Reviewerly/exHarmony huggingface dataset:
UV*: A Boustrophedon Pattern-Based Path Planning and Optimization Strategy for an Ultraviolet Disinfection Robot

COVID-19’s global spread had numerous negative consequences for the industry. At present, many antivirus products and technologies have been proposed, among which ultraviolet antivirus has been proven effective and does not pollute the environment. Most ultraviolet disinfection devices use fixed-point methods for disinfection, which is very labor-intensive and inflexible. Mobile ultraviolet disinfection robots are often not optimized because the movement trajectory of the robot is not optimized, resulting in insufficient disinfection areas. This study proposes a heuristic self-optimizing path planning algorithm called UV <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\ast} $ </tex-math></inline-formula> . UV <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\ast} $ </tex-math></inline-formula> generates a path trajectory based on the Boustrophedon pattern path planning to achieve full area coverage and then optimizes the path length of the robot’s movement trajectory through the path optimization procedure. This study compares the UV <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\ast} $ </tex-math></inline-formula> algorithm, the Random method, the Boustrophedon pattern algorithm, the CIRCLES algorithm, and the genetic algorithm (GA). In UV <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\ast} $ </tex-math></inline-formula> , we proposed a path optimization procedure that can optimize the existing path planning algorithms. The experimental results show that the UV <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\ast} $ </tex-math></inline-formula> path planning algorithm can reach the goal of full coverage, and the total path length is shorter than that of the Random, CIRCLES, GA, and Boustrophedon pattern path planning algorithms.
A Small Object Detection Network Based on Multiple Feature Enhancement and Feature Fusion

Due to the small size, high resolution, and complex background, small object detection has become a difficult point in computer vision. Making full use of high-resolution features and reducing information loss in the process of information propagation is of great significance to improve small object detection. In this article, to achieve the above two points, this work proposes a small object detection network based on multiple feature enhancement and feature fusion based on RetinaNet (MFEFNet). First, this work designs a densely connected dilated convolutions to adequately extract high-resolution features from C2. Then, this work utilizes subpixel convolution to avoid the loss of channel information caused by channel dimension reduction in the lateral connection. Finally, this article introduces a bidirectional fusion feature pyramid structure to shorten the propagation path of high-resolution features and reduce the loss of high-resolution features. Experiments show that our proposed MFEFNet achieves stable performance gains in object detection task. Specifically, the improved method improves RetinaNet from 34.4AP to 36.2AP on the challenging MS COCO dataset, and especially achieves excellent results in small object detection with an improvement of 2.9%.
FCP2Vec: Deep Learning-Based Approach to Software Change Prediction by Learning Co-Changing Patterns from Changelogs

As software systems evolve, they become more complex and larger, creating challenges in predicting change propagation while maintaining system stability and functionality. Existing studies have explored extracting co-change patterns from changelog data using data-driven methods such as dependency networks; however, these approaches suffer from scalability issues and limited focus on high-level abstraction (package level). This article addresses these research gaps by proposing a file-level change propagation to vector (FCP2Vec) approach. FCP2Vec is a recommendation system designed to aid developers by suggesting files that may undergo change propagation subsequently, based on the file being presently worked on. We carried out a case study utilizing three publicly available datasets: Vuze, Spring Framework, and Elasticsearch. These datasets, which consist of open-source Java-based software development changelogs, were extracted from version control systems. Our technique learns the historical development sequence of transactional software changelog data using a skip-gram method with negative sampling and unsupervised nearest neighbors. We validate our approach by analyzing historical data from the software development changelog for more than ten years. Using multiple metrics, such as the normalized discounted cumulative gain at K (NDCG@K) and the hit ratio at K (HR@K), we achieved an average HR@K of 0.34 at the file level and an average HR@K of 0.49 at the package level across the three datasets. These results confirm the effectiveness of the FCP2Vec method in predicting the next change propagation from historical changelog data, addressing the identified research gap, and show a 21% better accuracy than in the previous study at the package level.
Mirror-Brush Illusion: Creating Phantom Tactile Percepts on Intact Limbs

Haptic illusions provide unique insights into how we model our bodies separate from our environment. Popular illusions like the rubber-hand illusion and mirror-box illusion have demonstrated that we can adapt the internal representations of our limbs in response to visuo-haptic conflicts. In this manuscript, we extend this knowledge by investigating to what extent, if any, we also augment our external representations of the environment and its action on our bodies in response to visuo-haptic conflicts. Utilizing a mirror and a robotic brushstroking platform, we create a novel illusory paradigm that presents a visuo-haptic conflict using congruent and incongruent tactile stimuli applied to participants' fingers. Overall, we observed that participants perceived an illusory tactile sensation on their visually occluded finger when seeing a visual stimulus that was inconsistent with the actual tactile stimulus provided. We also found residual effects of the illusion after the conflict was removed. These findings highlight how our need to maintain a coherent internal representation of our body extends to our model of our environment.
Bridge of Trust: Cross Domain Authentication for Industrial Internet of Things (IIoT) Blockchain over Transport Layer Security (TLS)

The Industrial Internet of Things (IIoT) holds significant potential for improving efficiency, quality, and flexibility. In decentralized systems, there are no trust-based centralized authentication techniques, which are unsuitable for distributed networks or subnets, as they have a single point of failure. However, in a decentralized system, more emphasis is needed on trust management, which presents significant challenges in ensuring security and trust in industrial devices and applications. To address these issues, industrial blockchain has the potential to make use of trustless and transparent technologies for devices, applications, and systems. By using a distributed ledger, blockchains can track devices and their data exchanges, improving relationships between trading partners, and proving the supply chain. In this paper, we propose a model for cross-domain authentication between the blockchain-based infrastructure and industrial centralized networks outside the blockchain to ensure secure communication in industrial environments. Our model enables cross authentication for different sub-networks with different protocols or authentication methods while maintaining the transparency provided by the blockchain. The core concept is to build a bridge of trust that enables secure communication between different domains in the IIoT ecosystem. Our proposed model enables devices and applications in different domains to establish secure and trusted communication channels through the use of blockchain technology, providing an efficient and secure way to exchange data within the IIoT ecosystem. Our study presents a decentralized cross-domain authentication mechanism for field devices, which includes enhancements to the standard authentication system. To validate the feasibility of our approach, we developed a prototype and assessed its performance in a real-world industrial scenario. By improving the security and efficiency in industrial settings, this mechanism has the potential to inspire this important area.
Feasibility Study on Color Grading of Blue Iolite Based on GemDialogue Color Comparison Charts

To explore the feasibility of applying the GemDialogue color comparison charts to the color grading of blue iolite, the color of the charts was quantified by the integrating sphere handheld spectroscopy in this paper, and the screened data were classified into a total of 9 categories using the elbow rule and K-Means clustering analysis. Using Fisher’s discriminant to perform discriminant analysis on the 9 categories that have been classified, the discriminant function equation was established separately to verify the accuracy of this classification. Finally, the color parameters of iolite samples were substituted into the discriminant formula to obtain the color grade. By combining the classification results and visual observation, it is clear that applying the GemDialogue color charts to blue iolite color grading is feasible.
Multiphoton quantum van Cittert-Zernike theorem

Abstract Recent progress on quantum state engineering has enabled the preparation of quantum photonic systems comprising multiple interacting particles. Interestingly, multiphoton quantum systems can host many complex forms of interference and scattering processes that are essential to perform operations that are intractable on classical systems. Unfortunately, the quantum coherence properties of multiphoton systems degrade upon propagation leading to undesired quantum-to-classical transitions. Furthermore, the manipulation of multiphoton quantum systems requires nonlinear interactions at the few-photon level. Here, we introduce the quantum van Cittert-Zernike theorem to describe the scattering and interference effects of propagating multiphoton systems. This fundamental theorem demonstrates that the quantum statistical fluctuations, which define the nature of diverse light sources, can be modified upon propagation in the absence of light-matter interactions. The generality of our formalism unveils the conditions under which the evolution of multiphoton systems can lead to surprising photon statistics modifications. Specifically, we show that the implementation of conditional measurements may enable the all-optical preparation of multiphoton systems with attenuated quantum statistics below the shot-noise limit. Remarkably, this effect cannot be explained through the classical theory of optical coherence. As such, our work opens new paradigms within the established field of quantum coherence.
Container Scaling Strategy Based on Reinforcement Learning

Elasticity capability is one of the most important capabilities of cloud computing, which combines large-scale resource allocation capability to quickly achieve minute-level resource demand provisioning to meet the elasticity requirements of different scale scenarios. The elasticity capability is mainly determined by the container start-up speed and container scaling strategy together, where the container scaling strategy contains both vertical container scaling strategy and horizontal container scaling strategy. In order to make the container scaling policy more effective and improve the application service quality and resource utilization, we briefly introduce Kubernetes’ horizontal pod autoscaling (HPA) strategy, analyze the existing problem of HPA, and develop a container scaling strategy based on reinforcement learning. First, we analyze the problems of Kubernetes’ existing HPA container autoscaling strategy in the scale-up and scale-down phases, respectively. Second, the Markov decision model is used to model the container scaling problem. Then, we propose a model-based reinforcement learning algorithm to solve the container scaling problem. Finally, we compare the experimental results of the HPA scaling strategy and the model-based reinforcement learning strategy with the results from the resource utilization of the application, the change of the number of pods, and the application response time; through the experimental analysis, we verify that the reinforcement learning-based container scaling strategy can guarantee the application service quality and improve the utilization of the application resources more effectively than the HPA strategy.
KPRLN: deep knowledge preference-aware reinforcement learning network for recommendation

Abstract User preference information plays an important role in knowledge graph-based recommender systems, which is reflected in users having different preferences for each entity–relation pair in the knowledge graph. Existing approaches have not modeled this fine-grained user preference feature well, as affecting the performance of recommender systems. In this paper, we propose a deep knowledge preference-aware reinforcement learning network (KPRLN) for the recommendation, which builds paths between user’s historical interaction items in the knowledge graph, learns the preference features of each user–entity–relation and generates the weighted knowledge graph with fine-grained preference features. First, we proposed a hierarchical propagation path construction method to address the problems of the pendant entity and long path exploration in the knowledge graph. The method expands outward to form clusters centered on items and uses them to represent the starting and target states in reinforcement learning. With the iteration of clusters, we can better learn the pendant entity preference and explore farther paths. Besides, we design an attention graph convolutional network, which focuses on more influential entity–relation pairs, to aggregate user and item higher order representations that contain fine-grained preference features. Finally, extensive experiments on two real-world datasets demonstrate that our method outperforms other state-of-the-art baselines.
Strengthening Sustainability in Agile Education: Using Client-Sponsored Projects to Cultivate Agile Talents

The success of agile in software development (SD) has sparked the application of agile in non-SD domains such as business management to improve operational efficiency and innovation. Despite the rising industry demands for agile talents in the non-SD domains, agile education falls short of client-sponsored projects, calling into question the sustainability of agile education. This study makes up for the gap and illustrates an eight-month endeavor where scrum practices and values were imbued in a client-sponsored project. The analysis of qualitative and quantitative data gathered throughout the eight-month project illustrates a large disparity among students in their scrum application, reveals top challenges faced by students in their scrum application, and suggests the impact of the scrum application on the quality of student work. The findings of the study set a solid foundation based on which future agile education could be enhanced to strengthen the sustainability of agile education to meet industries’ rising demands for agile talents in non-SD domains.
Critical Factors Influencing Classroom Participation in Online Learning

Efficiency and engagement are considered to be the most critical components of teaching and learning. Teachers have always strived to maximize student involvement and quality participation with the help of various teaching techniques and strategies. The purpose of this study was to explore the ongoing online learning process in language classes and find out ways to enhance student engagement in the process. Research methods: a questionnaire survey and observation were used to determine students’ attitudes, expectations, and the level of involvement in online learning at Batumi Shota Rustaveli State University. The findings reveal the need to enhance student motivation. The research demonstrates that setting various authentic and interactive tasks with meaningful aims can significantly improve student focus. Additionally, incorporating social and emotional activities and providing teacher support and encouragement can facilitate student interaction and trigger an interest in lifelong learning.
Enabling interpretable machine learning for biological data with reliability scores

Machine learning tools have proven useful across biological disciplines, allowing researchers to draw conclusions from large datasets, and opening up new opportunities for interpreting complex and heterogeneous biological data. Alongside the rapid growth of machine learning, there have also been growing pains: some models that appear to perform well have later been revealed to rely on features of the data that are artifactual or biased; this feeds into the general criticism that machine learning models are designed to optimize model performance over the creation of new biological insights. A natural question arises: how do we develop machine learning models that are inherently interpretable or explainable? In this manuscript, we describe the SWIF(r) reliability score (SRS), a method building on the SWIF(r) generative framework that reflects the trustworthiness of the classification of a specific instance. The concept of the reliability score has the potential to generalize to other machine learning methods. We demonstrate the utility of the SRS when faced with common challenges in machine learning including: 1) an unknown class present in testing data that was not present in training data, 2) systemic mismatch between training and testing data, and 3) instances of testing data that have missing values for some attributes. We explore these applications of the SRS using a range of biological datasets, from agricultural data on seed morphology, to 22 quantitative traits in the UK Biobank, and population genetic simulations and 1000 Genomes Project data. With each of these examples, we demonstrate how the SRS can allow researchers to interrogate their data and training approach thoroughly, and to pair their domain-specific knowledge with powerful machine-learning frameworks. We also compare the SRS to related tools for outlier and novelty detection, and find that it has comparable performance, with the advantage of being able to operate when some data are missing. The SRS, and the broader discussion of interpretable scientific machine learning, will aid researchers in the biological machine learning space as they seek to harness the power of machine learning without sacrificing rigor and biological insight.
Incremental Bag of Words with Gradient Orientation Histogram for Appearance-Based Loop Closure Detection

This paper proposes a novel approach for appearance-based loop closure detection using incremental Bag of Words (BoW) with gradient orientation histograms. The presented approach involves dividing and clustering image blocks into local region features and representing them using gradient orientation histograms. To improve the efficiency of the loop closure detection process, the vocabulary Clustering Feature (CF) tree is generated and updated in real time using the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm, which is combined with an inverted index for the efficient selection of candidates and calculation of similarity. Moreover, temporally close and highly similar images are grouped to generate islands, which enhances the accuracy and efficiency of the loop closure detection process. The proposed approach is evaluated on publicly available datasets, and the results demonstrate high recall and precision.
Universum parametric $$\nu $$-support vector regression for binary classification problems with its applications

Abstract Universum data sets, a collection of data sets that do not belong to any specific class in a classification problem, give previous information about data in the mathematical problem under consideration to enhance the classifiers’ generalization performance. Recently, some researchers have integrated Universum data into the existing models to propose new models which result in improved classification performance. Inspired by these Universum models, an efficient parametric $$ \nu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ν</mml:mi></mml:math> -support vector regression with Universum data ( $$ {\mathfrak {U}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>U</mml:mi></mml:math> Par- $$ \nu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ν</mml:mi></mml:math> -SVR) is proposed in this work. This method, which finds two non-parallel hyperplanes by solving one optimization problem and considers heteroscedastic noise, overcomes some common disadvantages of the previous methods. The $$ {\mathfrak {U}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>U</mml:mi></mml:math> Par- $$ \nu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ν</mml:mi></mml:math> -SVR includes unlabeled samples that don’t belong to any class in the training process, which results in a quadratic programming problem. Two approaches are proposed to solve this problem. The first approach derives the dual formulation using the Lagrangian function and KKT conditions. Furthermore, a least squares parametric $$ \nu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ν</mml:mi></mml:math> -support vector regression with Universum data (named LS- $$ {\mathfrak {U}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>U</mml:mi></mml:math> Par- $$ \nu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ν</mml:mi></mml:math> -SVR) is suggested to further increase the generalization performance. The LS- $$ {\mathfrak {U}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>U</mml:mi></mml:math> Par- $$ \nu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ν</mml:mi></mml:math> -SVR solves a single system of linear equations, instead of addressing a quadratic programming problem in the dual formulation. Numerical experiments on artificial, UCI, credit card, NDC, and handwritten digit recognition data sets show that the suggested Universum model and its solving methodologies improve prediction accuracy.