Reviewerly/exHarmony huggingface dataset:
Payment Trees: Low Collateral Payments for Payment Channel Networks

The security of blockchain based decentralized ledgers relies on consensus protocols executed between mutually distrustful parties. Such protocols incur delays which severely limit the throughput of such ledgers. Payment and state channels enable execution of offchain protocols that allow interaction between parties without involving the consensus protocol. Protocols such as Hashed Timelock Contracts (HTLC) and Sprites (FC'19) connect channels into Payment Channel Networks (PCN) allowing payments across a path of payment channels. Such a payment requires each party to lock away funds for an amount of time. The product of funds and locktime is the collateral of the party, i.e., their cost of opportunity to forward a payment. In the case of HTLC, the locktime is linear to the length of the path, making the total collateral invested across the path quadratic in size of its length. Sprites improved on this by reducing the locktime to a constant by utilizing smart contracts. Atomic Multi-Channel Updates (AMCU), published at CCS'19, introduced constant collateral payments without smart contracts. In this work we present the Channel Closure attack on AMCU that allows a malicious adversary to make honest parties lose funds. Furthermore, we propose the Payment Trees protocol that allows payments across a PCN with linear total collateral without the aid of smart contracts; a competitive performance similar to Sprites, and yet compatible to Bitcoin.
Decentralized Distributed Deep Learning with Low-Bandwidth Consumption for Smart Constellations

For the space-based remote sensing system, onboard intelligent processing based on deep learning has become an inevitable trend. To adapt to the dynamic changes of the observation scenes, there is an urgent need to perform distributed deep learning onboard to fully utilize the plentiful real-time sensing data of multiple satellites from a smart constellation. However, the network bandwidth of the smart constellation is very limited. Therefore, it is of great significance to carry out distributed training research in a low-bandwidth environment. This paper proposes a Randomized Decentralized Parallel Stochastic Gradient Descent (RD-PSGD) method for distributed training in a low-bandwidth network. To reduce the communication cost, each node in RD-PSGD just randomly transfers part of the information of the local intelligent model to its neighborhood. We further speed up the algorithm by optimizing the programming of random index generation and parameter extraction. For the first time, we theoretically analyze the convergence property of the proposed RD-PSGD and validate the advantage of this method by simulation experiments on various distributed training tasks for image classification on different benchmark datasets and deep learning network architectures. The results show that RD-PSGD can effectively save the time and bandwidth cost of distributed training and reduce the complexity of parameter selection compared with the TopK-based method. The method proposed in this paper provides a new perspective for the study of onboard intelligent processing, especially for online learning on a smart satellite constellation.
UrbanPose: A New Benchmark for VRU Pose Estimation in Urban Traffic Scenes

Human pose, serving as a robust appearance-invariant mid-level feature, has proven to be effective and efficient for human action recognition and intention estimation. Pose features also have a great potential to improve trajectory prediction for the Vulnerable Road User (VRU) in ADAS or automated driving applications. However, the lack of highly diverse and large VRU pose datasets makes a transfer and application to the VRU rather difficult. This paper introduces the Tsinghua-Daimler Urban Pose dataset (TDUP), a large-scale 2D VRU pose image dataset collected in Chinese urban traffic environments from on-board a moving vehicle. The TDUP dataset contains 21k images with more than 90k high-quality, manually labeled VRU bounding boxes with pose keypoint annotations and additional tags. We optimize four state-of-the-art deep learning approaches (AlphaPose, Mask R-CNN, Pose-SSD and PitPaf) to serve as baselines for the new pose estimation benchmark. We further analyze the effect of using large pre-training datasets and different data proportions as well as optional labeled information during training. Our new benchmark is expected to lay the foundation for further VRU pose studies and to empower the development of accurate VRU trajectory prediction methods in complex urban traffic scenes. The dataset (including an evaluation server) is available on www.urbanpose-dataset.com for non-commercial scientific use.
Towards a Framework for Understanding the Performance of Blockchains

Blockchain and Distributed Ledger Technology (DLT) appears to be at a worldwide threshold of acceptance and adoption. Since their inception, several innovative projects have been proposing solutions to the blockchain trilemma, improving blockchain features and its technical limitations. However, the adoption of blockchain as a technology or a software component, requires a comprehensive understanding and characterization of their technical principles and characteristics. The latter introduces an uncertainty for an organization to decide which blockchain protocol best meets its needs and demands. In general, there is a lack of proper testing and software engineering practices for assessing the usage of blockchains usage and understanding their performance. Towards that direction, this paper presents an architecture for a blockchain benchmarking framework which aims at the deployment and evaluation of different blockchain protocols, focusing on different aspects such as security and scalability. A set of modules is introduced for testing and evaluating the behaviour of blockchain protocols under different test scenarios.
Entity and Relation Matching Consensus for Entity Alignment

Entity alignment aims to match synonymous entities across different knowledge graphs, which is a fundamental task for knowledge integration. Recently, researchers have devoted to leveraging rich information within relations to enhance entity alignment. They explicitly incorporate relations in entity representation and alignment, demonstrating remarkable results. However, affected by the semantic assumptions from early works, these works represent a relation by combining all the entities it connects, ignoring the semantic independence between entity and relation. Moreover, since these works perform alignment by comparing embedding similarity, they fail to consider a graph level alignment and tend to find local false correspondences.
Knowledge Distillation for Low-Power Object Detection: A Simple Technique and Its Extensions for Training Compact Models Using Unlabeled Data

The existing solutions for object detection distillation rely on the availability of both a teacher model and ground-truth labels. We propose a new perspective to relax this constraint. In our framework, a student is first trained with pseudo labels generated by the teacher, and then fine-tuned using labeled data, if any available. Extensive experiments demonstrate improvements over existing object detection distillation algorithms. In addition, decoupling the teacher and ground-truth distillation in this framework provides interesting properties such as: 1) using unlabeled data to further improve the student’s performance, 2) combining multiple teacher models of different architectures, even with different object categories, and 3) reducing the need for labeled data (with only 20% of COCO labels, this method achieves the same performance as the model trained on the entire set of labels). Furthermore, a by-product of this approach is the potential usage for domain adaptation. We verify these properties through extensive experiments.
Semi-supervised Learning Framework for UAV Detection

The use of supervised learning with various sensing techniques such as audio, visual imaging, thermal sensing, RADAR, and radio frequency (RF) have been widely applied in the detection of unmanned aerial vehicles (UAV) in an environment. However, little or no attention has been given to the application of unsupervised or semi-supervised algorithms for UAV detection. In this paper, we propose a semi-supervised technique and architecture for detecting UAVs in an environment by exploiting the RF signals (i.e., fingerprints) between a UAV and its flight-controller communication under wireless inference such as Bluetooth and WiFi. By decomposing the RF signals using a two-level wavelet packet transform, we estimated the second moment statistic (i.e., variance) of the coefficients in each packet as a feature set. We developed a local outlier factor model as the UAV detection algorithm using the coefficient variances of the wavelet packets from WiFi and Bluetooth signals. When detecting the presence of RF-based UAV, we achieved an accuracy of 96.7% and 86% at a signal-to-noise ratio of 30 dB and 18 dB, respectively. The application of this approach is not limited to UAV detection as it can be extended to the detection of rogue RF devices in an environment.
Emergence and structure of decentralised trade networks around dark web marketplaces

Dark web marketplaces (DWMs) are online platforms that facilitate illicit trade among millions of users generating billions of dollars in annual revenue. Recently, two interview-based studies have suggested that DWMs may also promote the emergence of direct user-to-user (U2U) trading relationships. Here, we carefully investigate and quantify the scale of U2U trading around DWMs by analysing 31 million Bitcoin transactions among users of 40 DWMs between June 2011 and Jan 2021. We find that half of the DWM users trade through U2U pairs generating a total trading volume greater than DWMs themselves. We then show that hundreds of thousands of DWM users form stable trading pairs that are persistent over time. Users in such stable pairs turn out to be the ones with the largest trading volume on DWMs. Then, we show that new U2U pairs often form while both users are active on the same DWM, suggesting the marketplace may serve as a catalyst for new direct trading relationships. Finally, we reveal that stable U2U pairs tend to survive DWM closures and that they were not affected by COVID-19, indicating that their trading activity is resilient to external shocks. Our work unveils sophisticated patterns of trade emerging in the dark web and highlights the importance of investigating user behaviour beyond the immediate buyer-seller network on a single marketplace.
Resilient Development of Models and Methods in Computing Space

The paper is devoted to the analysis of computer models and methods that dynamically evolve and therefore allow tracing certain patterns in their development. The considered examples of evolution testify to the objective processes of their integration into the natural world by structuring for its parallelism and fuzziness. These beacons determine the vector of resilient development, which is shown for the model of number, arithmetic operations, calculation results, on-line testing of digital circuits, safety-related systems and their problems from the standpoint of a resource-based approach. The mechanism of development of computer models by diversifying their concepts is shown. An example of the development of a circuit solution in its on-line testing based on the diversification of the values of the calculated results is given. Comparison of the obtained solutions, analyzed within the framework of the model of exact and approximate data, is carried out.
Automating incremental graph processing with flexible memoization

The ever-growing amount of dynamic graph data demands efficient techniques of incremental graph processing. However, incremental graph algorithms are challenging to develop. Existing approaches usually require users to manually design nontrivial incremental operators, or choose different memoization strategies for certain specific types of computation, limiting the usability and generality. In light of these challenges, we propose Ingress, an automated system for &lt;u&gt;in&lt;/u&gt;cremental &lt;u&gt;g&lt;/u&gt;raph proc&lt;u&gt;ess&lt;/u&gt;ing. Ingress is able to incrementalize batch vertex-centric algorithms into their incremental counterparts as a whole, without the need of redesigned logic or data structures from users. Underlying Ingress is an automated incrementalization framework equipped with four different memoization policies, to support all kinds of vertex-centric computations with optimized memory utilization. We identify sufficient conditions for the applicability of these policies. Ingress chooses the best-fit policy for a given algorithm automatically by verifying these conditions. In addition to the ease-of-use and generalization, Ingress outperforms state-of-the-art incremental graph systems by 15.93X on average (up to 147.14X) in efficiency.
Smart Contracts Refinement for Gas Optimization

Smart contracts facilitate the execution of programmable code on a blockchain. The cost for executing smart contract code is metered using gas - the exact amount of which is based on the computational complexity of the underlying smart contract. Hence, it is imperative to optimize smart contract code to reduce gas consumption and, in some instances, to even avoid malicious attacks. In this paper, we propose an approach to optimize the gas consumption of smart contracts, specifically loop control structures. We present a prototype implementation of our approach using off-the-shelf tools for Solidity smart contracts. We experimentally evaluate our technique using 72 Solidity smart contracts. Our evaluation demonstrates the average gas cost savings per transaction to be around 23,943 gas units, or an equivalent 21% decrease in gas costs. Although the approach causes a slight increase in deployment costs due to the additional internal functions, this is only 16,710 gas units on the average, or a 5% of the total deployment cost. As this overhead remains quite reasonable when compared to the gas cost savings for each transaction, it also confirms the efficacy, practicality and effectiveness of the proposed methodology.
You Look so Different: Finding Structural Clones and Subclones in Java Source Code

Code reuse and copying is a widespread practice in software development. Detecting code clones, i.e., identical or similar fragments of code, is thus an important task with many applications, ranging from code search to bug finding and malware detection. In this paper, we propose a new approach to detect code clones in source code. Instead of analyzing the code tokens or syntax, our technique is based upon control flow analysis and dominator trees. In this way, the technique not only detects exact and syntactically similar near-miss code clones but also two new types of clones, which we characterize as structural code clones and subclones. For implementation and evaluation, we have developed the tool StoneDetector, which finds code clones in Java source code. StoneDetector performs competitive with the state of the art as measured on the BigCloneBench benchmark and finds more structural clones and subclones.
Face Restoration via Plug-and-Play 3D Facial Priors

State-of-the-art face restoration methods employ deep convolutional neural networks (CNNs) to learn a mapping between degraded and sharp facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and only deal with task-specific face restoration (e.g., face super-resolution or deblurring). In this paper, we propose cross-tasks and cross-models plug-and-play 3D facial priors to explicitly embed the network with the sharp facial structures for general face restoration tasks. Our 3D priors are the first to explore 3D morphable knowledge based on the fusion of parametric descriptions of face attributes (e.g., identity, facial expression, texture, illumination, and face pose). Furthermore, the priors can easily be incorporated into any network and are very efficient in improving the performance and accelerating the convergence speed. Firstly, a 3D face rendering branch is set up to obtain 3D priors of salient facial structures and identity knowledge. Secondly, for better exploiting this hierarchical information (i.e., intensity similarity, 3D facial structure, and identity content), a spatial attention module is designed for the image restoration problems. Extensive face restoration experiments including face super-resolution and deblurring demonstrate that the proposed 3D priors achieve superior face restoration results over the state-of-the-art algorithms.
<i>𝜀</i> -weakened robustness of deep neural networks

Deep neural networks have been widely adopted for many real-world applications and their reliability has been widely concerned. This paper introduces a notion of ε-weakened robustness (briefly as ε-robustness) for analyzing the reliability and some related quality issues of deep neural networks. Unlike the conventional robustness, which focuses on the "perfect" safe region in the absence of adversarial examples, ε-weakened robustness focuses on the region where the proportion of adversarial examples is bounded by user-specified ε. The smaller the value of ε is, the less vulnerable a neural network is to be fooled by a random perturbation. Under such a robustness definition, we can give conclusive results for the regions where conventional robustness ignores. We propose an efficient testing-based method with user-controllable error bounds to analyze it. The time complexity of our algorithms is polynomial in the dimension and size of the network. So, they are scalable to large networks. One of the important applications of our ε-robustness is to build a robustness enhanced classifier to resist adversarial attack. Based on this theory, we design a robustness enhancement method with good interpretability and rigorous robustness guarantee. The basic idea is to resist perturbation with perturbation. Experimental results show that our robustness enhancement method can significantly improve the ability of deep models to resist adversarial attacks while maintaining the prediction performance on the original clean data. Besides, we also show the other potential value of ε-robustness in neural networks analysis.
Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation

In this paper, we address the problem of adaptive path planning for accurate semantic segmentation of terrain using unmanned aerial vehicles (UAVs). The usage of UAVs for terrain monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. However, a key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. To address this, we propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas on the terrain with fine details as they are detected in incoming images. This enables us to perform close inspections at low altitudes only where required, without wasting energy on exhaustive mapping at maximum resolution. A key feature of our approach is a new accuracy model for deep learning-based architectures that captures the relationship between UAV altitude and semantic segmentation accuracy. We evaluate our approach on the application of crop/weed segmentation in precision agriculture using real-world field data.
Single Image Haze Removal Based on transmission map estimation using Encoder-Decoder based deep learning architecture

Haze removal is an essential requirement in autonomous vehicle applications for identifying different objects on the road. Most of the available techniques are based on different constraints/priors. The important parameters required for recovering the ground truth from hazy image are transmission map and air light. In this paper, we proposed a learning-based Encoder-Decoder deep learning architecture for transmission map estimation. Based on the assumption that at least twenty percent of the outdoor image includes with sky region and hence airlight is calculated as average of the twenty percent brightest pixels of the image. These two parameters namely transmission map and airlight were applied in atmospheric scattering model for ground truth image recovery. In Encoder-Decoder architecture, Max pooling layer, dropout layer was used for feature learning and efficient generalization respectively. The proposed architecture was trained on different datasets like NYU Depth data set, FRIDA and RESIDE Dataset for better generalization on unseen data. Experimental results shows that the proposed method has shown better performance compared to the existing state of the art methods.
Discovering Interpretable Machine Learning Models in Parallel Coordinates

This paper contributes to interpretable machine learning via visual knowledge discovery in parallel coordinates. The concepts of hypercubes and hyper-blocks are used as easily understandable by end-users in the visual form in parallel coordinates. The Hyper algorithm for classification with mixed and pure hyper-blocks (HBs) is proposed to discover hyper-blocks interactively and automatically in individual, multiple, overlapping, and non-overlapping setting. The combination of hyper-blocks with linguistic description of visual patterns is presented too. It is shown that Hyper models generalize decision trees. The Hyper algorithm was tested on the benchmark data from UCI ML repository. It allowed discovering pure and mixed HBs with all data and then with 10-fold cross validation. The links between hyper-blocks, dimension reduction and visualization are established. Major benefits of hyper-block technology and the Hyper algorithm are in their ability to discover and observe hyperblocks by end-users including side by side visualizations making patterns visible for all classes. Another advantage of sets of HBs relative to the decision trees is the ability to avoid both data overgeneralization and overfitting.
Learn to Predict Sets Using Feed-Forward Neural Networks

This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a set is not fixed in advance. Many real-world problems, such as image tagging and object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors, matrices or tensors. We present a novel approach for learning to predict sets with unknown permutation and cardinality using deep neural networks. In our formulation we define a likelihood for a set distribution represented by a) two discrete distributions defining the set cardinally and permutation variables, and b) a joint distribution over set elements with a fixed cardinality. Depending on the problem under consideration, we define different training models for set prediction using deep neural networks. We demonstrate the validity of our set formulations on relevant vision problems such as: 1) multi-label image classification where we outperform the other competing methods on the PASCAL VOC and MS COCO datasets, 2) object detection, for which our formulation outperforms popular state-of-the-art detectors, and 3) a complex CAPTCHA test, where we observe that, surprisingly, our set-based network acquired the ability of mimicking arithmetics without any rules being coded.