Reviewerly/exHarmony huggingface dataset:
A Chebyshev polynomial approach to approximate solution of differential equations using differential evolution

Most of the problems associated with physical, biological and engineering systems are complex and are usually modeled with the help of linear as well as non-linear differential equations. In general, the exact solutions of non-linear differential equations may not be obtained by analytical methods and thus, in this paper, a numerical method is proposed to find approximate solutions of differential equations using Chebyshev polynomials and metaheuristic optimization algorithms. In this paper, Ordinary differential equations (ODEs) are modeled as optimization problems to determine their approximate solutions by considering Chebyshev polynomials as the base approximation function, taking initial and/or boundary conditions as constraints. The unknown coefficients of the polynomials are calculated using Differential Evolution (DE) in order to get the approximate solution with minimal error. The proposed method is applied to find the approximate solution to both Initial and Boundary Value Problems. For all test problems considered in this paper, the algorithms have been programmed using MATLAB software to run on computer. The effectiveness of the method is demonstrated by graphical comparison of the computed approximate solutions with that of the exact ones. Further, the performance of the method proposed in this paper is shown by finding out the Root Mean Square Error (RMSE) between the exact and approximate solution, which is found to be better than that of the related existing papers.
Privacy-Preserving User Pose Prediction for Safe and Efficient Human-Robot Interaction

Enhancing user privacy is crucial in improving the safety and efficiency of Human-Robot Interaction (HRI), as it is a key factor for establishing user trust in the robot. Using privacy-preserving sensors and local processing of the user's data are ways to enhance privacy in HRI. This paper presents a privacy-preserving sensing system for real-time tracking and predicting the user's movements in HRI. As privacy-preserving sensors, a thermal and a depth camera are used to monitor the user's movements and determine their current pose. In order to improve the robot's perception of the user's situation and enhance the quality of real-time user monitoring, a Deep Learning (DL) model has been developed to estimate the future poses of the user. The developed model is based on the Sequence to Sequence mechanism (Seq2Seq). Modifications have been made to Seq2Seq so it can be run locally on the robot. As a result of these modifications, the computational cost of the model has been reduced by 34%. Experimental studies have been conducted to evaluate the performance of the sensing system in tracking and predicting the user's movements in HRI. According to the test results, the proposed sensing system is able to track the movements of the user appropriately. Additionally, it is shown that the estimation of the user's movement through the proposed system with the prediction model can improve the safety and efficiency aspects of the HRI experiments by up to 24% and 17%, respectively.
A Model Checking Based Software Requirements Specification Approach for Embedded Systems

With the development of embedded techniques, embedded systems are becoming more and more complex along with complex requirements. Automatic software requirements specification approach is urgently required for embedded systems. Existing researches on automatic specification do not address timed-systems which are required in embedded systems. Therefore, this paper proposes a time related model checking based approach for software requirements specification in embedded systems. It tries to use a time related model checking technique to give a possible path of software behaviors to achieve expected effects. Specifically, this approach involves creating a domain-specific device library, expressing the system requirements, modeling the embedded system using timed automata, and deriving software requirements specifications through reachability analysis in the model checking tool UPPAAL. Additionally, a case study is conducted on a sun search control system to verify the feasibility of this approach.
Knowledge-Enhanced Neurosymbolic Artificial Intelligence for Cybersecurity and Privacy

Symbolic Artificial Intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and explicit, symbolic knowledge contained in knowledge graphs to enhance explainability and safety in AI systems.This approach addresses a key criticism of current generation systems, namely their inability to generate human-understandable explanations for their outcomes and ensure safe behaviors, especially in scenarios with unknown unknowns (e.g.cybersecurity, privacy).The integration of neural networks, which excel at exploring complex data spaces, and symbolic knowledge graphs, which represent domain knowledge, allows AI systems to reason, learn, and generalize in a manner understandable to experts.This article describes how applications in cybersecurity and privacy, two most demanding domains in terms of the need for AI to be explainable while being highly accurate in complex environments, can benefit from Neuro-Symbolic AI.
Time-Efficient Path Planning for Semi-Rigid Multi-Robot Formations

In this paper, we propose a novel approach for execution and planning-time efficient path planning for a formation of robots moving in complex indoor environments. Our approach combines the Voronoi Diagram (VD) with spline-based path planning to create smooth paths with limited curvature. We also incorporate free spaces into the VD to reduce the maximum curvature of the paths. Due to the high obstacle clearance enabled by VDs and the reduced curvature, we can reach higher velocities, given a permissible control error. Our simulation evaluation uses 200 randomly generated paths and compares the results with a splined Relaxed A* path planner. Our method saves several seconds for a 70-meter-long path with significantly reduced tracking error. These results demonstrate the potential of our approach for time-efficient path planning in complex indoor environments.
Additional Heatmap Constraint for Depth-Based 3D Hand Pose Estimation

In this paper, we propose two additional heatmap constraint methods that can be integrated into existing detection-based 3D Hand Pose Estimation backbone networks. Our methods effectively reduce the gap between the training and inferencing, which is often caused by the post processing in detection-based methods, by proposing a constraint on the intermediate dense representation-the heatmap. Our methods achieve excellent results on both the NYU and ICVL datasets, demonstrating the effectiveness of our proposed method.
Partially Observable Markov Decision Process Model for Dynamic Human Activity Recognition Using Radio Frequency Signals

Human activity recognition (HAR) using radio frequency signals and machine learning is a novel approach for sensing and detection of human activities in a privacy preserving scheme. This is particularly important in healthcare systems, since privacy of patients and staff is a priority. Another advantage of this technology is the non-contact activity recognition with no need of line-of-sight (LoS). However, limited attention has been paid to human activity dynamics and their impact on HAR. In this paper, we propose a framework for dynamic human activity recognition (DHAR) based on a partially observable Markov Decision Process (POMDP) model, motivated by potential inaccuracy of current HAR models in real-world environments and human activity dynamics. The POMDP model dynamically uses historical data of observed human activities and model selections to select an HAR model from a set of given models. An approximate method is proposed to solve the POMDP model. The simulation experiments show that the DHAR method can deliver better performance over each single HAR model.
A Q-Learning based Method for Energy-Efficient Monitoring of Activities of Daily Living using a Wearable Device and a Companion Robot

In this paper, we proposed a Q-learning based algorithm to trade off the activity recognition accuracy, energy cost, privacy concerns and robot resource consumption in monitoring activities of daily living (ADLs) for elderly care using a wearable device and a companion robot. The robot uses a Q-learning algorithm to decide when to turn on what sensors for data collection by considering the above four criteria. The wearable device follows the robot's decision to turn on the relevant sensors to collect data which are sent back to the robot for activity recognition. First, we described the overall design of the system in which a wearable device and a companion robot collaborate for activity monitoring. Second we developed the Q-learning framework based on the knowledge of transition motions. Third, we trained the model and evaluated the proposed method using offline data and real time data collected from our smart home testbed. The results showed that the proposed method could recognize ADLs with high accuracy while saving about half energy compared with periodic methods. The results from real time evaluation showed that the activity detection rate is about 85%.
Research on Billet Defect Marking Path Decision Method

For the billet defects marking path prob-lem, a random sampling algorithm with weights and an improved Vikor algorithm are used to solve it. The defect weight is introduced into the random sam-pling algorithm to improve the distinguishing ability of the algorithm and obtain a better initial feasi-ble solution. Applying an improved Vikor-G J-entropy weighted integrated decision-making method and the combined weights of the G J-entropy weighted method are calculated by the weighted average. Combining the combined weights with the Vikor evaluation system to evaluate each program. The effectiveness of the random sampling algorithm with weights and the improved Vikor algorithm is verified by simulation and practical application.
Explanation Needs in App Reviews: Taxonomy and Automated Detection

Explainability, i.e. the ability of a system to explain its behavior to users, has become an important quality of software-intensive systems. Recent work has focused on methods for generating explanations for various algorithmic paradigms (e.g., machine learning, self-adaptive systems). There is relatively little work on what situations and types of behavior should be explained. There is also a lack of support for eliciting explainability requirements. In this work, we explore the need for explanation expressed by users in app reviews. We manually coded a set of 1,730 app reviews from 8 apps and derived a taxonomy of Explanation Needs. We also explore several approaches to automatically identify Explanation Needs in app reviews. Our best classifier identifies Explanation Needs in 486 unseen reviews of 4 different apps with a weighted F-score of 86%. Our work contributes to a better understanding of users' Explanation Needs. Automated tools can help engineers focus on these needs and ultimately elicit valid Explanation Needs.
Vision: Requirements Engineering for Software Development in Aged Care

Technology can play a key role in enabling the aged care system to provide better care for the older adults. In the past few years we have collaborated with several industry partners working in the field of aged care. Some of these operate and maintain aged care facilities, and some provide technological solutions to aged care providers. These collaborations involved extensive requirements elicitation to identify the needs of aged care residents, carers, clinical staff and facility administrators, on projects including mobile apps, data analysis, workforce training, and smart homes. Based on these, we have come up with a vision on a number of opportunities, and exposed many significant challenges, for requirements engineering in the aged care software domain that must be addressed. We summarise some of these requirements engineering challenges and a propose a vision with a framework to help address them in next-generation aged care software projects.
Geometry Problem Solving Based on Counter-Factual Evolutionary Reasoning

As a representative topic in natural language processing and automated theorem proving, geometry problem solving requires an abstract problem understanding and symbolic reasoning. A major challenge here is to find a feasible reasoning sequence that is consistent with given axioms and the theorems already proved. Most recent methods have exploited neural network-based techniques to automatically discover eligible solving steps. Such a kind of methods, however, is greatly impacted by the expert solutions for training. To improve the accuracy, this paper proposes a new method called counterfactual evolutionary reasoning, which uses a generative adversarial network to generate initial reasoning sequences and then introduces counterfactual reasoning to explore potential solutions. By directly exploring theorem candidates rather than the neural network selection, the new method can sufficiently extend the searching space to get a more appropriate reasoning step. Through comparative experiments on the recent proposed Geometry3k, the largest geometry problem solving dataset, our method generally achieves a higher accuracy than most previous methods, bringing an overall improvement about 4.4% compared with the transformer models.
Trajectory Generation for Online Payload Estimation of Robot Manipulators: A Supervised Learning Based Approach

This paper studies the optimal trajectory generation problem for online payload estimation to enable flexible manipulation, where the robotic manipulators pick, transport, and place various types of workpieces. Prevailing work focuses on offline estimation and solve time-consuming optimization problem for the optimal trajectory and initial configuration. By contrast, online estimation requires a quick trajectory generation process where the initial configuration, largely determined by the workpiece and environment layout, is not a design variable. Parameterizing joint trajectories by sinusoidal functions with the amplitudes being design variables, we adopt a supervised learning based approach to fulfill realtime trajectory generation where the mapping from the initial joint positions to the optimal amplitudes is established. This approach shifts the burden of solving computationally intensive and time-consuming trajectory design problems offline and facilitates the fast online generation of identification trajectories. The effectiveness of the trajectory generation method is demonstrated through simulation.
Trajectory Optimization of Manipulator for Loading and Unloading the Ladle Nozzle Based on Decomposition Multi-Objective Improved Whale Optimization Algorithm

In recent years, industrial manipulators are widely used in factories. To solve the problems of low efficiency and high joint jitter in ladle nozzle loading and unloading, a new trajectory optimization method is proposed. Firstly, a manipulator model is established on the base of mechanical construction and parameters of KR1000L750. And the 3-5-3 segmented polynomial interpolation approach is used to design the trajectory. Then a trajectory optimization model is proposed based on time and impact as optimization objectives. Secondly, the traditional whale optimization algorithm is improved by introducing a nonlinear adaptive convergence factor to balance the exploration and development of the algorithm. The improved algorithm has also added a new search mechanism during the exploration phase. To reduce the waste of exploration resources, the information on the current optimal solution is added by the Cauchy mutation method. Then, a decomposition-based multi-objective improved whale optimization algorithm (DMOIWOA) is proposed. The algorithm combines a penalty-based boundary intersection approach with the improved whale optimization algorithm. Six representative optimization algorithms are selected for comparison on the test functions. The DMOIWOA has evident superiority in convergence and distribution. Finally, the algorithm is applied to the trajectory optimization model to obtain a better Pareto front. In conclusion, the DMOIWOA can reasonably optimize the manipulator trajectory, improving the manipulator operation efficiency and reducing the manipulator jitter.
Voxel Based Motion Prediction for Efficient HRC Utilizing Latent Space

Safe human-robot-collaboration requires the robot not to pose a risk to the operator in the shared workspace. To avoid collisions different approaches such as sekleton-model based motion predictions of the human operator have been explored. Those approaches limit themselves to the motion of the operator and neglect other dynamic or movable objects in the workspace. We propose an approach that utilizes a purely voxel based prediction of motion in an arbitrary workspace without the use of models to predict any type of motion. This is done by encoding the voxelized 3D space as a latent vector and predict future occupation of the workspace by predicting possible future latent vectors. To archive this a combination of a Variational Autoencoder (VAE) and a GRU based prediction network is utilized. Through the nature of the used latent vectors it is possible to remove noise and complete hidden voxels from the input data and allow interpolation between similar voxel configurations. Interpolation is key to enable a meaningful decoding of prediced latent vectors back into the voxel domain. The VAE and GRU training are evaluated on two complex workspaces without prior knowledge. With our approach we can reconstruct complex environments without the need of any models with high accuracy and can predict human and object motion. As a second contribution the extensive workspace data sets will be made publicly available.
Modularized and scalable compilation for double quantum dot quantum computing

Abstract Any quantum program on a realistic quantum device must be compiled into an executable form while taking into account the underlying hardware constraints. Stringent restrictions on architecture and control imposed by physical platforms make this very challenging. In this paper, based on the quantum variational algorithm, we propose a novel scheme to train an Ansatz circuit and realize high-fidelity compilation of a set of universal quantum gates for singlet-triplet qubits in semiconductor double quantum dots, a fairly heavily constrained system. Furthermore, we propose a scalable architecture for a modular implementation of quantum programs in this constrained systems and validate its performance with two representative demonstrations, the Grover’s algorithm for the database searching (static compilation) and a variant of variational quantum eigensolver for the Max-Cut optimization (dynamic compilation). Our methods are potentially applicable to a wide range of physical devices. This work constitutes an important stepping-stone for exploiting the potential for advanced and complicated quantum algorithms on near-term devices.
A Transformer-based Approach for Abstractive Summarization of Requirements from Obligations in Software Engineering Contracts

Software Engineering (SE) contracts are a valuable source of software requirements. Seed requirements derived from SE contracts can provide a starting point to the Requirements Engineering (RE) phase. To extract such a seed however, a correct interpretation of contracts text is crucial. A major challenge with contracts text interpretation is that the text is lengthy, convoluted, and it incorporates a complex Legalese. If a summary of the high-level requirements from obligations present in SE contracts is available to the requirement analysts in a language that is comprehensible to them, they can use this seed requirements knowledge to ask the right questions to the stakeholders. In this paper, we propose an approach for summarizing the requirements present in obligations in a language comprehensible to requirement analysts. We use the principles of Prompt Engineering to prompt GPT-3 to generate summaries for training Natural Language Generation (NLG) models for generating SE-specific summaries. Experiments using NLG models such as BART, GPT-2, T5, and Pegasus indicate that Pegasus generates the most accurate summaries with the highest ROUGE score as compared to other models.
Research on Online Multi-Target Tracking Algorithm Combining Full Convolutional Twinnetworks and ReID Networks

Multi-target tracking algorithms play a huge role in intelligent video surveillance, unmanned driving, and new retail user portraits. This research has broad market prospects and value. However, the current multi-target tracking field has problems such as target positioning, target occlusion and half occlusion, operating speed and lighting conditions. Therefore, this paper proposes to introduce a single target tracking algorithm based on a fully convolutional twin network into the field of multi-target tracking, and proposes an online intelligent monitoring scheme with the multi-target tracking algorithm as the core.
An Investigation of Requirements Engineering Teaching in Higher Education in Switzerland

Requirements Engineering (RE) has emerged as an independent discipline in recent decades and is no longer considered a peripheral topic in software engineering. However, teaching this discipline to students poses challenges for educators in higher education, as it requires developing soft skills, decision-making abilities, practical application knowledge, and providing theoretical content. This Ph.D. project aims to develop a comprehensive understanding of RE teaching and investigate how Swiss universities address these challenges to produce “industry-ready” students. The study will use an Exploratory Sequential Mixed Methods Design involving interviews with RE lecturers and analysis of module descriptions to gather information on curricula, degree programs, subject integration, prerequisites, and instructional approaches. An online questionnaire will assess former students' perception of the RE course, suitable prerequisites, and instructional methods. Examining the collected data will reveal potential relationships between prerequisites, instructional approaches, and students' comprehension of RE. Text mining will be employed to compare industry needs with university curricula, analyzing Swiss job advertisements for RE positions to gain insights into desired skills and certifications. By analyzing the collected data, potential relationships between prerequisites, instructional approaches, and students' comprehension of RE will be examined. The findings may enhance teaching approaches in Requirements Engineering Education (REE) in Switzerland and beyond.