Reviewerly/exHarmony huggingface dataset:
Towards an integrated approach for managing the variability and evolution of both software and hardware components

Although the development of mass-customized products has been successfully applied to both hardware and software, companies struggle managing the variability and evolution of software-intensive products within a coherent product engineering approach. While the variability and evolution of software alone is manageable, managing both software and hardware within one product line is a complex task and requires an integrated approach. Moreover, as the release cycle for hardware is longer than for software, a product line of hardware and software is usually developed in generations. While one generation is validated and produced, the next generation is already planned and designed, reusing both software and hardware of previous generations. Thus, the different generations and artifacts shared between them must be managed together. Finally, when approaches directly assign software to hardware, managing their evolution becomes increasingly complex. Evolved resource demands may be missed, exhausting the resources provided by the hardware, possibly leading to degraded or faulty functionality. To remedy this, we refine the Unified Conceptual Model to our Variability Model for both Software and Hardware capturing the notion of product line generations, versions and variants of both software and hardware components, as well as resource demands of software on hardware. This is the first step towards the development of an integrated product engineering approach for managing the variability and evolution of software-intensive products.
<i>spl-js-engine</i>

In 2015, our research laboratory started the definition and implementation of a Software Product Line (SPL) for the generation of web-based Geographic Information Systems. Tooling support for SPL was scarce, and we did not found any suitable alternative to implement the mentioned product line. Therefore, we built spl-js-engine, a JavaScript library that, following the annotative approach, can generate final product source code from the annotated code, the feature model of the product line, and a product specification. spl-js-engine validates the specification of the product against the feature model prior to the generation. Since its first implementation, we have used this tool in many occasions both in the academia and the industry contexts.
Using Augmented Reality in AEC Tertiary Education: A Collaborative Design Case

The architecture, engineering, and construction (AEC) industry involves substantial requirements for visualization of complex three-dimensional models. There is a significant opportunity to expand the range of tools used to prepare students for this reality, while increasing engagement in the learning process. This study details several potential benefits of advanced visualization tools for AEC design courses, and proposes augmented reality (AR) as an addition to current teaching methods. An AR prototype that facilitates a mock building project design meeting has been developed, and a user evaluation (N=60) was carried out to measure its impact on visualization and engagement. The results demonstrate that AR enables students to visualize buildings with greater speed, accuracy, and confidence than paper drawings during a mock design meeting. User perspectives strongly favored AR across fields such as attention span, affinity for the building design, and ease of both visualization and communication. These results show promise for the use of AR in facilitating visualization practice during design education.
Introducing Students to Scientific Computing in the Laboratory through Python and Jupyter Notebooks

Described in this chapter are three undergraduate laboratory activities designed to introduce undergraduate chemistry students to scientific computing through Python and Jupyter notebooks. The activities include simulating first-order radioactive decay kinetics using random number generators in a General Chemistry course, having students calculate the entropy of a substance using specific heat capacity and enthalpy data in a first-year or intermediate-level course, and performing non-linear curve fitting of real gas data in an advanced-level physical chemistry course. None of these activities assume students have previous computer programming experience and all walk the students through simulations, loading, processing, analyzing, and visualizing data. To compensate for the lack of student programming experience, the activities leverage the Jupyter notebook code and markdown cell structure to provide additional prompting and explanation and sometimes include prewritten code. Each activity incorporates computing through a different model ranging from students simply modifying and running code to writing their own Python code. This chapter will discuss not only the activities, but lessons learned from teaching the activities and challenges faced in incorporating computing into the undergraduate chemistry curriculum.
Improving the Efficiency of Multi-Objective Grasshopper Optimization Algorithm to Enhance Ontology Alignment

Ontology alignment is an essential and complex task to integrate heterogeneous ontology. The meta-heuristic algorithm has proven to be an effective method for ontology alignment. However, it only applies the inherent advantages of meta-heuristics algorithm and rarely considers the execution efficiency, especially the multi-objective ontology alignment model. The performance of such multi-objective optimization models mostly depends on the well-distributed and the fast-converged set of solutions in real-world applications. In this paper, two multi-objective grasshopper optimization algorithms (MOGOA) are proposed to enhance ontology alignment. One is ε -dominance concept based GOA (EMO-GOA) and the other is fast Non-dominated Sorting based GOA (NS-MOGOA). The performance of the two methods to align the ontology is evaluated by using the benchmark dataset. The results demonstrate that the proposed EMO-GOA and NS-MOGOA improve the quality of ontology alignment and reduce the running time compared with other well-known metaheuristic and the state-of-the-art ontology alignment methods.
Performance Comparison of Deep Learning and Boosted Trees for Cryptocurrency Closing Price Prediction

The emergence of cryptocurrencies has drawn significant investment capital in recent years with an exponential increase in market capitalization and trade volume. However, the cryptocurrency market is highly volatile and burdened with substantial heterogeneous datasets characterized by complex interactions between predictors, which may be difficult for conventional techniques to achieve optimal results. In addition, volatility significantly impacts investment decisions; thus, investors are confronted with how to determine the price and assess their financial investment risks reasonably. This study investigates the performance evaluation of a genetic algorithm tuned Deep Learning (DL) and boosted tree-based techniques to predict several cryptocurrencies' closing prices. The DL models include Convolutional Neural Networks (CNN), Deep Forward Neural Networks, and Gated Recurrent Units. We assess and benchmark the performance of the DL models with boosted tree-based models on six cryptocurrency datasets from multiple data sources using relevant performance metrics. The results reveal that the CNN model has the least mean average percentage error of 0.06 and produces a consistent and highest explained variance score of 0.97 (on average) compared to other models. Hence, CNN is more reliable with limited training data and easily generalizable for predicting several cryptocurrencies' daily closing prices. Also, the results will help practitioners obtain a better understanding of crypto market challenges and offer practical strategies to lower risks.
Formally Compensating Performance Limitations for Imprecise 2D Object Detection

In this paper, we consider the imperfection within machine learning-based 2D object detection and its impact on safety. We address a special sub-type of performance limitations related to the misalignment of bounding-box predictions to the ground truth: the prediction bounding box cannot be perfectly aligned with the ground truth. We formally prove the minimum required bounding box enlargement factor to cover the ground truth. We then demonstrate that this factor can be mathematically adjusted to a smaller value, provided that the motion planner uses a fixed-length buffer in making its decisions. Finally, observing the difference between an empirically measured enlargement factor and our formally derived worst-case enlargement factor offers an interesting connection between quantitative evidence (demonstrated by statistics) and qualitative evidence (demonstrated by worst-case analysis) when arguing safety-relevant properties of machine learning functions.
Impact of Machine Learning on Safety Monitors

Machine Learning components in safety-critical applications can perform some complex tasks that would be unfeasible otherwise. However, they are also a weak point concerning safety assurance. An aspect requiring study is how the interactions between machine-learning components and other non-ML components evolve with training of the former. It is theoretically possible that learning by Neural Networks may reduce the effectiveness of error checkers or safety monitors, creating a major complication for safety assurance. We present an initial exploration of this problem focused on automated driving, where machine learning is heavily used. We simulated operational testing of a standard vehicle architecture, where a machine learning-based Controller is responsible for driving the vehicle and a separate Safety Monitor is provided to detect hazardous situations and trigger emergency action to avoid accidents. Among the results, we observed that indeed improving the Controller could make the Safety Monitor less effective; it is even possible for a training increment to make the Controller’s own behaviour safer but the vehicle’s less safe. We discuss implications for practice and for research.
A Practical Account into Counting Dung’s Extensions by Dynamic Programming

Abstract argumentation and Dung’s framework are popular for modeling and evaluating arguments in artificial intelligence. We consider various counting problems in abstract argumentation under practical aspects. We revisit algorithms and establish a framework that employs dynamic programming on tree decompositions for counting extensions of abstract argumentation frameworks under admissible, stable, and complete semantics. We provide an empirical evaluation and investigate conditions under which our approach is useful.
QMaxSATpb: A Certified MaxSAT Solver

While certification has been successful in the context of satisfiablity solving, with most state-of-the-art solvers now able to provide proofs of unsatisfiability, in maximum satisfiability, such techniques are not yet widespread. In this paper, we present QMaxSATpb, an extension of QMaxSAT that can produce proofs of optimality in the VeriPB proof format, which itself builds on the well-known cutting planes proof system. Our experiments demonstrate that proof logging is possible without much overhead.
A Machine Learning System to Improve the Performance of ASP Solving Based on Encoding Selection

Answer set programming (ASP) has long been used for modeling and solving hard search problems. Experience shows that the performance of ASP tools on different ASP encodings of the same problem may vary greatly from instance to instance and it is rarely the case that one encoding outperforms all others. We describe a system and its implementation that given a set of encodings and a training set of instances, builds performance models for the encodings, predicts the execution time of these encodings on new instances, and uses these predictions to select an encoding for solving.
Exploring the Experience of Traveling to Familiar Places in VR: An Empirical Study Using Google Earth VR

Considered to be an emerging topic in tourism research, traveling to familiar places benefits both the travelers and destinations. However, the current development of familiar tourism is severely affected by the epidemic. Recognizing the potential of virtual reality (VR), this research aims to explore VR’s relevance in the application of visiting familiar places and the empirical features of VR beyond on-site familiar tourism. We first reviewed the literature and developed an experience framework. Secondly, we conducted an exploratory activity in which participants (N = 16) used Google Earth VR to travel to their familiar places with two conditions (free exploration and task-oriented travel). In the activity, we employed think-aloud protocols, a scale, and semi-structured interviews. With our framework, we categorized findings into five dimensions and indicated the similarities and differences between familiar tourism in VR and that in actual places. Particularly, three empirical features (the sense of distance, multi-view space, and montage time) were recognized and formed VR’s opportunity to outperform on-site travel. We conclude by discussing the impacts of VR on familiar tourism and providing implications, including designing the timeline, sounds, tasks, and virtual guides. These suggestions inspire designers to acknowledge VR limitations and identify directions for future tourism applications. As a beginning of empirical investigation on VR familiar tourism, this study opens up a new field of discussion around VR tourism experience. We invite the Human-Computer Interaction (HCI) community to collectively probe into the VR experience and design of visiting familiar places. Efforts in this area will stretch beyond the current understandings of various forms of tourism and contribute to crafting rich travel experience by immersive technologies.
Strengths and Weaknesses of Emergency Remote Teaching in Higher Education From the Students’ Perspective: The Portuguese Case

Motivated by the COVID-19 pandemic, most students of higher education institutions (HEIs) in Portugal experienced online learning from March 2020 to July 2020. Based on the answers obtained from students to a set of two open questions included in a questionnaire, this article aims to identify the positive (strengths) and negative (weaknesses) aspects of online learning during this period, which is also known as emergency remote teaching (ERT). A total of 2,107 valid answers were gathered. Issues related to comfort and time management were the topics most frequently mentioned by students as strengths, particularly for those who are simultaneously workers. In contrast, the assessments, interaction, and self-confidence comprised the set of the most frequently mentioned by students as weaknesses. In this latter context, the most evident differences were found by age, type of course, and students’ status. The breakdown by gender did not show any relevant difference, regardless of the item under analysis. These findings may be useful for decision-makers to plan their actions, particularly regarding the new challenges for the future of higher education programs. Those actions may include the options regarding the most proper learning model among face-to-face, online, or blended learning by case, as well as the measures to improve the overall quality of the online learning to increase the students’ satisfaction.
Everyone is victimized or only the naïve? The conflicting discourses surrounding identity theft victimization

Identity theft impacts millions of North Americans annually and has increased over the last decade. Victims of identity theft can face various consequences, including losses of time and money, as well as emotional, physical, and relational effects. Scholars have found that institutional messaging surrounding identity theft places responsibility on individuals for their own protection, which can mask institutions’ roles in identity theft’s prevalence. This paper presents findings from interviews with Canadian victims of identity theft and argues that conflicting discourses surround this crime. While identity theft victimizations are viewed as inevitable in the digital age, victims are often simultaneously stereotyped as old, naïve, or non-technologically savvy. Within this context, this research also finds that victims can express varying degrees of self-blame for having provided perpetrators with information or for having not better protected themselves. Finally, this paper argues that victims’ embarrassment and self-blame may impede help-seeking and reporting.
Learning Aspect-Aware High-Order Representations from Ratings and Reviews for Recommendation

Textual reviews contain rich semantic information that is useful for making better recommendation, as such semantic information may indicate more fine-grained preferences of users. Recent efforts make considerable improvement on recommendation by integrating textual reviews in rating-based recommendations. However, there still exist major challenges on integrating textual reviews for recommendation. On the one hand, most existing works focus on learning a single representation from reviews but ignoring complex relations between users (or items) and reviews, which may fail to capture user preferences and item attributes together. On the other hand, these works independently learn latent representations from ratings and reviews while omitting correlations between rating-based features and review-based features, which may harm recommendation performance. In this article, we capture the aspect-aware relations by constructing heterogeneous graphs from reviews. Furthermore, we propose a new recommendation model, namely AHOR, to jointly distill rating-based features and review-based features, which are derived from ratings and reviews, respectively. To explore the multi-hop connectivity information between users, items, and aspects, a novel graph neural network is introduced to learn aspect-aware high-order representations. Experiments based on public datasets show that our approach outperforms state-of-the-art methods. We also provide detailed analysis on the high-order signals and the aspect importance to show the interpretability of our proposed model.
Afterword: Audit Society 2.0?

Purpose The purpose of this study is to briefly reflect on the continuing relevance of the arguments of “ The Audit Society ” thesis after 25 years and to consider whether they need to be adapted in the face of advances in digitization. Design/Methodology/approach This essay develops a series of critical reflections on the main arguments in “ The Audit Society ” (Oxford University Press, 1997). Findings There is a need for critical accounting and management research to explore how audit society themes require adjustment in the face of “surveillance capitalism” and how the boundaries between accounting, security and surveillance are becoming blurred. Originality/value This essay is a personal and self-critical account of the genesis of The Audit Society thesis which looks forward to how this thesis can develop. It also offers a defence of the value of books in enabling the travelling of ideas across fields.
Document Clustering Using Graph Based Fuzzy Association Rule Generation

With the wider growth of web-based documents, the necessity of automatic document clustering and text summarization is increased.Here, document summarization that is extracting the essential task with appropriate information, removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task.In this research, a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation (gFAR).Initially, the graph model is used to map the relationship among the data (multi-source) followed by the establishment of document clustering with the generation of association rule using the fuzzy concept.This method shows benefit in redundancy elimination by mapping the relevant document using graph model and reduces the time consumption and improves the accuracy using the association rule generation with fuzzy.This framework is provided in an interpretable way for document clustering.It iteratively reduces the error rate during relationship mapping among the data (clusters) with the assistance of weighted document content.Also, this model represents the significance of data features with class discrimination.It is also helpful in measuring the significance of the features during the data clustering process.The simulation is done with MATLAB 2016b environment and evaluated with the empirical standards like Relative Risk Patterns (RRP), ROUGE score, and Discrimination Information Measure (DMI) respectively.Here, DailyMail and DUC 2004 dataset is used to extract the empirical results.The proposed gFAR model gives better trade-off while compared with various prevailing approaches.
A New Multirobot Path Planning With Priority Order Based on the Generalized Voronoi Diagram

This paper proposes a new path planning method called the priority order navigation algorithm (PONA) for multi-robot navigation in a large flat space.The PONA can guarantee collision-free and efficient travel in the space with fixed or/and dynamic obstacles.The priority order of robots is assigned by the user based on the importance degree of the robots' tasks and the objective is to make the higher priority robot reach its target faster than the lower priority robot.This study uses the generalized Voronoi diagram (GVD) to establish the initial map for PONA and links the navigation points in GVD to plan the path for each robot.Further, we modify the navigation point links to shorten feasible paths for the lower priority robot and its shortest two feasible paths can be switched to each other based on a certain condition to avoid hitting the higher priority robot.The proposed PONA is compared to several benchmark path planning methods, which are the shortest distance algorithm (SDA) and reciprocal orientation algorithm (ROA), in the simulation section and it is found that the PONA can reduce the average length of the trajectory by more than 10% compared with ROA and SDA.INDEX TERMS Voronoi diagram, Yen's algorithm, multi-robot path planning, collision-free, path-priority order.
Unsupervised Domain Adaptation for 3D Point Clouds by Searched Transformations

Input-level domain adaptation reduces the burden of a neural encoder without supervision by reducing the domain gap at the input level. Input-level domain adaptation is widely employed in 2D visual domain, <i>e.g.</i>, images and videos, but is not utilized for 3D point clouds. We propose the use of input-level domain adaptation for 3D point clouds, namely, point-level domain adaptation. Specifically, we propose to learn a transformation of 3D point clouds by searching the best combination of operations on point clouds that transfer data from the source domain to the target domain while maintaining the classification label without supervision of the target label. We decompose the learning objective into two terms, resembling domain shift and preserving label information. On the PointDA-10 benchmark dataset, our method outperforms state-of-the-art, unsupervised, point cloud domain adaptation methods by large margins (up to + 3.97 &#x0025; in average).