OpenRaiser/Intern-Atlas huggingface dataset:
Flexible Software Protection
Venue: arXiv cs.CR 2020
Year: 2020

To counter software reverse engineering or tampering, software obfuscation tools can be used. However, such tools to a large degree hard-code how the obfuscations are deployed. They hence lack resilience and stealth in the face of many attacks. To counter this problem, we propose the novel concept of flexible obfuscators, which implement protections in terms of data structures and APIs already present in the application to be protected. The protections are hence tailored to the application in which they are deployed, making them less learnable and less distinguishable. In our research, we concretized the flexible protection concept for opaque predicates. We designed an interface to enable the reuse of existing data structures and APIs in injected opaque predicates, we analyzed their resilience and stealth, we implemented a proof-of-concept flexible obfuscator, and we evaluated it on a number of real-world use cases. This paper presents an in-depth motivation for our work, the design of the interface, an in-depth security analysis, and a feasibility report based on our experimental evaluation. The findings are that flexible opaque predicates indeed provide strong resilience and improved stealth, but also that their deployment is costly, and that they should hence be used sparsely to protect only the most security-sensitive code fragments that do not dominate performance. Flexible obfuscation therefor delivers an expensive but also more durable new weapon in the ever ongoing software protection arms race.
Automatic Scansion of Spanish Poetry without Syllabification
Venue: arXiv cs.CL 2020
Year: 2020

In recent years, several systems of automated metric analysis of Spanish poetry have emerged. These systems rely on complex methods of syllabification and stress assignment, which use PoS-tagging libraries, whose computational cost is high. This cost increases with the calculation of metric ambiguities. Furthermore, they do not consider determining issues in syllabic count such as the phenomena of compensation between hemistichs of verses of more than eleven syllables. However, it is possible to carry out an informative and accurate metric analysis without using these costly methods. We propose an algorithm that performs accurate scansion (number of syllables, stress pattern and type of verse) without syllabification. It addresses metric ambiguities and takes into account the hemistichs compensation. Our algorithm outperforms the current state of the art by 2% in fixed-metre poetry, and 25% in mixed-metre poetry. It also runs 21 and 25 times faster, respectively. Finally, a desktop application is offered as a tool for researchers of Spanish poetry.
Enabling Secure and Effective Biomedical Data Sharing through Cyberinfrastructure Gateways
Venue: arXiv cs.CR 2020
Year: 2020

Dynaswap project reports on developing a coherently integrated and trustworthy holistic secure workflow protection architecture for cyberinfrastructures which can be used on virtual machines deployed through cyberinfrastructure (CI) services such as JetStream. This service creates a user-friendly cloud environment designed to give researchers access to interactive computing and data analysis resources on demand. The Dynaswap cybersecurity architecture supports roles, role hierarchies, and data hierarchies, as well as dynamic changes of roles and hierarchical relations within the scientific infrastructure. Dynaswap combines existing cutting-edge security frameworks (including an Authentication Authorization-Accounting framework, Multi-Factor Authentication, Secure Digital Provenance, and Blockchain) with advanced security tools (e.g., Biometric-Capsule, Cryptography-based Hierarchical Access Control, and Dual-level Key Management). The CI is being validated in life-science research environments and in the education settings of Health Informatics.
Evaluating structural edge importance in temporal networks
Venue: arXiv cs.CE 2020
Year: 2020

To monitor risk in temporal financial networks, we need to understand how individual behaviours affect the global evolution of networks. Here we define a structural importance metric - which we denote as $l_e$ - for the edges of a network. The metric is based on perturbing the adjacency matrix and observing the resultant change in its largest eigenvalues. We then propose a model of network evolution where this metric controls the probabilities of subsequent edge changes. We show using synthetic data how the parameters of the model are related to the capability of predicting whether an edge will change from its value of $l_e$. We then estimate the model parameters associated with five real financial and social networks, and we study their predictability. These methods have application in financial regulation whereby it is important to understand how individual changes to financial networks will impact their global behaviour. It also provides fundamental insights into spectral predictability in networks, and it demonstrates how spectral perturbations can be a useful tool in understanding the interplay between micro and macro features of networks.
Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset
Venue: arXiv cs.CV 2020
Year: 2020

We introduce a new outdoor urban 3D pointcloud dataset, covering a total area of 2.7 $km^2$, sampled from three Swiss cities with different characteristics. The dataset is manually annotated for semantic segmentation with per-point labels, and is built using photogrammetry from images acquired by multirotors equipped with high-resolution cameras. In contrast to datasets acquired with ground LiDAR sensors, the resulting point clouds are uniformly dense and complete, and are useful to disparate applications, including autonomous driving, gaming and smart city planning. As a benchmark, we report quantitative results of PointNet++, an established point-based deep 3D semantic segmentation model; on this model, we additionally study the impact of using different cities for model generalization.
K-Means Kernel Classifier
Venue: arXiv cs.LG 2020
Year: 2020

We combine K-means clustering with the least-squares kernel classification method. K-means clustering is used to extract a set of representative vectors for each class. The least-squares kernel method uses these representative vectors as a training set for the classification task. We show that this combination of unsupervised and supervised learning algorithms performs very well, and we illustrate this approach using the MNIST dataset
Rotation Equivariant Siamese Networks for Tracking
Venue: arXiv cs.CV 2020
Year: 2020

Rotation is among the long prevailing, yet still unresolved, hard challenges encountered in visual object tracking. The existing deep learning-based tracking algorithms use regular CNNs that are inherently translation equivariant, but not designed to tackle rotations. In this paper, we first demonstrate that in the presence of rotation instances in videos, the performance of existing trackers is severely affected. To circumvent the adverse effect of rotations, we present rotation-equivariant Siamese networks (RE-SiamNets), built through the use of group-equivariant convolutional layers comprising steerable filters. SiamNets allow estimating the change in orientation of the object in an unsupervised manner, thereby facilitating its use in relative 2D pose estimation as well. We further show that this change in orientation can be used to impose an additional motion constraint in Siamese tracking through imposing restriction on the change in orientation between two consecutive frames. For benchmarking, we present Rotation Tracking Benchmark (RTB), a dataset comprising a set of videos with rotation instances. Through experiments on two popular Siamese architectures, we show that RE-SiamNets handle the problem of rotation very well and out-perform their regular counterparts. Further, RE-SiamNets can accurately estimate the relative change in pose of the target in an unsupervised fashion, namely the in-plane rotation the target has sustained with respect to the reference frame.
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user's private attribute inference in social networks, and community detection. Various methods such as pairwise Markov Random Fields (pMRF) and graph neural networks were developed for semi-supervised node classification. pMRF is more efficient than graph neural networks. However, existing pMRF-based methods are less accurate than graph neural networks, due to a key limitation that they assume a heuristics-based constant edge potential for all edges. In this work, we aim to address the key limitation of existing pMRF-based methods. In particular, we propose to learn edge potentials for pMRF. Our evaluation results on various types of graph datasets show that our optimized pMRF-based method consistently outperforms existing graph neural networks in terms of both accuracy and efficiency. Our results highlight that previous work may have underestimated the power of pMRF for semi-supervised node classification.
Learning with Retrospection
Venue: arXiv cs.LG 2020
Year: 2020

Deep neural networks have been successfully deployed in various domains of artificial intelligence, including computer vision and natural language processing. We observe that the current standard procedure for training DNNs discards all the learned information in the past epochs except the current learned weights. An interesting question is: is this discarded information indeed useless? We argue that the discarded information can benefit the subsequent training. In this paper, we propose learning with retrospection (LWR) which makes use of the learned information in the past epochs to guide the subsequent training. LWR is a simple yet effective training framework to improve accuracies, calibration, and robustness of DNNs without introducing any additional network parameters or inference cost, only with a negligible training overhead. Extensive experiments on several benchmark datasets demonstrate the superiority of LWR for training DNNs.
Upper Confidence Bounds for Combining Stochastic Bandits
Venue: arXiv cs.LG 2020
Year: 2020

We provide a simple method to combine stochastic bandit algorithms. Our approach is based on a "meta-UCB" procedure that treats each of $N$ individual bandit algorithms as arms in a higher-level $N$-armed bandit problem that we solve with a variant of the classic UCB algorithm. Our final regret depends only on the regret of the base algorithm with the best regret in hindsight. This approach provides an easy and intuitive alternative strategy to the CORRAL algorithm for adversarial bandits, without requiring the stability conditions imposed by CORRAL on the base algorithms. Our results match lower bounds in several settings, and we provide empirical validation of our algorithm on misspecified linear bandit and model selection problems.
Hausdorff Point Convolution with Geometric Priors
Venue: arXiv cs.CV 2020
Year: 2020

Without a shape-aware response, it is hard to characterize the 3D geometry of a point cloud efficiently with a compact set of kernels. In this paper, we advocate the use of Hausdorff distance as a shape-aware distance measure for calculating point convolutional responses. The technique we present, coined Hausdorff Point Convolution (HPC), is shape-aware. We show that HPC constitutes a powerful point feature learning with a rather compact set of only four types of geometric priors as kernels. We further develop a HPC-based deep neural network (HPC-DNN). Task-specific learning can be achieved by tuning the network weights for combining the shortest distances between input and kernel point sets. We also realize hierarchical feature learning by designing a multi-kernel HPC for multi-scale feature encoding. Extensive experiments demonstrate that HPC-DNN outperforms strong point convolution baselines (e.g., KPConv), achieving 2.8% mIoU performance boost on S3DIS and 1.5% on SemanticKITTI for semantic segmentation task.
The effect of the toplolgy adaptation on search performance in overlay network
Venue: arXiv cs.NI 2020
Year: 2020

The work presented in this research paper has focused on the effect of network to-pology adaptation on search performance in peer to peer overlay network. Guided search vs. blind search have been studied with the aim of improving the search re-sults and decreasing the time a search message would take to reach the destination. The network has been formulated as a bi-direction graph with vertices represent network nodes and edges represent connections. The level of network subject of this study is on application layer, that means two nodes are connected if they know each other contact addresses. A good example of this kind of network is the social network where all the lower layers are hidden from the end user. Two different search algorithms have been studied under these circumstances, namely: depth first algorithm and breadth first algorithm. Furthermore, the algorithms performance is examined under random topology (scale free network topology) and under topology adaptation. A simulation scenario has been designed to investigate the fidelity of the system and study the suggested solutions. Simulation results have shown that the search algorithms are performing better under topology adaptation in terms of re-sults quality and search time.
Graph Theory in the Classification of Information Systems
Venue: arXiv cs.DM 2020
Year: 2020

Risk classification plays an important role in many regulations and standards. However, a general method that provides an optimal classification has not been proposed yet. Also, the criteria of optimality are not defined in these regulations. In this work, we will propose a mathematical model that is sufficient to describe this problem, and we also propose an algorithm that classifies graph vertices based on their risk value in polynomial time.
SoK: Lending Pools in Decentralized Finance
Venue: arXiv cs.CR 2020
Year: 2020

Lending pools are decentralized applications which allow mutually untrusted users to lend and borrow crypto-assets. These applications feature complex, highly parametric incentive mechanisms to equilibrate the loan market. This complexity makes the behaviour of lending pools difficult to understand and to predict: indeed, ineffective incentives and attacks could potentially lead to emergent unwanted behaviours. Reasoning about lending pools is made even harder by the lack of executable models of their behaviour: to precisely understand how users interact with lending pools, eventually one has to inspect their implementations, where the incentive mechanisms are intertwined with low-level implementation details. Further, the variety of existing implementations makes it difficult to distill the common aspects of lending pools. We systematize the existing knowledge about lending pools, leveraging a new formal model of interactions with users, which reflects the archetypal features of mainstream implementations. This enables us to prove some general properties of lending pools, such as the correct handling of funds, and to precisely describe vulnerabilities and attacks. We also discuss the role of lending pools in the broader context of decentralized finance.
Detecting Hateful Memes Using a Multimodal Deep Ensemble
Venue: arXiv cs.LG 2020
Year: 2020

While significant progress has been made using machine learning algorithms to detect hate speech, important technical challenges still remain to be solved in order to bring their performance closer to human accuracy. We investigate several of the most recent visual-linguistic Transformer architectures and propose improvements to increase their performance for this task. The proposed model outperforms the baselines by a large margin and ranks 5$^{th}$ on the leaderboard out of 3,100+ participants.
Statistical modeling of pneumothorax deformation by mapping CT and cone-beam CT images
Venue: arXiv cs.CG 2020
Year: 2020

In this study, we introduce statistical modeling methods for pneumothorax deformation using paired cone-beam computed tomography (CT) images. We designed a deformable mesh registration framework for shape changes involving non-linear deformation and rotation of the lungs. The registered meshes with local correspondences are available for both surgical guidance in thoracoscopic surgery and building statistical deformation models with inter-patient variations. In addition, a kernel-based deformation learning framework is proposed to reconstruct intraoperative deflated states of the lung from the preoperative CT models. This paper reports the findings of pneumothorax deformation and evaluation results of the kernel-based deformation framework.
Linear complementary pair of group codes over finite principal ideal rings
Venue: arXiv cs.IT 2020
Year: 2020

A pair $(C, D)$ of group codes over group algebra $R[G]$ is called a linear complementary pair (LCP) if $C \oplus D =R[G]$, where $R$ is a finite principal ideal ring, and $G$ is a finite group. We provide a necessary and sufficient condition for a pair $(C, D)$ of group codes over group algebra $R[G]$ to be LCP. Then we prove that if $C$ and $D$ are both group codes over $R[G]$, then $C$ and $D^{\perp}$ are permutation equivalent.
Robotic Following of Flexible Extended Objects: Relevant Technical Facts on the Kinematics of a Moving Continuum
Venue: arXiv cs.RO 2020
Year: 2020

The paper offers general technical facts on the kinematics of a moving continuum involved in research on robotic following of flexible extended objects.
Leave Zero Out: Towards a No-Cross-Validation Approach for Model Selection
Venue: arXiv cs.LG 2020
Year: 2020

As the main workhorse for model selection, Cross Validation (CV) has achieved an empirical success due to its simplicity and intuitiveness. However, despite its ubiquitous role, CV often falls into the following notorious dilemmas. On the one hand, for small data cases, CV suffers a conservatively biased estimation, since some part of the limited data has to hold out for validation. On the other hand, for large data cases, CV tends to be extremely cumbersome, e.g., intolerant time-consuming, due to the repeated training procedures. Naturally, a straightforward ambition for CV is to validate the models with far less computational cost, while making full use of the entire given data-set for training. Thus, instead of holding out the given data, a cheap and theoretically guaranteed auxiliary/augmented validation is derived strategically in this paper. Such an embarrassingly simple strategy only needs to train models on the entire given data-set once, making the model-selection considerably efficient. In addition, the proposed validation approach is suitable for a wide range of learning settings due to the independence of both augmentation and out-of-sample estimation on learning process. In the end, we demonstrate the accuracy and computational benefits of our proposed method by extensive evaluation on multiple data-sets, models and tasks.
Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains
Venue: arXiv cs.RO 2020
Year: 2020

Robot gait optimization is the task of generating an optimal control trajectory under various internal and external constraints. Given the high dimensions of control space, this problem is particularly challenging for multi-legged robots walking in complex and unknown environments. Existing literatures often regard the gait generation as an optimization problem and solve the gait optimization from scratch for robots walking in a specific environment. However, such approaches do not consider the use of pre-acquired knowledge which can be useful in improving the quality and speed of motion generation in complex environments. To address the issue, this paper proposes a transfer learning-based evolutionary framework for multi-objective gait optimization, named Tr-GO. The idea is to initialize a high-quality population by using the technique of transfer learning, so any kind of population-based optimization algorithms can be seamlessly integrated into this framework. The advantage is that the generated gait can not only dynamically adapt to different environments and tasks, but also simultaneously satisfy multiple design specifications (e.g., speed, stability). The experimental results show the effectiveness of the proposed framework for the gait optimization problem based on three multi-objective evolutionary algorithms: NSGA-II, RM-MEDA and MOPSO. When transferring the pre-acquired knowledge from the plain terrain to various inclined and rugged ones, the proposed Tr-GO framework accelerates the evolution process by a minimum of 3-4 times compared with non-transferred scenarios.
Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training
Venue: arXiv cs.AI 2020
Year: 2020

Online reviews are a vital source of information when purchasing a service or a product. Opinion spammers manipulate these reviews, deliberately altering the overall perception of the service. Though there exists a corpus of online reviews, only a few have been labeled as spam or non-spam, making it difficult to train spam detection models. We propose an adversarial training mechanism leveraging the capabilities of Generative Pre-Training 2 (GPT-2) for classifying opinion spam with limited labeled data and a large set of unlabeled data. Experiments on TripAdvisor and YelpZip datasets show that the proposed model outperforms state-of-the-art techniques by at least 7% in terms of accuracy when labeled data is limited. The proposed model can also generate synthetic spam/non-spam reviews with reasonable perplexity, thereby, providing additional labeled data during training.
GIS-Based Estimation of Seasonal Solar Energy Potential for Parking Lots and Roads
Venue: arXiv cs.OH 2020
Year: 2020

The amount of sun cast on roads and parking lots determines the charging opportunities for solar vehicles and impacts the efficiency of conventional vehicles. Estimates of solar energy potential on urban surfaces to assess parking and driving conditions need to account for the shadows cast by surrounding trees and buildings. However, though existing GIS tools can calculate solar potential on surfaces that have buildings and trees, these tools do not estimate the conditions beneath trees and do not consider the seasonal changes in deciduous trees. We introduce a new approach to address these factors using pixel substitution and a light penetration factor. In this paper, we describe how to integrate these techniques into a workflow for computing solar potential estimates for parking and driving conditions. We demonstrate the methodology in an urban setting in North Carolina that includes a mixture of urban structures and trees. We provide code samples so that this workflow is easily repeatable. The solar maps produced with our method are a useful resource for planning solar vehicle parking and routing, and identifying shaded conditions for conventional vehicles.