Reviewerly/exHarmony huggingface dataset:
Establishing Virtual Test-Driven Development Environments in the Automotive Domain: A Continuous Engineering Approach

In the recent decade, the growing importance of software-based features particularly in the automotive domain has led to an increased demand for innovative development concepts and software methodologies which facilitate the design, implementation, and testing of the involved embedded systems. The evolution from single-purpose controllers over distributed networks of multi-function ECUs up to centralized software-heavy computing devices has shifted the focus of development environments from hardware-centric testbeds to virtualized x-in-the-loop evaluation facilities enabling opportunities for early prototyping, virtual integration, and continuous engineering of the system components under consideration. However, determining the proper set of tools which support the required test-driven development processes and infrastructure automation is a challenging task, particularly when considering the individual needs of different product lines. This is due to the plethora of available solutions, each usually dedicated to only certain development aspects, which need to be seamlessly integrated to cope with the complexity of nowadays system architectures and volatility of requirements regarding the functionality and quality of the aspired products. In this context, modern product development requires correspondingly flexible evaluation environments with traceability support, where usually downstream testing activities are performed upfront and continuously on an iterative basis. In this paper, we propose a pragmatic approach for establishing such development environments which can be tailored to the constitution of specific corporate product lines by means of tool chain instantiation and integration. This approach is based on a recurrent 3-phase procedure and an object-oriented data model facilitating the management of relevant details and relations in terms of organizational data, use case descriptions, requirement specifications, as well as tool qualifications. The associated process includes the analysis of relevant practices, recommendations for possible improvements, and implementation of proof of concepts which can be built into a continuous engineering pipeline for the sake of complementing a holistic product development lifecycle.
Learning to Write User Stories with the 4C Model: Context, Card, Conversation, and Confirmation

Writing user stories that effectively support software product development is difficult for students new to the practice. This position paper addresses this challenge by extending the existing 3C model for defining user stories with an extra C for ‘context’. This format is targeted to interactive software systems and inspired by a grounded theory research study where the observed product managers provided context by basing most user stories on concrete and validated design artifacts, such as wireframes. After experimenting with the 4C model during four semesters with positive initial results, we posit that the model helps students generate stories that are easier to create and review while supporting the development of innovative solutions that satisfy the stakeholders’ needs.
Bot With Interactions: Improving GitHub Pull-Request Feedback Through Two-Way Communication

Projects in our software-engineering course require students to submit GitHub pull requests to an open-source software project containing more than 30,000 lines of source code. Once submitted, code is checked by a static code analyzer, as well as a bot named Danger Bot. The Danger Bot is able to detect more than 40 programmable system-specific guideline violations (which are different from static analysis rules). Although use of the Danger Bot was associated with a decrease of 40% in guideline violations, it also emitted some false positives. Neither staff nor students could change the feedback given by the Danger Bot on pull-request pages, because there was only one-way communication, from bot to humans. In this paper, we discuss how we bypass the limitations of the Danger Bot by introducing the Danger Bot 2.0 (hereinafter, ”the bot”) with two-way communication between humans and the bot. In this way, if students or teaching staff find some false positives produced by the bot, they can tell the bot about this. After teaching staff cancel a particular message as a false positive, the bot will not report that message again until it is re-enabled by the staff. We conducted a pilot study for the bot with two-way communication. Results showed that the bot with two-way communication was associated with a significant 70% decrease of unresolved guideline violations and the elimination of 100% false positives in them. The majority of two-way communications happened between the teaching staff and the bot, especially when teaching staff cancelled all the false positive violations after inspection. There was only one communication between students and the bot.
Verifiable and Robust Monitoring and Alerting System for Road Safety by AI based Consensus Development on Blockchain

The integration of AI and the Blockchain has been explored by existing solutions that cooperatively detect and alert a dangerous situation for road safety purposes. However, most such solutions endorse a transaction without considering the context conveyed. Alternatively, their concept has not been implemented as a deployable system for a trial in the field. Considering a use case to be the safety of a pedestrian in a traffic junction, this paper proposes a novel approach, the VErifiable and Robust Monitoring and Alerting (VERMA) system, that 1) detects a potentially dangerous situation (like a fallen pedestrian on the road), 2) immediately alerts the other stakeholders, the driver, and the driving assistance system in the proximity, and 3) records the detected danger in the Blockchain with auditable evidence. The key feature of the proposed system is the multi-AI PBFT-based voting mechanism that enables the other participating nodes to verify the context of the reported dangerous situation using their own AI from different angles and locations in the junction. The VERMA system was implemented by emulating a pseudo vehicle using a smartphone and a GPU-enabled laptop computer that satisfies the requirement to be tested in the field. The evaluation results through the field trial confirm the end-to-end integration of the proposed system with the fast consensus development. This paper also organizes the lessons that should be considered for real-world deployment.
Latent Dynamic Token Vision Transformer for Pedestrian Attribute Recognition

Pedestrian attribute recognition(PAR), as a subarea of multi-label classification, requires models to qualify multiple class-specific features learning ability. However, existing multi-label classification models, such as those based on CNN or Transformer, tend to form a single class-specific attention map for all labels. In order to further improve the model’s ability to capture multiple class-specific attention regions, we propose a Latent Dynamic Token Vision Transformer, termed LDT-ViT, which constructs a sequential LDT attention module to generate multiple latent tokens dynamically. Compared with the single class-token, the multiple latent tokens produced by the LDT module can learn more class-specific attention regions corresponding to different attributes of a pedestrian. Moreover, we also design a parallel multi-stage voting mechanism to aggregate hierarchical ignored feature information from the intermediate layers. The voting mechanism can produce the maximum responses across different attention layers to take advantage of complementary intermediate features, which are more suitable for multi-label tasks. Comprehensive experiments show that the proposed LDT-ViT model achieves state-of-the-art performance on PETA and PA-100K pedestrian attribute datasets.
Urban-StyleGAN: Learning to Generate and Manipulate Images of Urban Scenes

A promise of Generative Adversarial Networks (GANs) is to provide cheap photorealistic data for training and validating AI models in autonomous driving. Despite their huge success, their performance on complex images featuring multiple objects is understudied. While some frameworks produce high-quality street scenes with little to no control over the image content, others offer more control at the expense of high-quality generation. A common limitation of both approaches is the use of global latent codes for the whole image, which hinders the learning of independent object distributions. Motivated by SemanticStyleGAN (SSG), a recent work on latent space disentanglement in human face generation, we propose a novel framework, Urban-StyleGAN, for urban scene generation and manipulation. We find that a straightforward application of SSG leads to poor results because urban scenes are more complex than human faces. To provide a more compact yet disentangled latent representation, we develop a class grouping strategy wherein individual classes are grouped into super-classes. Moreover, we employ an unsupervised latent exploration algorithm in the $\mathcal{S}$-space of the generator and show that it is more efficient than the conventional ${\mathcal{W}^ + }$-space in controlling the image content. Results on the Cityscapes and Mapillary datasets show the proposed approach achieves significantly more controllability and improved image quality than previous approaches on urban scenes and is on par with general-purpose non-controllable generative models (like StyleGAN2) in terms of quality.
Adaptive local adversarial attacks on 3D point clouds

Modern artificial intelligence systems rely heavily on deep learning techniques. However, deep neural networks are easily disturbed by adversarial objects. Adversarial examples are beneficial to improve the robustness of the 3D neural network model and enhance the stability of the artificial intelligence system. At present, most 3D adversarial attack methods perturb the entire point cloud to generate adversarial examples, which results in high perturbation costs and low operability in the physical world. In this paper, we propose an adaptive local adversarial attack method (AL-Adv) on 3D point clouds to generate adversarial point clouds. First, we analyze the vulnerability of the 3D network model and extract the salient regions of the input point cloud, namely the vulnerable regions. Second, we propose an adaptive gradient attack algorithm that targets salient regions. The proposed attack algorithm adaptively assigns different disturbances in different directions of the three-dimensional coordinates of the point cloud. Experimental results show that our proposed AL-Adv method achieves a higher attack success rate than the global attack method. Specifically, the adversarial examples generated by AL-Adv demonstrate good imperceptibility and small generation costs.
Towards Explainability in Modular Autonomous System Software

Safety-critical Autonomous Systems require trustworthy and transparent decision-making process to be deployable in the real world. The advancement of Machine Learning introduces high performance but largely through black-box algorithms. In this position paper, we focus the discussion of explainability specifically with Autonomous Vehicles (AVs). As a safety-critical system, AVs provide the unique opportunity to utilize cutting-edge Machine Learning techniques while requiring transparency in decision making. Interpretability in every action the AV takes becomes crucial in post-hoc analysis where blame assignment might be necessary. In this paper, we provide positioning on how researchers could consider incorporating explainability and interpretability into design and optimization of separate Autonomous Vehicle modules including Perception, Planning, and Control.
Mixed Poisson-Gaussian noise reduction using a time-space fractional differential equations

Images are frequently corrupted by various sorts of mixed or unrecognized noise, including mixed Poisson-Gaussian noise, rather than just a single kind of noise. In this work, we propose a time-space fractional differential equation to remove mixed Poisson-Gaussian noise. Combining fixed- and variable-order fractional derivatives allows us to maintain an image's high- and low-frequency components while eliminating noise. The current model, although primarily intended for mixed noise reduction, can indeed be utilized with great efficacy on images that have been solely degraded by Gaussian noise. In addition to this, a stable discretization strategy is presented. The illustrative results demonstrate that our scheme performs better than earlier models, reduces the staircase effect, and is applicable to electron microscopy and CT images.
Design an Adaptive Virtual Reality Game to Promote Elderly Health

The elderly population in cities worldwide is experiencing significant growth each year, primarily due to advancements in medical technology and increased life expectancy. Consequently, the health of the elderly has become a paramount concern. To lead healthy and fulfilling lives, the elderly must engage in more active lifestyles. However, many activities that promote well-being, such as fishing, golfing, or hiking, may be restricted due to dangerous outdoor environments that pose a risk to their safety. This presents a critical challenge that needs to be addressed. To tackle this issue, our research combines two cutting-edge technologies, namely virtual reality (VR) and artificial intelligence (AI), to develop an adaptive VR game specifically designed for elderly individuals seeking to enhance their overall health. VR, as an innovative electronic device, offers an immersive experience that allows users to engage with 3D 360-degree environments through VR glasses, providing a lifelike sensation. By leveraging the highly interactive nature of VR, users can partake in various movements and activities to effectively promote their health without encountering any physical risks. Furthermore, we have incorporated AI into the game to provide real-time guidance to users and dynamically adjust the game content as needed. This AI integration enables personalized advice based on the user's current situation, allowing the game to adapt to their specific health promotion goals. Through AI's analytical capabilities, the game content can be continuously optimized to better align with the user's objectives and ensure an engaging and effective experience. By combining VR and AI, our research aims to create a safe and immersive virtual environment for elderly individuals, enabling them to actively participate in activities that contribute to their well-being. This novel approach addresses the challenges faced by the elderly in accessing health-promoting activities, providing them with an engaging and personalized means of improving their overall health.
Metric Learning Based Class Specific Experts for Open-Set Recognition of Traffic Participants in Urban Areas Using Infrastructure Sensors

Sensors installed in the infrastructure can make a significant contribution to the advancement of Advanced Driver Assistance Systems (ADAS) and connected mobility. Thermal cameras provide protection against the abuse of personalised data and perform robustly in challenging environmental conditions, making them an excellent choice for infrastructural perception. The goal of this work is to solve the crucial problem of Open-Set Recognition (OSR) for thermal camera-based perception systems installed in the infrastructure. In this paper, a novel modular architecture for OSR called Class Specific Experts (CSE) is proposed, in which, class specialization is achieved using individual feature spaces. The proposed methodology can be easily embedded in an object detection setting and provides as a main advantage, the possibility of online incremental learning without catastrophic forgetting. This work also introduces a open-source classification dataset called Infrastructure Thermal Dataset (ITD) containing image snippets captured by a thermal camera mounted in the infrastructure. The proposed approach outperforms the compared baselines for the task of OSR on many publicly available thermal and non-thermal datasets, as well as the new ITD dataset.
Research on path planning of autonomous manganese nodule mining vehicle based on lifting mining system

Deep-sea manganese nodules are abundant in the ocean, with high exploitation potential and commercial value, and have become mineral resources that coastal countries compete to develop. The pipeline-lifting mining system is the most promising deep-sea mining system at present. A deep-sea mining vehicle is the core equipment of this system. Mining quality and efficiency rely on mining vehicles to a great extent. According to the topographic and geomorphic environmental characteristics of deep-sea manganese nodules at the bottom of the ocean, a new deep-sea mining system based on an autonomous manganese nodule mining vehicle is proposed in this paper. According to the operating environment and functional requirements of the seabed, a new mining method is proposed, and the global traverse path planning research of the autonomous manganese nodule mining vehicle based on this mining method is carried out. The arc round-trip acquisition path planning method is put forward, and the simulation verification shows that the method effectively solves the problems of low efficiency of mining vehicle traversing acquisition and obstacle avoidance.
Orthogonal decomposition of multivariate densities in Bayes spaces and relation with their copula-based representation

Bayes spaces were initially designed to provide a geometric framework for the modeling and analysis of distributional data. It has recently come to light that this methodology can be exploited to construct an orthogonal decomposition of bivariate probability densities into an independence and an interaction part. In this paper, new insights into these results are given by reformulating them using Hilbert space theory, and a multivariate extension is developed using a distributional analog of the Hoeffding–Sobol identity. A connection is also made between the resulting decomposition of a multivariate density and its copula-based representation.
Practical Range Sensing with Thermal Light

Many quantum sensing suggestions rely on temporal correlations found in photon pairs generated by parametric down-conversion. In this work, we show that the temporal correlations in light with a thermal photon statistics can be equally useful for such applications. Using a sub-threshold laser diode as an ultrabright source of thermal light, we demonstrate optical range finding to a distance of up to 1.8 km.
Speed Inversion in a Potts Glass Model of Cortical Dynamics

The authors of this study model the cortex as a network of Potts units. They observe a phenomenon, known as ``speed inversion'' where the ``frontal'' units, initially slower in isolation, become faster than the ``posterior'' units when interacting, which may play a role during learning.
Multimodal feature fusion for illumination-invariant recognition of abnormal human behaviors

This paper presents a fusion of feature maps extracted from visible and thermal infrared surveillance videos to recognize abnormal human behaviors in low illumination conditions. Abnormal human behaviors often occur at night or in darkness, where a visible camera alone may not capture sufficient visual information for analysis. The proposed scheme uses additional data captured by a thermal infrared camera to enhance information on human appearance and motion. Thermal and visible data are assumed to be informative for analyzing human behaviors, yet they contribute differently to certain areas of video frames. This paper proposes a multimodal feature fusion (MFF) to produce a weight matrix that determines how much thermal or visible input should contribute to obtain optimal performance. Experiment results demonstrate that MFF effectively recognizes abnormal human behaviors in darkness. MFF achieves an overall accuracy of 91.01%, which is at least 4.53% higher than individual thermal or visible data and 2.17% higher than the state-of-the-art fusion-based abnormal human behavior recognition techniques.
Bi-Neighborhood Graph Neural Network for cross-lingual entity alignment

The objective of the cross-lingual entity alignment (EA) task is to identify equivalent entities in knowledge graphs (KGs) across different languages. Most existing works rely on relation triples to learn entity representations. In addition, some methods introduce attribute information to improve the performance of EA, but they model relation triples and attribute triples separately in different networks, which prevents the alignment information in different triples from spreading across the networks and limits their mutual interaction and enhancement. In this paper, we propose integrating entities and attributes into a unified entity-attribute graph, where the neighbors of an entity include not only other entities but also its attributes. To model the entity-attribute graph, we further design a novel Bi-Neighborhood Graph Neural Network (BNGNN). Specifically, BNGNN applies attribute-aware self-attention to neighborhood aggregation and generates two types of neighborhood features in each layer, i.e., entity neighborhood features and attribute neighborhood features. Then, the two features are combined through a highway gate to generate more representative entity embeddings. Finally, the outputs of multiple layers are integrated to obtain global-aware entity representations with high-level structural information. Furthermore, a two-stage training strategy is adopted to jointly perform entity alignment and attribute alignment in a semi-supervised framework. Extensive experiments conducted on two real-world datasets (regular and sparse) demonstrate that our BNGNN model consistently outperforms existing EA methods.
ydata-profiling: Accelerating data-centric AI with high-quality data

ydata-profiling is an open-source Python package for advanced exploratory data analysis that enables users to generate data profiling reports in a simple, fast, and efficient manner, fostering a standardized and visual understanding of the data. Beyond traditional descriptive properties and statistics, ydata-profiling follows a Data-Centric AI approach to exploratory analysis, as it focuses on the automatic detection and highlighting of complex data characteristics often associated with potential data quality issues, such as high ratios of missing or imbalanced data, infinite, unique, or constant values, skewness, high correlation, high cardinality, non-stationarity, seasonality, duplicate records, and other inconsistencies. The source code, documentation, and examples are available in the GitHub repository: https://github.com/ydataai/ydata-profiling.