Reviewerly/exHarmony huggingface dataset:
Memento: Facilitating Effortless, Efficient, and Reliable ML Experiments

Running complex sets of machine learning experiments is challenging and time-consuming due to the lack of a unified framework. This leaves researchers forced to spend time implementing necessary features such as parallelization, caching, and checkpointing themselves instead of focussing on their project. To simplify the process, in this paper, we introduce Memento, a Python package that is designed to aid researchers and data scientists in the efficient management and execution of computationally intensive experiments. Memento has the capacity to streamline any experimental pipeline by providing a straightforward configuration matrix and the ability to concurrently run experiments across multiple threads. A demonstration of Memento is available at: wickerlab.org/publication/memento .
Multimodal construction of human movement dataset under the development of physical dance teaching in colleges and universities

Abstract First, this paper proposes a multimodal fusion-based human motion recognition technique applied to college physical dance teaching, where feature-level or decision-level fusion maps human kinematic semantic information from datasets to classifiers under a single-level stage. Secondly, a multi-level multimodal fusion approach for human motion recognition is proposed to make it more adaptable to practical application scenarios. The depth data is converted into a depth motion projection map at the input side, and the inertial data is converted into a signal image. Finally, all the modalities are trained by a convolutional neural network for extracting features, and the extracted features are fused at the feature level by discriminant correlation analysis. The results show that the multi-level multimodal fusion framework achieves 99.8% and 99.9% recognition accuracy on the two datasets, respectively, with 100% accuracy in the Throw and Catch action recognition and the lowest recognition rate of 95.36% in the Clap action test, with an average recognition rate of 97.89%, which has a high recognition accuracy. The multi-level multimodal fusion model can obtain movement data close to the actual movement data after optimization in low-precision movement data, which provides data support for physical dance teaching and learning.
An Inquiry into the Evolution of the Project-based Instructional Approach “Three” from “A Program” to “A High-quality Course”

Within the context of education reform, there is a pressing need to overhaul and enhance the existing educational approach. As part of this endeavor, it is imperative to establish a more adaptable and multifaceted project-based teaching methodology. However, it is important to acknowledge that this approach also exhibits certain inherent limits, which become increasingly apparent during its implementation. However, it is worth considering if this particular pedagogical approach can effectively address its inherent limits and successfully facilitate the desired transformation and advancement of the educational system. This research aims to examine the efficacy of the “three” teaching approach in facilitating the transition from a mere instructional plan to an effective classroom experience.
Reading promotion mode of university libraries in the context of information development

Abstract This paper mainly proposes a technical route of user profiling based on multi-view clustering and constructs a multidimensional and multi-view reader feature system based on reader behavior data. According to the constructed reader feature system and the defects of the classical K-means algorithm in multi-view clustering, a multi-view dichotomous K-means algorithm based on Marxian distance is proposed. Using the proposed algorithm for multi-view clustering of readers, the user portrait features of readers can be obtained, and finally, a university library user portrait system is designed and implemented. By analyzing the behavior of college students through the user portrait technique, we get that the most preferred book genre of college students is literature accounting for 71.45%, followed by science and history with 57.64% and 51.37%, respectively. The most popular form of reading promotion among college students is classical film and television exhibitions, accounting for 63.73%, followed by lectures by celebrities and experts, accounting for 60.91%. The highest influence on college students going to the library is too many spare time activities, accounting for 64.38%, and not choosing books, accounting for 36.95%. These data can help libraries understand readers’ needs and provide help for reading accurate recommendations and services based on readers’ user profiles.
Scale and efficiency of China’s radiation industry based on LightGBM model

Abstract In the digitalization of big data information, how to use big data and China’s radiation industry combined have become a key topic of social concern. This paper first constructs LightGBM model by gradient boosting tree and deep neural network. Gradient boosting tree optimizes the convergence and stability of LightGBM model, while the deep neural network improves the accuracy and efficiency of the LightGBM model by updating the weights on each edge. Then the radiation safety management method is proposed according to the danger of the radiation industry, the characteristic data of radiation industry scale and efficiency are obtained through research work, the research indexes are determined, and finally, the data analysis of China’s radiation industry scale and efficiency is conducted by using LightGBM model. Take the medical isotope industry as an example: from 2010 to 2020, the number of nuclear medicine industries increased from 857 to 1148, and the deviation rate of prediction was always maintained at 10%. In terms of radiation industry benefits: the annual growth rate of PET diagnostics, SPECT diagnostics and Nuclide Therapy benefit is 56%, 14% and 5.3%, respectively, the radiation industry has great market demand, and the benefit will continue to grow in the future. This study is a comprehensive and accurate analysis of the scale and benefits of China’s radiation industry and has a guiding reference value for the development and research of China’s radiation industry.
SHAMROQ: A Software Engineering Methodology to Extract Deontic Expressions from the Code of Federal Regulations — A Single-Case, Embedded Case Study

This research provides a comprehensive analysis of deontic expressions within the Code of Federal Regulations (CFR) Title 48, Federal Acquisition Regulations System, specifically focusing on obligations, permissions, prohibitions, and dispensations. Utilizing SHAMROQ, a systematic and rigorous methodology, the authors extract, classify, and analyze these expressions, quantify their prevalence, and identify common linguistic patterns within the legal text. The results show that obligations (71.3%) form most deontic expressions in CFR 48, indicating the heavily prescriptive nature of the document. Permissions also form a significant part (21.9%), suggesting the liberties and allowances are embedded within the regulatory framework. In contrast, prohibitions (5.4%) and dispensations (1.4%) are less frequent, indicating that the document leans more towards defining what is required or allowed rather than what is explicitly forbidden or exempted. This research also highlights the challenges encountered during the extraction process, providing insights into the complexities of parsing legal texts and the intricacies of deontic language. These challenges range from the technical difficulties of parsing a complex hierarchical document to the conceptual challenges of defining precise rulesets for regulations and provisions. In summary, the results deepen the understanding of regulatory compliance in software engineering and contribute to the development of more effective and efficient automated extraction tools.
Quantum Zeno and Anti‐Zeno Effects in the Dynamics of Non‐Degenerate Hyper‐Raman Processes Coupled to Two Linear Waveguides

Abstract The effect of the presence of two probe waveguides on the dynamics of hyper‐Raman processes is studied in terms of quantum Zeno and anti‐Zeno effects. Specifically, the enhancement (diminution) of the evolution of the hyper‐Raman processes due to interaction with the probe waveguides via evanescent waves is viewed as quantum Zeno (anti‐Zeno) effect. This study considers the two probe waveguides interacting with only one of the optical modes at a time. For instance, as a specific scenario, it is considered that the two non‐degenerate pump modes interact with each probe waveguide linearly, while Stokes and anti‐Stokes modes do not interact with the probes. Similarly, in another scenario, it is assumed both the probe waveguides interact with Stokes (anti‐Stokes) mode simultaneously. The present results show that quantum Zeno (anti‐Zeno) effect is associated with phase‐matching (mismatching). However, it do not find any relation between the presence of the quantum Zeno effect and antibunching in the bosonic modes present in the hyper‐Raman processes.
Communicating engineering heritage through immersive technology: A VR framework for enhancing users' interpretation process in virtual immersive environments

The recent advancement in immersive technologies opens up opportunities for the way individuals perceive and engage with information in public spaces to be innovated. This article discusses a study that investigates the application of Virtual Reality (VR) as an instrument for communicating engineering heritage in museum environments with the aim to enhance visitor experience. The study adopted Shannon’s communication theory as the main principle for contextualising heritage objects within virtual environments. This approach can benefit curators in informing the way the intended meaning, value, and context behind museum artefacts to be delivered through visual narratives and aesthetics. In this study, three VR scenarios have been developed using the Unreal engine to investigate the aspects of learning, interaction, and immersion during the virtual experience. One-way ANOVA approach was used to determine the significant differences between the proposed factors in the study. The study found that the absence of interaction in the immersive scenario reduced the mean score leading to a lack of constructive guidance during navigation. Whereas using Gamified and narrated approaches significantly increased the mean value of the participants compared to the control group. While many researchers argue that the utilisation of VR could improve the users’ level of presence, the study outcomes suggest that there are certain conditions that should be structured during the development process to facilitate better engagement with virtual content. To achieve these conditions, gamification and storytelling strategies have been found to be effective in delivering an interactive immersive experience for engaging with heritage artefacts and contents.
SE‐Swin: An improved Swin‐Transfomer network of self‐ensemble feature extraction framework for image retrieval

Abstract The Swin‐Transformer is a variant of the Vision Transformer, which constructs a hierarchical Transformer that computes representations with shifted windows and window multi‐head self‐attention. This method can handle the scale invariance problem and performs well in many computer vision tasks. In image retrieval, high‐quality feature descriptors are necessary to improve retrieval accuracy. This paper proposes a self‐ensemble Swin‐Transformer network structure to fuse the features of different layers of the Swin‐Transformer network, eliminating noise points present in a single layer, and improving the retrieval effect. Two experiments were conducted, one on the In‐shop Clothes Retrieval dataset and another on the Stanford Online Product dataset. The experiments showed that the proposed method significantly increased the retrieval effect of features extracted using Vision Transformer, surpassing previous state‐of‐the‐art image retrieval methods. In the second experiment, the feature map of the trained model was visualized, revealing that the improved network significantly reduces focus on some noise points and enhances focus on image features compared to the original network.
Ink painting style transfer using asymmetric cycle-consistent GAN

Chinese ink painting, an artistic and cultural treasure, necessitates automatic generation for its preservation and evolution. We've innovatively observed and validated that the domain information between photographs and ink paintings, is asymmetrical, which has been overlooked by current style transfer algorithms. We propose an innovative generative adversarial network featuring an asymmetric cyclic consistency structure to address this in ink painting style transfer. This structure uses generators of differing capabilities to align with the asymmetry in transformation directions, improving image quality and model optimization speed. Additionally, we introduce two unique loss functions within the network. The salient edge loss intensifies the subject in the real photo and enhances the edge stroke of the drawn subject, a distinct attribute of ink painting. The feature-wise cycle consistency loss is designed to speed up model optimization. We've also built a Chinese bird ink painting dataset to validate effectiveness of the model. Extensive experiments on this and a public dataset demonstrate that our algorithm can comprehensively learn various stylistic features of ink painting, especially regarding brushstroke style, ink diffusion, and detail preservation. Furthermore, the quantitative results indicate our approach achieves superior results in generation quality and model efficiency compared to existing methods. For instance, compared to the most recent style transfer method, our method achieves an average decrease of 9.44% and 25.32% for FID and KID metrics across three datasets and reduces training and inference time costs by 46.81% and 22.71% respectively.
New players in intelligent transportation: Autonomous Segway in a dynamic environment

This paper heralds a mathematical treatment of Segways as autonomous robots for personal transportation and deliveries and courier services in constrained dynamic environments from a bird’s-eye view. New velocity-based stabilizing controllers of an autonomous nonholonomic two-wheeled self-balancing personalized Segway robot are extracted from a total potential developed by employing the Lyapunov-based Control Scheme (LbCS) for navigation in a partially known environment. Velocity controllers’ cost and time effectiveness and efficiency result from the interaction of the three prominent pillars of LbCS: smoothest, shortest, and safest path for motion planning. Furthermore, the autonomous personal transporter has an obstacle avoidance sensor with a limited detection range ideal for fast navigation in dynamic environments with narrow corridors, tracks, and pathways. This also successfully facilitates navigation in a partially known environment where the sensors only receive and avoid static and dynamic obstacles in a limited range. The results are numerically validated, and the efficacy of the new controllers is exemplified via computer simulations, which illustrate the forward, backward, and zero-turn radius maneuvers of the Segway robot. Introducing the particular autonomous personal transporter would contribute to transportation systems of smart cities.
Who Is Afraid of Test Smells? Assessing Technical Debt from Developer Actions

Test smells are patterns in test code that may indicate poor code quality. Some recent studies have cast doubt on the accuracy and usefulness of the test smells proposed and studied by the research community. In this study, we aimed to determine whether developers view these test smells as sources of technical debt worth spending effort to remove. We selected 12 substantial open-source software systems and mapped how 19 test smells from the literature were introduced and removed from the code base over time. Out of these 19 smells, our results show that: 1) four test smells were rarely detected in our selected projects; 2) three test smells are removed rapidly from the projects while another three are removed from code bases slowly; 3) the remaining nine test smells did not show a consistent pattern of quick or delayed removal. Our results suggest that the test smells currently being studied by researchers do not capture the true concerns of developers regarding test quality, with current testing tool sets, with only three of the 19 smells studied showing clear evidence of developer concern.
CATANA: Replay Testing for the Ethereum Blockchain

Blockchain technology is increasingly being adopted in various domains where the immutability of recorded information can foster trust among stakeholders. However, upgradeability mechanisms such as the proxy pattern permit modifying the terms encoded by a Smart Contract even after its deployment. Ensuring that such changes do not impact previous users is of paramount importance. This paper introduces CATANA, a replay testing approach for proxy-based Ethereum applications. Experiments conducted on real-world projects demonstrate the viability of using the public history of transactions to evaluate new versions of a deployed contract and perform more reliable upgrades.
How Do Different Types of Testing Goals Affect Test Case Design?

Test cases are designed in service of goals, e.g., functional correctness or performance. Unfortunately, we lack a clear understanding of how specific goal types influence test design. In this study, we explore this relationship through interviews and a survey with software developers, with a focus on identification and importance of goal types, quantitative relations between goals and tests, and personal, organizational, methodological, and technological factors. We identify nine goal types and their importance, and perform further analysis of three—correctness, reliability, and quality. We observe that test design for correctness forms a “default” design process that is modified when pursuing other goals. For the examined goal types, test cases tend to be simple, with many tests targeting a single goal and each test focusing on 1–2 goals at a time. We observe differences in testing practices, tools, and targeted system types between goal types. In addition, we observe that test design can be influenced by organization, process, and team makeup. This study provides a foundation for future research on test design and testing goals.
RQCODE: Security Requirements Formalization with Testing

Secure software systems are crucial in today’s digital world, where there is an ever-increasing amount of IT systems, leading to more risks of exposing sensitive data and service outages. One of the key aspects of secure software development is ensuring that security requirements are met through the various stages of software development. The process of testing security requirements is often complex and time-consuming, notably because of the gap between the verification process of security requirements and the testing process. To address this issue and simplify the testing of security requirements, this paper proposes to use the Requirements as Code approach (RQCODE). RQCODE combines security requirements with code in a way to support automated testing and continuous verification of security requirements throughout the software development life cycle. This paper contributes to the field of software security by providing a practical and effective approach to bridge the gap between verification of security requirements and testing, ultimately leading to more secure software systems. Additionally, it discusses the benefits of this approach, such as its ability to improve the accuracy and consistency of testing, enabling the early detection of security issues, and reducing the time and effort required for security testing. It also discusses the challenges and limitations of the approach.
Understanding Problem Solving in Software Testing: An Exploration of Tester Routines and Behavior

Software testing is a difficult, intellectual activity performed in a social environment. Naturally, testers use and allocate multiple cognitive resources towards this task. The goal of this study is to understand better the routine and behaviour of human testers and their mental models when performing testing. We investigate this topic by surveying 38 software testers and developers in Sweden. The survey explores testers’ cognitive processes when performing testing by investigating the knowledge they bring, the activities they select and perform, and the challenges they face in their routine. By analyzing the survey results, we provide a characterization of tester practices and identify insights regarding the problem-solving process. We use these descriptions to further enhance a cognitive model of software testing.
On the Evaluation of Photometric Stereo Applications Testing Using Image Modifications

Computer vision is vital for various applications like object tracking for autonomous driving or quality assurance. Hence, assuring that computer vision fulfills given quality criteria is essential and requires sufficient testing. In previous work, authors introduced a testing method relying on image modifications for a photometric stereo application. Image modifications include pixel errors or the rotation of images to be analyzed, revealing a substantial impact on the computed outcome of the photometric stereo application, depending on the applied modification. This paper focuses on whether we can reproduce the impact of image modifications in a real-world setup. In particular, we compare the impact of the rotation of the analyzed sample with the rotation modification applied to the image of the sample. The comparison indicates a similar effect when using rotation, showing that testing based on image modifications is valuable for verifying computer vision applications.
Generating Synthetic Dialogues from Prompts to Improve Task-Oriented Dialogue Systems

Recently, the research into language models fine-tuned to follow prompts has made notable advances. These are commonly used in the form of chatbots. One special case of chatbots is that of Task-Oriented Dialogue (TOD) systems that aim to help the user achieve specific tasks using external services. High quality training data for these systems is costly to come by. We thus evaluate if the new prompt-following models can generate annotated synthetic dialogues and if these can be used to train a TOD system. To this end we generate data based on descriptions of a dialogues goal. We train a state-of-the-art TOD system to compare it in a low resource setting with and without synthetic dialogues. The evaluation shows that using prompt-following language models to generate synthetic dialogues could help training better TOD systems.
Using knowledge graphs and deep learning algorithms to enhance digital cultural heritage management

Abstract Cultural heritage management poses significant challenges for museums due to fragmented data, limited intelligent frameworks, and insufficient applications. In response, a digital cultural heritage management approach based on knowledge graphs and deep learning algorithms is proposed to address the above challenges. A joint entity-relation triple extraction model is proposed to automatically identify entities and relations from fragmented data for knowledge graph construction. Additionally, a knowledge completion model is presented to predict missing information and improve knowledge graph completeness. Comparative simulations have been conducted to demonstrate the effectiveness and accuracy of the proposed approach for both the knowledge extraction model and the knowledge completion model. The efficacy of the knowledge graph application is corroborated through a case study utilizing ceramic data from the Palace Museum in China. This method may benefit users since it provides automated, interconnected, visually appealing, and easily accessible information about cultural heritage.