Reviewerly/exHarmony huggingface dataset:
Managing knowledge loss: a systematic literature review and future research directions

Purpose Job market shifts, such as workforce mobility and aging societies, cause the exit of knowledgeable personnel from organizations. The ensuing knowledge loss (K-loss) has broad negative effects. This study analyzes the knowledge management literature on K-loss published from 2000 to 2021 and identifies fruitful directions for future research. Design/methodology/approach The authors conduct a systematic literature review of 74 peer-reviewed articles published between 2000 and 2021. These articles were retrieved from ProQuest Central, Science Direct, EBSCOhost and Emerald databases. The analysis utilizes Jesson et al .’s (2011) six principles: field mapping, comprehensive search, quality assessment, data extraction, synthesis and write-up. Findings Three sub-topics emerge from the systematic literature review: K-loss drivers, positive and negative impacts of K-loss and mitigation strategies. Over half of the literature addresses mitigation strategies and provides solutions for K-loss already in progress, rather than proposing preventive measures. Research limitations/implications This study has limitations related to the time span covered. Moreover, it focuses on articles published in refereed journals. Therefore, important contributions from conference papers, books and professional reports were excluded. Originality/value This research comprehensively synthesizes the K-loss literature and proposes future avenues of research to address under-investigated areas and potentially lead to theoretical and empirical advancements in the field. This study also provides suggestions for improving managerial practices.
Digital interactive app and students’ mathematics self-efficacy, anxiety, and achievement in the “new normal”

The pandemic brought changes in the educational setup leading all learning institutions to rely on online learning. In instruction, using the e-learning method has increased due to the pandemic. This study investigated the effects of the Digital Interactive Math Comics (DIMaC) mobile App on students’ self-efficacy, anxiety, and achievement in General Mathematics. The study employed quasi-experimental research, specifically a one-group pre-posttest design. Two adapted questionnaires were employed in the study. Results revealed that students’ self-efficacy remained the same as “Moderate,” anxiety significantly decreased, and their achievement improved from “Satisfactory” to “Very Satisfactory” when utilizing the DIMaC App for 6 weeks. A negative correlation was observed between students’ mathematics self-efficacy and anxiety, while no significant association was found in the combination of the other variables. Results revealed that interactive instructional materials like the DIMaC app help students decrease their learning anxiety in the “New Normal” setup and improve learning gain. Learning materials that students manipulate and interact with significantly affect Mathematics Performance. Therefore, school stakeholders, especially the subject teachers, may develop interactive materials to aid students in the new setup in education.
Autoscaler Evaluation and Configuration: A Practitioner's Guideline

Autoscalers are indispensable parts of modern cloud deployments and determine the service quality and cost of a cloud application in dynamic workloads. The configuration of an autoscaler strongly influences its performance and is also one of the biggest challenges and showstoppers for the practical applicability of many research autoscalers. Many proposed cloud experiment methodologies can only be partially applied in practice, and many autoscaling papers use custom evaluation methods and metrics. This paper presents a practical guideline for obtaining meaningful and interpretable results on autoscaler performance with reasonable overhead. We provide step-by-step instructions for defining realistic usage behaviors and traffic patterns. We divide the analysis of autoscaler performance into a qualitative antipattern-based analysis and a quantitative analysis. To demonstrate the applicability of our guideline, we conduct several experiments with a microservice of our industry partner in a realistic test environment.
Two No-Reference Image Quality Assessment methods based on Possibilistic Choquet Integral and Entropy: Application to Automatic Fingerprint Identification Systems

Quickly assessing image quality and rejecting poor-quality images can substantially improve current biometric systems. The main idea behind our proposed context-based Image Quality Assessment (IQA) approaches is to offer an alternative to learning by using contextual information (domain knowledge). This is achieved by using a set of Contextual Quality Indicators (CQIs) and transforming them into Quality Assessment Criteria (QAC). Proper transforming ways are via the Possibilistic Choquet Integral (PCI) and Possibilistic Entropy (PE). The choice of the CQIs to be used is based on a priori analysis of the context of application. We use ‘fingerprint’ as the context of application. The performance of an Automatic Fingerprint Identification System (AFIS) is impacted by poor-quality fingerprint images. The main advantage of our context-based approaches over other IQA methods is that it requires no learning stage, use only four statistical CQIs, and can act very early in the processing chain (i.e., on raw images) to reject quickly poor-quality images so that the subsequent AFIS can process only good-quality images, which result in better recognition rate (RR) performance. The functioning of two methods (PCI, PE) is illustrated using image samples from PolyUHRF database. The performance of over two proposed NR-IQA (PCI, PE) is compared with two well known learning-based NR-IQA methods (NIQE, SSEQ) and the well known Fingerprint-IQA (F-IQA) methods (NFIQ, RPS, OCL, GaborShen, Gabor, RVU and FDA). Four experimental Data sets, FVC2000, FVC2002, FVC2004 and FVC2006, have been used to make the comparison. The results obtained with the context-based approaches compare favorably with those obtained with learning-based methods for fingerprint domain of applications.
The metaverse applications for the finance industry, its challenges, and an approach for the metaverse finance industry

With the rapid development of digital technology, all areas of society may accelerate their entry into the virtual world, thus blurring the boundary between the physical and digital worlds and promoting a Metaverse. The more financial opportunities in metaverse offers its members, the more choices they have. Whether you're a visitor or a company that has invested in the metaverse to sell your goods, there are many ways to generate money. The potential of the metaverse to build virtual spaces for people to connect may have a significant negative impact on the financial and banking sector, among other useful applications it offers. For the reason of metaverse, people can interact with cutting-edge technology using extended technology (VR, AR, MR) etc. With these developing ideas rise to the metaverse, a virtual economy where things may be produced and purchased. Metaverse increased the financial trust-ability of the people for its reliable and safe technologies. Metaverse uses digital virtual identifications, extended reality, blockchain decentralization techniques, AI applications, IoT systems, and digital twins for metaverse finances. In this paper, we are going to discuss metaverse applications, their challenges, and a state of art approach for the finance industry.
Chained Deep Learning Using Generalized Cross-Entropy for Multiple Annotators Classification

Supervised learning requires the accurate labeling of instances, usually provided by an expert. Crowdsourcing platforms offer a practical and cost-effective alternative for large datasets when individual annotation is impractical. In addition, these platforms gather labels from multiple labelers. Still, traditional multiple-annotator methods must account for the varying levels of expertise and the noise introduced by unreliable outputs, resulting in decreased performance. In addition, they assume a homogeneous behavior of the labelers across the input feature space, and independence constraints are imposed on outputs. We propose a Generalized Cross-Entropy-based framework using Chained Deep Learning (GCECDL) to code each annotator's non-stationary patterns regarding the input space while preserving the inter-dependencies among experts through a chained deep learning approach. Experimental results devoted to multiple-annotator classification tasks on several well-known datasets demonstrate that our GCECDL can achieve robust predictive properties, outperforming state-of-the-art algorithms by combining the power of deep learning with a noise-robust loss function to deal with noisy labels. Moreover, network self-regularization is achieved by estimating each labeler's reliability within the chained approach. Lastly, visual inspection and relevance analysis experiments are conducted to reveal the non-stationary coding of our method. In a nutshell, GCEDL weights reliable labelers as a function of each input sample and achieves suitable discrimination performance with preserved interpretability regarding each annotator's trustworthiness estimation.
A Genetic Algorithm-Controlled Solar Tracker Robot with Increased Precision Due to Evolution

The aim of this paper is to present the genetic algorithm used in programming a solar tracker robot. The solar tracker robot can be used to increase the efficiency of solar panels by rotating them toward the Sun. When the efficiency of solar panels is increased, they can reduce the space occupied by them. Efficiency can also be increased by genetic algorithms, where a solar panel can evolve and thus track the Sun more precisely. The genetic algorithm implemented in the solar tracker is the main focus of this paper. The genetic algorithm is presented with a flow chart and formulas. After this, the system is implemented on a solar tracker robot and validated with real experiments. The solar tracker robot algorithm is implemented in the C language on a microcontroller built on an FPGA platform.
Reliability of distance estimation in virtual reality space: A quantitative approach for construction management

This paper reports a study aimed at assessing the reliability of decision-making for construction management based on visual observation of the built environment in virtual reality. The focus of this study is on egocentric distance perception, a critical factor in construction management decisions based on visual observation. The study evaluates the accuracy of distance estimation and walking a target distance in virtual reality, as compared to the real world, through experiments. The experimental results demonstrate that participants consistently underestimate distance and walk beyond the target distance. Notably, in virtual reality, they underestimated the distance by 22% more than they did in the real world, and they overwalked 53% more than they did in the real world. These findings offer valuable insights for construction managers reviewing digital twin models in virtual reality to make informed decisions. Furthermore, this study lays a foundation for future research on the reliability of understanding space occupancy in virtual reality.
Lattice-based group signatures with forward security for anonymous authentication

Group signatures allow users to sign messages on behalf of a group without revealing authority is capable of identifying the user who generated it. However, the exposure of the user's signing key will severely damage the group signature scheme. In order to reduce the loss caused by signing key leakage, Song proposed the first forward-secure group signature. If a group signing key is revealed at the current time period, the previous signing key will not be affected. This means that the attacker cannot forge group signatures regarding messages signed in the past. To resist quantum attacks, many lattice-based forward-secure group signatures have been proposed. However, their key-update algorithm is expensive since they require some costly computations such as the Hermite normal form (HNF) operations and conversion from a full-rank set of lattice vectors into a basis. In this paper, we propose the group signature with forward security from lattice. In comparison with previous works, we have several advantages: Firstly, our scheme is more effective since we only need to sample some vectors independently from a discrete Gaussian during the key-update algorithm. Secondly, the derived secret key size is linear instead of quadratic with the lattice dimensions, which is more friendly towards lightweight applications. Anonymous authentication plays an increasingly critical role in protecting privacy and security in the environment where private information could be collected for intelligent analysis. Our work contributes to the anonymous authentication in the post-quantum setting, which has wide potential applications in the IoT environment.
A systematic literature review on augmented reality in mathematics education

Technology, in particular augmented reality (AR), has the potential to greatly enhance interactive learning environments for mathematics in both classrooms and other teaching environments. The purpose of the present study was to investigate existing literature on AR in mathematics education. We selected papers from 10 databases, Scopus, Web of Science Core Collection, ERIC, IEEE Xplore Digital Library, Teacher Reference Center, SpringerLink, zbMATH Open, Taylor &amp;amp; Francis Online Journals, JSTOR, and MathSciNet. From these databases, 42 related studies were selected by the method of preferred reporting items for systematic reviews and meta-analysis (PRISMA2020). The results from all the papers showed positive outcomes as a result of AR implementation in mathematics education. They were also analyzed into different themes according to quantitative and qualitative criteria. The advantages and challenges of AR usage in mathematics education are also discussed in detail.
Modeling Extractive Question Answering Using Encoder-Decoder Models with Constrained Decoding and Evaluation-Based Reinforcement Learning

Extractive Question Answering, also known as machine reading comprehension, can be used to evaluate how well a computer comprehends human language. It is a valuable topic with many applications, such as in chatbots and personal assistants. End-to-end neural-network-based models have achieved remarkable performance on these tasks. The most frequently used approach to extract answers with neural networks is to predict the answer’s start and end positions in the document, independently or jointly. In this paper, we propose another approach that considers all words in an answer jointly. We introduce an encoder-decoder model to learn from all words in the answer. This differs from previous works. which usually focused on the start and end and ignored the words in the middle. To help the encoder-decoder model to perform this task better, we employ evaluation-based reinforcement learning with different reward functions. The results of an experiment on the SQuAD dataset show that the proposed method can outperform the baseline in terms of F1 scores, offering another potential approach to solve the extractive QA task.
SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment

Navigating robots through large-scale environments while avoiding dynamic obstacles is a crucial challenge in robotics. This study proposes an improved deep deterministic policy gradient (DDPG) path planning algorithm incorporating sequential linear path planning (SLP) to address this challenge. This research aims to enhance the stability and efficiency of traditional DDPG algorithms by utilizing the strengths of SLP and achieving a better balance between stability and real-time performance. Our algorithm generates a series of sub-goals using SLP, based on a quick calculation of the robot's driving path, and then uses DDPG to follow these sub-goals for path planning. The experimental results demonstrate that the proposed SLP-enhanced DDPG path planning algorithm outperforms traditional DDPG algorithms by effectively navigating the robot through large-scale dynamic environments while avoiding obstacles. Specifically, the proposed algorithm improves the success rate by 12.33% compared to the traditional DDPG algorithm and 29.67% compared to the A*+DDPG algorithm in navigating the robot to the goal while avoiding obstacles.
Banging Interaction: A Ubimus-Design Strategy for the Musical Internet

We introduce a new perspective for musical interaction tailored to a specific class of sonic resources: impact sounds. Our work is informed by the field of ubiquitous music (ubimus) and engages with the demands of artistic practices. Through a series of deployments of a low-cost and highly flexible network-based prototype, the Dynamic Drum Collective, we exemplify the limitations and specific contributions of banging interaction. Three components of this new design strategy—adaptive interaction, mid-air techniques and timbre-led design—target the development of creative-action metaphors that make use of resources available in everyday settings. The techniques involving the use of sonic gridworks yielded positive outcomes. The subjects tended to choose sonic materials that—when combined with their actions on the prototype—approached a full rendition of the proposed soundtrack. The results of the study highlighted the subjects’ reliance on visual feedback as a non-exclusive strategy to handle both temporal organization and collaboration. The results show a methodological shift from device-centric and instrumental-centric methods to designs that target the dynamic relational properties of ubimus ecosystems.
Conversational agents in language learning

Abstract Due to advances in technology, conversational agents are emerging as intelligent spoken dialogue systems that simulate natural conversation with human beings. A growing body of literature has investigated the potential of conversational agents in enhancing language learning across multiple contexts. In this paper, a broad scoping review examining the current literature on conversational agents and language learning was conducted. This review mapped APA PsycINFO, ERIC and ProQuest Dissertations &amp; Theses databases, which yielded 23 papers for further analysis. Our examination of these papers suggests that there are three main ways in which conversational agents are used for language learning. This review discusses these three approaches and points to directions that require further research to fully exploit the potential of conversational agents in language learning.
Sequence-to-sequence pretraining for a less-resourced Slovenian language

Large pretrained language models have recently conquered the area of natural language processing. As an alternative to predominant masked language modeling introduced in BERT, the T5 model has introduced a more general training objective, namely sequence to sequence transformation, which more naturally fits text generation tasks. The monolingual variants of T5 models have been limited to well-resourced languages, while the massively multilingual T5 model supports 101 languages.We trained two different-sized T5-type sequence-to-sequence models for morphologically rich Slovene language with much fewer resources. We analyzed the behavior of new models on 11 tasks, eight classification ones (named entity recognition, sentiment classification, lemmatization, two question answering tasks, two natural language inference tasks, and a coreference resolution task), and three text generation tasks (text simplification and two summarization tasks on different datasets). We compared the new SloT5 models with the multilingual mT5 model, multilingual mBART-50 model, and with four encoder BERT-like models: multilingual BERT, multilingual XLM-RoBERTa, trilingual Croatian-Slovene-English BERT, and monolingual Slovene RoBERTa model.Concerning the classification tasks, the SloT5 models mostly lag behind the monolingual Slovene SloBERTa model. However, these models are helpful for generative tasks and provide several useful results. In general, the size of models matters, and currently, there is not enough training data for Slovene for successful pretraining of large models.While the results are obtained on Slovene, we believe that they may generalize to other less-resourced languages, where such models will be built. We make the training and evaluation code, as well as the trained models, publicly available.
A novel image steganography algorithm based on hybrid machine leaning and its application in cyberspace security

As an important technology in the field of cyberspace security, image steganography is of great strategic significance to the research of image steganography. With the wider application of image steganography technology, there are fewer and fewer cases of image steganography using a single algorithm, and more adaptive steganography of images is used instead. According to the different sensitivities of human eyes to the foreground area and background area of the image, the key to solving this problem is to segment the foreground and background of the carrier image. Aiming at the problem of low maximum embedding capacity of traditional steganography algorithm and poor perception quality of carrier image after steganography, this paper firstly uses adaptive threshold segmentation to process carrier removal image, and divides the image into foreground and background regions. Secondly, in order to improve the embedding capacity, high-capacity steganography is used for pixels in the foreground area of the image, and low-capacity steganography is used for the background area of the image. At the same time, in order to improve the security of the secret information, hybrid machine learning is used to encrypt before the secret information is embedded, and finally BFS (Breadth First Search) is used to complete the steganography of the carrier image. The experimental results show that the PSNR (Peak Signal to Noise Ratio) value of the pictures after steganography by this algorithm is above 52.71 dB, which has high image perception quality. Better protection of confidential information.