OpenRaiser/Intern-Atlas huggingface dataset:
Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms
Venue: arXiv cs.CR 2021
Year: 2021

With the rapid advancement of technology, different biometric user authentication, and identification systems are emerging. Traditional biometric systems like face, fingerprint, and iris recognition, keystroke dynamics, etc. are prone to cyber-attacks and suffer from different disadvantages. Electroencephalography (EEG) based authentication has shown promise in overcoming these limitations. However, EEG-based authentication is less accurate due to signal variability at different psychological and physiological conditions. On the other hand, keystroke dynamics-based identification offers high accuracy but suffers from different spoofing attacks. To overcome these challenges, we propose a novel multimodal biometric system combining EEG and keystroke dynamics. Firstly, a dataset was created by acquiring both keystroke dynamics and EEG signals from 10 users with 500 trials per user at 10 different sessions. Different statistical, time, and frequency domain features were extracted and ranked from the EEG signals and key features were extracted from the keystroke dynamics. Different classifiers were trained, validated, and tested for both individual and combined modalities for two different classification strategies - personalized and generalized. Results show that very high accuracy can be achieved both in generalized and personalized cases for the combination of EEG and keystroke dynamics. The identification and authentication accuracies were found to be 99.80% and 99.68% for Extreme Gradient Boosting (XGBoost) and Random Forest classifiers, respectively which outperform the individual modalities with a significant margin (around 5 percent). We also developed a binary template matching-based algorithm, which gives 93.64% accuracy 6X faster. The proposed method is secured and reliable for any kind of biometric authentication.
Non-ambiguous trees: new results and generalisation (Full version)
Venue: arXiv cs.DM 2021
Year: 2021

We present a new definition of non-ambiguous trees (NATs) as labelled binary trees. We thus get a differential equation whose solution can be described combinatorially. This yields a new formula for the number of NATs. We also obtain q-versions of our formula. We finally generalise NATs to higher dimension.
Vectorial Parameterizations of Pose
Venue: arXiv cs.RO 2021
Year: 2021

Robotics and computer vision problems commonly require handling rigid-body motions comprising translation and rotation - together referred to as pose. In some situations, a vectorial parameterization of pose can be useful, where elements of a vector space are surjectively mapped to a matrix Lie group. For example, these vectorial representations can be employed for optimization as well as uncertainty representation on groups. The most common mapping is the matrix exponential, which maps elements of a Lie algebra onto the associated Lie group. However, this choice is not unique. It has been previously shown how to characterize all such vectorial parameterizations for SO(3), the group of rotations. Some results are also known for the group of poses, where it is possible to build a family of vectorial mappings that includes the matrix exponential as well as the Cayley transformation. We extend what is known for these pose mappings to the 4 x 4 representation common in robotics, and also demonstrate three different examples of the proposed pose mappings: (i) pose interpolation, (ii) pose servoing control, and (iii) pose estimation in a pointcloud alignment problem. In the pointcloud alignment problem our results lead to a new algorithm based on the Cayley transformation, which we call CayPer.
XAMG: A library for solving linear systems with multiple right-hand side vectors
Venue: arXiv cs.MS 2021
Year: 2021

This paper presents the XAMG library for solving large sparse systems of linear algebraic equations with multiple right-hand side vectors. The library specializes but is not limited to the solution of linear systems obtained from the discretization of elliptic differential equations. A corresponding set of numerical methods includes Krylov subspace, algebraic multigrid, Jacobi, Gauss-Seidel, and Chebyshev iterative methods. The parallelization is implemented with MPI+POSIX shared memory hybrid programming model, which introduces a three-level hierarchical decomposition using the corresponding per-level synchronization and communication primitives. The code contains a number of optimizations, including the multilevel data segmentation, compression of indices, mixed-precision floating-point calculations, vector status flags, and others. The XAMG library uses the program code of the well-known hypre library to construct the multigrid matrix hierarchy. The XAMG's own implementation for the solve phase of the iterative methods provides up to a twofold speedup compared to hypre for the tests performed. Additionally, XAMG provides extended functionality to solve systems with multiple right-hand side vectors.
Monocular Quasi-Dense 3D Object Tracking
Venue: arXiv cs.CV 2021
Year: 2021

A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer's actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform. The object association leverages quasi-dense similarity learning to identify objects in various poses and viewpoints with appearance cues only. After initial 2D association, we further utilize 3D bounding boxes depth-ordering heuristics for robust instance association and motion-based 3D trajectory prediction for re-identification of occluded vehicles. In the end, an LSTM-based object velocity learning module aggregates the long-term trajectory information for more accurate motion extrapolation. Experiments on our proposed simulation data and real-world benchmarks, including KITTI, nuScenes, and Waymo datasets, show that our tracking framework offers robust object association and tracking on urban-driving scenarios. On the Waymo Open benchmark, we establish the first camera-only baseline in the 3D tracking and 3D detection challenges. Our quasi-dense 3D tracking pipeline achieves impressive improvements on the nuScenes 3D tracking benchmark with near five times tracking accuracy of the best vision-only submission among all published methods. Our code, data and trained models are available at https://github.com/SysCV/qd-3dt.
A Unified Game-Theoretic Interpretation of Adversarial Robustness
Venue: arXiv cs.LG 2021
Year: 2021

This paper provides a unified view to explain different adversarial attacks and defense methods, i.e. the view of multi-order interactions between input variables of DNNs. Based on the multi-order interaction, we discover that adversarial attacks mainly affect high-order interactions to fool the DNN. Furthermore, we find that the robustness of adversarially trained DNNs comes from category-specific low-order interactions. Our findings provide a potential method to unify adversarial perturbations and robustness, which can explain the existing defense methods in a principle way. Besides, our findings also make a revision of previous inaccurate understanding of the shape bias of adversarially learned features.
From Nano-Communications to Body Area Networks: A Perspective on Truly Personal Communications
Venue: arXiv cs.NI 2021
Year: 2021

This article presents an overview of future truly personal communications, ranging from networking inside the human body to the exchange of data with external wireless devices in the surrounding environment. At the nano- and micro-scales, communications can be realized with the aid of molecular mechanisms, Forster resonance energy transfer phenomenon, electromagnetic or ultrasound waves. At a larger scale, in the domain of Body Area Networks, a wide range of communication mechanisms is available, including smart-textiles, inductive- and body-couplings, ultrasounds, optical and wireless radio transmissions, a number of mature technologies existing already. The main goal of this article is to identify the potential mechanisms that can be exploited to provide interfaces in between nano- and micro-scale systems and Body Area Networks. These interfaces have to bridge the existing gap between the two worlds, in order to allow for truly personal communication systems to become a reality. The extraordinary applications of such systems are also discussed, as they are strong drivers of the research in this area.
Genetic Improvement of Routing Protocols for Delay Tolerant Networks
Venue: arXiv cs.NI 2021
Year: 2021

Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of e.g. (possibly, unmanned) vehicles and humans where, despite a lack of continuous connectivity, data must be transmitted while the network conditions change due to the nodes' mobility. In these contexts, routing is NP-hard and is usually solved by heuristic "store and forward" replication-based approaches, where multiple copies of the same message are moved and stored across nodes in the hope that at least one will reach its destination. Still, the existing routing protocols produce relatively low delivery probabilities. Here, we genetically improve two routing protocols widely adopted in DTNs, namely Epidemic and PRoPHET, in the attempt to optimize their delivery probability. First, we dissect them into their fundamental components, i.e., functionalities such as checking if a node can transfer data, or sending messages to all connections. Then, we apply Genetic Improvement (GI) to manipulate these components as terminal nodes of evolving trees. We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes, and find that GI produces consistent gains in delivery probability in four cases. We then verify if this improvement entails a worsening of other relevant network metrics, such as latency and buffer time. Finally, we compare the logics of the best evolved protocols with those of the baseline protocols, and we discuss the generalizability of the results across test cases.
Towards Risk Modeling for Collaborative AI
Venue: arXiv cs.SE 2021
Year: 2021

Collaborative AI systems aim at working together with humans in a shared space to achieve a common goal. This setting imposes potentially hazardous circumstances due to contacts that could harm human beings. Thus, building such systems with strong assurances of compliance with requirements domain specific standards and regulations is of greatest importance. Challenges associated with the achievement of this goal become even more severe when such systems rely on machine learning components rather than such as top-down rule-based AI. In this paper, we introduce a risk modeling approach tailored to Collaborative AI systems. The risk model includes goals, risk events and domain specific indicators that potentially expose humans to hazards. The risk model is then leveraged to drive assurance methods that feed in turn the risk model through insights extracted from run-time evidence. Our envisioned approach is described by means of a running example in the domain of Industry 4.0, where a robotic arm endowed with a visual perception component, implemented with machine learning, collaborates with a human operator for a production-relevant task.
3D Semantic Scene Completion: a Survey
Venue: arXiv cs.CV 2021
Year: 2021

Semantic Scene Completion (SSC) aims to jointly estimate the complete geometry and semantics of a scene, assuming partial sparse input. In the last years following the multiplication of large-scale 3D datasets, SSC has gained significant momentum in the research community because it holds unresolved challenges. Specifically, SSC lies in the ambiguous completion of large unobserved areas and the weak supervision signal of the ground truth. This led to a substantially increasing number of papers on the matter. This survey aims to identify, compare and analyze the techniques providing a critical analysis of the SSC literature on both methods and datasets. Throughout the paper, we provide an in-depth analysis of the existing works covering all choices made by the authors while highlighting the remaining avenues of research. SSC performance of the SoA on the most popular datasets is also evaluated and analyzed.
How Developers Choose Names
Venue: arXiv cs.SE 2021
Year: 2021

The names of variables and functions serve as implicit documentation and are instrumental for program comprehension. But choosing good meaningful names is hard. We perform a sequence of experiments in which a total of 334 subjects are required to choose names in given programming scenarios. The first experiment shows that the probability that two developers would select the same name is low: in the 47 instances in our experiments the median probability was only 6.9%. At the same time, given that a specific name is chosen, it is usually understood by the majority of developers. Analysis of the names given in the experiment suggests a model where naming is a (not necessarily cognizant or serial) three-step process: (1) selecting the concepts to include in the name, (2) choosing the words to represent each concept, and (3) constructing a name using these words. A followup experiment, using the same experimental setup, then checked whether using this model explicitly can improve the quality of names. The results were that names selected by subjects using the model were judged by two independent judges to be superior to names chosen in the original experiment by a ratio of two-to-one. Using the model appears to encourage the use of more concepts and longer names.
FES: A Fast Efficient Scalable QoS Prediction Framework
Venue: arXiv cs.AI 2021
Year: 2021

Quality-of-Service prediction of web service is an integral part of services computing due to its diverse applications in the various facets of a service life cycle, such as service composition, service selection, service recommendation. One of the primary objectives of designing a QoS prediction algorithm is to achieve satisfactory prediction accuracy. However, accuracy is not the only criteria to meet while developing a QoS prediction algorithm. The algorithm has to be faster in terms of prediction time so that it can be integrated into a real-time recommendation or composition system. The other important factor to consider while designing the prediction algorithm is scalability to ensure that the prediction algorithm can tackle large-scale datasets. The existing algorithms on QoS prediction often compromise on one goal while ensuring the others. In this paper, we propose a semi-offline QoS prediction model to achieve three important goals simultaneously: higher accuracy, faster prediction time, scalability. Here, we aim to predict the QoS value of service that varies across users. Our framework consists of multi-phase prediction algorithms: preprocessing-phase prediction, online prediction, and prediction using the pre-trained model. In the preprocessing phase, we first apply multi-level clustering on the dataset to obtain correlated users and services. We then preprocess the clusters using collaborative filtering to remove the sparsity of the given QoS invocation log matrix. Finally, we create a two-staged, semi-offline regression model using neural networks to predict the QoS value of service to be invoked by a user in real-time. Our experimental results on four publicly available WS-DREAM datasets show the efficiency in terms of accuracy, scalability, fast responsiveness of our framework as compared to the state-of-the-art methods.
Königsberg Sightseeing: Eulerian Walks in Temporal Graphs
Venue: arXiv cs.DM 2021
Year: 2021

An Eulerian walk (or Eulerian trail) is a walk (resp. trail) that visits every edge of a graph $G$ at least (resp. exactly) once. This notion was first discussed by Leonhard Euler while solving the famous Seven Bridges of Königsberg problem in 1736. What if Euler had to take a bus? In a temporal graph $(G,λ)$, with $λ: E(G)\to 2^{[τ]}$, an edge $e\in E(G)$ is available only at the times specified by $λ(e)\subseteq [τ]$, in the same way the connections of the public transportation network of a city or of sightseeing tours are available only at scheduled times. In this scenario, even though several translations of Eulerian trails and walks are possible in temporal terms, only a very particular variation has been exploited in the literature, specifically for infinite dynamic networks (Orlin, 1984). In this paper, we deal with temporal walks, local trails, and trails, respectively referring to edge traversal with no constraints, constrained to not repeating the same edge in a single timestamp, and constrained to never repeating the same edge throughout the entire traversal. We show that, if the edges are always available, then deciding whether $(G,λ)$ has a temporal walk or trail is polynomial, while deciding whether it has a local trail is NP-complete even if it has lifetime~2. In contrast, in the general case, solving any of these problems is NP-complete, even under very strict hypothesis.
Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers
Venue: arXiv cs.CL 2021
Year: 2021

The abolitionist movement of the nineteenth-century United States remains among the most significant social and political movements in US history. Abolitionist newspapers played a crucial role in spreading information and shaping public opinion around a range of issues relating to the abolition of slavery. These newspapers also serve as a primary source of information about the movement for scholars today, resulting in powerful new accounts of the movement and its leaders. This paper supplements recent qualitative work on the role of women in abolition's vanguard, as well as the role of the Black press, with a quantitative text modeling approach. Using diachronic word embeddings, we identify which newspapers tended to lead lexical semantic innovations -- the introduction of new usages of specific words -- and which newspapers tended to follow. We then aggregate the evidence across hundreds of changes into a weighted network with the newspapers as nodes; directed edge weights represent the frequency with which each newspaper led the other in the adoption of a lexical semantic change. Analysis of this network reveals pathways of lexical semantic influence, distinguishing leaders from followers, as well as others who stood apart from the semantic changes that swept through this period. More specifically, we find that two newspapers edited by women -- THE PROVINCIAL FREEMAN and THE LILY -- led a large number of semantic changes in our corpus, lending additional credence to the argument that a multiracial coalition of women led the abolitionist movement in terms of both thought and action. It also contributes additional complexity to the scholarship that has sought to tease apart the relation of the abolitionist movement to the women's suffrage movement, and the vexed racial politics that characterized their relation.
On the list decodability of rank-metric codes containing Gabidulin codes
Venue: arXiv cs.IT 2021
Year: 2021

Wachter-Zeh in [42], and later together with Raviv [31], proved that Gabidulin codes cannot be efficiently list decoded for any radius $τ$, providing that $τ$ is large enough. Also, they proved that there are infinitely many choices of the parameters for which Gabidulin codes cannot be efficiently list decoded at all. Subsequently, in [41] these results have been extended to the family of generalized Gabidulin codes and to further family of MRD-codes. In this paper, we provide bounds on the list size of rank-metric codes containing generalized Gabidulin codes in order to determine whether or not a polynomial-time list decoding algorithm exists. We detect several families of rank-metric codes containing a generalized Gabidulin code as subcode which cannot be efficiently list decoded for any radius large enough and families of rank-metric codes which cannot be efficiently list decoded. These results suggest that rank-metric codes which are $\mathbb{F}_{q^m}$-linear or that contains a (power of) generalized Gabidulin code cannot be efficiently list decoded for large values of the radius.
Network Environment Design for Autonomous Cyberdefense
Venue: arXiv cs.CR 2021
Year: 2021

Reinforcement learning (RL) has been demonstrated suitable to develop agents that play complex games with human-level performance. However, it is not understood how to effectively use RL to perform cybersecurity tasks. To develop such understanding, it is necessary to develop RL agents using simulation and emulation systems allowing researchers to model a broad class of realistic threats and network conditions. Demonstrating that a specific RL algorithm can be effective for defending a network under certain conditions may not necessarily give insight about the performance of the algorithm when the threats, network conditions, and security goals change. This paper introduces a novel approach for network environment design and a software framework to address the fundamental problem that network defense cannot be defined as a single game with a simple set of fixed rules. We show how our approach is necessary to facilitate the development of RL network defenders that are robust against attacks aimed at the agent's learning. Our framework enables the development and simulation of adversaries with sophisticated behavior that includes poisoning and evasion attacks on RL network defenders.
Slip-Based Autonomous ZUPT through Gaussian Process to Improve Planetary Rover Localization
Venue: arXiv cs.RO 2021
Year: 2021

The zero-velocity update (ZUPT) algorithm provides valuable state information to maintain the inertial navigation system (INS) reliability when stationary conditions are satisfied. Employing ZUPT along with leveraging non-holonomic constraints can greatly benefit wheeled mobile robot dead-reckoning localization accuracy. However, determining how often they should be employed requires consideration to balance localization accuracy and traversal rate for planetary rovers. To address this, we investigate when to autonomously initiate stops to improve wheel-inertial odometry (WIO) localization performance with ZUPT. To do this, we propose a 3D dead-reckoning approach that predicts wheel slippage while the rover is in motion and forecasts the appropriate time to stop without changing any rover hardware or major rover operations. We validate with field tests that our approach is viable on different terrain types and achieves a 3D localization accuracy of more than 97% over 650 m drives on rough terrain.