OpenRaiser/Intern-Atlas huggingface dataset:
Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving
Venue: arXiv cs.AI 2021
Year: 2021

Balancing safety and efficiency when planning in dense traffic is challenging. Interactive behavior planners incorporate prediction uncertainty and interactivity inherent to these traffic situations. Yet, their use of single-objective optimality impedes interpretability of the resulting safety goal. Safety envelopes which restrict the allowed planning region yield interpretable safety under the presence of behavior uncertainty, yet, they sacrifice efficiency in dense traffic due to conservative driving. Studies show that humans balance safety and efficiency in dense traffic by accepting a probabilistic risk of violating the safety envelope. In this work, we adopt this safety objective for interactive planning. Specifically, we formalize this safety objective, present the Risk-Constrained Robust Stochastic Bayesian Game modeling interactive decisions satisfying a maximum risk of violating a safety envelope under uncertainty of other traffic participants' behavior and solve it using our variant of Multi-Agent Monte Carlo Tree Search. We demonstrate in simulation that our approach outperforms baselines approaches, and by reaching the specified violation risk level over driven simulation time, provides an interpretable and tunable safety objective for interactive planning.
Removing biased data to improve fairness and accuracy
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning systems are often trained using data collected from historical decisions. If past decisions were biased, then automated systems that learn from historical data will also be biased. We propose a black-box approach to identify and remove biased training data. Machine learning models trained on such debiased data (a subset of the original training data) have low individual discrimination, often 0%. These models also have greater accuracy and lower statistical disparity than models trained on the full historical data. We evaluated our methodology in experiments using 6 real-world datasets. Our approach outperformed seven previous approaches in terms of individual discrimination and accuracy.
"I Don't Think So": Summarizing Policy Disagreements for Agent Comparison
Venue: arXiv cs.AI 2021
Year: 2021

With Artificial Intelligence on the rise, human interaction with autonomous agents becomes more frequent. Effective human-agent collaboration requires users to understand the agent's behavior, as failing to do so may cause reduced productivity, misuse or frustration. Agent strategy summarization methods are used to describe the strategy of an agent to its destined user through demonstration. A summary's objective is to maximize the user's understanding of the agent's aptitude by showcasing its behaviour in a selected set of world states. While shown to be useful, we show that current methods are limited when tasked with comparing between agents, as each summary is independently generated for a specific agent. In this paper, we propose a novel method for generating dependent and contrastive summaries that emphasize the differences between agent policies by identifying states in which the agents disagree on the best course of action. We conduct user studies to assess the usefulness of disagreement-based summaries for identifying superior agents and conveying agent differences. Results show disagreement-based summaries lead to improved user performance compared to summaries generated using HIGHLIGHTS, a strategy summarization algorithm which generates summaries for each agent independently.
Deep Reinforcement Learning for Energy-Efficient Beamforming Design in Cell-Free Networks
Venue: arXiv cs.IT 2021
Year: 2021

Cell-free network is considered as a promising architecture for satisfying more demands of future wireless networks, where distributed access points coordinate with an edge cloud processor to jointly provide service to a smaller number of user equipments in a compact area. In this paper, the problem of uplink beamforming design is investigated for maximizing the long-term energy efficiency (EE) with the aid of deep reinforcement learning (DRL) in the cell-free network. Firstly, based on the minimum mean square error channel estimation and exploiting successive interference cancellation for signal detection, the expression of signal to interference plus noise ratio (SINR) is derived. Secondly, according to the formulation of SINR, we define the long-term EE, which is a function of beamforming matrix. Thirdly, to address the dynamic beamforming design with continuous state and action space, a DRL-enabled beamforming design is proposed based on deep deterministic policy gradient (DDPG) algorithm by taking the advantage of its double-network architecture. Finally, the results of simulation indicate that the DDPG-based beamforming design is capable of converging to the optimal EE performance. Furthermore, the influence of hyper-parameters on the EE performance of the DDPG-based beamforming design is investigated, and it is demonstrated that an appropriate discount factor and hidden layers size can facilitate the EE performance.
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning
Venue: arXiv cs.RO 2021
Year: 2021

Interaction-aware planning for autonomous driving requires an exploration of a combinatorial solution space when using conventional search- or optimization-based motion planners. With Deep Reinforcement Learning, optimal driving strategies for such problems can be derived also for higher-dimensional problems. However, these methods guarantee optimality of the resulting policy only in a statistical sense, which impedes their usage in safety critical systems, such as autonomous vehicles. Thus, we propose the Experience-Based-Heuristic-Search algorithm, which overcomes the statistical failure rate of a Deep-reinforcement-learning-based planner and still benefits computationally from the pre-learned optimal policy. Specifically, we show how experiences in the form of a Deep Q-Network can be integrated as heuristic into a heuristic search algorithm. We benchmark our algorithm in the field of path planning in semi-structured valet parking scenarios. There, we analyze the accuracy of such estimates and demonstrate the computational advantages and robustness of our method. Our method may encourage further investigation of the applicability of reinforcement-learning-based planning in the field of self-driving vehicles.
Over 100 Bugs in a Row: Security Analysis of the Top-Rated Joomla Extensions
Venue: arXiv cs.CR 2021
Year: 2021

Nearly every second website is using a Content Management System (CMS) such as WordPress, Drupal, and Joomla. These systems help to create and modify digital data, typically within a collaborative environment. One common feature is to enrich their functionality by using extensions. Popular extensions allow developers to easily include payment gateways, backup tools, and social media components. Due to the extended functionality, it is not surprising that such an expansion of complexity implies a bigger attack surface. In contrast to CMS core systems, extensions are usually not considered during public security audits. However, a Cross-Site Scripting (XSS) or SQL injection (SQLi) attack within an activated extension has the same effect on the security of a CMS as the same issue within the core itself. Therefore, vulnerabilities within extensions are a very attractive tool for malicious parties. We study the security of CMS extensions using the example Joomla; one of the most popular systems. We discovered that nearly every second installation of such a system also includes Joomla's official top-10 rated extensions as a per se requirement. Moreover, we have detected that every single extension of the official top-10 rated extensions is vulnerable to XSS and 30% of them against SQLi. We show that our findings are not only relevant to Joomla; two of the analyzed extensions are available within systems like WordPress or Drupal, and introduce the same vulnerabilities. Finally, we pinpoint mitigation strategies that can be realized within extensions to achieve the same security level as the core CMS.
Securing emergent behaviour in swarm robotics
Venue: arXiv cs.RO 2021
Year: 2021

Swarm robotics is the study of how a large number of relatively simple robots can be designed so that a desired collective behaviour emerges from the local interactions among robots and between the robots and their environment. While many aspects of a swarm may be modelled as various types of ad hoc networks, and accordingly many aspects of security of the swarm may be achieved by conventional means, here we will focus on swarm emergent behaviour as something that most distinguishes swarm robotics from ad hoc networks. We discuss the challenges emergent behaviour poses on communications security, and by classifying a swarm by types of robots, types of communication channels, and types of adversaries, we examine what classes may be secured by traditional methods and focus on aspects that are most relevant to allowing emergent behaviour. We will examine how this can be secured by ensuring that communication is secure. We propose a simple solution using hash chains, and by modelling swarm communications using a series of random graphs, we show that this allows us to identify rogue robots with a high probability.
Interpretable Neural Networks based classifiers for categorical inputs
Venue: arXiv cs.LG 2021
Year: 2021

Because of the pervasive usage of Neural Networks in human sensitive applications, their interpretability is becoming an increasingly important topic in machine learning. In this work we introduce a simple way to interpret the output function of a neural network classifier that take as input categorical variables. By exploiting a mapping between a neural network classifier and a physical energy model, we show that in these cases each layer of the network, and the logits layer in particular, can be expanded as a sum of terms that account for the contribution to the classification of each input pattern. For instance, at the first order, the expansion considers just the linear relation between input features and output while at the second order pairwise dependencies between input features are also accounted for. The analysis of the contributions of each pattern, after an appropriate gauge transformation, is presented in two cases where the effectiveness of the method can be appreciated.
BlockNet Report: Curriculum Guidance Document
Venue: arXiv cs.CY 2021
Year: 2021

Blockchain is a challenging topic since it is novel and fosters potential innovation. The blockchain is attractive for various disciplines, and, because of its cross-cutting nature, needs knowledge stemming from various disciplines. The devised curriculum can be instantiated specifically to meet the needs of students' groups from various disciplines. The pedagogical innovation of the project is the inclusion of interdisciplinary project groups with participant's interaction via online platforms for project-based learning activities. MOOCs and SNOCs allow blended-learning for interdisciplinary and geographically distributed student groups.
A Framework for Federated SPARQL Query Processing over Heterogeneous Linked Data Fragments
Venue: arXiv cs.DB 2021
Year: 2021

Linked Data Fragments (LDFs) refer to Web interfaces that allow for accessing and querying Knowledge Graphs on the Web. These interfaces, such as SPARQL endpoints or Triple Pattern Fragment servers, differ in the SPARQL expressions they can evaluate and the metadata they provide. Client-side query processing approaches have been proposed and are tailored to evaluate queries over individual interfaces. Moreover, federated query processing has focused on federations with a single type of LDF interface, typically SPARQL endpoints. In this work, we address the challenges of SPARQL query processing over federations with heterogeneous LDF interfaces. To this end, we formalize the concept of federations of Linked Data Fragment and propose a framework for federated querying over heterogeneous federations with different LDF interfaces. The framework comprises query decomposition, query planning, and physical operators adapted to the particularities of different LDF interfaces. Further, we propose an approach for each component of our framework and evaluate them in an experimental study on the well-known FedBench benchmark. The results show a substantial improvement in performance achieved by devising these interface-aware approaches exploiting the capabilities of heterogeneous interfaces in federations.
Generating automatically labeled data for author name disambiguation: An iterative clustering method
Venue: arXiv cs.DL 2021
Year: 2021

To train algorithms for supervised author name disambiguation, many studies have relied on hand-labeled truth data that are very laborious to generate. This paper shows that labeled training data can be automatically generated using information features such as email address, coauthor names, and cited references that are available from publication records. For this purpose, high-precision rules for matching name instances on each feature are decided using an external-authority database. Then, selected name instances in target ambiguous data go through the process of pairwise matching based on the rules. Next, they are merged into clusters by a generic entity resolution algorithm. The clustering procedure is repeated over other features until further merging is impossible. Tested on 26,566 instances out of the population of 228K author name instances, this iterative clustering produced accurately labeled data with pairwise F1 = 0.99. The labeled data represented the population data in terms of name ethnicity and co-disambiguating name group size distributions. In addition, trained on the labeled data, machine learning algorithms disambiguated 24K names in test data with performance of pairwise F1 = 0.90 ~ 0.92. Several challenges are discussed for applying this method to resolving author name ambiguity in large-scale scholarly data.
Pedestrian Simulation: A Review
Venue: arXiv cs.RO 2021
Year: 2021

This article focuses on different aspects of pedestrian (crowd) modeling and simulation. The review includes: various modeling criteria, such as granularity, techniques, and factors involved in modeling pedestrian behavior, and different pedestrian simulation methods with a more detailed look at two approaches for simulating pedestrian behavior in traffic scenes. At the end, benefits and drawbacks of different simulation techniques are discussed and recommendations are made for future research.
Predictive Relay Selection: A Cooperative Diversity Scheme Using Deep Learning
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we propose a novel cooperative multi-relay transmission scheme for mobile terminals to exploit spatial diversity. By improving the timeliness of measured channel state information (CSI) through deep learning (DL)-based channel prediction, the proposed scheme remarkably lowers the probability of wrong relay selection arising from outdated CSI in fast time-varying channels. It inherits the simplicity of opportunistic relaying by selecting a single relay, avoiding the complexity of multi-relay coordination and synchronization. Numerical results reveal that it can achieve full diversity gain in slow-fading channels and substantially outperforms the existing schemes in fast-fading wireless environments. Moreover, the computational complexity brought by the DL predictor is negligible compared to off-the-shelf computing hardware.
Positioning in 5G networks
Venue: arXiv cs.NI 2021
Year: 2021

In this paper we describe the recent 3GPP Release 16 specification for positioning in 5G networks. It specifies positioning signals, measurements, procedures, and architecture to meet requirements from a plethora of regulatory, commercial and industrial use cases. 5G thereby significantly extends positioning capabilities compared to what was possible with LTE. The indicative positioning performance is evaluated in agreed representative 3GPP simulation scenarios, showing a 90 percentile accuracy of a few meters down to a few decimeters depending on scenarios and assumptions.
Reproducibility in Evolutionary Computation
Venue: arXiv cs.AI 2021
Year: 2021

Experimental studies are prevalent in Evolutionary Computation (EC), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflecting similar concerns in other scientific fields. In this article, we discuss, within the context of EC, the different types of reproducibility and suggest a classification that refines the badge system of the Association of Computing Machinery (ACM) adopted by ACM Transactions on Evolutionary Learning and Optimization (https://dlnext.acm.org/journal/telo). We identify cultural and technical obstacles to reproducibility in the EC field. Finally, we provide guidelines and suggest tools that may help to overcome some of these reproducibility obstacles.
MudrockNet: Semantic Segmentation of Mudrock SEM Images through Deep Learning
Venue: arXiv cs.CV 2021
Year: 2021

Segmentation and analysis of individual pores and grains of mudrocks from scanning electron microscope images is non-trivial because of noise, imaging artifacts, variation in pixel grayscale values across images, and overlaps in grayscale values among different physical features such as silt grains, clay grains, and pores in an image, which make their identification difficult. Moreover, because grains and pores often have overlapping grayscale values, direct application of threshold-based segmentation techniques is not sufficient. Recent advances in the field of computer vision have made it easier and faster to segment images and identify multiple occurrences of such features in an image, provided that ground-truth data for training the algorithm is available. Here, we propose a deep learning SEM image segmentation model, MudrockNet based on Google's DeepLab-v3+ architecture implemented with the TensorFlow library. The ground-truth data was obtained from an image-processing workflow applied to scanning electron microscope images of uncemented muds from the Kumano Basin offshore Japan at depths < 1.1 km. The trained deep learning model obtained a pixel-accuracy about 90%, and predictions for the test data obtained a mean intersection over union (IoU) of 0.6591 for silt grains and 0.6642 for pores. We also compared our model with the random forest classifier using trainable Weka segmentation in ImageJ, and it was observed that MudrockNet gave better predictions for both silt grains and pores. The size, concentration, and spatial arrangement of the silt and clay grains can affect the petrophysical properties of a mudrock, and an automated method to accurately identify the different grains and pores in mudrocks can help improve reservoir and seal characterization for petroleum exploration and anthropogenic waste sequestration.
Multi-state MRAM cells for hardware neuromorphic computing
Venue: arXiv cs.ET 2021
Year: 2021

Magnetic tunnel junctions (MTJ) have been successfully applied in various sensing application and digital information storage technologies. Currently, a number of new potential applications of MTJs are being actively studied, including high-frequency electronics, energy harvesting or random number generators. Recently, MTJs have been also proposed in designs of a new platforms for unconventional or bio-inspired computing. In the present work, it is shown that serially connected MTJs forming a multi-state memory cell can be used in a hardware implementation of a neural computing device. A behavioral model of the multi-cell is proposed based on the experimentally determined MTJ parameters. The main purpose of the mutli-cell is the formation of the quantized weights of the network, which can be programmed using the proposed electronic circuit. Mutli-cells are connected to CMOS-based summing amplifier and sigmoid function generator, forming an artificial neuron. The operation of the designed network is tested using a recognition of the hand-written digits in 20x20 pixel matrix and shows detection ratio comparable to the software algorithm, using the weight stored in a multi-cell consisting of four MTJs or more.
Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs
Venue: arXiv cs.AI 2021
Year: 2021

Real-world knowledge graphs are often characterized by low-frequency relations - a challenge that has prompted an increasing interest in few-shot link prediction methods. These methods perform link prediction for a set of new relations, unseen during training, given only a few example facts of each relation at test time. In this work, we perform a systematic study on a spectrum of models derived by generalizing the current state of the art for few-shot link prediction, with the goal of probing the limits of learning in this few-shot setting. We find that a simple zero-shot baseline - which ignores any relation-specific information - achieves surprisingly strong performance. Moreover, experiments on carefully crafted synthetic datasets show that having only a few examples of a relation fundamentally limits models from using fine-grained structural information and only allows for exploiting the coarse-grained positional information of entities. Together, our findings challenge the implicit assumptions and inductive biases of prior work and highlight new directions for research in this area.
Understanding and Fixing Complex Faults in Embedded Cyberphysical Systems
Venue: arXiv cs.SE 2021
Year: 2021

Understanding fault types can lead to novel approaches to debugging and runtime verification. Dealing with complex faults, particularly in the challenging area of embedded systems, craves for more powerful tools, which are now becoming available to engineers.
LION: Lidar-Inertial Observability-Aware Navigator for Vision-Denied Environments
Venue: arXiv cs.RO 2021
Year: 2021

State estimation for robots navigating in GPS-denied and perceptually-degraded environments, such as underground tunnels, mines and planetary subsurface voids, remains challenging in robotics. Towards this goal, we present LION (Lidar-Inertial Observability-Aware Navigator), which is part of the state estimation framework developed by the team CoSTAR for the DARPA Subterranean Challenge, where the team achieved second and first places in the Tunnel and Urban circuits in August 2019 and February 2020, respectively. LION provides high-rate odometry estimates by fusing high-frequency inertial data from an IMU and low-rate relative pose estimates from a lidar via a fixed-lag sliding window smoother. LION does not require knowledge of relative positioning between lidar and IMU, as the extrinsic calibration is estimated online. In addition, LION is able to self-assess its performance using an observability metric that evaluates whether the pose estimate is geometrically ill-constrained. Odometry and confidence estimates are used by HeRO, a supervisory algorithm that provides robust estimates by switching between different odometry sources. In this paper we benchmark the performance of LION in perceptually-degraded subterranean environments, demonstrating its high technology readiness level for deployment in the field.