Reviewerly/exHarmony huggingface dataset:
Epistemic Planning for Heterogeneous Robotic Systems

In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite of capabilities. However, within these application spaces, communication is often unreliable, causing inefficiencies or outright failures to arise in most MRS algorithms. Many researchers tackle this problem by requiring all robots to either maintain communication using proximity constraints or assuming that all robots will execute a predetermined plan over long periods of disconnection. The latter method allows for higher levels of efficiency in a MRS, but failures and environmental uncertainties can have cascading effects across the system, especially when a mission objective is complex or time-sensitive. To solve this, we propose an epistemic planning framework that allows robots to reason about the system state, leverage heterogeneous system makeups, and optimize information dissemination to disconnected neighbors. Dynamic epistemic logic formalizes the propagation of belief states, and epistemic task allocation and gossip is accomplished via a mixed integer program using the belief states for utility predictions and planning. The proposed framework is validated using simulations and experiments with heterogeneous vehicles.
Monte-Carlo Tree Search with Prioritized Node Expansion for Multi-Goal Task Planning

Symbolic task planning for robots is computationally challenging due to the combinatorial complexity of the possible action space. This fact is amplified if there are several sub-goals to be achieved due to the increased length of the action sequences. In this work, we propose a multi-goal symbolic task planner for deterministic decision processes based on Monte Carlo Tree Search. We augment the algorithm by prioritized node expansion which prioritizes nodes that already have fulfilled some sub-goals. Due to its linear complexity in the number of sub-goals, our algorithm is able to identify symbolic action sequences of 145 elements to reach the desired goal state with up to 48 sub-goals while the search tree is limited to under 6500 nodes. We use action reduction based on a kinematic reachability criterion to further ease computational complexity. We combine our algorithm with object localization and motion planning and apply it to a real-robot demonstration with two manipulators in an industrial bearing inspection setting.
Management of the Cyber Range Functionality Based on the Analysis of the Digital Footprint of Students

The article discusses new approaches to teaching students and conducting scientific research in the context of information security. It is crucial to provide students with hands-on learning: emulating and simulating a variety of information assets, resources and a complex network infrastructure. One of the options for solving the problem can be the usage of a cyber range that imitates the structure of the information system of some hypothetical organization. Cyber ranges provide an opportunity for students to act both as defending or intruding parties, teaching different skills and mindsets. The paper considers the development of an intellectual service - a virtual assistant, backed with cybersecurity ontology, which is capable of supporting the cyber range’s users with educational material and advice. Such an approach eases the teachers’ job on the one hand, and allows us to analyze the learning process along the digital trail of students to adjust it and make it more efficient. It is assumed that the description of the protection technology, as well as the virtual assistant’s ontology database, are not secret, however, they must be reliably protected from deliberate forgery. In our case, such protection is provided by blockchain technology.
Complete Coverage Path Planning for Omnidirectional Expand and Collapse Robot Panthera

Autonomous mobile robots (AMRs) face challenges in efficiently covering complex environments. To navigate narrow and expansive areas, AMRs must have two essential attributes: compact size for confined spaces and larger size with omnidirectional locomotion for broader spaces. This study utilizes omnidirectional expand and collapse robots (OECRs) to demonstrate efficient area coverage. OECRs can collapse to navigate through confined spaces and expand for efficient coverage in broad spaces. However, current complete coverage path planning (CCPP) methods do not account for the expanded and collapsed states of OECRs. To address this, a depth-first search (DFS) approach is proposed for OECRs' CCPP, which can adjust the robotic footprint along the CCPP path to reduce path length. The proposed DFS outperforms the state-of-the-art CCPP in terms of increased area coverage and reduced distance traveled on a selected map.
Exploring the Benefits of Geography on Power Network Diagrams

This paper introduces an interactive visualisation that combines a spatial element to the single line diagram (SLD). SLDs are conceptual maps of the power network used by power engineers to understand the connectivity between assets of the network, study power flow, and maintain grid stability and security. Enabling users with varying degrees of electrical knowledge to understand the geographical aspect of the SLD was the key design goal. Developed through an iterative process, the visualisation intuitively transitions from an SLD view to a map view. Evaluation of the visualisation during, and following development revealed that the prototype was well-liked and that having a spatial element to the SLD was useful in understanding the geographical relationships in the power network. The evaluation also helped identify stakeholders with an interest in the hybrid view and showed the prototype’s potential utility in communicating technical data to electrical non-technical users.
UPPLIED: UAV Path Planning for Inspection Through Demonstration

In this paper, a new demonstration-based path-planning framework for the visual inspection of large structures using UAVs is proposed. We introduce UPPLIED: UAV Path PLanning for InspEction through Demonstration, which utilizes a demonstrated trajectory to generate a new trajectory to inspect other structures of the same kind. The demonstrated trajectory can inspect specific regions of the structure and the new trajectory generated by UPPLIED inspects similar regions in the other structure. The proposed method generates inspection points from the demonstrated trajectory and uses standardization to translate those inspection points to inspect the new structure. Finally, the position of these inspection points is optimized to refine their view. Numerous experiments were conducted with various structures and the proposed framework was able to generate inspection trajectories of various kinds for different structures based on the demonstration. The trajectories generated match with the demonstrated trajectory in geometry and at the same time inspect the regions inspected by the demonstration trajectory with minimum deviation. The experimental video of the work can be found at https://youtu.be/YqPx-cLkv04.
Interpretable Trajectory Prediction for Autonomous Vehicles via Counterfactual Responsibility

The ability to anticipate surrounding agents' behaviors is critical to enable safe and seamless autonomous vehicles (AVs). While phenomenological methods have successfully predicted future trajectories from scene context, these predictions lack interpretability. On the other hand, ontological approaches assume an underlying structure able to describe the interaction dynamics or agents' internal decision processes. Still, they often suffer from poor scalability or cannot reflect diverse human behaviors. This work proposes an interpretability framework for a phenomenological method through responsibility evaluations. We formulate responsibility as a measure of how much an agent takes into account the welfare of other agents through counterfactual reasoning. Additionally, this framework abstracts the computed responsibility sequences into different responsibility levels and grounds these latent levels into reward functions. The proposed responsibility-based interpretability framework is modular and easily integrated into a wide range of prediction models. To demonstrate the utility of the proposed framework in providing added interpretability, we adapt an existing AV prediction model and perform a simulation study on a real-world nuScenes traffic dataset. Experimental results show that we can perform offline ex-post traffic analysis by incorporating the responsibility signal and rendering interpretable but accurate online trajectory predictions.
See What the Robot Can't See: Learning Cooperative Perception for Visual Navigation

We consider the problem of navigating a mobile robot towards a target in an unknown environment that is endowed with visual sensors, where neither the robot nor the sensors have access to global positioning information and only use first-person- view images. In order to overcome the need for positioning, we train the sensors to encode and communicate relevant viewpoint information to the mobile robot, whose objective it is to use this information to navigate to the target along the shortest path. We overcome the challenge of enabling all the sensors (even those that cannot directly see the target) to predict the direction along the shortest path to the target by implementing a neighborhood-based feature aggregation module using a Graph Neural Network (GNN) architecture. In our experiments, we first demonstrate generalizability to previously unseen environments with various sensor layouts. Our results show that by using communication between the sensors and the robot, we achieve up to 2.0 × improvement in SPL (Success weighted by Path Length) when compared to a communication-free baseline. This is done without requiring a global map, positioning data, nor pre-calibration of the sensor network. Second, we perform a zero-shot transfer of our model from simulation to the real world. Laboratory experiments demonstrate the feasibility of our approach in various cluttered environments. Finally, we showcase examples of successful navigation to the target while both the sensor network layout as well as obstacles are dynamically reconfigured as the robot navigates. We provide a video demo <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://www.youtube.com/watch?v=kcrnr6RUgucw, the dataset, trained models, and source code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> https://github.com/proroklab/sensor-guided-visual-nav.
An Interacting Multiple Model Approach Based on Maximum Correntropy Student's T Filter

This paper presents a novel approach called the Interacting Multiple Model (IMM)-based Maximum Correntropy Student's T Filter (MCStF), which addresses the challenges posed by non-Gaussian measurement noises. The MCStF demonstrates superior performance compared to the IMM algorithm based on Kalman Filters (KFs) in both simulation environments and real-time systems. The Crazyflie 2.0 nano Unmanned Air Vehicle (UAV) model is used in the simulation validation, and results from 3000 independent Monte Carlo runs are shown. After getting the simulation results under monotonously changed non-Gaussian distribution, their performance results have been compared to each other. The same scenario has been applied in the real-time system using Crazyflie 2.0. Next, results from real-time tests are presented in which the position of Crazyflie 2.0 is estimated online.
A GM-PHD Filter with Estimation of Probability of Detection and Survival for Individual Targets

This paper proposes a modification of the Gaussian mixture probability hypothesis density (GM-PHD) filter to compute online the probability of detection <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(P_{D})$</tex> and probability of survival <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(P_{S})$</tex> of targets. This eliminates the need for predetermined and/or constant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{D}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{S}$</tex> values, that may degrade the estimation. The proposed filter estimates the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{D}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{S}$</tex> values for each individual target based on newly introduced parameters, which are updated during the measurement update process. The effectiveness of the proposed filter was validated through an in-lab experiment using four unmanned ground robots with varying <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{D}$</tex> values and a real-world lidar-based obstacle tracking system implemented on an Automated Surface Vehicle operating in a lake with real-time boat traffic. The results of the experiments demonstrate that the proposed filter outperforms the standard PHD filter with incorrect <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{D}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{S}$</tex> values. These findings highlight the potential benefits of the proposed filter in improving target tracking performance in complex environments.
Professional Standards in Questioned Documents Expertises

Criminalistic/forensic expert examination of documents is a segment of criminal technique, i.e. forensic science. In modern countries, these activities are undertaken by highly specialized services that are frequently under the control of various security structures (judiciary, police, immigration services, etc.). In Bosnia and Herzegovina (as well as in the countries of the former Yugoslavia), these jobs have traditionally been associated with police agencies whose actions fall into the sphere of criminal techniques (more recently, forensics). Certain methods of behaving, ways of functioning, as well as processes linked to staff training and ways of acting within individual activities of this sort of expertise, have been formed via the accomplishment of the aforementioned duties. The purpose of this article is to introduce the field of criminal expert examination of questioned documents, namely the knowledge and conditions that a welltrained expert in this field should have. Furthermore, the work demonstrates in greater detail how the necessary insights and knowledge should be gathered. The fundamental reason for this work´s theme is the goal to describe this field from the standpoint of modern science, profession, and established standards that are used around the world. Another motive is the goal to explain the settings of this sector in a scientific and practical manner from the standpoint of international experience. Specifically, the article will examine some of the international standards and procedures for this field of expertise. The preceding study will be based on a survey of comparative literature as well as an examination of the standards and practices of organizations such as: The Scientific Working Group for Forensic Document Examination (SWGDOC), American Board of Forensic Document Examiners (ABFDE), American Academy of Forensic Sciences (AAFS), American National Standards Institute (ANSI), The American Society of Questioned Document Examiners (ASQDE), Southwestern Association of Forensic Document Examiners (SWAFDE), Southeastern Association of Forensic Document Examiners (SAFDE) and The European Network of Forensic Science Institutes (ENFSI).
Do trademarks reduce search costs in the age of information?*

This chapter questions the continuing relevance of the search cost theory of trademarks as markets shift online where algorithms shape and influence consumer purchasing decisions. Long the dominant approach to trademarks, the search cost theory posits that trademarks help consumers quickly and easily identify the precise product they desire in a crowded marketplace. As markets shift online, surveillance capitalism keeps careful track of what consumers look at, what they buy, and what they re-buy. To a computer, a trademark is just a particular collection of zeros and ones, and could be readily replaced by any other set of zeros and ones, uniquely associated with the consumer’s shopping and purchasing decisions. This chapter explores how the law should approach trademarks in this new digital age.
Thinking Beyond the Default User: The Impact of Gender, Stereotypes, and Modality on Interpretation of User Needs

Abstract Throughout the mechanical design process, designers, the majority of whom are men, often fail to consider the needs of women, resulting in consequences ranging from inconvenience to increased risk of serious injury or death. Although these biases are well-studied in other fields of research, the mechanical design field lacks formal investigation into this phenomenon. In this study, engineering students (n = 300) took a survey in which they read a Persona describing a student makerspace employee and a Walkthrough describing their interaction with the makerspace while completing a project. During the Walkthrough, the user encountered various obstacles, or Pain Points. Participants were asked to recall and evaluate the Pain Points that the user encountered, then evaluated their perceptions of the makerspace and user. The independent variables under investigation were the gender of the user Persona (woman, gender-neutral, or man), Walkthrough room case (crafting or woodworking makerspace), and modality of the Persona and Walkthrough (text- /audio-based). Results showed that participants from the Text-based modality were better able to recall Pain Points compared to participants from the Audio-based modality, although the Pain Points were assessed as more severe when they impacted women users. In addition to finding that the gender of a user impacted the way a task environment was perceived, results confirmed the presence of androcentrism, or “default man” assumptions, in the way designers view end users of unknown gender. Promisingly, providing user Persona information in an audio modality eliminated this bias compared to text-based modalities.
Explainability in User Sentiment Analysis with CoreNLP

This project introduces an innovative approach to user sentiment analysis and explainability, using a Natural Language Processing technique, Stanford’s CoreNLP sentiment analysis tool. The project accurately determines user sentiment utilizing a pre-labelled dataset, by integrating CoreNLP features. Emphasizing transparency and interpretability, it provides valuable insights into sentiment predictions and the factors influencing each classification.
How to use a CAS for Hardware Design Automation

With the rise of new technologies, the demand for efficient cryptographic hardware is rising. This work is focusing on finite field based cryptography. To explore the vast design space obtained from possible finite field parameters, automated generation of hardware submodules using designer-specified algorithms for finite field arithmetic is needed. This work presents a hardware design automation framework exploiting symbolic computation capabilities of GAP to generate the expressions, needed for hardware implementations, on-the-fly, and compiles them into synthesizable datapaths, test-vectors, and testbences.
gRoMA: A Tool for Measuring the Global Robustness of Deep Neural Networks

Deep neural networks (DNNs) are at the forefront of cutting-edge technology, and have been achieving remarkable performance in a variety of complex tasks. Nevertheless, their integration into safety-critical systems, such as in the aerospace or automotive domains, poses a significant challenge due to the threat of adversarial inputs: perturbations in inputs that might cause the DNN to make grievous mistakes. Multiple studies have demonstrated that even modern DNNs are susceptible to adversarial inputs, and this risk must thus be measured and mitigated to allow the deployment of DNNs in critical settings. Here, we present gRoMA (global Robustness Measurement and Assessment), an innovative and scalable tool that implements a probabilistic approach to measure the global categorial robustness of a DNN. Specifically, gRoMA measures the probability of encountering adversarial inputs for a specific output category. Our tool operates on pre-trained, black-box classification DNNs, and generates input samples belonging to an output category of interest. It measures the DNN’s susceptibility to adversarial inputs around these inputs, and aggregates the results to infer the overall global categorial robustness of the DNN up to some small bounded statistical error. We evaluate our tool on the popular Densenet DNN model over the CIFAR10 dataset. Our results reveal significant gaps in the robustness of the different output categories. This experiment demonstrates the usefulness and scalability of our approach and its potential for allowing DNNs to be deployed within critical systems of interest.
Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets

Image retrieval is the process of searching and retrieving images from a datastore based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or healthcare images by extracting features from the images, such as deep features, colour-based features, shape-based features, and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images. The datasets employed in this paper contain wind turbine blade images with defects, chest computerised tomography scans with COVID-19 infections, heatsink images with defects, and lake ice images. The proposed framework was evaluated with different feature extraction methods (DefChars, resized raw image, local binary pattern, and scale-invariant feature transforms) and distance metrics to determine the most efficient parameters in terms of retrieval performance across datasets. The retrieval results show that the proposed framework using the DefChars and the Manhattan distance metric achieves a mean average precision of 80% and a low standard deviation of ±0.09 across classes of irregular patterns, outperforming alternative feature–metric combinations across all datasets. Our proposed ImR framework performed better (by 8.71%) than Super Global, a state-of-the-art deep-learning-based image retrieval approach across all datasets.
Development of Economic E-Modules Based on Contextual Teaching and Learning (CTL) to Improve Learning Outcomes of Class XI Students of SMA Negeri 7 Medan

This study aims to determine: (1) the feasibility of economic e-modules based on Contextual Teaching and Learning to improve student learning outcomes in class XIIPS SMA Negeri 7 Medan; (2) the effectiveness of economic e-modules based on Contextual Teaching and Learning to improve student learni