regularizing the insertion time of blocks. This would facilitate the emergence of small pools or autonomous miners.
HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle
Venue: arXiv cs.DC 2022
Year: 2022

Accurate protein structure prediction can significantly accelerate the development of life science. The accuracy of AlphaFold2, a frontier end-to-end structure prediction system, is already close to that of the experimental determination techniques. Due to the complex model architecture and large memory consumption, it requires lots of computational resources and time to implement the training and inference of AlphaFold2 from scratch. The cost of running the original AlphaFold2 is expensive for most individuals and institutions. Therefore, reducing this cost could accelerate the development of life science. We implement AlphaFold2 using PaddlePaddle, namely HelixFold, to improve training and inference speed and reduce memory consumption. The performance is improved by operator fusion, tensor fusion, and hybrid parallelism computation, while the memory is optimized through Recompute, BFloat16, and memory read/write in-place. Compared with the original AlphaFold2 (implemented with Jax) and OpenFold (implemented with PyTorch), HelixFold needs only 7.5 days to complete the full end-to-end training and only 5.3 days when using hybrid parallelism, while both AlphaFold2 and OpenFold take about 11 days. HelixFold saves 1x training time. We verified that HelixFold's accuracy could be on par with AlphaFold2 on the CASP14 and CAMEO datasets. HelixFold's code is available on GitHub for free download: https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold, and we also provide stable web services on https://paddlehelix.baidu.com/app/drug/protein/forecast.
Skeletal Human Action Recognition using Hybrid Attention based Graph Convolutional Network
Venue: arXiv cs.CV 2022
Year: 2022

In skeleton-based action recognition, Graph Convolutional Networks model human skeletal joints as vertices and connect them through an adjacency matrix, which can be seen as a local attention mask. However, in most existing Graph Convolutional Networks, the local attention mask is defined based on natural connections of human skeleton joints and ignores the dynamic relations for example between head, hands and feet joints. In addition, the attention mechanism has been proven effective in Natural Language Processing and image description, which is rarely investigated in existing methods. In this work, we proposed a new adaptive spatial attention layer that extends local attention map to global based on relative distance and relative angle information. Moreover, we design a new initial graph adjacency matrix that connects head, hands and feet, which shows visible improvement in terms of action recognition accuracy. The proposed model is evaluated on two large-scale and challenging datasets in the field of human activities in daily life: NTU-RGB+D and Kinetics skeleton. The results demonstrate that our model has strong performance on both dataset.
Modality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detection
Venue: arXiv cs.CV 2022
Year: 2022

Weakly-supervised audio-visual violence detection aims to distinguish snippets containing multimodal violence events with video-level labels. Many prior works perform audio-visual integration and interaction in an early or intermediate manner, yet overlooking the modality heterogeneousness over the weakly-supervised setting. In this paper, we analyze the modality asynchrony and undifferentiated instances phenomena of the multiple instance learning (MIL) procedure, and further investigate its negative impact on weakly-supervised audio-visual learning. To address these issues, we propose a modality-aware contrastive instance learning with self-distillation (MACIL-SD) strategy. Specifically, we leverage a lightweight two-stream network to generate audio and visual bags, in which unimodal background, violent, and normal instances are clustered into semi-bags in an unsupervised way. Then audio and visual violent semi-bag representations are assembled as positive pairs, and violent semi-bags are combined with background and normal instances in the opposite modality as contrastive negative pairs. Furthermore, a self-distillation module is applied to transfer unimodal visual knowledge to the audio-visual model, which alleviates noises and closes the semantic gap between unimodal and multimodal features. Experiments show that our framework outperforms previous methods with lower complexity on the large-scale XD-Violence dataset. Results also demonstrate that our proposed approach can be used as plug-in modules to enhance other networks. Codes are available at https://github.com/JustinYuu/MACIL_SD.
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels
Venue: arXiv cs.LG 2022
Year: 2022

Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data plays a significant role in tracking vessel activity and mapping mobility patterns such as those found in fishing. Accordingly, this paper proposes a geometric-driven semi-supervised approach for fishing activity detection from AIS data. Through the proposed methodology we show how to explore the information included in the messages to extract features describing the geometry of the vessel route. To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry highlighting the changes in the vessel's moving pattern which tends to indicate fishing activity. The labels obtained by the proposed unsupervised approach are used to detect fishing activities, which we approach as a time-series classification task. In this context, we propose a solution using recurrent neural networks on AIS data streams with roughly 87% of the overall $F$-score on the whole trajectories of 50 different unseen fishing vessels. Such results are accompanied by a broad benchmark study assessing the performance of different Recurrent Neural Network (RNN) architectures. In conclusion, this work contributes by proposing a thorough process that includes data preparation, labeling, data modeling, and model validation. Therefore, we present a novel solution for mobility pattern detection that relies upon unfolding the trajectory in time and observing their inherent geometry.
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
Venue: arXiv cs.CR 2022
Year: 2022

While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures and tools for evaluating its security in different application contexts. In this article, we discuss how to develop automated and scalable security evaluations of machine learning using practical attacks, reporting a use case on Windows malware detection.
Making Python Code Idiomatic by Automatic Refactoring Non-Idiomatic Python Code with Pythonic Idioms
Venue: arXiv cs.SE 2022
Year: 2022

Compared to other programming languages (e.g., Java), Python has more idioms to make Python code concise and efficient. Although pythonic idioms are well accepted in the Python community, Python programmers are often faced with many challenges in using them, for example, being unaware of certain pythonic idioms or do not know how to use them properly. Based on an analysis of 7,638 Python repositories on GitHub, we find that non-idiomatic Python code that can be implemented with pythonic idioms occurs frequently and widely. Unfortunately, there is no tool for automatically refactoring such non-idiomatic code into idiomatic code. In this paper, we design and implement an automatic refactoring tool to make Python code idiomatic. We identify nine pythonic idioms by systematically contrasting the abstract syntax grammar of Python and Java. Then we define the syntactic patterns for detecting non-idiomatic code for each pythonic idiom. Finally, we devise atomic AST-rewriting operations and refactoring steps to refactor non-idiomatic code into idiomatic code. We test and review over 4,115 refactorings applied to 1,065 Python projects from GitHub, and submit 90 pull requests for the 90 randomly sampled refactorings to 84 projects. These evaluations confirm the high-accuracy, practicality and usefulness of our refactoring tool on real-world Python code. Our refactoring tool can be accessed at 47.242.131.128:5000.
Contrastive Learning for Online Semi-Supervised General Continual Learning
Venue: arXiv cs.LG 2022
Year: 2022

We study Online Continual Learning with missing labels and propose SemiCon, a new contrastive loss designed for partly labeled data. We demonstrate its efficiency by devising a memory-based method trained on an unlabeled data stream, where every data added to memory is labeled using an oracle. Our approach outperforms existing semi-supervised methods when few labels are available, and obtain similar results to state-of-the-art supervised methods while using only 2.6% of labels on Split-CIFAR10 and 10% of labels on Split-CIFAR100.
Attacking (and defending) the Maritime Radar System
Venue: arXiv cs.CR 2022
Year: 2022

Operation of radar equipment is one of the key facilities used by navigators to gather situational awareness about their surroundings. With an ever increasing need for always-running logistics and tighter shipping schedules, operators are relying more and more on computerized instruments and their indications. As a result, modern ships have become a complex cyber-physical system in which sensors and computers constantly communicate and coordinate. In this work, we discuss novel threats related to the radar system, which is one of the most security-sensitive component on a ship. In detail, we first discuss some new attacks capable of compromising the integrity of data displayed on a radar system, with potentially catastrophic impacts on the crew' situational awareness or even safety itself. Then, we present a detection system aimed at highlighting anomalies in the radar video feed, requiring no modifications to the target ship configuration. Finally, we stimulate our detection system by performing the attacks inside of a simulated environment. The experimental results clearly indicate that the attacks are feasible, rather easy to carry out, and hard-to-detect. Moreover, they prove that the proposed detection technique is effective.
DWTCP: Ultra Low Latency Congestion Control Protocol for Data Centers
Venue: arXiv cs.NI 2022
Year: 2022

Congestion control algorithms rely on a variety of congestion signals (packet loss, Explicit Congestion Notification, delay, etc.) to achieve fast convergence, high utilization, and fairness among flows. A key limitation of these congestion signals is that they are either late in feedback or they incur significant overheads. An ideal congestion control must discover any available bandwidth in the network, detect congestion as soon as link utilization approaches full capacity, and react timely to avoid queuing and packet drops, without significant overheads. To this end, this work proposes Scout service that leverages priority queues to infer bandwidth availability and link busyness at the host. The key observation here is that as the high priority queue (HPQ) gets busier, the low priority queue (LPQ) is served less. Therefore, the state of the link can be observed from the LPQ and any congestion can be detected several RTTs earlier than observing the HPQ. We propose a new transport protocol, Double-Window Transmission Control Protocol (DWTCP) that builds upon the Scout service to dynamically adjust its congestion window. Our testbed and simulation-based evaluation demonstrates that Scout enables a data center transport to achieve high throughput, near-zero queues, lower latency, and high fairness.
TreeExplorer: a coding algorithm for rooted trees with application to wireless and ad hoc routing
Venue: arXiv cs.NI 2022
Year: 2022

Routing tables in ad hoc and wireless routing protocols can be represented using rooted trees. The constant need for communication and storage of these trees in routing protocols demands an efficient rooted tree coding algorithm. This efficiency is defined in terms of the average code length, and the optimality of the algorithm is measured by comparing the average code length with the entropy of the source. In this work, TreeExplorer is introduced as an easy-to-implement and nearly optimal algorithm for coding rooted tree structures. This method utilizes the number of leaves of the tree as an indicator for choosing the best method of coding. We show how TreeExplorer can improve existing routing protocols for ad hoc and wireless systems, which normally entails a significant communication overhead.