Reviewerly/exHarmony huggingface dataset:
Human Values-Driven User Requirements with the ACT Matrix: An eExam Case Study

Software systems are increasingly being used to mediate stressful life events that have lasting implications on individuals' well-being. To assist users in attaining their best outcomes, while managing uncomfortable feelings, requirements engineers need to consider the sociotechnical aspects of a system. They should consider the values driving user needs as well as the technical aspects. In this paper, we present a novel approach of using the ACT (Acceptance and Commitment Therapy) Matrix as a method for eliciting human values-driven user requirements. The ACT Matrix is a visual mapping tool, originally used by psychotherapists to assist their clients to focus on valued actions despite uncomfortable feelings, to achieve long-term health and well-being outcomes. Our main research question aimed to answer whether the ACT Matrix instrument is an effective tool for requirements engineers to elicit values-driven user requirements that can be implemented to improve a sociotechnical system. We present our case study of applying this method to a system which mediates university exams. We report on methodology and study design, and present preliminary results with examples of values-driven user requirements. The pros and cons of our approach are discussed from both a human values and requirements engineering perspective, and recommendations are made.
TAGRAM: A Framework for Tagging User Stories

User stories are a popular notation for representing requirements, especially in agile development. However, how they can be used further during the development process is still under examination. In recent years, user stories research has focused on how to apply machine learning to user stories to understand their quality, the required implementation efforts, and the ability to generate software artifacts. One primary concern in this direction is the lack of datasets and specially labeled ones. To bridge this gap, in this work, we introduce a framework for tagging user stories to construct labeled datasets for user stories for various tasks.
Boosting Unsupervised Domain Adaptation for 3D Object Detection in Point Clouds with 2D Image Semantic Information

Both 3D and RGB-D data are applicable for 3D object detection, yet there exists significant geometric bias between these two data representations owing to the different reconstruction procedures. The geometric bias between these two data types induces performance drops for cross-domain testing; hence we propose an unsupervised domain adaption (UDA) framework to leverage annotated data in different data formats for indoor 3D object detection. Our method inverse-projects the pixel-wise semantic labels predicted from 2D images onto point clouds for object detection and UDA in both directions. For the more challenging UDA from 3D to RGB-D data, we propose some additional strategies to reduce the domain gap by aligning the extracted features from two domains with adversarial training. Our method reduces the domain gap between two types of data and leverages the semantic label information predicted from 2D RGB images to boost the accuracy of the 3D object detection model. In our experiments, we validate our approach with ScanNet and SUN RGB-D as the source and the target datasets in both directions of domain adaptation. The proposed method improves the mAP@0.25 by 6.4% and 10.3% for the two directions of cross-dataset testing compared with that without applying any domain adaptation.
Vision-Based Potential Field Path Planning for Robot Obstacle Avoidance Under Field-of-View Constraints

Path planning improves the performance and robustness of vision-based robot control in unstructured environments. Visual servo path planning usually focuses only on the path of the camera in the Cartesian space or the path of the feature points in the image space, which means that the collision between robots and obstacles in the workspace cannot be avoided. This paper proposes a vision-based potential field path planning method for robot manipulators with field-of-view constraints in the image space and obstacle avoidance in the Cartesian space. A hybrid potential field function is proposed to integrate Fo V limits in the image space and obstacle avoidance in the Cartesian space. A probability-based search strategy is presented for path planning to avoid oscillation caused by gradient descent. The proposed method is applied to a collaborative robot with 7 degrees of freedom equipped with an eye-in-hand camera. Experiments in two different environments have verified the superiority of the proposed method regarding spanned image areas, camera path lengths, iteration times, and obstacle avoidance compared to the classical gradient descent-based method.
Secure Co-Creation of Industrial Knowledge Graph: Graph Complement Method with Federated Learning and ChatGPT

Industrial areas have increasingly developed their own Knowledge Graph (KG) for organizing and leveraging vast amounts of data. One major challenge in constructing KG is the heavy reliance on available resources, restricting the scalability and accuracy of the resulting graphs. To address this issue, an end-to-end method is proposed to create a multi-benefit ecosystem by integrating Federated Learning with ChatGPT (a popular language model). Different stakeholders may leverage ChatGPT to search for novel knowledge that complements their existing KGs, however, this approach could potentially introduce ambiguous and wrong triples into the KG. To overcome this, Federated Learning is applied to align and disambiguate the triples using other industrial KGs as super-vision. The proposed method applies a multi-field hyperbolic embedding method to vectorize entities and edges, which are then associatively aggregated to achieve edge replenishment and entity fusion for each KG encrypted. Finally, an incentive win-win mechanism is proposed to motivate diverse stakeholders to contribute to this co-creation actively. A case study is conducted on different industrial KG to evaluate the proposed method. Results demonstrate that this method provides a practical solution for KG co-creation and no compromise to data security.
Improved YOLOv5s Network for Traffic Object Detection with Complex Road Scenes

Aiming at the problems of low target detection accuracy and prone to false detection and missed detection in complex traffic scenes, a road target detection algorithm based on improved YOLOv5s is proposed. Firstly, a feature extraction layer based on Spatial Pyramid Pooling(SPP) is adopted for increasing shallow feature receptive fields named as Multi-scale SPP module (M-SPP). M-SPP is designed to fully integrate feature information of different scales for improving feature extraction ability of our network. Secondly, a Global Context Attention Module(GCAM) is proposed, in focusing on important features in channel and spatial dimensions, allowing the network to focus on more useful information. Finally, experimental results show that detection accuracy of the improved YOLOv5s model we proposed on the UA-DETRAC dataset, compared with the original algorithm, improved by 1.83% and 2.54% respectively. Our improved model abtain good detection accuracy while ensuring real-time performance, and solves problem of missed detection and false detection of small targets in complex road scenes to a certain extent.
An Experiment on the Effects of Using Color to Visualize Requirements Analysis Tasks

Recent approaches have investigated assisting users in making early trade-off decisions when the future evolution of project elements is uncertain. These approaches have demon-strated promise in their analytical capabilities; yet, stakeholders have expressed concerns about the readability of the models and resulting analysis, which builds upon Tropos. Tropos is based on formal semantics enabling automated analysis; however, this creates a problem of interpreting evidence pairs. The aim of our broader research project is to improve the process of model comprehension and decision making by improving how analysts interpret and make decisions. We extend and evaluate a prior approach, called EVO, which uses color to visualize evidence pairs. In this scientific evaluation paper, we explore the effectiveness and usability of EVO. We conduct an experiment (n = 32) to measure any effect of using colors to represent evidence pairs. We find that with minimal training, untrained modelers were able to use the color visualization for decision making. The visualization significantly improves the speed of model comprehension and users found it helpful.
A Tiny Graph Neural Network for Inverse Graph Partitioning with Imbalance Constraints

Recent years have witnessed an interest in inverse graph partitioning (IGP), which stems from many real-world applications such as anti-community detection and detention room allocation. Previous studies typically focus on the balanced IGP problem that divides a graph into multiple subgraphs of the same size. However, many practical scenarios require partitioning a graph into subgraphs of different sizes, which limits the feasibility of existing IGP methods. In this paper, we present an end-to-end model to solve IGP with imbalance constraints, called IIGPN, which utilizes a tiny graph neural network to learn subgraph assignments for nodes. IIGPN is iteratively trained against an unsupervised loss function that encodes the imbalanced IGP problem. Empirical experiments show that IIGPN significantly outperforms existing algorithms and approximates the optimal solution on large-scale graphs. Moreover, IIGPN reduces the computation time by about 90 percent compared to the best heuristic algorithm.
Expansive Spatial-Temporal Kernel Convolution Network for Skeleton-Based Action Recognition

In skeleton-based action recognition, graph convolutional networks (GCNs) have become the most common method due to their strong modeling ability on graph data and the utilization of prior knowledge on human skeleton. However, convolutional methods have been overlooked in this field despite their wide applications in visual tasks, mature libraries, and learning resources. In this paper, we propose an Expansive Spatial-Temporal Kernel Convolution Network (ESTCN) consisting of Expansive Spatial Kernel Convolution and Expansive Temporal Kernel Convolution for spatial feature extraction. Our ESTCN adopts large kernel convolution to achieve global receptive field and utilize prior knowledge on human joints. Extensive experiments on NTU-RGB+D dataset demonstrate that our method achieves comparable performance with GCN-based methods and outperforms other convolutional methods.
An RE'23 Workshop on Environment-Driven Requirements Engineering (EnviRE'23)

We organize a one-day workshop on Environment-Driven Requirements Engineering (EnviRE'23) in conjunction with the 31st IEEE International Requirements Engineering Conference. With the rising influence of AI, IoT, and cyber-physical systems, we realize that the environment, in which the software operates, becomes more open and evolves rapidly with stakeholders' changing needs. EnviRE'23 features one keynote and seven accepted papers. In addition, we organize an interactive session with workshop participants to explore the role of large language models (LLMs), specifically ChatGPT, in requirements elicitation and modeling. Overall, the workshop is aimed at bringing the interested researchers and practitioners together, exchanging ideas and visions, and exploring a set of open problems to pursue in the years to come. Since the first edition of EnviRE in 2021, this is the first time that our workshop is held in person. We are excited to continue our workshop after the pandemic.
Obstacle-Avoiding Path Planning and Tracking for Intelligent Vehicles Based on Kinematic Model

With the rapid development of intelligent driving technology, people have higher requirements for the safety of vehicle driving. The framework of this paper is divided into two layers. The upper layer is responsible for the path planning of the driving trajectory, and the lower layer is the trajectory tracking control. In the path planning layer, an obstacle model based on the potential energy function is proposed, while considering the sensing range of the vehicle, so that the vehicle can make an obstacle avoidance response in advance. The search efficiency is improved by combining PSO to segment the trajectory. The differential flatness of the intelligent vehicles is used to smooth the whole trajectory and segment boundaries, making the driving more comfortable. The trajectory tracking layer utilizes the kinematic model and ensures that the inputs are within the control boundaries. Simulation and real experiment based on the Quanser self-driving car (QCar) have been accomplished to prove the effectiveness of the proposed algorithm.
Requirements Analysis of Variability Constraints in a Configurable Flight Software System

Variability constraints are an integral part of the requirements for a configurable system. The constraints specified in the requirements on the legal combinations of options define the space of potential valid configurations for the system-to-be. This paper reports on our experience with the variability-related requirements constraints of a flight software framework used by multiple space missions. A challenge that we saw for practitioners using the current framework, now open-sourced, is that the specifications of its variability-related requirements and constraints are dispersed across several documents, rather than being centralized in the software requirements specification. Such dispersion can contribute to misunderstandings of the side-effects of design choices, increased effort for developers, and bugs during operations. Based on our experience, we propose a new software variability model, similar to a product-line feature model, in the flight software framework. We describe the structured technique by which our model is developed, demonstrate its use, and evaluate it on a key service module of the flight software. Results show that our lightweight modeling technique helped find missing and inconsistent variability-related requirements and constraints. More generally, we suggest that a variability modeling technique such as this can be an efficient way for developers to centralize the specification and improve the analysis of dispersed variability-related requirements and constraints in other configurable systems.
Transformer Based Approach for Sample Generation in Motion Planning

Efficient motion planning algorithms are critical in real-world robot applications, but existing methods become less effective as the robot's dimensionality and workspace increase, primarily due to the computational cost of collision checking during sample generation. We propose a framework that generates robot samples directly for the planning problem to overcome the high cost of expensive operations in sampling-based motion planning. The framework utilizes a combination of an AutoEncoder (AE) and a Transformer-based model, which efficiently generates valid robot samples using the output from the AE, capturing a latent space representation of the robot's environments represented as point clouds. The framework is tested on various ground and air robot planning problems in 3D environments and performs better than comparable methods across all the tested problems. It also demonstrates the ability to generalize to new and unseen environments.
Automatic Domain-Specific Corpora Generation from Wikipedia - A Replication Study

Replication studies help mature our knowledge and attempt to validate the findings of a prior piece of research. However, these studies are still rare in the Requirements Engineering field. Additionally, the rapidly advancing realm of Natural Language Processing (NLP) is creating new opportunities for efficient, machine-assisted workflows application which can bring new perspectives and results to the forefront. Thus, in this paper, we replicate and extend a previous study (baseline), a tool, WikiDoMiner, which automatically generated domain-specific corpora by crawling Wikipedia. In this study, we investigated and executed the implementation of WikiDoMiner (open-sourced code from the original paper) to recreate the results. This allowed us to strengthen the external validity of the original study. We extended the baseline to evaluate additional data sets and generated nuanced results using state-of-the-art NLP techniques such as Bidirectional Encoder Representations from Transformers (BERT). Results showed that due to the growing content in Wikipedia, the corpus generated for the Railways and Networks domains did not precisely match the results from the baseline. However, utilizing the state-of-the-art KeyBERT library from the Huggingface AI community enhanced the results, eventually generating a meaningful corpus compared to the baseline.
Augmenting the Problem Frames Approach with Explicit Data Descriptions Using ChatGPT

Problem Frames (PF) as a mainstream requirements analysis method has attracted extensive attention and research in the field of requirements engineering. However, the current problem diagram as a PF visualization tool is more abstract and lacks the ability to express data descriptions. In this paper, we propose a method to extend problem diagrams based on ChatGPT. The aim is to detail the requirements to ensure a more accurate and comprehensive representation of the complex requirements of the system. We combine problem diagrams and ChatGPT by letting them interact with each other, and obtain the cause table describing causal relationships that exist within shared phenomena, then we model the cause table and calculate the structural coefficient of each cause and rank the causes from the largest to the smallest. Finally the ranked causes are used as an extension of the shared phenomenon, thus using the property that they can help improve the detailed descriptions of the requirements. A case study of an insulin injection control system demonstrates the applicability and viability of this method.
Domain-Based Graph Neural Network: Evaluations of the Effectiveness of Graph Representation Based on Domain Knowledge

In this study, we propose a method to represent relationships between features as a graph for domain knowledge in manufacturing processes, and evaluate its impact on the prediction accuracy of machine learning models for varying granularities of domain knowledge. In industrial fields such as manufacturing, utilizing domain knowledge to understand the prediction results and ensure sufficient prediction accuracy by adding domain knowledge is important when constructing models from small amounts of data. By evaluating how much prediction accuracy is affected by differences in the granularity of the graph, how much knowledge is required for industrial applications can be estimated and industrial applications of models can be considered in terms of their cost effectiveness. Previous studies have confirmed that adding domain knowledge through graphs to graph neural networks improves prediction accuracy compared to deep neural networks due to inductive bias. However, the effect of domain knowledge granularities on prediction accuracy has not been confirmed. In this study, we applied two representations of domain knowledge to graphs and loss functions, and verified the changes in prediction accuracy. We experimentally evaluated the effectiveness of the granularity of domain knowledge for prediction accuracy using failure classification for machine failures in the manufacturing industry. Additionally, we also describe the advantage of adding domain knowledge in terms of improving prediction accuracy and its effect.
High-Resolution Image Anomaly Detection via Spatiotemporal Consistency Incorporated Knowledge Distillation

This paper introduces a new, practical, and challenging scenario of high-resolution (HR) image anomaly detection. Anomaly detection methods cooperated with sliding windows are typical solutions for this task, but they fail to capture long-term dependencies. This paper proposes a Spatiotemporal Consistency Incorporated Knowledge Distillation (STCIKD) method, which translates HR images into video sequences and exploits spatial and temporal consistency between them to capture both local spatial and long-term dependencies. STCIKD consists of a teacher network and two student networks. Among the two students, a spatial student network captures spatial consistency by reconstructing the current video frame, and another temporal student network learns temporal consistency by predicting the future frame. This paper benchmarks several state-of-the-art image anomaly detection methods and evaluates STCIKD for HR image anomaly detection. The results show that by incorporating spatial and temporal consistency, STCIKD significantly outperforms other methods.
Collision-Free Motion Planning for Multiple Robot Arms by Combining Deep Q-Network and Graph Search Algorithm

In recent years, multiple industrial robots have been introduced into factories, and they are required to perform complex tasks. In order to improve work efficiency, it is also important to generate a collision-free trajectory in a limited space where the robots interfere with each other. However, in the actual collision avoidance motion, the robot may collide due to motion delay or disturbances. In particular, collisions between industrial robots and humans have caused fatal accidents in the past, and a collision avoidance motion planning method for multiple robot arms is required to avoid such accidents. Previous research has achieved a real time avoidance method for multiple robot arms using a graph search algorithm and Q-learning which is one of the reinforcement learning method. In this paper, we propose a collision avoidance motion planning method using DQN (Deep Q-Network) instead of Q-learning. By using DQN, it is possible to deal with the case where the input is an image and continuous state space. We compare the performance of the proposed method and the conventional method by simulation experiments. As a result, the proposed method shows its usefulness in the cases where the conventional method could not avoid collisions.
Towards Quantum Requirements Engineering

Quantum computing is rapidly transitioning from a scientific curiosity to an industrial reality, offering the potential to overcome classical computation limitations. This shift has given rise to the field of quantum software engineering (QSE), focused on developing novel approaches for engineering large-scale quantum applications. Traditional software engineering methods fall short in addressing the unique characteristics of quantum computing, necessitating innovative QSE methodologies and tools. While progress has been made in testing and debugging quantum programs, very little work has been done on analyzing if and how existing requirements engineering techniques can address the challenges that designing quantum software raises. This paper discusses some of the challenges for requirements engineers while eliciting requirements for quantum software, modeling the collected requirements, and analyzing them, and introduces some initial solutions for eliciting, modeling, and analyzing quantum requirements.