Reviewerly/exHarmony huggingface dataset:
A new taxonomy of global software development best practices using prioritization based on a fuzzy system

Abstract Effective management of development projects is crucial to delivering high‐quality software within time and budget constraints. However, organizing geographically distributed software development activities presents unique challenges, including difficulties in face‐to‐face interaction and coordination. To assist the global software development community in updating and developing new project management techniques, identifying and prioritizing best practices is essential. This study aims to develop a taxonomy based on the prioritization of management practices for software development, drawing from empirical data. Fifty‐five best practices associated with the Project Management Body of Knowledge (PMBOK) fields were identified from the existing literature. The study also empirically examines the acceptability and relevance of these practices within the industry. An analytic hierarchy process within a fuzzy system is employed to prioritize these practices based on their importance in managing global software development.
A Software for Learning Thai Fingerspelling Sign Language into Thai Consonants with Machine Learning

Children with hearing impairment or deafness often face difficulties in spelling and reading. This research aims to develop a program that translates Thai fingerspelling sign language into Thai consonants. The objective is to use this program as a teaching tool to help children with hearing impairment understand and learn the Thai language more easily. The program created is a utilization of Artificial Intelligence (AI) techniques, where the program captures real-time hand gesture images in fingerspelling sign language and then translates the results into Thai consonants. It enables them to accurately create hand signs and makes the learning process more engaging and interesting. The program development begins with Hand Detection algorithm, which involves detecting the positions of 21 landmarks on the hand after using image processing techniques. After that, the hand gesture images for all the Thai consonants are captured and saved to be used as ground truth datasets. Next, the ground truth datasets are fed into the machine learning process to create a trained model. This enables the program to match the hand gesture images accurately with the correct consonant flashcards, using Google’s Teachable Machine. The results of testing the program revealed that the program has an average accuracy of 79.50%. The program was deployed with 12 users, consisting of 2 teachers and 10 elementary school students from Setsatian School for the Deaf under the Royal Patronage of His Royal Highness Crown Prince Maha Vajiralongkorn. After conducting the user satisfaction survey and applying statistical analysis methods, the results revealed an overall satisfaction rating of “very satisfactory,” scoring 83.80% across all aspects. The program developed can be used as a teaching aid for children with hearing impairments to facilitate their learning and academic progress.
Pepper, just show me the way! How robotic shopping assistants should look and act

Abstract Artificial intelligence enables modern robots to serve as service and sales assistants. Today's robotic shopping assistants (RSAs) can appear either humanoid or non‐humanoid and possess utilitarian and/or hedonic attributes. However, many questions remain unexplored regarding an effective customer‐centric RSA design. Do customers prefer a humanoid or non‐humanoid RSA with hedonic or utilitarian attributes? To answer those questions, the research deploys a mixed‐method approach involving a survey of customers who have interacted with the Pepper Robot, a humanoid robot (Study 1), and follow‐up experiments examining customer responses to a humanoid/non‐humanoid RSA with hedonic/utilitarian attributes (Studies 2 and 3). The research employs an innovative approach that analyzes both unstructured and structured data simultaneously. Study results suggest that customers prefer humanoid RSAs with utilitarian attributes over those with hedonic attributes. The research contributes to the literature by proposing hedonic (vs. utilitarian) attributes of RSAs as new drivers of anthropomorphic perceptions.
Long-term creep monitoring of composite wing leading edge using embedded fiber Bragg grating

The creep behavior of composite wing leading edges resulting from nonequilibrium residual stresses and material viscoelasticity needs to be evaluated comprehensively as it significantly affects assembly. In this study, long-term creep monitoring of a composite wing leading edge used in an actual airplane for 710 h is conducted using embedded fiber Bragg grating arrays and a creep extraction algorithm. The spectra and Bragg wavelength shifts of two embedded arrays, which involve temperature, thermal expansion, and creep, are recorded and analyzed. The creep curve of the composite wing leading edge is reconstructed and further predicted using the creep extraction algorithm based on multiparameter decoupling and the Burgers model. This study elucidates the enlargement of the opening size in the composite wing leading edge by measuring the tensile strain above the neutral axis. The predicted creep time serves as a valuable reference for determining the appropriate assembly timing.
Personal knowledge graphs: an introduction

Artificial Intelligence (AI) is receiving significant attention in various domains, such as precision medicine, mental healthcare, manufacturing, education, finance, and many others. Most applications in these domains are concerned with the classification or language generation tasks, wherein confidence in the outcome is measured through traditional metrics and stakeholder agreement. When 91% of companies in these domains started a call for explainability in AI, it propelled the infusion of Knowledge Graphs (KGs) to make AI explainable. Further, the synthesis of AI and KGs added new capabilities in data-driven AI, such as (a) improved performance with minimal training data, (b) user-level explainability, (c) modeling rare events and handling uncertainty, (d) inducing context sensitivity in AI, and (e) better control over the behavior of AI system to ensure safety. Furthermore, with AI and KG together, personalization became possible. The induction of Personal Knowledge Graphs or Personalized Knowledge Graphs (PKGs) began to endure the capabilities of AI that could customize the outcome based on users&apos; persona and enhance user engagement. Moreover, with PKGs, the community of AI could implement trustworthy systems safeguarding user security and ensuring consistency and robustness in outcomes. This chapter allows interdisciplinary researchers and practitioners in AI to contribute their research on methodologies, tools, and applications that discuss PKG&apos;s construction, utilization, and inference functionality.
Integrating personal knowledge graphs into the enterprise

This chapter covers the topic of integrating personal knowledge graphs (PKGs) into a large enterprise. When the term refers to a personal knowledge graph (PKG) that is integrated into the enterprise, it is used as integrated personal knowledge graph (IPKG). The proposed chapter consists of four parts: Background on PKGs to explore about the fundamentals of PKGs; IPKG challenges to explore the challenges of integrating PKGs into an enterprise knowledge graph and the role that machine learning can play in the future; IPKG steps to show the flow of steps in performing the integration; Security to find grain access control - using role and authorization.
Applications of personal knowledge graphs

A blog post at Google on 16 May 2012 with the title "Introducing the Knowledge Graph: things, not strings" represented one of the first references concerning the current definition of Knowledge Graphs. On the other hand, the article by Krisztian Balog et al. "Personal Knowledge Graphs: A Research Agenda" represented one of the very first references clearly defining the commonalities of Personal Knowledge Graphs (PKGs) such as presenting a "Spider-Web" graph layout having as the user its "center of gravity." To date, the literature related to PKGs is currently scarce given that it is still a virgin and promising research field. In this chapter, we present a survey including a classification of different types of applications of PKGs, spanning from E-learning Systems to Personal Information Managers (PIMs), to the Decentralized Web (e.g. the "Social Linked Data" (SOLID) stack), and so on. This classification identifies nine overlapping categories given that PKGs may belong to one or more categories. In each classification, we focus/highlight common and outstanding architectural components as reference architectures for each category type. We end-up the chapter by including and suggesting a reference architecture depicting desired main components for a semantic web (SW)-based PKG.
Daniela Pettersson-Traba, The development of the concept of SMELL in American English: A usage-based view of near-synonymy (Applications of Cognitive Linguistics 51). Berlin and Boston: De Gruyter Mouton, 2022. Pp. xviii + 270. ISBN 9783110792201.

Daniela Pettersson-Traba, The development of the concept of SMELL in American English: A usage-based view of near-synonymy (Applications of Cognitive Linguistics 51). Berlin and Boston: De Gruyter Mouton, 2022. Pp. xviii + 270. ISBN 9783110792201.
A Perspective Taking Supported AR Learning Platform with a User Study on Learning Three-View Drawing

Perspective taking has various effects on students’ learning, including the development of innovative capabilities, improved communication efficiency in distance education, and enhanced learning performances in practical tasks. Current collaboration systems often provide only a single third-person or first-person perspective and require the placement of markers on physical objects to achieve perspective alignment. These limitations restrict users’ observation and participation in the collaboration process, resulting in a less-than-ideal experience. To address this issue, a perspective taking supported augmented reality collaboration learning platform centered around a general technology course is proposed in this article. Users have the ability to view each other’s perspectives and utilize various collaboration cues, including sketches, voice, models, gestures, and gaze. In addition, a comparative study was conducted for the first time, comparing the effects of different perspective collaborations on students’ learning performance, internal motivation, collaboration experience, and intention to continue using the system. The study revealed significant improvements in learning performance when using the proposed system. Students also reported higher levels of intrinsic motivation, better collaborative experiences, and a greater willingness to continue using the proposed system. User interviews further confirmed the preference for and enjoyment of the proposed system.
Underground abnormal sensor condition detection based on gas monitoring data and deep learning image feature engineering

Underground gas sensors as the most intuitive tool for monitoring gas concentrations in underground mining, yet they are subject to frequent anomalies due to ground pressure, constructions, even malicious masking by workers. Due to the depth of underground mining and the complexity of the environment, it is almost impossible to manually monitor the status of the all the sensors. Thus, the ability to accurately identify the working status of gas sensors at the working face are critical importance to mining safety. In this paper, we propose a deep learning feature engineering based approach to coupling the relationship between underground sensors. Experiment results show that the relationship between gas sensors can be expressed by position and time, so that when a sensor such as upper corner T0 malfunctions, it can be detected by other sensors such as T1 and T2. By converting the gas concentration into the form of recurrence plots (RPs), we are able to transform time-series gas concentration data into images with more dimensions in time lag, and enabling the application of more efficient and accurate machine vision methods. Based on the location of sensors at the working face, we found that the sensors at positions T0, T1 and T2 are correlated as the wind flows through the tunnel and have a higher correlation in the subsections of the time series. And those correlation can directly use to check the operating status of the sensors. We also discuss whether the relationships between the data itself can be preserved at the feature level during the mapping of gas concentrations to features, since deep learning (DL) looks like the next promise future after digitization in the mining industrialization with more and more data analysis and placing the results under a larger decision. This feature-based approach for gas concentration analysis can also be used for prediction and early warning.
A Theory of Secure and Efficient Implementation of Electronic Money

Abstract Scalable and secure implementation of central bank digital currencies (CBDC) has been a challenge. Blockchains provide high operator-independent security and enable central banks to outsource CBDC operations while retaining control over the amount of circulating money. Scalability of blockchain depends on the possibility of decomposing the blockchain. We study how the choice of money scheme: accounts, bills, or unspent transaction outputs (UTXOs) influences the existence of secure and decomposable blockchain implementations of CBDC. We give formal definitions to money schemes, their decompositions, atomic decompositions inspired from the properties of blockchain implementations. For our formalism, we use tools from universal algebra and category theory. We present a general decomposition theory and conditions under which money schemes have atomic decompositions. Bill money schemes meet these conditions but account and UTXO schemes do not. Bill schemes enable scalable and secure implementations of CBDC while the more traditional schemes have some issues.
2BiVQA: Double Bi-LSTM-based Video Quality Assessment of UGC Videos

Recently, with the growing popularity of mobile devices as well as video sharing platforms (e.g., YouTube, Facebook, TikTok, and Twitch), User-Generated Content (UGC) videos have become increasingly common and now account for a large portion of multimedia traffic on the internet. Unlike professionally generated videos produced by filmmakers and videographers, typically, UGC videos contain multiple authentic distortions, generally introduced during capture and processing by naive users. Quality prediction of UGC videos is of paramount importance to optimize and monitor their processing in hosting platforms, such as their coding, transcoding, and streaming. However, blind quality prediction of UGC is quite challenging, because the degradations of UGC videos are unknown and very diverse, in addition to the unavailability of pristine reference. Therefore, in this article, we propose an accurate and efficient Blind Video Quality Assessment (BVQA) model for UGC videos, which we name 2BiVQA for double Bi-LSTM Video Quality Assessment. 2BiVQA metric consists of three main blocks, including a pre-trained Convolutional Neural Network to extract discriminative features from image patches, which are then fed into two Recurrent Neural Networks for spatial and temporal pooling. Specifically, we use two Bi-directional Long Short-term Memory networks, the first is used to capture short-range dependencies between image patches, while the second allows capturing long-range dependencies between frames to account for the temporal memory effect. Experimental results on recent large-scale UGC VQA datasets show that 2BiVQA achieves high performance at lower computational cost than most state-of-the-art VQA models. The source code of our 2BiVQA metric is made publicly available at https://github.com/atelili/2BiVQA .
Adaptive Gaussian Kernel-Based Incremental Scheme for Outlier Detection

An outlier, known as an error state, can bring valuable cognitive analytic results in many industrial applications. Aiming at detecting outliers as soon as they appear in data streams that continuously arrive from data sources, this paper presents an adaptive-kernel-based incremental scheme. Specifically, the Gaussian kernel function with an adaptive kernel width is employed to ensure smoothness in local measures and to improve discriminability between objects. The dynamical Gaussian kernel density is presented to describe the gradual process of changing density. When new data arrives, the method updates the relevant density measures of the affected objects to achieve outlier computation of the arrived object, which can significantly reduce the computational burden. Experiments are performed on five commonly used datasets, and experimental results illustrate that the proposed method is more effective and robust for incremental outlier mining automatically.
Eye Control and Motion with Deep Reinforcement Learning: In Virtual and Physical Environments

Attention mechanism in computer vision refers to scan, detect, and track a target object. This paper aims to develop and virtually train a machine learning model for object attention mechanism, combining object detection and mechanical automation. For this, we use Unity 3D Engine to model a simple scene in which two virtual cameras align together to realize a monocular attention in specific objects. Deep reinforcement learning, via ML-agent’s library, was used to train a model that aligns the virtual cameras. Moreover, the model was transferred to a physical camera to replicate the performance of attention mechanism.
Benchmark Analysis for Backbone Optimization in a Facial Reconstruction Model

Lightweight model development has emerged as an important study subject in computer vision in response to the need for resource-efficient solutions. These models attempt to strike a balance between model size, computing requirements, and accuracy. They give benefits such as efficient resource use, faster inference times, and improved accessibility. For 3D facial reconstruction models, lightweight architectures present an opportunity for implementation in less demanding hardware, since these algorithms usually rely on powerful processors such as NVIDIA graphic cards. The following research paper provides a benchmark comparison between diverse state-of-the-art lightweight models in a facial reconstruction model, with the aim to reduce its computational complexity so that it can be tested on a mobile device.
Dense reconstruction of substation room with LSD SLAM

With the development of digital transmission and transformation systems, digital substations have become a prevalent trend in the electric power industry. However, the presence of many electrical components in substation environments with varying textures poses challenges for the reconstruction of a digital three-dimensional model and robot operation. To deal with that problem, this paper introduces the rich line features present in substation environments into the frontend feature tracking of 3D reconstruction in order to improve the accuracy of the 3D reconstruction. The optimized LSD algorithm is utilized for extracting line features in weak texture scenes. Additionally, a line segment merging algorithm is proposed to reduce the rate of mismatched line features and avoid redundancy through the combination of similar or repeated line segments. Finally, the paper presents a novel line feature matching method based on geometric constraints during the stage of online feature matching, which has the ability to improve matching accuracy while decreasing matching time. The experimental results demonstrate that the front-end visual odometer's optimization method proposed in this paper can offer better feature extraction and matching initial values compared to traditional methods for nonlinear optimization of the back-end of 3D reconstruction and loopback detection.
Depth completion of transparent objects based on convolutional attention mechanism

Transparent objects have two unique optical properties of reflection and light transmission. Current depth cameras cannot perceive them effectively, and the obtained depth images contain a lot of holes and noise information. Robotic grasping and 3D reconstruction of transparent objects becomes difficult. Recent methods mainly focus on depth completion for nontransparent objects, while transparent objects are rarely studied. This paper presents a deep learning approach to obtain a complete depth image from raw RGB-D images of transparent objects. Specifically, our algorithm uses an encoder-decoder architecture, the encoder is used to extract the feature information of transparent objects, and the decoder uses deconvolution operations to complete the depth of transparent objects. In the encoder stage, this paper adopts the Joint Convolutional Attention and Transformer block (JCAT). It consists of two parts: the convolutional attention layer and the Vision Transformer, which is beneficial to extract local and global features. In the decoder stage, Convolutional Block Attention Module (CBAM) is added to enhance feature fusion in channel and spatial dimensions. We conduct a large number of experiments on the ClearGrasp dataset. The experimental results show that our method is reliable.
Research on object detection algorithm in complex scene based on improved YOLOv5

The main task of object detection is the recognition and localisation of objects in images. It is the cornerstone of image understanding and the preparation for vision tasks such as image segmentation, target tracking and pose recognition. YOLOv5 is a object detection technique that is widely used in military, medical and traffic applications. However, in complex and changing real-world scenes, there are often multiple interfering factors, so the detection effect of YOLOv5 is greatly affected. In this paper, we propose an improved YOLOv5 algorithm for complex scenes from the perspectives of background information interference and inaccurate bounding box localisation. Firstly, the SimAM attention module is added to YOLOv5 to enable the model to focus on the differences in information in the feature maps and to enhance the anti-interference ability of the network; then the EIoU loss is used in the loss function and the Focal EIoU loss function is proposed by combining the optimization idea of Focal loss to deal with the category imbalance and improve the convergence speed of the algorithm; finally ,a comparison experiment is conducted on the PASCAL VOC dataset to demonstrate the reliability of the improved algorithm.
Background and foreground attention maps for small object detection

We observe representative information of large, medium objects and the vast background overwhelm that of small objects, which leads to poor performance of small object detection. To this end, a new module has been proposed in this paper, named Background and Foreground Attention Maps(BFAM) module, composing of three sub-modules: segmentation, background and foreground attention sub-modules. The segmentation maps positions which have the top strong semantic information in the deep layer of the backbone to the bottom layer maintaining more small object details and mask them to obtain background map and foreground map. Apply two tailored attention sub-modules on them respectively and then fuse them with different weights to detect final results. Experiments demonstrate BFAM achieves promising gains in small object detection on PASCAL VOC 2012 and Seaship datasets.