Reviewerly/exHarmony huggingface dataset:
Can knowledge management processes support business transformation? The mediating role of business agility

Purpose This study aims to examine the impact of knowledge management processes (KMPs) on business transformation (BT) as mediated by business agility (BA). Design/methodology/approach The study is a quantitative, descriptive and analytical study. A two-stage sampling was selected. A total of 380 questionnaires were distributed using a self-administered survey to six companies representing the study context. A total of 362 questionnaires were collected and analyzed using SPSS and Path analysis (AMOS-22), with a response rate of 95.2%. Findings The study revealed that creation, storage, sharing and application of knowledge had statistically significant impact on BT. Moreover, the results revealed that BA had a positive effect on BT. Finally, this study confirms the mediating role of BA. Practical implications This study has several practical implications. These include, but are not limited to, more focus on both the KMPs and BT dimensions to improve their levels. There is also a need to search for other factors that can support BT efforts to enhance the growth potential and survival of business organizations. Originality/value The study confirms that KMPs can facilitate transforming of business to cope with unending environmental changes. Considering the scarcity of the available empirical as well as theoretical studies within the Jordanian as well as the overall Arab countries context, the study and its findings are expected to provide some insights and useful contribution concerning the potential mediating impact of BA, which, as revealed by the literature review, has rarely been considered in previous studies. This could open the door for further research to understand the mechanisms of BT and its predictive variables.
The ART and LOGIC of scholarly communication: Effective skills for publication and beyond

Based on a series of well-recognized workshops around the world conducted by the author with more than 15 years of EIC experience, this article highlights the essence and spirit of publishing scholarly outcomes for authors in science and engineering. It is vital for all scientists who intend to upgrade their publications to bear in mind that, additional to KISS (Keep It Simple and Straightforward), the contents of a manuscript should be prepared based on the core concept of ART and LOGIC, namely: Thoroughness (be thorough when compiling) oComplete the sentence, paragraph, and articleReadability (keep the expression readable) oKnow what you mean and mean what you knowArticulateness (be articulate in wording) oBe polite yet firm, when necessary, with controversies and Lock one in ASAP oIdentify and be identical with the readersOffer useful information oJustification and reasoning are most criticalGain confidence oPut forth best methodology and know-howIndication of good will oBe constructive even when criticism is neededConclusive statement oProvide solid conclusion with proper outlook.
Noisiness Estimation of Classroom Students Based on RGB Images

This study aims to create a system that can detect and visualize the environment of each student and the entire classroom as if the assistant homeroom teachers were present in the classroom and to provide teachers awareness during class. This study also attempts to estimate the noise level in the class-room. The developed approach uses a background subtraction method and skin color extraction to extract the body movements of the students with the aim of noise estimation. In this paper, a method is proposed to estimate the noisiness by extracting the body movements of students using the background subtraction method and skin color extraction and by fusing the dot-like noise generated by the logical product with a morphological transformation, area division of the image, and the number of students facing the front. The result a confirm that the subjective noisiness judgment of humans can be estimated with an accuracy of 80%. In the future, this study will attempt to improve the accuracy of the noisiness estimation by optimizing the threshold based on the judgment results of the teacher.
An Examination on Reduction of Displayed Character Shake while Walking in Place with AR Glasses

In recent years, augmented reality (AR) has started to be used in our daily lives. AR glasses are used when walking, which is a normal part of daily life, but walking causes the text displayed on the glasses to shake. This reduces both readability and our attention to what is in front of us, and increases discomfort. We propose a method of fixing the text to take account of shaking while walking to reduce these adverse effects. Experiments revealed the effectiveness of our reduction method and its influence on the distance of the text display.
A Pinch-based Text Entry Method for Head-mounted Displays

Pinch gestures have been used for text entry in Head-mounted dis-plays (HMDs), enabling a comfortable and eyes-free text entry. However, the number of pinch gestures is limited, making it difficult to input all characters. In addition, the common pinch-based meth-ods with a QWERTY keyboard require accurate control of the hand position and angle, which could be affected by natural tremors and the Heisenberg effect. So, we propose a new text entry method for HMDs, which combines hand positions and pinch gestures with a condensed key-based keyboard, enabling one-handed text entry for HMDs. With this method, users move their hands with a naturally comfortable posture between three large different spaces in the air to choose one key set and then execute one of the pinch gestures to choose one character, where hand jitter does not affect the selection, helping to improve the input speed. The results of a primary study show that the mean input speed of the proposed method is 7.60 words-per-minute (WPM).
Sign Language Recognition Based on Gesture Recognition/Holistic Features Recognition: A Review of Techniques

Sign Language is now a part of important means of communication, covering the domain of deaf-mute communication, controlling robots through pose movements, exoskeleton, medical application, and home automation. There is plenty of research going on gesture recognition methods/techniques (using wearable sensors, computer vision), stacking it with other technologies for automation, and forming a means of communication between deaf/mute/blind individuals. Our paper highlights some of the best techniques that have surfaced in the past years and their limitations. Also, it tabulates the performance of those techniques their drawbacks and how to improve it, the dataset used, background condition. This study offers an in-depth survey of gesture recognition systems with a short discussion on use of sign language translation.
[DC] A Tangible Augmented Reality Programming Learning Environment (TARPLE) for Active, Guided Learning

This PhD project aims to bring together technological and educational perspectives by understanding how objectives of programming learning and principles of active learning and embodied learning can be supported and enhanced with AR and TUI technologies. This work presents an approach to the design and evaluation of a TARPLE prototype with enhanced functionality to encourage active guided learning of a text-based OOP language. The system supports natural interaction with learning material, and embodiment and contextualisation of information in 3D space. One of the goals of this thesis has been to understand how empirical studies can inform the design of a TARPLE that supports learning of text-based programming languages and development of a basic debugging skillset.
Implementing Augmented Reality Using OpenCV

Continuous attempts have been made to make livelihood efficient and effortless. Augmented reality (AR) provides extra information to the user by applying virtual image onto the real environment. Augmenting the real world around us with computer-generated perceptual information can simplify the way we interact with our surroundings. This will help in comprehending concepts in an immersive environment. Thus, simplifying concepts and making learning, visualization and representation easy. People's ability to understand what they've learned could be enhanced by a combination of interactivity and engagement, resulting in rapid acquisition of information and skills. The ability to engage yourself in the data within our own comfortable surroundings, sharing and discussing the knowledge and visualization with others in the same room is not only a fantastic experience, but it also leads to an increased incentive to discuss the data with others. A three-dimensional image helps students understand abstract concepts. The paper deals with a proposed model that shall inculcate Marker detection algorithm for image projection on the target surface.
A Novel Recurrent Neural Network Model for the Identification of Time-Delayed Nonlinear System

This paper presents a locally recurrent neural network with input feed through (LRNNIFT) for the identification of nonlinear time-delayed system. The proposed network model parameters are tuned using a Back-propagation (BP) algorithm. The performance of the proposed model is compared with the well known recurrent Elman neural network (ENN) and a single layer feed-forward neural network (FFNN). The simulation results showed that the proposed model has shown better accuracy as compared to the other two models.
Path planning algorithms for robotic aquaculture monitoring

Aerial drones have great potential to monitor large areas quickly and efficiently. Aquaculture is an industry that requires continuous water quality data to successfully grow and harvest fish. The Hybrid Aerial Underwater Robotic System (HAUCS) is designed to collect water quality data of aquaculture ponds to reduce labor costs for farmers. The routing of drones to cover each fish pond on an aquaculture farm can be reduced to the Vehicle Routing Problem. A dataset is created to simulate the distribution of ponds on a farm and is used to assess the HAUCS Path Planning Algorithm (HPP). Its performance is compared with the Google Linear Optimization Package (GLOP) and a Graph Attention Model (GAM) for routing problems. GLOP is the most efficient solver for 50 to 200 ponds at the expense of long run times, while HPP outperforms the other methods in solution quality and run time for instances larger than 200 ponds.
Analysis and Observation of Behavioral Factors Contributing to Improvement of Embodiment to a Supernumerary Limb

Research has been conducted on attaching a robotic arm to a human body and using it as a supernumerary limb. However, it remains unclear what type of behaviors contribute to the induction of embodiment in a supernumerary limb. In this study, we created a virtual environment where we could observe behaviors that contribute to the embodiment of avatars and tools, such as ”looking at one’s own body in a mirror,” ”moving the supernumerary limb quickly and accurately,” and ”paying constant attention to the supernumerary limb,” and verified whether these behaviors are also effective in inducing the embodiment of the supernumerary limb. After spending 40 min in a behavior observation space, the left hand, the side with the supernumerary limb, was used significantly less frequently. In addition to these behaviors, ”self-touching,” ”transferring objects between their own hand and the supernumerary limb,” and ”whole-body movement” were also observed. The observations suggest that these behaviors may change the awareness of the supernumerary limb and contribute to the improvement of its embodiment.
Enhanced App Based Sorting Algorithm Visualizer

Computer Science could be a field of not fair creating arrangements to day-to-day exercises but moreover to enhance is to the fullest. Calculations are the fundamental down to earth unit for moving forward any action that offer work with particular efficiency, and to get increment the efficiency of errand. Sorting is the key component of Information structures and Algorithms. In any case, it is complicated and we couldn’t able to recognize the entire calculation and code for the primary time. So the use of this application has numerous predominant understanding on instruction. The key component of this project is to assist tenderfoots to be able to imagine the sorting calculations so our brain can get it quicker and keep in mind way better. Conjointly get superior understanding of basic operations.
Self super-resolution autostereoscopic 3D measuring system using deep convolutional neural networks

Autostereoscopy technology can provide a rapid and accurate three-dimensional (3D) measurement solution for micro-structured surfaces. Elemental images (EIs) are recorded within one snapshot and the measurement accuracy can be quantified from the disparities existing in the 3D information. However, a trade-off between the spatial and the angular resolution of the EIs is a major obstacle to the improvement on the measurement results. To address this issue, an angular super-resolution algorithm based on deep neural networks is proposed to construct a self super-resolution autostereoscopic (SSA) 3D measuring system. The proposed super-resolution algorithm can generate novel perspectives between the neighboring EIs so that the angular resolution is enhanced. The proposed SSA 3D measuring system can achieve self super-resolution on its measurement data. A comprehensive comparison experiment was conducted to verify the feasibility and technical merit of the proposed measuring system. The results show that the proposed SSA system can significantly improve the resolution of the measuring data by around 4 folds and enhance the measurement accuracy to a sub-micrometer level with lower standard deviations and biases.
Medium-specific aspects of digital reading and their impact on reading comprehension

Abstract The paper analyses empirical research results comparing the impact of the medium (traditional, paper-based vs. screen-based) on the reading process and text comprehension. It focuses on two analytical approaches—the first looks at the construction of cognitive maps of texts and the other the material aspects of the medium – which enable readers to orientate themselves and to explore and interpret the text more comprehensively. The paper discusses differences in how readers approach textual meaning according to experience of using digital technologies, the text, and reader experience and abilities. It concludes by arguing that strengthening these individual experiences and abilities is key to effective inference of textual meaning.
View-Adaptive Asymmetric Image Detail Enhancement for 360-degree Stereoscopic VR Content

We present a simple VR-specific image detail enhancement method that improves the viewing experience of 360-degree stereoscopic photographed VR contents. By exploiting the fusion characteristics of binocular vision, we propose an asymmetric process that applies detail enhancement to one single image channel only. Our method can dynamically apply the enhancement in a view-adaptive fashion in real-time on most low-cost standalone VR headsets. We discuss the benefits of this method with respect to authoring possibilities, storage and bandwidth issues of photographed VR contents.
Critical literacy: prevents negative impact of clickbait news on news agency website

Purpose This study aims to describe the critical literacy of online newsreaders on the viva.co.id website in dealing with clickbait news. Critical literacy is oriented to the critical power of online newsreaders by analyzing, evaluating and reflecting on the relationship of online newsreaders, texts and social and political contexts. This research will focus on the type of clickbait exaggeration or news headlines that use exaggeration. Design/methodology/approach This study uses a quantitative method with a descriptive approach. The purpose of quantitative research is to provide information on a situation by using scientific procedures to answer a question in an actual way (Sugiyono, 2010). Scientific observation is carried out carefully to obtain accurate results on a phenomenon. This study presents an overview of the situation or a phenomenon regarding the critical literacy of online newsreaders on the viva.co.id website in dealing with clickbait titles in Surabaya. The population in this study were all online newsreaders entitled clickbait on the viva.co.id website, where the number of online newsreaders on the viva.co.id website was not known. Sampling in this study used the Lemeshow formula (Lemeshow, 1997, as cited in Priyambodo, 2019). This formula determines the number of samples if the population is not known. Based on the calculation using the Lemeshow formula, it can be seen that the minimum sample size required is 96 respondents who are rounded up to 100 respondents with a 95% confidence level. Findings Based on the findings of the data recapitulation of the overall dimensions of critical literacy, this study shows that newsreaders entitled clickbait on the website viva.co.id have critical literacy skills that fall into the high category with an overall average of 4.07. This finding shows positive results because critical literacy skills are skills needed by newsreaders entitled clickbait on the website viva.co.id. Currently, online news is very much in number, and it may be that the news is not by the existing facts; therefore, this ability is considered very important because, with this ability, the reader can find out the ideology or interests that affect a news publication (Priyatni, 2010). Research limitations/implications This research only uses an online news provider, namely, viva.co.id, it is an Indonesia-based online news provider that covers all news subjects. Practical implications This study will give understanding about the use of critical literacy to prevent negative impact on clickbait and is very useful for all online newsreaders, information practitioners and scientists. Originality/value The use of critical literacy to assess online news is rarely studied in Indonesia, especially when dealing with clickbait news that can harm or bring disadvantages for readers. So, this study is very important to help readers to prevent clickbait news.
Book review: Soumyen Sikdar, Ramesh Chandra Das and Rajib Bhattacharyya (Eds), Role of IT–ITES in Economic Development of Asia: Issues of Growth, Sustainability and Governance

Soumyen Sikdar, Ramesh Chandra Das and Rajib Bhattacharyya (Eds), Role of IT–ITES in Economic Development of Asia: Issues of Growth, Sustainability and Governance. Springer, 2020, pp. 329, Ä109.99 (Hardback), Ä79.99 (Paperback), ISBN 978-981-15-4205-3.
Inflation Improves Graph Neural Networks

Graph neural networks (GNNs) have gained significant success in graph representation learning and become the go-to approach for many graph-based tasks. Despite their effectiveness, the performance of GNNs is known to decline gradually as the number of layers increases. This attenuation is mainly caused by noise propagation, which refers to the useless or negative information propagated (directly or indirectly) from other nodes during the multi-layer graph convolution for node representation learning. This noise increases more severely as the layers of GNNs deepen, which is also a main reason of over-smoothing. In this paper, we propose a new convolution strategy for GNNs to address this problem via suppressing the noise propagation. Specifically, we first find that the feature propagation process of GNNs can be taken as a Markov chain. And then, based on the idea of Markov clustering, we introduce a new graph inflation layer (i.e., using a power function over the distribution) into GNNs to prevent noise propagating from local neighbourhoods to the whole graph with the increase of network layers. Our method is simple in design, which does not require any changes on the original basis and therefore can be easily extended. We conduct extensive experiments on real-world networks and have a stable improved performance as the network depth increases over existing GNNs.
DeepCARSKit: A deep learning based context-aware recommendation library

Recent development in recommender systems has demonstrated the effectiveness of deep learning in recommendation algorithms. There are several existing open-source libraries for recommendation research, but not in the area of context-aware recommendations using deep learning. Therefore, we develop and release DeepCARSKit which is an open-source and deep learning based context-aware recommendation library. DeepCARSKit supports evaluations specifically designed in context-aware recommender systems, and it provides a standard platform for researchers to develop, execute and evaluate different context-aware recommendation models based on deep learning.
Adapter Based Fine-Tuning of Pre-Trained Multilingual Language Models for Code-Mixed and Code-Switched Text Classification

Abstract Code-mixing and code-switching (CMCS) are frequent features in online conversations. Classification of such text is challenging if one of the languages is low-resourced. Fine-tuning pre-trained multilingual language models (PMLMs) is a promising avenue for code-mixed text classification. In this paper, we explore adapter-based fine-tuning of PMLMs for CMCS text classification. We introduce sequential and parallel stacking of adapters, continuous fine-tuning of adapters, and training adapters without freezing the original model as novel techniques with respect to single-task CMCS text classification. We also present a newly annotated dataset for the classification of Sinhala-English code-mixed and code-switched text data, where Sinhala is a low-resourced language. Our dataset of 10000 user comments has been manually annotated for five classification tasks: sentiment analysis, humor detection, hate speech detection, language identification, and aspects identification, thus making it the first publicly available Sinhala-English CMCS dataset with the largest number of annotation types. In addition to this dataset, we also carried out experiments on our proposed techniques with Kannada-English and Hindi-English datasets. These experiments confirm that our adapter-based PMLM fine-tuning techniques outperform, or are on par with the basic fine-tuning of PMLM models.
Supervised Attention in Sequence-to-Sequence Models for Speech Recognition

Attention mechanism in sequence-to-sequence models is designed to model the alignments between acoustic features and output tokens in speech recognition. However, attention weights produced by models trained end to end do not always correspond well with actual alignments, and several studies have further argued that attention weights might not even correspond well with the relevance attribution of frames. Regardless, visual similarity between attention weights and alignments is widely used during training as an indicator of the models quality. In this paper, we treat the correspondence between attention weights and alignments as a learning problem by imposing a supervised attention loss. Experiments have shown significant improved performance, suggesting that learning the alignments well during training critically determines the performance of sequence-to-sequence models.