Reviewerly/exHarmony huggingface dataset:
Enterprise architecture contribution in distributed agile software development

Abstract Geographically distributed agile development may experience a high failure rate due to communication issues, which has a negative influence on project performance. One suggested solution in the literature is to enhance both communication and project performance by implementing agile enterprise architecture. However, the empirical evidence that supports this claim is still scarce. To address this gap, this study empirically explores the role of agile enterprise architecture as an artifact in distributed agile development. The findings of an in‐depth qualitative case study from a dispersed agile development organization that involves teams distributed over three locations are used in this work. Over 2 months, data was gathered by interviewing 12 key members of the team and watching three Sprint sessions of agile software development. Text analysis qualitative approach was used to analyze the data. The findings imply that agile enterprise architecture has a positive effect on distributed agile software development communication, quality, and functionality. Agile enterprise architecture may also support on‐time completion, but a trade‐off with on‐budget may be necessary. These findings provide valuable insights, frameworks, and best practices that support organizations in achieving greater agility, collaboration, and success in their distributed software development initiatives. As this is one of the first studies to look at the influence of agile enterprise architecture on distributed agile software development communication and performance, further research is needed to confirm and expand on the conclusions of this study.
Insegnare la scrittura ad apprendenti sinofoni: tra voce, identità e testualità

Writing is a social act of construction of meanings, which makes no sense in the absence of a addressee, so it is inherent in a process of negotiation in which the writer is constantly engaged in a dialogue to construct his voice and identity in the production of a text. This paper aims to analyse how Chinese learners of Italian LS construct their voice in the production of a argument essay. Given the specificity of the textual typology, we also focus on how much Chinese textuality can influence the argument structure in LS. The article concludes with some considerations for teaching writing to Chinese students.
Assessment of Digital Teaching Competence in Non-University Education

The enormous influence that Information and Communication Technologies have in society, as well as the pandemic caused by COVID-19, have caused teachers to need to adapt to new educational contexts in recent years, in addition to evidencing the enormous deficiencies in the use of technologies. The quarantine situation made it necessary to organize the educational system so that students could continue their training away from the classroom. This article aims to assess whether teachers’ perceptions about their level of digital competence have changed after quarantine in non-university education teachers in Spain. For this, a nonexperimental quantitative method was applied using descriptive statistical techniques. The sample is made up of 168 teachers, and for data collection, a questionnaire was used covering different areas established by the DigCompEdu framework. The results show that the perception teachers have about their digital competence is that it has improved due to free training, which implies the necessary training of teachers in new technologies. These data encourage the design of a training plan from universities to comply with the resolution of 4 May 2022, of the General Directorate for Territorial Evaluation and Cooperation on updating the reference framework of digital teaching competence.
Special Issue on Photonic State Tomography: Methods and Applications

The realm of quantum engineering has undergone a remarkable transformation in recent years [...]
Measuring student and educator digital competence beyond self-assessment: Developing and validating two rubric-based frameworks

Abstract Over the past decade, self-assessment tools have garnered significant attention in the interest of measuring the skillset required by educators and students to function productively and ethically in digitally mediated environments, particularly in relation to education policy implementation. Since stated beliefs do not always align with actual practice, gaps have been shown to occur between self-reporting and performance in practice. Having an external assessor can counteract this imbalance; however, both perspectives should be taken into consideration as both are equally important. Against this background, this study develops and validates two rubric-based frameworks that supplement self perceived student and educator digital competence with classroom observation and task performance analysis. The DigComp and DigCompEdu self-assessment frameworks were used as a starting point to develop a student rubric and an educator rubric, respectively, underpinned by criteria validated in previous frameworks. The expert technique, which is the base for the Delphi Method, was used to validate each rubric, after which the rubrics were implemented at a Spanish university to test their reliability. The results indicated that the force of agreement across raters was consistent and both rubrics had a high degree of internal consistency, therefore both instruments are reliable.
Language usage analysis for EMF metamodels on GitHub

Abstract Context EMF metamodels lie at the heart of model-based approaches for a variety of tasks, notably for defining the abstract syntax of modeling languages. The language design of EMF metamodels itself is part of a design process, where the needs of its specific range of users should be satisfied. Studying how people actually use the language in the wild would enable empirical feedback for improving the design of the EMF metamodeling language. Objective Our goal is to study the language usage of EMF metamodels in public engineered projects on GitHub. We aim to reveal information about the usage of specific language constructs, whether they match the language design. Based on our findings, we plan to suggest improvements in the EMF metamodelling language. Method We adopt a sample study research strategy and collect data from the EMF metamodels on GitHub. After a series of preprocessing steps including filtering out non-engineered projects and deduplication, we employ an analytics workflow on top of a graph database to formulate generalizing statements about the artifacts under study. Based on the results, we also give actionable suggestions for the EMF metamodeling language design. Results We have conducted various analyses on metaclass, attribute, feature/relationship usage as well as specific parts of the language: annotations and generics. Our findings reveal that the most used metaclasses are not the main building blocks of the language, but rather auxiliary ones. Some of the metaclasses, metaclass features and relations are almost never used. There are a few attributes which are almost exclusively used with a single value or illegal values. Some of the language features such as special forms of generics are very rarely used. Based on our findings, we provide suggestions to improve the EMF language, e.g. removing a language element, restricting its values or refining the metaclass hierarchy. Conclusions In this paper, we present an extensive empirical study into the language usage of EMF metamodels on GitHub. We believe this study fills a gap in the literature of model analytics and will hopefully help future improvement of the EMF metamodeling language.
Market and Sharing Alternatives for the Provision of Massive Machine-Type and Ultra-Reliable Low-Latency Communications Services over a 5G Network

The objective of this paper is to analyze economic alternatives for the provision of ultra-reliable low-latency communication (URLLC) and massive machine-type communication (mMTC) services over a fifth-generation (5G) network. Two business models, a monopoly and a duopoly, are studied and two 5G network scenarios are analyzed: a 5G network where the network resources are shared between the two services without service priority, and a 5G network with network slicing that allows for URLLC traffic to have a higher priority. Microeconomics is used to model the behavior of users and operators, and game theory is used to model the strategic interaction between users and operators. The results show that a monopoly over a 5G network with network slicing is the most efficient way to provide both URLLC and mMTC services.
Innovative Optimal Control Path Planning for PMCU in Cruise Ship Construction: A Kinematic and Obstacle Avoidance Model

In cruise ship manufacturing, it is common practice for prefabricated modular cabin units (PMCU) to be assembled in the workshop and transported to a designated installation location on board the ship. This paper proposed a Radau pseudospectral method based on a probabilistic roadmap (PRM) for PMCU path planning in complex deck environments. Firstly, a kinematic model is constructed, and the continuous-time optimal control path planning problem is established. Among them, an area-based obstacle avoidance constraint is applied to the approach. Then, the continuous-time optimal control problem is discretized into a nonlinear programming (NLP) problem using the orthogonal collocation method. For the initial value-sensitive NLP problem solving, a strategy using PRM combined with a heuristic rule is used to obtain a high-quality initial guess. Finally, experiments under two operating scenarios of PMCU pushing demonstrate that the method is capable of generating a better path while ensuring that the whole process is collision-free. The results show that the algorithm proposed in this paper can effectively solve the PMCU pushing path planning problem in the complex deck environment, and the path considering kinematic characteristics can be used for cabin installation, which has strong operability and applicability.
UTDRM: unsupervised method for training debunked-narrative retrieval models

Abstract A key task in the fact-checking workflow is to establish whether the claim under investigation has already been debunked or fact-checked before. This is essentially a retrieval task where a misinformation claim is used as a query to retrieve from a corpus of debunks. Prior debunk retrieval methods have typically been trained on annotated pairs of misinformation claims and debunks. The novelty of this paper is an Unsupervised Method for Training Debunked-Narrative Retrieval Models () in a zero-shot setting, eliminating the need for human-annotated pairs. This approach leverages fact-checking articles for the generation of synthetic claims and employs a neural retrieval model for training. Our experiments show that tends to match or exceed the performance of state-of-the-art methods on seven datasets, which demonstrates its effectiveness and broad applicability. The paper also analyses the impact of various factors on ’s performance, such as the quantity of fact-checking articles utilised, the number of synthetically generated claims employed, the proposed entity inoculation method, and the usage of large language models for retrieval.
Development of a High-Precision and Lightweight Detector and Dataset for Construction-Related Vehicles

Effective vehicle detection plays a crucial role in various applications in cities, including traffic management, urban planning, vehicle transport, and surveillance systems. However, existing vehicle detection methods suffer from low recognition accuracy, high computational costs, and excessive parameters. To address these challenges, this paper proposed a high-precision and lightweight detector along with a new dataset for construction-related vehicles. The dataset comprises 8425 images across 13 different categories of vehicles. The detector was based on a modified version of the You Only Look Once (YOLOv4) algorithm. DenseNet was utilized as the backbone to optimize feature transmission and reuse, thereby improving detection accuracy and reducing computational costs. Additionally, the detector employed depth-wise separable convolutions to optimize the model structure, specifically focusing on the lightweight neck and head components. Furthermore, H-swish was used to enhance non-linear feature extraction. The experimental results demonstrated that the proposed detector achieves a mean average precision (mAP) of 96.95% on the provided dataset, signifying a 4.03% improvement over the original YOLOv4. The computational cost and parameter count of the detector were 26.09GFLops and 16.08 MB, respectively. The proposed detector not only achieves lower computational costs but also provides higher detection when compared to YOLOv4 and other state-of-the-art detectors.
Enhancing Security and Accountability in Autonomous Vehicles through Robust Speaker Identification and Blockchain-Based Event Recording

As the deployment of Autonomous Vehicles (AVs) gains momentum, ensuring both security and accountability becomes paramount. This paper proposes a comprehensive approach to address these concerns. With the increasing importance of speaker identification, our first contribution lies in implementing a robust mechanism for identifying authorized users within AVs, enhancing security. To counter the threat of voice spoofing, an ensemble-based approach leveraging speaker verification techniques is presented, ensuring the authenticity of user commands. Furthermore, in scenarios of accidents involving AVs, the need for accurate accountability and liability allocation arises. To address this, we introduce a novel application of blockchain technology, enabling an event recording system that ensures transparent and tamper-proof records. The proposed system enhances AV security and establishes a framework for reliable accident investigation using speakers’ records. In addition, this paper presents an innovative concept where vehicles act as impartial judges during accidents, utilizing location-based identification. Results show the viability of the proposed solution for accident investigation and analysis.
A note on analyzing the stability of oscillator Ising machines

Abstract The rich non‐linear dynamics of the coupled oscillators (under second harmonic injection) can be leveraged to solve computationally hard problems in combinatorial optimization such as finding the ground state of the Ising Hamiltonian. While prior work on the stability of the so‐called Oscillator Ising Machines (OIMs) has used the linearization method, in this letter, the authors present a complementary method to analyze stability using the second‐order derivative test of the energy/cost function. The authors establish the equivalence between the two methods, thus augmenting the tool kit for the design and implementation of OIMs.
SECANet: A structure‐enhanced attention network with dual‐domain contrastive learning for scene text image super‐resolution

Abstract In this letter, we developed novel Structure Enhanced Channel Attention Network (SECANet) for scene text image super‐resolution (STISR). The newly proposed SECANet integrates a group of Structure‐Enhanced Attention Modules to focus more on both local and global structural features in the character regions of text images. Moreover, we elaborately formulate a Dual‐Domain Contrastive Learning framework that integrates one pixel‐level contrastive loss and the other semantic‐level contrastive loss to jointly optimize the SECANet for generating more visually pleasing yet better recognizable high‐quality SR images without introducing any additional prior generators in both the training and testing stages, showing promising computational efficiency. Experimental results on the Textzoom dataset indicate that our method can achieve both decent performance in super‐resolving more impressive scene text images from low‐resolution ones and better recognition accuracy than other competitors.
Breast Cancer Screening Using Artificial Intelligence Techniques: Enhancing Biochemical Insights and Diagnostic Accuracy

Breast cancer, the most prevalent cancer in women worldwide, demands effective screening for early identification and improved treatment outcomes. Recent advances in artificial intelligence (AI) have resulted in dramatic developments in a variety of fields, including healthcare. In this review paper, we look at how AI approaches can be used in breast cancer screening to improve diagnostic accuracy and provide deeper molecular insights. We dig into the complex terrain of breast cancer treatment, which has transformed as a result of the discovery of prognostic and predictive biomarkers, allowing for personalised therapeutic methods based on molecular subgroups. We emphasise the importance of AI-driven approaches in optimising screening procedures and providing quick and exact findings. The potential of AI to revolutionise breast cancer screening is highlighted, including its applications in diagnostic imaging, lesion identification, and standardised imaging data interpretation. The analysis highlights AI’s critical role in tackling issues associated with the integration of new technologies, providing solutions for worldwide standardisation in cancer detection.
Image-based Regularization for Action Smoothness in Autonomous Miniature Racing Car with Deep Reinforcement Learning

Deep reinforcement learning has achieved signif-icant results in low-level controlling tasks. However, for some applications like autonomous driving and drone flying, it is difficult to control behavior stably since the agent may suddenly change its actions which often lowers the controlling sys-tem's efficiency, induces excessive mechanical wear, and causes uncontrollable, dangerous behavior to the vehicle. Recently, a method called conditioning for action policy smoothness (CAPS) was proposed to solve the problem of jerkiness in low-dimensional features for applications such as quadrotor drones. To cope with high-dimensional features, this paper proposes image-based regularization for action smoothness (1-RAS) for solving jerky control in autonomous miniature car racing. We also introduce a control based on impact ratio, an adaptive regularization weight to control the smoothness constraint, called IR control. In the experiment, an agent with 1- RAS and IR control significantly improves the success rate from 59% to 95%. In the real-world-track experiment, the agent also outperforms other methods, namely reducing the average finish lap time, while also improving the completion rate even without real world training. This is also justified by an agent based on I-RAS winning the 2022 AWS DeepRacer Final Championship Cup.
PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation

Reliable LiDAR panoptic segmentation (LPS), including both semantic and instance segmentation, is vital for many robotic applications, such as autonomous driving. This work proposes a new LPS framework named PANet to eliminate the dependency on the offset branch and improve the performance on large objects, which are always over-segmented by clustering algorithms. Firstly, we propose a non-learning Sparse Instance Proposal (SIP) module with the “sampling-shifting-grouping” scheme to directly group thing points into instances from the raw point cloud efficiently. More specifically, balanced point sampling is introduced to generate sparse seed points with more uniform point distribution over the distance range. And a shift module, termed bubble shifting, is proposed to shrink the seed points to the clustered centers. Then we utilize the connected component label algorithm to generate instance proposals. Furthermore, an instance aggregation module is devised to integrate potentially fragmented instances, improving the performance of the SIP module on large objects. Extensive experiments show that PANet achieves state-of-the-art performance among published works on the SemanticKITII validation and nuScenes validation for the panoptic segmentation task. Code is available at https://github.com/Jieqianyu/PANet.git.
Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov Model

Deep reinforcement learning (DRL) algorithms have proven effective in robot navigation, especially in unknown environments, by directly mapping perception inputs into robot control commands. However, most existing methods ignore the local minimum problem in navigation and thereby cannot handle complex unknown environments. In this paper, we propose the first DRL-based navigation method modeled by a semi-Markov decision process (SMDP) with continuous action space, named Adaptive Forward Simulation Time (AFST), to overcome this problem. Specifically, we reduce the dimensions of the action space and improve the distributed proximal policy optimization (DPPO) algorithm for the specified SMDP problem by modifying its GAE to better estimate the policy gradient in SMDPs. Experiments in various unknown environments demonstrate the effectiveness of AFST.
Recognition algorithms based on the construction of threshold rules using two-dimensional representative pseudo-objects

The development of recognition algorithms is discussed in the article; they are built using threshold rules based on representative pseudo-objects that provide a solution to the recognition problem in conditions of high dimensionality of feature space. A new approach is proposed, based on the formation of a set of two-dimensional base pseudo-objects and the determination of a relevant set of two-dimensional threshold proximity functions when constructing an extreme recognition algorithm. A parametric description of the proposed recognition algorithms is given, presented in the form of a sequence of computational procedures, the main of which are procedures for determining: 1) groups of tightly coupled features; 2) a set of representative features (RF); 3) groups of tightly coupled pseudo-objects in the RF subspace; 4) difference functions between objects in the two-dimensional subspace of RF; 5) groups of tightly coupled pseudo-objects in the RF subspace; 6) a set of basic pseudo-objects; 7) difference functions between the basic and simple pseudo-object in the two-dimensional RF subspace; 8) functions that differentiate between a pseudo-object and a class; 9) discriminant functions in the two-dimensional subspace of RF; 10) groups of tightly coupled separating functions; 11) basic separating functions in each group and 12) integral recognition operator for basic discriminant proximity functions. The results of a comparative analysis of the proposed and known recognition algorithms are presented. The main conclusion is that the implementation of the approach proposed in this study allows us to move from a given feature space to a space of RFs of lesser dimension.
Development of E-LKPD on Smartphone Using Kodular and its Effect Test on Learning Activities and Learning Outcomes of Class X Students MAN 2 Levels on Ecosystem Materials

The aims of this study to produce E-LKPD with Smartphones using Kodular on Ecosystem material for students that is valid, practical, and effective on learning activities and learning outcomes based on material, visual communication, learning design and software utilization in class X MAN Langkat
Phase transformations of Bi, Sb, Se, Te and U in the oxidation model of wolframite rocks of the Sherlovaya Gora deposit (Eastern Transbaikalia)

On the basis of the data of the chemical composition of wolframite rocks of the Sherlovaya Gora deposit, a model of their oxidation in an aqueous medium under standard conditions in the Selector PC is constructed. For Bi, Sb, Se, Te and U, equilibrium mineral phases and compounds are presented depending on the redox conditions of the “water-rock” systems.