Reviewerly/exHarmony huggingface dataset:
Role of Social Media in Perspective of Media Information Literacy During Pandemic Covid-19

Emphasizing the significance of media information literacy, social media is playing a pivotal role in disseminating information and creating literacy among users.This research offers a momentous contribution to both researchers and policy makers as it sheds light on how social media creates awareness among masses during pandemic.This Study ensures to understand the relationship between social media and information literacy and also analyze the association between media literacy and youth behavior.The study employs quantitative research by using survey method to find out the behavior of youth towards disseminated information by social media regarding pandemic COVID-19.Convenience sampling benefits the research to draw a sample conveniently in relevance to social media usage and an exposure to the information of COVID-19.Theoretically, uses and gratification along with media literacy theory has been applied to analyze the current study which intends to focus the need to understand that how behavior of youth changes after getting exposed to the disseminated information regarding COVID-19 in terms of start taking precautions i.e wearing masks, using sanitizers, maintaining social distance.It clearly concludes that social media shows a great contribution in creating literacy among masses which eventually helps people to use precautionary measure against COVID-19.
Cricket Videos Summary generation using a Novel Convolutional Neural Network

Video is the combination of frames, many sequences of images known as frames are grouped together to form a video. The exponential growth of video data requires constant exploration from original videos by extracting only informative content present in the video. Traditional video processing applications process frame one by one and consume a lot of time, however, video summarization techniques based on deep learning extract only key-frames in the video based on classification for informative content. Supervised and unsupervised video processing techniques help people to reduce their efforts for video summarization. In this paper, we propose a novel customized convolutional neural network i.e. a supervised model of deep learning for summarization of cricket videos. Our proposed Cricket- Convolutional Neural Network (C-CNN) learns the most informative features from video frames and performs binary classification into positive and negative class. We have performed an extensive experimentation which ensures that our proposed C-CNN network outperforms the existing techniques for cricket video summarization.
ETHICAL FOCUS IN TRAINING FUTURE BUSINESS LEADERS AT UNIVERSITY LEVEL

In the contemporary world, there is a growing need for socially responsible and ethical leadership. Therefore, training future business leaders in the field of professional ethics and social responsibility is an important task of modern university education. To solve it, the participation of scientists and university teachers in the creation and implementation of advanced methods for the development of professional ethics of students is necessary. The aim of the study was to check the effectiveness of training future business leaders in professional ethics at university level using a new methodology created by the authors. In order to verify the hypotheses, an experiment was conducted. Additionally, the questionnaires and tests completed by the participants were analysed. The results of learning according to the new methodology, which confirm the expediency of its use for the development of parameters of professional ethics in future business leaders, are presented. The methodology described in the article can be used by university teachers to train future business leaders, as well as by organizational psychologists to conduct trainings with business leaders and personnel of enterprises and institutions.
Hand Gesture Recognition with ConvNets for School-Aged Children to Learn Basic Arithmetic Operations

Hand Gesture and Sign Language Recognition are recognized as non-trivial problems in the computer vision community. Deep Learning (DL) has provided both, a novel analytical framework and solutions. Moreover, Convolutional Neural Networks (CNN) have shown there are still goals to be met and solutions to be given regarding Hand Posture Recognition with the architectures and technologies we have today. This work proposes building a Hand Gesture Recognizer able to identify 13 classes: numbers from 0 to 9, and the signs corresponding to the arithmetic operations of addition, subtraction, and multiplication. The core model aims to translate digits and math symbols signed by students and teachers in a school environment to both make the teaching-learning process more engaging and to promote sign language learning in the student community. Based on Object Detection and leveraging a pretrained model using Transfer Learning, this DL model was retrained with a data set of 1,365 images (105 per class/sign) of Panamanian Sign Language hand-shapes. An accuracy of 88% was reached on the validation data, with proved usefulness on American Sign Language (ASL) for these hand-shapes, and being easily adaptable to other sign systems, including two-handed finger spelling ones like International Sign System (IS).
The Generalized Microscopic Image Reconstruction Problem for Hypergraphs

In this paper we study a particular case of the microscopic image reconstruction problem, first introduced in [6, 10] and then extended to undirected unweighted graphs in [2]. We consider a general hypergraph $$H = (V,E)$$ such that each node v has assigned a physical value $$l_v$$ that we would determine. Since in many applications it may be difficult or almost impossible to directly extract these values, we study how to retrieve them starting from the set of probes $$P_v = \sum _{w \in N(w)} l_w$$ , i.e. the sum of labels of v's neighbors. In particular, we prove that the values $$l_v$$ can be found in polynomial time using linear algebra tools and that the problem can be shifted to undirected weighted graphs trough the concept of 2-section of a hypergraph. Finally, we provide some classes of hypergraphs whose 2-intersection graphs have a specific form (a line or a s-tree) and whose related reconstruction problem from the probes can be performed with the minimum number of zero or one surgical probe.
Exploring the Realverse: Building, Deploying, and Managing QoE in XR Communications

Immersive communication based on extended Reality (XR) is foreseen to be the next step in human communication. For that challenge, we propose the “Realverse”: an approach where physically distant people can interact as if they were in the same physical space. This can be achieved by implementing a distributed reality solution, including four fundamental elements: remote presence, embodied interaction, visual communication, and shared immersion. In this paper we describe our recent research and approach to this topic, as well as its implications in terms of implementation, network architecture, and quality of experience. We also propose some insights for future standardization efforts.
Preliminary Study and Assessment of Drinking Water from Almaty, Kazakhstan

Drinking water samples from eight districts of Almaty, Kazakhstan was collected and physical and chemical analysis of the samples was carried out. Quality indicators of drinking water, such as organoleptic characteristics of water (smell, taste, color, and turbidity), general characteristics (pH, total hardness, permanganate demand, and dry residue), inorganic substances (cations and anions) and contaminants (heavy metals and total petroleum hydrocarbons) were determined, except pesticide residues which will be analyzed for further analysis with a wide range of pollutants. According to all indicators obtained for all districts of Almaty, the anthropogenic impact on drinking water in Almaty districts is assessed as low, not exceeding the permissible maximum allowable concentrations (MAC) values, and drinking water in Almaty corresponds to the approved standards and rules for drinking water of Kazakhstan. Despite of the fact that studied pollutants are below their MAC values, they still pose threat to public health due to their accumulative properties. The study of drinking water in the districts of Almaty made it possible to assess the ecological state in the studied districts of Almaty, as well as to propose recommendations for improving the quality of drinking water in areas where water quality indicators are closer to their MAC values.
Multidomain Joint Learning of Pedestrian Detection for Application to Quadrotors

Pedestrian detection and tracking are critical functions in the application of computer vision for autonomous driving in terms of accident avoidance and safety. Extending the application to drones expands the monitoring space from 2D to 3D but complicates the task. Images captured from various angles pose a great challenge for pedestrian detection, because image features from different angles tremendously vary and the detection performance of deep neural networks deteriorates. In this paper, this multiple-angle issue is treated as a multiple-domain problem, and a novel multidomain joint learning (MDJL) method is proposed to train a deep neural network using drone data from multiple domains. Domain-guided dropout, a critical mechanism in MDJL, is developed to self-organize domain-specific features according to neuron impact scores. After training and fine-tuning the network, the accuracy of the obtained model improved in all the domains. In addition, we also combined the MDJL with Markov decision-process trackers to create a multiobject tracking system for flying drones. Experiments are conducted on many benchmarks, and the proposed method is compared with several state-of-the-art methods. Experimental results show that the MDJL effectively tackles many scenarios and significantly improves tracking performance.
THE DEVELOPMENT PROSPECT OF RETAIL VIRTUAL STORE

In recent years, the retail virtual store has become the main trend in social services. More and more people tend to shop in retail virtual stores. With the development of 3D virtual reality, this trend is getting stronger and stronger. Therefore, the development prospect of virtual retail stores has attracted much attention. This paper examines the impact of companies’ and users’ popularization of helmet gadgets on in-store traffic and analyzes how virtual reality (VR) could enhance the customer experience throughout the shopping trip. A qualitative research design has been used, which also included conversations with both professionals and consumers. Moreover, this paper seeks to break new ground by attempting to use the current literature to help predict future directions and trends for online shopping.
Achieving Usability: Looking for Connections between User-Centred Design Practices and Resultant Usability Metrics in Agile Software Development

This article searches for correlations between the practices of usability professionals from software development teams and the resulting usability of the system they are working on. After reviewing the literature of the past decade, we can conclude that most researchers try to achieve better usability with process proposals, but measurements focus on perceived usability. The level of involvement of users in the requirements capture phase is unclear. There is a trend to reduce costs of usability engineering, either by choosing more lightweight and informal methods or by trying to substitute user involvement. Looking at decade's worth of 452 articles about "agile AND usability" in Scopus, we selected 133 which deal with the integration of usability, UX and User Centred Design practices and Agile development.
Gap Analysis of existing managerial skill set and the skills imparted by the management institutes for the IT Industry

Nowadays, it becomes more important to produce rightly skilled individuals with the skills to survive and flourish in the industry. This paper focuses on the skill set that the management institutes are developing and how well that skill set is catering to the current era's IT industry. Such a sample population covers all the IT firms of India, registered as authorized companies and management institutes of the country that follow the overall accepted curriculum for management studies. A sample of 54 respondents was taken. The survey was done, questionnaires were floated to gather data, and further IBM SPSS was used for statistical analysis and to interpret the results. This research reveals a huge gap between the skills required and the skill set provided. It also brings out the key factors to address to fill the gap. Not only the factors but also the best possible methods to overcome such gaps have been brought to light in this paper. It gives an insight into the industry's dynamics and how important it is to cope with them for survival.
Efficient quantum interpolation of natural data

We present efficient methods to interpolate data with a quantum computer to complement uploading techniques and quantum postprocessing. The quantum algorithms make use of the efficient quantum Fourier transform (QFT) as well as classical signal and imaging processing techniques, and may facilitate quantum advantages for some relevant families of data. We showcase a QFT interpolation method, a quantum cosine transform (QCT) interpolation geared towards natural data, and we improve upon them by utilizing a quantum circuit's capabilities of processing data in superposition. A circuit for the QCT is presented. We illustrate the methods on probability distributions and quantum encoded images, and discuss the precision of the resulting interpolations.
Uncovering Factors Influencing Millennials’ Use of Non-Standard Words in Twitter

Social media communication has its own language features and one aspect is modified spelling of standard words. Social media users use shortened words with full awareness of the meanings, and new non-standard words are constantly added to the repertoire of social media language. A pertinent question is whether social media users learn these non-standard words to use or whether they also contribute to the vocabulary used in social media communication. The study examined Malaysian millennials’ use of non-standard words in Twitter and their reasons for shortening words. For the non-standard words, data were collected from 200 active Twitter users whereas data on reasons for shortening words were collected from 30 users. The results showed that the Malaysian millennials frequently used non-standard spelling of words. The three top words were “ni” (this), “nak” (want), and X (negation). The main reasons for the Twitter users to shorten words were the 280-character limit per tweet, user convenience, and characteristics of words. The Twitter users felt free to create new spellings of standard words at times for fun, but most of the time, they use the common non-standard words. The Malaysian millennials reported that they were inclined to shorten long and complex words, and words with many vowels. The study suggests that Twitter users balance between speed in communication and preservation of meaning when using non-standard words. Keywords: Social media, Twitter, non-standard words, word formation processes, shortening of words.
Learning representation via indirect feature decorrelation with bi-vector-based contrastive learning for clustering

Clustering is an essential task in machine learning, and learning clustering-friendly representation is crucial for clustering performance. Recently, methods that combine contrastive learning with feature decorrelation have demonstrated their efficacy in learning representation for clustering. However, the feature decorrelation is usually performed in an additional feature space, making the optimization more complex. Moreover, the dimension of this extra feature space is linked to the batch size, but this essential correlation that affects feature decorrelation has been less studied. This paper explores the connection between batch size and feature decorrelation in contrastive learning, then proposes to utilize the linear independence created by contrastive learning with small batch sizes to promote feature decorrelation indirectly. Further, this work introduces bi-vector-based contrastive learning to enable contrastive learning with small batch sizes, which is also of interest to researchers with limited resources. Besides, spectral clustering is utilized to explain the advantage of using linear independence for feature decorrelation and clarify its condition. Experiment results on five benchmark datasets demonstrate the efficacy of the proposed method against state-of-the-art methods. As for the CIFAR-10 and CIFAR-100, our method achieves the clustering accuracy of 88.9% and 52.1%, which are 7.4% and 9.6% higher than the non-prior SOTA method.
Model selection with bootstrap validation

Abstract Model selection is one of the most central tasks in supervised learning. Validation set methods are the standard way to accomplish this task: models are trained on training data, and the model with the smallest loss on the validation data is selected. However, it is generally not obvious how much validation data is required to make a reliable selection, which is essential when labeled data are scarce or expensive. We propose a bootstrap‐based algorithm, bootstrap validation (BSV), that uses the bootstrap to adjust the validation set size and to find the best‐performing model within a tolerance parameter specified by the user. We find that BSV works well in practice and can be used as a drop‐in replacement for validation set methods or k ‐fold cross‐validation. The main advantage of BSV is that less validation data is typically needed, so more data can be used to train the model, resulting in better approximations and efficient use of validation data.
A Practical Approach to Discretised PDDL+ Problems by Translation to Numeric Planning

PDDL+ models are advanced models of hybrid systems and the resulting problems are notoriously difficult for planning engines to cope with. An additional limiting factor for the exploitation of PDDL+ approaches in real-world applications is the restricted number of domain-independent planning engines that can reason upon those models. With the aim of deepening the understanding of PDDL+ models, in this work, we study a novel mapping between a time discretisation of pddl+ and numeric planning as for PDDL2.1 (level 2). The proposed mapping not only clarifies the relationship between these two formalisms but also enables the use of a wider pool of engines, thus fostering the use of hybrid planning in real-world applications. Our experimental analysis shows the usefulness of the proposed translation and demonstrates the potential of the approach for improving the solvability of complex PDDL+ instances.
AI-SUPPORTED NATURAL LANGUAGE PROCESSING IN PROJECT MANAGEMENT — CAPABILITIES AND RESEARCH AGENDA

AI-based natural language processing (NLP) models show remarkable performance in tasks like question answering or text generation in general. We argue that recent NLP-AI models will play a major role in the transformation of our societies, an endeavor mainly shaped by projects, project managers and project teams. We present the results of an experiment, in which we fed state-of-the-art NLP models like GPT-3 project management-related questions and had an expert team rate the maturity of the answers. Results indicate that the size of the model and the text corpora seem to make substantial difference to the performance. The best model seems to be able to answer most project management-related questions convincingly to the judgement of the expert panel. Attempts to train a model without extensive training data preparation resulted in poorer results. A research agenda is derived and presented.
Augmented Reality (AR) for Deaf and Hard of Hearing (DHH) for Animation

Augmented Reality (AR) technology in education faces a significant gap, especially for deaf and hard-of-hearing (DHH) students. Research has shown the vast growing technologies for AR in many sectors; however, implementing the technologies towards education for DHH students is still lacking in Malaysia. This study aims to provide a rationale and justification for using AR technologies for DHH students for Animation. Based on the literature review, five important keys drive the research questions. For the context of this paper, the three considerable keys are AR as a teaching aid (1), the importance of Art and Design (2), and social stigma (3). These primary keys are to support and justify this research. An interview was conducted with teachers who teach DHH students to further help the three primary keys. An interview with the AR developer was also conducted for this research. The study developed an AR with Animation based on the interview and record-keeping. The results show that implementing this technology will significantly impact DHH students, especially in Art and Design. When exploring and using the AR Application, DHH students felt awkward. They were unfamiliar with the use of AR applications in their class. Nevertheless, they all enjoyed using it as it will have vast potential and possibility. As technology grows, DHH students will be one step ahead of others. It is worth mentioning the social stigma among them and how future research would continue to diminish the social stigma.
The Influence of Accounting Information System Quality, Accounting Information Quality and Accounting Information System Security on End User Satisfaction of S4/Hana System Application Product (SAP) with Perceived Usefulness as a Moderating Variable at PT Hakaaston

This study aims to determine the effect of the quality of accounting information systems, the quality of accounting information, the security of accounting information systems on end user satisfaction of the S4/HANA System Application Product (SAP) with perceived usefulness as a moderating variable at PT Hakaaston. This study took a population and samples from internal users (users) System Application Product (SAP) S4/HANA on PT Hakaaston. The results of this study indicate that quality of accounting information system and quality of accounting information take effect positive significant on end user satisfaction of S4/HANA System Application Product (SAP) , meanwhile accounting information system security is not take effect positive significant on S4/HANA System Application Product (SAP) end user satisfaction . Then, the quality of accounting information systems, the quality of accounting information and the security of accounting information systems have an effect positive significant on perceived usefulness. Next, perceived usefulness no take effect positive significant on S4/HANA System Application Product (SAP) end user satisfaction. Perceived Usefulness no take effect positive significant in moderate connection Among quality system information accounting, quality of accounting information and security of accounting information systems with satisfaction user end System Application Product (SAP) S4/HANA at PT Hakaaston.