Reviewerly/exHarmony huggingface dataset:
Risk-Aware Emergency Landing Planning for Gliding Aircraft Model in Urban Environments

An in-flight loss of thrust poses a risk to the aircraft, its passengers, and people on the ground. When a loss of thrust happens, the (auto)pilot is forced to perform an emergency landing, possibly toward one of the reachable airports. If none of the airports is reachable, the aircraft is forced to land at another location, which can be risky in urban environments. In this work, we present a generalization of the previous work on planning safe emergency landing in the case of in-flight loss of thrust such that the risk induced by the loss of thrust can be assessed if none of the airports are reachable. The proposed method relies on planning space discretization and efficient risk propagation through the risk map. The approach can find the least risky landing site and corresponding forced landing trajectory for any configuration in the planning space. The method has been empirically evaluated in a realistic urban scenario. The results support its suitability for risk-aware planning of an emergency landing in the case of in-flight loss of thrust.
Generalized Few-shot Semantic Segmentation for LiDAR Point Clouds

Semantic segmentation of LiDAR point clouds can provide assistance for precise perception in autonomous driving, but traditional segmentation methods face challenges such as unbalanced class distribution and insufficient labeling. Generalized few-shot learning has been researched on image data, but these methods are difficult to apply directly to LiDAR point clouds. To tackle these challenges, we propose a generalized few-shot semantic segmentation method based on LiDAR point cloud data, enabling us to predict base and novel classes simultaneously. To improve the performance with limited novel class samples, we integrate semantic vectors and leverage the intrinsic relationship between base and novel class vectors to facilitate learning. We conduct comprehensive comparisons with other methods on the SemanticKITTI and constantly surpass them with higher mIoU, demonstrating the effectiveness of our method.
Optimizing Algorithms from Pairwise User Preferences

Typical black-box optimization approaches in robotics focus on learning from metric scores. However, that is not always possible, as not all developers have ground truth available. Learning appropriate robot behavior in human-centric contexts often requires querying users, who typically cannot provide precise metric scores. Existing approaches leverage human feedback in an attempt to model an implicit reward function; however, this reward may be difficult or impossible to effectively capture. In this work, we introduce SortCMA to optimize algorithm parameter configurations in high dimensions based on pairwise user preferences. SortCMA efficiently and robustly leverages user input to find parameter sets without directly modeling a reward. We apply this method to tuning a commercial depth sensor without ground truth, and to robot social navigation, which involves highly complex preferences over robot behavior. We show that our method succeeds in optimizing for the user's goals and perform a user study to evaluate social navigation results.
ConSOR: A Context-Aware Semantic Object Rearrangement Framework for Partially Arranged Scenes

Object rearrangement is the problem of enabling a robot to identify the correct object placement in a complex environment. Prior work on object rearrangement has explored a diverse set of techniques for following user instructions to achieve some desired goal state. Logical predicates, images of the goal scene, and natural language descriptions have all been used to instruct a robot in how to arrange objects. In this work, we argue that burdening the user with specifying goal scenes is not necessary in partially-arranged environments, such as common household settings. Instead, we show that contextual cues from partially arranged scenes (i.e., the placement of some number of pre-arranged objects in the environment) provide sufficient context to enable robots to perform object rearrangement without any explicit user goal specification. We introduce ConSOR, a Context-aware Semantic Object Rearrangement framework that utilizes contextual cues from a partially arranged initial state of the environment to complete the arrangement of new objects, without explicit goal specification from the user. We demonstrate that ConSOR strongly outperforms two baselines in generalizing to novel object arrangements and unseen object categories. The code and data are available at https://github.com/kartikvrama/consor.
Global Map Assisted Multi-Agent Collision Avoidance via Deep Reinforcement Learning around Complex Obstacles

State-of-the-art multi-agent collision avoidance algorithms face limitations when applied to cluttered public environments, where obstacles may have a variety of shapes and structures. The issue arises because most of these algorithms are agent-level methods. They concentrate solely on preventing collisions between the agents while the obstacles are handled merely out-of-policy. Obstacle-aware policies output an action considering both agents and obstacles. Current obstacle-aware algorithms, mainly based on Lidar sensor data, struggle to handle collision avoidance around complex obstacles. To resolve this issue, this paper investigates how to find a better way to travel around diverse obstacles. In particular, we present a global map assisted collision avoidance algorithm which, following the lead of a high-level goal guide and using an obstacle representation called distance map, considers other agents and obstacles simultaneously. Moreover, our model can be loaded into each agent individually, making it applicable to large maps or more agents. Simulation results indicate that our model outperforms the state-of-the-art algorithms, showing in scenarios with complex obstacles. We present a notion for incorporating global information in decentralized decision-making, along with a method for extending agent-level algorithms to cluttered environments in real-world scenarios.
DoubleBee: A Hybrid Aerial-Ground Robot with Two Active Wheels

In this paper, we present the dynamic model and control of DoubleBee, a novel hybrid aerial-ground vehicle consisting of two propellers mounted on tilting servo motors and two motor-driven wheels. DoubleBee exploits the high energy efficiency of a bicopter configuration in aerial mode, and enjoys the low power consumption of a two-wheel self-balancing robot on the ground. Furthermore, the propeller thrusts act as additional control inputs on the ground, enabling a novel decoupled control scheme where the attitude of the robot is controlled using thrusts and the translational motion is realized using wheels. A prototype of DoubleBee is constructed using commercially available components. The power efficiency and the control performance of the robot are verified through comprehensive experiments. Challenging tasks in indoor and outdoor environments demonstrate the capability of DoubleBee to traverse unstructured environments, fly over and move under barriers, and climb steep and rough terrains.
Target Attribute Perception Based UAV Real-Time Task Planning in Dynamic Environments

In this paper, a comprehensive solution for enabling unmanned aerial vehicle (UAV) to autonomously fly through complex and dynamic environments is proposed. Moving objects all have unique property information, we propose a method that utilizes deep learning for 3D dynamic environment perception, while taking into account limitations in computing resources. For safer dynamic avoidance, we first model the dynamic target and integrate it into a static grid occupancy map, and then construct a gradient field based on its attribute information. To achieve autonomous UAV flight in dynamic environments, we design an adaptive planning method based on gradient optimization, which achieves significant computational savings by autonomously adjusting the planning frequency and using manually constructed gradients instead of maintaining a signed distance field (SDF). We have integrated the above approach into a customised quadrotor system and thoroughly tested it in real-world, verifying its flexibility to handle multiple objects with variable speed motion in complex enviroment.
InterTracker: Discovering and Tracking General Objects Interacting with Hands in the Wild

Understanding human interaction with objects is an important research topic for embodied Artificial Intelligence and identifying the objects that humans are interacting with is a primary problem for interaction understanding. Existing methods rely on frame-based detectors to locate interacting objects. However, this approach is subjected to heavy occlusions, background clutter, and distracting objects. To address the limitations, in this paper, we propose to leverage spatio-temporal information of hand-object interaction to track interactive objects under these challenging cases. Without prior knowledge of the general objects to be tracked like object tracking problems, we first utilize the spatial relation between hands and objects to adaptively discover the interacting objects from the scene. Second, the consistency and continuity of the appearance of objects between successive frames are exploited to track the objects. With this tracking formulation, our method also benefits from training on large-scale general object-tracking datasets. We further curate a video-level hand-object interaction dataset for testing and evaluation from 100DOH. The quantitative results demonstrate that our proposed method outperforms the state-of-the-art methods. Specifically, in scenes with continuous interaction with different objects, we achieve an impressive improvement of about 10% as evaluated using the Average Precision (AP) metric. Our qualitative findings also illustrate that our method can produce more continuous trajectories for interacting objects.
Autonomous Exploration Using Ground Robots with Safety Guarantees

Autonomous exploration in an unknown environment is widely studied, and many exploration strategies exist. However, in most of the works, safety is not usually given top priority. The reason behind the violation of safety by most of the exploration algorithms in real-world applications is the ignorance of some or all of the following factors (i) the mathematical model of the robot, (ii) practical constraints on states (like constrained steering angle, speed, etc.) and inputs (like actuator saturation), and (iii) hardware constraints such as sampling time, sensor noise, modelling uncertainties, etc. In this work, we propose an autonomous exploration framework for the 2-D exploration problem that considers the factors above to provide safety guarantees for the robot and the environment. The effectiveness of this method is shown in a high-fidelity simulation of different robots with onboard sensors in different simulation environments and real-world implementation.
Efectos de la Educación a Distancia en Educación Primaria durante el confinamiento y propuestas para el desarrollo de la Competencia Digital Docente

The lockdown caused by the COVID-19 pandemic in March 2020 forced the transfer of face-to-face teaching from schools to home education, thanks to the Information and Communication Technologies (ICT). However, not all students or teachers faced the situation with the same conditions. This article pretends to analyze the impact caused by the closure of schools, and, consequently, the implementation of Distance Learning in Primary Education in Spain. For this, different scientific articles extracted from academic sources have been consulted, applying search terms and selection criteria for their inclusion. The results show that, with lockdown and Distance Education, educational inequality, socio-family and digital inequalities, already present in Spanish society, have increased exponentially. In addition, this modality of education assumes both advantages and disadvantages. A practical part is also exposed in this article, where recommendations are presented to help educational agents who have to teach Distance Learning and who lack prior digital knowledge. The purpose is to prevent, where appropriate, some of the problems caused by the lack of training in this field. Thus, it is estimated that the manual offers a greater number of strengths and opportunities than weaknesses and threats, although work must continue on its improvement.
Future Fusion+ UNet (R2U-Net) Deep Learning Architecture for Breast Mass Segmentation

R2U-Net, or Recurrent Residual U-Net, is a U-Net extension that includes both residual and recurrent connections for image segmentation tasks. R2U-Net is an image segmentation task-focused network that mixes residual and recurrent connections to boost performance and manage sequential data. Semantic segmentation algorithms based on deep learning (DL) have demonstrated state-of-the-art performance recently. Specifically, these methods have proven effective for tasks like medical image segmentation, classification, and detection. U-Net is one of the most prominent deep learning techniques for these applications. These proposed structures for segmentation problems have various advantages. In addition, better feature representation for segmentation tasks is provided by accumulating features using recurrent residual convolutional layers. Moreover allows us to design a more effective U-Net architecture for medical picture segmentation using the same amount of network parameters. The experimental results reveal that the model outperforms analogous models such as R2U-Net on segmentation tasks. The accuracy of the R2UNet model was 95.6%, while the FF + (AlexResNet + R2Unet) result was more than 97%, with an accuracy (%) of 97.4, AUC (%) of 97.35, precision (%) of 97.4, F1-score (%) of 95.26, and recall (%) of 97.16. The employment of these segmentation approaches in the identification and diagnosis of breast cancer produced outstanding results. Our proposed method could provide a more precise diagnosis of breast cancer, perhaps improving patient outcomes.
Multi-Instance Classification of Breast Tumor Ultrasound Images Using Convolutional Neural Networks and Transfer Learning

Breast cancer is arguably one of the leading causes of death among women around the world. The automation of the early detection process and classification of breast masses has been a prominent focus for researchers in the past decade. The utilization of ultrasound imaging is prevalent in the diagnostic evaluation of breast cancer, with its predictive accuracy being dependent on the expertise of the specialist. Therefore, there is an urgent need to create fast and reliable ultrasound image detection algorithms to address this issue.This paper aims to compare the efficiency of six state-of-the-art, fine-tuned deep learning models that can classify breast tissue from ultrasound images into three classes: benign, malignant, and normal, using transfer learning. Additionally, the architecture of a custom model is introduced and trained from the ground up on a public dataset containing 780 images, which was further augmented to 3900 and 7800 images, respectively. What is more, the custom model is further validated on another private dataset containing 163 ultrasound images divided into two classes: benign and malignant. The pre-trained architectures used in this work are ResNet-50, Inception-V3, Inception-ResNet-V2, MobileNet-V2, VGG-16, and DenseNet-121. The performance evaluation metrics that are used in this study are as follows: Precision, Recall, F1-Score and Specificity.The experimental results show that the models trained on the augmented dataset with 7800 images obtained the best performance on the test set, having 94.95 ± 0.64%, 97.69 ± 0.52%, 97.69 ± 0.13%, 97.77 ± 0.29%, 95.07 ± 0.41%, 98.11 ± 0.10%, and 96.75 ± 0.26% accuracy for the ResNet-50, MobileNet-V2, InceptionResNet-V2, VGG-16, Inception-V3, DenseNet-121, and our model, respectively.Our proposed model obtains competitive results, outperforming some state-of-the-art models in terms of accuracy and training time.
The Hamilton Compression of Highly Symmetric Graphs

Abstract We say that a Hamilton cycle $$C=(x_1,\ldots ,x_n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>C</mml:mi> <mml:mo>=</mml:mo> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>x</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>x</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> in a graph G is k -symmetric, if the mapping $$x_i\mapsto x_{i+n/k}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>x</mml:mi> <mml:mi>i</mml:mi> </mml:msub> <mml:mo>↦</mml:mo> <mml:msub> <mml:mi>x</mml:mi> <mml:mrow> <mml:mi>i</mml:mi> <mml:mo>+</mml:mo> <mml:mi>n</mml:mi> <mml:mo>/</mml:mo> <mml:mi>k</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> for all $$i=1,\ldots ,n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>i</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> , where indices are considered modulo n , is an automorphism of G . In other words, if we lay out the vertices $$x_1,\ldots ,x_n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>x</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>x</mml:mi> <mml:mi>n</mml:mi> </mml:msub> </mml:mrow> </mml:math> equidistantly on a circle and draw the edges of G as straight lines, then the drawing of G has k -fold rotational symmetry, i.e., all information about the graph is compressed into a $$360^\circ /k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>360</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> <mml:mo>/</mml:mo> <mml:mi>k</mml:mi> </mml:mrow> </mml:math> wedge of the drawing. The maximum k for which there exists a k -symmetric Hamilton cycle in G is referred to as the Hamilton compression of G . We investigate the Hamilton compression of four different families of vertex-transitive graphs, namely hypercubes, Johnson graphs, permutahedra and Cayley graphs of abelian groups. In several cases, we determine their Hamilton compression exactly, and in other cases, we provide close lower and upper bounds. The constructed cycles have a much higher compression than several classical Gray codes known from the literature. Our constructions also yield Gray codes for bitstrings, combinations and permutations that have few tracks and/or that are balanced.
Unveiling the Dynamics of Financial Institutions and Markets in Shaping Economic Prosperity in MENA

This research explored the relationship between financial development and economic growth in the MENA region from 1996 to 2022. Using panel data, it assessed whether financial institutions and financial markets had differing impacts on economic growth. Various statistical methods, including OLS, GMM, quantile, and U-tests, were employed to analyze this correlation. Our findings revealed a nonlinear relationship between financial development (institutions and markets) and economic growth, characterized by an inverted U-shaped curve. This relationship was influenced by the MENA region’s limited financial regulations and the “vanishing effect”. Financial institutions were found to have an insignificant impact on economic growth but played a role in constraining it. Conversely, financial markets significantly contributed to growth initially, but this effect diminished over time, eventually turning negative. Additionally, this research highlighted the positive influences of liquidity and exports on economic growth, while noting that the rule of law and political stability had adverse effects.
A Pilot Study on the Use of Virtual Reality to Educate Community Members about Dementia

Aim To understand the impact of using an immersive dementia video via a virtual reality (VR) device to educate community members.
Promoting Human-centred Machine Translation Quality Assessment in NLP education

This research advocates for the integration of Human Evaluation (HE) in Machine Translation Quality Assessment (MTQA), countering the over-reliance on Automatic Evaluation Metrics (AEM) and their associated risks. Highlighting the limitations of AEMs and stressing the strength of HE, this project proposes a mixed-methods training approach for Natural Language Processing (NLP) students, using the ADDIE framework. It aims to equip NLP students with robust HE approaches for a more comprehensive MTQA process, so future developers have the skills to ensure the reliability of MT systems and preventing risks and biases to be propagated.
Immersive Neural Network Exploration: A VR Approach to Human-Centered AI Understanding

In today's rapidly evolving artificial intelligence (AI) landscape, the complexity of neural networks, especially in deep learning, presents significant challenges for intuitive human understanding. Compelling visualization methods have become crucial with AI systems integrating into everyday experiences and environments. Virtual Reality (VR), as an immersive and interactive technology, offers a novel approach to visualizing intricate AI processes. This work introduces transformative updates to the DeepVisionVR platform, a pioneering tool for 3D visualization of Convolutional Neural Networks (CNNs) in VR. A cornerstone of our enhancements is the Sensitivity Analysis module, which offers real-time interactivity, allowing users to adjust pixels within the VR space, shedding light on the intricacies of model outcomes. In addition, advanced model interpretation methodologies have been integrated, including Integrated Gradients, GradientShap, and Occlusion, enriching the depth of insight into model rationale. Our Adversarial Analysis, utilizing the Fast Gradient Sign Method (FGSM), unveils potential weak points in models, emphasizing their vulnerability to minor input alterations. The new features aim to provide a deeper understanding of how neural networks interpret and react to various inputs, thereby bridging the gap between complex machine learning models and human interpretability.
Generative AI-Based Tutoring System for Upper Egypt Community Schools

This work introduces design for a Generative AI (Gen-AI) based tutoring system customized for Egyptian community schools needs. Community schools are managed by NGOs as an alternative education to unprivileged students who missed the formal government education. Our research involved working with an Egyptian NGO and a community school in upper Egypt from March 2023 to September 2023. Several workshops were conducted with involved stakeholders and end users to collect the requirements for such a system. The proposed design reflects the views of management, teachers and student parents.It also adhere to Haman-Centered AI (HCAI) principles which prioritize human values and experiences. This paper lists the outcomes of this process and surveys potential tools that can be used in this system. The findings emphasize the need for Gen-AI to act as a teacher's assistant rather than a replacement, it also highlights the need for localization as well as the need for an end-to-end solution rather than developing an isolated AI-based module. The proposed Gen-AI tutoring system integrates Generative AI, a Learning Management System (LMS), and comprehensive reporting dashboards. This design aims to meet the technical and pedagogical requirements of community school environments and produces a trusted tutoring system that empowers teachers and students.