Reviewerly/exHarmony huggingface dataset:
Unleashing employees’ tacit knowledge toward performance-driven culture in a Saudi Arabian organisation

Purpose Performance-driven culture has received extensive attention from both academics and practitioners because of its impact on organisations’ performance. Employees’ tacit knowledge about performance-driven culture is pivotal to identify, as it contributes to increasing the organisation’s performance. With the aim of enriching ongoing debate in human resources and knowledge management research, this paper proposes a conceptual model for emancipating and investigating the main factors of employees’ tacit knowledge that shape and affect performance-driven culture of a Saudi Arabian organisation. Design/methodology/approach The validity of the conceptual model is examined through a qualitative study developed using a thematic analysis of 134 employees’ unbridged computerised typewritten comments about organisation’s performance-driven culture. Findings Findings confirm the utility of conceptual models in explaining and categorising employees’ emancipated tacit knowledge, providing a potential contribution to academics and practitioners interested in developing managerial processes for improving organisation’s performance-driven culture. Originality/value Both the conceptual reflections and empirical-based evidence herein enrich ongoing debate in the area of human resources and knowledge management about employees’ tacit knowledge and performance-driven culture.
Development of a landslide detection surveillance system

A landslide is a natural disaster that causes deaths and loss of life and property. Before the main landslide, several characteristic precursor events like the fall of small stones and rocks occur up to ~180sec before the actual landslide. In this paper, we propose a landslide surveillance system. The surveillance system input is processed by an image processing algorithm that continuously monitors the area and detects and tracks any such precursor events of a landslide. The system issues an alert as soon as it detects any such event. The proposed system requires cheap, low-frame rate cameras, and less memory and bandwidth - making it an effective landslide warning system.
Expression for the Probability of Correlation Error in Data Fusion

In a previous paper we proposed an expression for the probability of association (or correlation) error between two lists of objects, subject to known Gaussian distributions and a Poisson field of such objects. The expression requires only summing a few terms in a series, and is quite accurate, especially so when the probability of such an error is low. It does, however, depend on an assumption that correlation errors were caused by isotropic observations of the truth. Hence, in this paper, we extend the analysis to non-isotropic sensor noise, and find that in many practical situations of interest it is quite simple to adapt the isotropic analysis thereto. A natural extension of our results is to the case of multiple (more than two) sensors, and we find that in the case of a simple sequential fusion strategy the analysis is straightforward. These multi-sensor results suggest that the analysis might fruitfully be applied to suggest a good sequential ordering; we do so, and find that significant benefits accrue even when based on observations alone (no need for clairvoyant knowledge of target &#x201C;truth&#x201D;). Finally, we explore translational sensor bias.
Multi-Target Custody via Distributed Multiple-Model Multiple-Hypothesis Tracking

The challenge of multi-target tracking is made more onerous when there are nontrivial sensor coverage gaps. Most automatic trackers will drop track outside of coverage, due to the large track state uncertainty. Recently, use of the Ornstein-Uhlenbeck (OU) process has been proposed to extend object custody outside regions of sensor coverage, by exploiting context information that supplements available sensor data. This paper continues this line of research using a multiple-model OU filter within a distributed multiple-hypothesis tracking paradigm, with local (context-unaware) trackers and downstream (context-aware) track fusion. Crucially, the context-aware tracking filter must allow for objects that deviate from expected behavior; we achieve this via a context-unaware mode with limited interaction with context-aware modes. Further, as context may include multiple competing modes, it is necessary to generalize existing tracking performance metrics to account for partial object custody. Accordingly, we introduce multiple-model extensions to target completeness, track completeness, and optimal sub-pattern assignment (OSPA) metrics. We quantify improved performance of context-aware surveillance over baseline processing using variations of an air surveillance scenario.<sup>1</sup><sup>1</sup>IEEE Aerospace Conference, Big Sky MT, March 2022. <sup>2</sup><sup>2</sup>IEEEAC paper &#x0023;2705, Final Version, Updated 2021-11-24.
Thinking Aloud or Screaming Inside: Exploratory Study of Sentiment Around Work

Millions of workers experience work-related ill health every year. The loss of working days often accounts for poor well-being because of discomfort and stress caused by the workplace. The ongoing pandemic and postpandemic shift in socioeconomic and work culture can continue to contribute to adverse work-related sentiments. Critically investigating state-of-the-art technologies, this study identifies the research gaps in recognizing workers' need for well-being support, and we aspire to understand how such evidence can be collected to transform the workforce and workplace.Building on recent advances in sentiment analysis, this study aims to closely examine the potential of social media as a tool to assess workers' emotions toward the workplace.This study collected a large Twitter data set comprising both pandemic and prepandemic tweets facilitated through a human-in-the-loop approach in combination with unsupervised learning and meta-heuristic optimization algorithms. The raw data preprocessed through natural language processing techniques were assessed using a generative statistical model and a lexicon-assisted rule-based model, mapping lexical features to emotion intensities. This study also assigned human annotations and performed work-related sentiment analysis.A mixed methods approach, including topic modeling using latent Dirichlet allocation, identified the top topics from the corpus to understand how Twitter users engage with discussions on work-related sentiments. The sorted aspects were portrayed through overlapped clusters and low intertopic distances. However, further analysis comprising the Valence Aware Dictionary for Sentiment Reasoner suggested a smaller number of negative polarities among diverse subjects. By contrast, the human-annotated data set created for this study contained more negative sentiments. In this study, sentimental juxtaposition revealed through the labeled data set was supported by the n-gram analysis as well.The developed data set demonstrates that work-related sentiments are projected onto social media, which offers an opportunity to better support workers. The infrastructure of the workplace, the nature of the work, the culture within the industry and the particular organization, employers, colleagues, person-specific habits, and upbringing all play a part in the health and well-being of any working adult who contributes to the productivity of the organization. Therefore, understanding the origin and influence of the complex underlying factors both qualitatively and quantitatively can inform the next generation of workplaces to drive positive change by relying on empirically grounded evidence. Therefore, this study outlines a comprehensive approach to capture deeper insights into work-related health.
A Fourier Descriptor for Bone Point Segmentation using inner distance in remote sensing images

In most cases, especially in remote sensing targets, the contour of the object is used to describe the features of the object, so the shape descriptor plays an indispensable role in the target detection and recognition. In recent years, more and more shape descriptors have come to the fore, but many descriptors ignore the details of the shape, such as the traditional shape descriptor of centroid and contour descriptor (CCD) and shape context descriptor (SC). The inner distance of a shape has strong robustness for describing shape features, and the inner distance shape context9(IDSC) is a good example. Therefore, this paper proposes a method of using the inner distance to find shape bones and segment the shape contour by these bones, finally performing Fourier transform to form shape feature (FD-IDBS). In matching time, we using the distance difference to perform shape matching. What is commendable is that its discriminability and robustness is strong, process is simple and matching speed is fast. More importantly, the experiment results show that the shape descriptor has higher retrieval accuracy.
Deep Transfer Learning for Question Classification Based on Semantic Information Features of Category Labels

Question classification is an important component of the question answering system (QA system), which is designed to restrict the answer types and accurately locate the answers. Therefore, the classification results of the questions affect the quality and performance of the QA system. Most question classification methods in the past have relied on a large amount of manually labeled training data. However, in real situations, especially in new domains, it is very difficult to obtain a large amount of labeled data. Transfer learning is an effective approach to solve the problem with the scarcity of annotated data in new domains. We compare the effects of different deep transfer learning methods on cross-domain question classification. On the basis of the ALBERT fine-tuning model, we extract the category labels of the source domain, the question text, and the predicted category labels of the target domain as input to extract the category labels. Additionally, the semantic information of the category labels is extracted to achieve cross-domain question classification. Furthermore, WordNet is used to expand the question, which further improves the classification accuracy of the target domain. Experimental results show that the above methods can further improve the classification accuracy in new domains based on deep transfer learning.
Collaborative Music Creation Method Based on Image Collection of Lattice Code

With the development of science and technology, more and more emerging technologies have been integrated into electronic music, forming a rich and diverse form of interactive electronic music. This research mainly discusses the collaborative music creation method based on bit matrix code image acquisition. In the decoding process, the displacement value of each point should be determined according to the direction and distance from the point to the reference point of the corresponding grid raster. This article marks the position characteristics of image symbols according to the dot matrix code notation defined in the improved coding method. After searching for a pair of dot codes with the shortest distance in the horizontal or vertical direction, starting from the first code point in the vertical direction and following the second code point. Through the above method, it is possible to accurately construct the grid raster lines of the dot matrix position encoding symbol image formed after the improved encoding, so that the decoding of the dot matrix symbol image is more accurate, and the composition translation is reduced. The improved coding method reduces the error of the origin matrix code by about 0.11 mm as a whole. This research will realize the collaborative recording of data in the music creation process, which will bring great convenience to creators.
‘For (…) a leader like this Prime Minister to talk about morals and morality is a disgrace’: offensive action, uptake and moral implications in the context of parliamentary debates

This contribution examines the negotiation of offensive action, its uptake and moral implications in the context of parliamentary debate and user comments on social media, by drawing data from the British Parliament. Being institutional discourse, public discourse and media discourse, communicative action in parliament is constrained within a society's code of conduct, describing what may count as offensive action and which institutional sanctions an offender would need to expect. Adopting a discourse-pragmatic approach, debates on offence are distinguished with respect to metadebates in parliament, and metadebates following up on the offensive action in users' comments in social media. The study shows that parliamentary debate is imbued with complex moral issues and that participants draw on a variety of strategies to assign offence the status of an object of discourse in order to initiate metadebates on its (in-)appropriateness, such as the strategic use of challenges, presuppositions, and of explicit and implicit references to morality. Due to the pluralism of communicative action and the multilayeredness of participant roles and interactional frames, participants voice and take offence as representatives of political groups and as individuals, and in spite of the seeming regulatedness of parliamentary interactions engage in metadebates about what they consider as (in-)appropriate and morally right or wrong.
Adaptive Classification Algorithm Design for Online Teaching Resources of Opera Singing

In the context of the integration of each professional course’s online education platform and teaching knowledge system, teaching resources are experiencing explosive growth, and classroom-based instruction is gradually being replaced by resource-based instruction. In the modern era, the optimization, integration, and efficient use of online teaching resources have become central concerns in education. In particular, the categorization and integration of resources have become the central focus of teaching work. The cognitive stratification theory classifies and orders the educational objectives in the cognitive domain in a scientific manner, which provides teachers with important ideas and foundations for implementing online and offline integrated teaching designs centered on the students’ skill acquisition. Adaptive classification algorithm is introduced to subdivide the objectives of online singing teaching resources, and on this basis, the online resources are classified and integrated according to the characteristics of different teaching sessions, thereby achieving the efficient use of resources, which is an innovative path of singing resources classification and integration in the new stage of online resources teaching.
Mathematical Model Design of the Traditional Dress Recognition Algorithm Based on Digital Watermarking Technology

According to the characteristics of traditional clothing, clothing identification is studied, and clothing identification and clothing culture learning are effectively combined to find a new method for the inheritance of national culture and strive to make contributions to the inheritance of national culture. According to the requirement of the traditional garment identification watermark monitoring system, a self-synchronous digital watermarking algorithm is designed and implemented. Watermark is embedded in the time domain, and feature information is extracted from traditional clothing by means of mean filtering and replaced by watermark to achieve the purpose of embedding information. Blind detection is realized without the participation of the original image. The difference between the traditional costume embedded with watermark and the original traditional costume is almost imperceptible. It can effectively resist synchronous attacks including clipping and time shifting, showing good robustness. Imperceptibility and robustness can be adjusted freely by embedding strength. The HOG + SVM algorithm is applied to minority clothing classification and recognition. By comparing different classifiers, it is concluded that the classifier trained by the support vector machine algorithm has the best classification effect on ethnic clothing. In order to improve the classification effect, the classical algorithm of color, texture, and shape feature extraction was combined with SVM to conduct experiments on the clothing database collected and sorted out in Yunnan ethnic minority communities, and finally, we verified that the HOG feature combined with the SVM classification algorithm achieved good results in the classification of ethnic clothing.
Open science in China: Openness, economy, freedom &amp; innovation

Taking credit for digitalization and platformization, China has initiated its open science infrastructure implementation and made an effort to focus on open access (OA) journals and data sharing over the past two decades. With the continuous development need, issues and concerns have caught in attention, including data accessibility, research transparency, general population awareness and communication of science, public trust in science, and scientific research and innovation efficiency. This paper has unfolded the maze of open science stance in China and elaborated on its current economy, openness and freedom extents, and future innovation potential towards a global open science community, within depth and scope of both the Chinese and Western scholars’ interpretations.
An Incremental Approach to Classify Healthcare URLs Using a Novel ‘Web Document Classification Model’

In the present research work, we proposed a novel Web document classification model (WDCM) to classify healthcare-related URLs. This is required as the Web is flooded with millions of URLs and they may be duplicated or irrelevant most of the time. It is a challenging task to extract and organize Web documents specific to a certain domain. Another challenge is to provide the updated and latest information to the users. In this research, the authors have proposed WDCM which tackles the above-mentioned challenges with the incremental learning approach. Authors have also used sentiment analysis and document similarity measures to classify URLs in the healthcare domain. The analysis of the experimental results shows that with machine learning classifiers, cosine similarity measure with logistic regression showed the highest accuracy of 93.75%. Euclidean distance (ED) measure with logistic regression showed minimum accuracy of 86.6%. When implemented as an algorithm ED measure showed the highest accuracy of 96.60% for training and 95.83% for the testing dataset in five iterations. Jaccard distance measure showed lower accuracy of 65.71% for training and 85.33 for the testing dataset in five iterations. It is also observed that the maximum URLs fetched are with the .com domain.
BMCR: A collaborator recommendation model based on Scholars’ academic level

Collaborator recommendation helps scholars find suitable collaborators, which is useful for the development of scientific research and the transformation of scientific and technological achievements. However, the gap between scholars' academic level seriously affects the establishment of cooperative relations. In this paper, a new model named Best Matching Collaborator Recommendation (BMCR) is proposed. BMCR model looks for the best matching collaborators for scholars from a new perspective of academic level. First, we define the academic level index of scholars and cluster scholars with this index. Next, the research topics of scholars are extracted from their published papers by using the topic extraction model. Then, for each cluster, a scholar-topic graph is built with the scholars and their topics. Finally, given the target scholar, the biased random walk with restart algorithm is carried out on the scholar-topic graph to find potential collaborators. Experiments on the MAG, DBLP, C-DBLP datasets show that our model improves the feasibility of cooperation between the target scholars and recommended scholars. Compared with other methods, our model has improved precision rate, recall rate and F1 score by 5.3%, 2.5%, and 4% respectively. The experimental results indicate that scholars' academic level is a key reference factor in the collaborator recommendation, and that scholars with similar academic level are more likely to achieve practical cooperation. The findings are not only of great significance in recommending collaborators to scholars, but also can be extended to other fields to solve the problem of collaborator recommendation.
Effects of CLEM cloud based E-learning management system for teaching and learning process in mechatronics

Due to the wide usage of Internet and the needs of learning. And since the Internet can be accessed by multiple devices like iPad, phones and Tabs so using the cloud computing will make it so easy. e-learning is an important issue since it may reduce the efforts of cost and time consumption of actual real learning. For mechatronics (which is a discipline of computer, mechanics and electronics). Using cloud e learning will make it easier for teachers, learners and students to develop their knowledge of mechatronics and also, they may create and develop courses in addition to idea exchanging in reliable and easy way. The effects of cloud-based electronic-learning for mechatronics will be discussed in this proposal.
Improvement of accuracy of under-performing classifier in decision making using discrete memoryless channel model and Particle Swarm Optimization

In-spite of availability of wide range of algorithms for constructing multi-class classifier, there are applications like Decision support system, Game prediction in the sports, where the multi-class classifier performs relatively poor in terms of achieving the reasonable classification accuracy. In this paper, poorly performing multi-class classifier (constructed using the classical methods like Artificial Neural Network, cascade of Support Vector Machine, etc.) is treated as the discrete memoryless channel model with known transition probabilities (channel matrix) and the unknown priors. It is further used to construct M-ary Mini-Max technique based randomized decision rule to improve the performance of the multi-class classifier in terms of the classification accuracy. The prior probabilities and the probabilities associated with the M-ary randomized decision rule are further solved using the Particle Swarm Optimization. The experimental results based on the Monte-Carlo simulation using the synthetic data set and the real data set reveal the consistent improvement in the performance of the poorly performing classifier using the proposed technique.
Creating Informative and Successful Human Robot Interaction in a Real-World Service Environment

This study explores how to best utilize service robots when deploying them in customer facing contexts for information requests. Many companies investing in such robots unfortunately fail to integrate them successfully in a way where they facilitate meaningful interactions with users. Copenhagen Visitor Service is one of those companies who have tried to integrate a Pepper robot into their service environment but failed due to several factors relating to both the environment, the robot and lack of a strategical implementation. Through an exploratory field study with in-situ feedback from the participants, it became evident that the robot in this specific setting needed a more focused purpose and an interaction flow that was not reliant on verbal communication. A prototype was developed consisting of an informative quiz targeting families. A subsequent field deployment demonstrated a better and more meaningful user experience, indicating that careful consideration of the use context is paramount for a successful integration as well as a strategical implementation.