Reviewerly/exHarmony huggingface dataset:
Analysis of Information Security Measures Embedded in the GDPR

This paper takes a viewpoint of the information security officer whose task is to ensure the optimal level of privacy protection for personal information that the organisation processes. We analyse the requirements of the EU's General Data Protection Regulation (GDPR) with the aim of finding out whether all the main aspects of information security management process (prevention, detection, response and recovery) are covered in a well-balanced manner. Our main finding is that recovery is under-emphasised in the GDPR compared to the other aspects.
Collision-Free Trajectory Planning of Mobile Robots by Integrating Deep Reinforcement Learning and Model Predictive Control

In this paper, we present an efficient approach to real-time collision-free navigation for mobile robots. By integrating deep reinforcement learning with model predictive control, our aim is to achieve both collision avoidance and computational efficiency. The methodology begins with training a preliminary agent using deep Q-learning, enabling it to generate actions for next time steps. Instead of executing these actions, a reference trajectory is generated based on them, which avoids obstacles present on the original reference path. Subsequently, this local trajectory is employed within an MPC trajectory-tracking framework to provide collision-free guidance for the mobile robot. Experimental results demonstrate that the proposed DQN-MPC hybrid approach outperforms pure MPC in terms of time efficiency and solution quality.
Motion Planning to Cartesian Targets Leveraging Large-Scale Dynamic Roadmaps

In this paper, we achieve reliable, sub-second motion planning to Cartesian end-effector targets in changing real-world environments for a high degree-of-freedom <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{12} + \mathbf{7}$</tex> DoF), redundant robot. Several recent works have tackled a subset of these challenges, leveraging innovations in optimization, deep learning, and/or intelligent sampling. However, deployment of robots in real-world environments demands reliably tackling all three challenges: changing environments, fast planning, and high DoF robots. In this work, we leverage (1) large-scale Dynamic Roadmaps (DRM) enabled through GPU-accelerated collision-checking, combined with (2) an optimization-based local inverse kinematics (IK) solver. The high-level principle behind our approach is to offload as much computation either offline or onto the GPU as possible in order to simplify and speed up online planning. Even in changing environments, this enables fast planning in configuration space to a neighborhood of a target pose(s) specified in Cartesian space, whereby the final connection to the target pose is made via a local IK solver. We ran several experiments in an unmodified real-world grocery store with this motion planner on a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{12} + \mathbf{7}$</tex> DoF mobile manipulation robot executing a grocery fulfillment task, achieving ≈0.3s average planning times with 100% success rate across 950 motion plans.
A Hierarchical Filter Transfer Learning Algorithm for Heterogeneous Domains

A main goal of heterogeneous transfer learning algorithms is to solve the domain adaptation problem of different feature spaces. However, some existing heterogeneous transfer learning methods usually only extract common features from the source domain and target domain, ignoring specific features, which may damage the performance of transfer learning. Therefore, a hierarchical filter transfer learning algorithm (HFTLA) for heterogeneous domains in is proposed. First, a nonlinear mapping is constructed to learn the potential relationship between the features of different domains. Then, the feature space can be aligned by learning common features and specific features, which can ensure the integrity of the features. Second, a hierarchical filter framework is developed to play different roles in different stages of transfer learning. In the pretransfer phase, a knowledge filter based on genetic principle is designed to increase the diversity of knowledge with different genetic operators. In the post-transfer phase, a guided filter is established to achieve a coupling balance between source knowledge and target information. Finally, experimental results on heterogeneous domains illustrate the effectiveness of HFTLA.
Eliciting a Security Architecture Requirements Baseline from Standards and Regulations

Recently, numerous security standards, laws, and regulations have been issued to address growing security concerns for critical systems. As a result, assuring that a system software architecture complies with security standards and regulations has become a top priority, critical to an organization's success. This study focuses on a SCADA system use case to identify the practical challenges in eliciting adequate security requirements to ensure that a system software architecture complies with security standards and accommodate the evolving nature of both the standards and the systems. To address these challenges, a systemic and iterative process for eliciting security requirements is proposed. The findings shed light on improving the effectiveness and efficiency of the security requirements elicitation process, ensuring compliance with standards, policies, and regulations in system software architecture.
Visualizations for User-supported State Space Exploration of Goal Models

Automated analysis has been used in goal-oriented requirements engineering (GORE) to evaluate scenarios and make trade-off decisions. For higher complexity problems (e.g., backwards analysis), using a search-based solver may be more efficient than custom algorithms. When these black-box solvers produce a single solution, users may be suspicious about whether the given answer is ideal or believable. Users would like to explore the potential solutions but are prevented from doing so because these inquiries often suffer from a state explosion problem. In this RE@Next! paper, we introduce the use of valuation-based filtering and coloring to assist users in understanding a solution space and selecting custom states from it. We use the concrete semantics of modeling requirements in the Evolving Intentions framework and its associated goal modeling tool, BloomingLeaf, to explore the application of these visualization techniques. In our initial evaluation, we demonstrate how these techniques can be used on a fully worked out example. We conduct initial measurements of the time savings and state space reduction created by the valuations and color filtering, and discuss future directions of this project.
Asymptotically Optimized Multi-Surface Coverage Path Planning for Loco-Manipulation in Inspection and Monitoring

Regular inspection and monitoring of aging assets are crucial to safe operation in industrial facilities, with remote robotic monitoring being a particularly promising approach for asset inspection. However, vessels, pipework, and surfaces to be monitored can follow complex 3D surfaces, and frequently no 3D as-built models exist. In this paper, we present an end-to-end solution that uses an optimization method for coverage path planning of multiple complex surfaces for mobile robot manipulators. The system includes a two-layer hierarchical structure of optimization: mission planning and motion planning. The surface sequence is optimized with a mixed-integer linear programming formulation while motion planning solves a whole-body optimal control problem considering the robot as a floating-base system. The loco-manipulation system automatically plans a full-coverage trajectory over multiple surfaces for contact-based non-destructive monitoring after unrolling the 3D-mesh region-of-interest selected from the user interface and projects it back to the surface. Our pipeline aims at offshore asset inspection and remote monitoring in industrial applications, and is also applicable in manufacturing and maintenance where area coverage is critical. We demonstrate the generality and scalability of our solution in a variety of robotic coverage path planning applications, including for multi-surface asset inspection using a quadrupedal manipulator.
Accountable Design for Individual, Societal, and Regulated Values in the UAV Domain

Software systems are increasingly expected to address a broad range of stakeholder values representing both personal and societal values as well as values ensconced as laws and regulations. Whereas laws and regulations must be fully addressed, other human values need to be carefully analyzed and prioritized within the context of candidate architectural designs. The majority of prior work has investigated requirements engineering techniques for either regulatory compliance or for human-values, we take an integrated approach which simultaneously considers laws and regulations as well as societal and personal human values throughout the system analysis, specification, and design process. We illustrate our approach through detailed examples drawn from a multi-drone system regulated by the USA Federal Aviation Authority (FAA) and operating in a domain rich with human and societal values. We then discuss requirements engineering challenges and solutions unique to identifying analyzing, and prioritizing human, societal, and regulatory requirements, and ultimately for designing accountable software systems.
Using Motivational Models to Promote Emotional Goals Among Software Engineering Students

There has been increasing awareness in recent years of the importance of considering broader, social and personal goals in software engineering. One important aspect is the need to engender certain feelings in users when they interact with software. Such emotional goals are relatively new to software development. While there are established methods for teaching requirements elicitation for standard goals and evaluating whether they have been met, there is much less work on evaluating whether emotional goals have been met through software. For the past five years, emotional goals have been explicitly included while teaching software requirements with students at The University of Melbourne and Swinburne University of Technology, both in dedicated subjects and in project subjects. Students have been strongly encouraged to incorporate emotional goals in the design of projects' requirements through the use of motivational models. In this paper we discuss and reflect on our experience teaching motivational modelling that is aimed at supporting students in the creation and evaluation of emotional goals in the software requirements phase.
ILLOD Replication Package: An Open-Source Framework for Abbreviation-Expansion Pair Detection and Term Consolidation in Requirements

ILLOD is a tool for detecting abbreviation-expansion pairs (AEPs) in requirement sets. It utilizes syntactic features such as Initial Letters, term Lengths, Order, and Distribution of characters to determine if a term is a potential long form to a given abbreviation. The artifact bundles all source code and data resources to replicate evaluation results presented for ILLOD in two research papers published at the REFSQ2022 Conference and in the Information and Software Technology (IST) journal. In addition, ILLOD can be used to detect AEPs, perform abbreviation detection, and the input data-set can be used for further research in requirements engineering or other related fields. The repository is organized into different directories containing data, Python sources, and notebooks for experiments and evaluations. Detailed instructions are provided to load and use the tool on a local system, and the results generated by ILLOD are stored in output files. The tool demonstrates its effectiveness in detecting AEPs and consolidating glossary terms, and the evaluation results provide insights into the performance of different classifiers. The artifact repository is a valuable resource for researchers and practitioners in the field of requirements engineering and related areas.
Autonomous Navigation and Obstacle Avoidance for Mobile Robots in a Tax Environment

This paper focuses on the navigation and obstacle avoidance problems of intelligent mobile robots in a tax environment. Through combining the improved jump point search (JPS) and dynamic window approach (DWA) algorithms, a fusion path planning method is proposed for the mobile robots in tax services. Specially, a bi-directional JPS algorithm with an adaptive heuristic function is provided to decrease the search complexity and improve search flexibility in the global path planning. Meanwhile, considering the features of the tax environment, several evaluation subfunctions are particularly designed to leverage the global information and optimize the local path planning. Finally, various simulations and experiments in a Turtlebot3 robot validate the feasibility and effectiveness of the proposed method. The obtained results provide valuable guidance for the autonomous navigation research of the tax service robots.
Towards a Basic Security Framework for SMEs – Results From an Investigation of Cybersecurity Challenges in Denmark

Denmark is one of the most digitised countries in Europe. However, according to recent reports, IT security in Danish companies has not followed along. To shed light into the challenges that companies experience with implementing IT security, we conducted interviews with four employees from four different companies, asking about their IT security and their needs to become better protected. Our results show that the companies are lacking fundamental security protection and are in need of basic security help and raising awareness of cyber threats in the organisations. Based on our findings, we discuss directions for future steps in terms of addressing these issues.
Welcome to the Seventh International Workshop on Crowd-Based Requirements Engineering (CrowdRE'23)

Welcome to the 7th International Workshop on Crowd-Based Requirements Engineering (CrowdRE'23), where scientists and representatives of industry meet to exchange ideas, experiences, and other contributions regarding the state of the art of Crowd-Based Requirements Engineering (CrowdRE). The discipline of CrowdRE seeks to address the challenges of traditional requirements engineering (RE) in scaling up to settings with thousands to millions of users of (software) products or (software-driven) services, who form a large and heterogeneous group that can be denoted as a ‘crowd’ [1], [2]. The user feedback generated by the crowd, such as texts or usage data, can be a valuable source of requirements, problems, wishes, and needs. Responding quickly, effectively, and iteratively to this feedback can greatly increase a product's success. CrowdRE comprises any approach that provides RE with suitable means for this crowd paradigm, especially by involving the crowd and by collecting, harmonizing, analyzing, and Interpreting their user feedback.
Binocular active stereo matching based on multi-scale random forest

With the rapid development of 3D vision technology, the existing passive binocular cameras can no longer meet the practical needs of depth perception. Therefore, this paper proposes a binocular active stereo matching method based on multi-scale random forest. Firstly, a binocular active vision system consists of a near-infrared random speckle projector and a binocular camera is constructed, and the system was calibrated using Zhang's calibration method. Secondly, gamma image enhancement and image difference method are used for processing to reduce the impact of ambient light on the measurement results. Next, extract the points of interest and use the multi-scale random forest algorithm to match the window where the points of interest are located to generate a sparse structured light anchor disparity map globally. Finally, using the Census transforms as the matching cost, iterate continuously, refine the disparity and get a dense disparity map. The experimental results show that this system can achieve good depth perception accuracy and robustness under complex indoor lighting conditions.
Indoors Traversability Estimation with RGB-Laser Fusion

We propose a dual-stream, semi-supervised, attention-based approach that employs feature fusion of RGB and Laser Range Finder (LRF) modalities. Our method lever-ages the strength of two powerful transformer-based networks, i.e. Vision Transformer (ViT) and SegFormer, along with LRF information, to adequately predict whether the scene encountered in the image is safe for a robot to traverse. Towards this effort, we introduce an automated labelling system profiting from the combination of raw velocity readings and laser scanning information. Moreover, we show that overall GOINO-GO detection is enhanced by fusing RGB and laser modalities. Feature fusion is achieved through the employment of a Multi-Head Self-Attention (MHSA) module. Through cross-domain validation, we show that the proposed traversability estimation method can achieve decent amounts of transferability even with limited amount of training data.
Can Videos be Used to Communicate Non-functional Requirements? An Early Empirical Investigation

Non-functional requirements (NFRs) are not easy to elicit and formulate. Technology development and social media platforms made videos one of the primary means to communicate different information. Also, for software products, videos are used to, e.g., validate the idea of products before development-vision videos, to present them-product videos. Thus, a question arises can we use videos to communicate NFRs within Requirements Engineering activities? We designed an empirical study to answer the stated question and conducted an early investigation of 5 vision videos. We found that requirements about interoperability, time behavior, and usability were the most frequently mentioned in vision videos. We identified also several methods of expressing NFRs, e.g., by showing end-user reactions, using captions or narrator. Our study is the first attempt to investigate the research question. It follows from it that videos seem to be a useful means to communicate NFRs. We identified several challenges in using this medium and in analyzing videos that allowed us to suggest areas that can be included in the roadmap for further research in this area.
A Comparative Evaluation of Requirement Template Systems

Context: Multiple semi-formal syntax templates for natural language requirements foster to reduce ambiguity while preserving readability. Yet, existing studies on their effectiveness do not allow to systematically investigate quality benefits and compare different notations. Objectives: We strive for a comparative benchmark and evaluation of template systems to support practitioners in selecting template systems and enable researchers to work on pinpoint improvements and domain-specific adaptions. Methods: We conduct a comparative experiment with a control group of free-text requirements and treatment groups of their variants following different templates. We compare effects on metrics systematically derived from quality guidelines. Results: We present a benchmark consisting of a systematically derived metric suite over seven relevant quality categories and a dataset of 1764 requirements, comprising 249 free-text forms from five projects and variants in five template systems. We evaluate effects in comparison to free text. Except for one template system, all have solely positive effects in all categories. Conclusions: The proposed benchmark enables the identification of the relative strengths and weaknesses of different template systems. Results show that templates can generally improve quality compared to free text. Although MASTER leads the field, there is no conclusive favourite choice, as overall effect sizes are relatively similar.
Design of a Seedling Planting Robot Using a CNC Controller

Planting large numbers of seedlings in a short period improves the quality of life and meets basic human needs, especially in areas where forests have been damaged by fires or human activities. Planting seedlings has always been a very exhausting and time-consuming process that requires a significant amount of human effort. This article discusses the steps of designing and building an autonomous seedling planting robot (known as Nahal) that performs pit digging, precise planting, and irrigating are discussed. In addition, the idea of using a closed-loop control system of CNC machines to control the seedling robot’s operation is proposed. In this approach, the robot controller initially receives the CAD file of the land where the seedlings are to be planted along with the details of the land and the points marked for planting the seedlings and, finally, the CNC control software is used for the robot to navigate the robot through the entire planting stages.
Assessment of Lightweight Cryptography Algorithms on ARM Cortex-M Processors

The proliferation of sensitive information and data processing in the Internet of Things edge and end devices is accelerating at a rapid pace. As a result, ensuring security becomes increasingly crucial. Although encryption is essential, edge devices often cannot accommodate large overheads. This study assesses the efficiency of ten lightweight cryptography (LWC) algorithms by comparing their power consumption, performance, and memory footprints in ARM Cortex architectures. The goal is to guide designers working with LWC and ARM processor architectures. The LWC algorithms, along with the baseline Advanced Encryption Standard (AES), are implemented and executed on ARM Cortex-M processors, intended for edge devices. Results reveal trade-offs associated with cipher properties and the inherent architectural resources of each processing device.
An Attention-ReXNet Network for Long Tail Road Scene Classification

With the development of assisted driving and autonomous driving, road scene classification has become increasingly important due to its ability to provide environmental information for drivers and autonomous driving, which is beneficial to reduce traffic accidents. However, road scene classification is limited by dataset imbalance and running speed, making it difficult to apply practically. To solve the problems mentioned above, we proposed an Attention-ReXNet network to conduct road scene classification. A balance Softmax cross-entropy is introduced to solve the class imbalance problem in road scene classification. The proposed method is evaluated in the Road Surface Classification Dataset (RSCD), which contains 27 categories and 1 million images with class imbalance. Experimental results show that Attention-ReXNet and balance Softmax cross entropy can achieve the best classification performance, with accuracy and top-1 being 87.67% and 88.52%, respectively.
Cross-Modal Self-Supervised Feature Extraction for Anomaly Detection in Human Monitoring

This paper proposes to extract cross-modal self-supervised features to detect anomalies in human monitoring. Our previous works that use deep captioning in addition to monitoring images were successful. However, their use of unimodally trained image and text features shows deficiencies in capturing contextual information across the modalities. We devise a self-supervised method that creates cross-modal features by maximizing the mutual information between both modalities in a common subspace. It allows capturing different complex distributions between modalities, improving the detection performance of clustering methods. Extensive experimental results show improvements in both AUC and AUPRC scores when compared to the best baselines on two real-world datasets. The AUC has improved from 0.895 to 0.969, and from 0.97 to 0.98. The AUPRC has improved from 0.681 to 0.850, and from 0.840 to 0.894.