Reviewerly/exHarmony huggingface dataset:
Job relevance or perceived usefulness? What features of immersive virtual reality software predict intention to use in a future project-based-learning scenario: a mixed method approach

Not only since COVID-19, the topic of decentralized working and learning methods is becoming increasingly important for various reasons. New virtual reality technologies enable learning in immersive scenarios, which is good when learning from home is advised. However, not all immersive Virtual Reality (iVR) training incorporates learning systems that support complex, realistic, practical tasks that lead to a product or enable acquiring knowledge and life-enhancing skills like project-based learning. Although there are many iVR applications available that support project management, the specific features of these applications that lead to the intention to use (and therefore life-enhancing skills) have yet to be discovered. In this exploratory mixed-method study, we investigated the question of the importance of perceived usefulness (PU) and job relevance (JR) as predictors of intention to use (ItU) in a selection of immersive iVR application features. We started with market research and aggregated 88 software features in 13 categories of 34 professional iVR applications. After an expert selection and ranking procedure, a survey was developed. After deriving from the TAM 2 model and with a sample n = 103, we computed the relationship of JR, PU, and ItU. Although high values were generally observed, we found that the importance of PU is higher than JR when it comes to ItU. Limitations of the study are discussed, and suggestions for further research are given.
An Investigation into the Field of Cloud Accounting

The accounting sector is at the forefront of the widespread adoption of cloud computing. "the cloud" means remote servers and computing resources that may be accessed online. It is critical to keep abreast of new technology developments. Your company might reach new heights thanks to the competitive edge of this innovative technology. Scalability, remote access, low maintenance, and cost reduction are all features it offers. Everything you need to automate, streamline, and improve the efficiency of your company is right here. We must participate in cloud accounting as it is the wave of the future. The article delves into the conceptual notion and examination of cloud accounting
Service Vision and Values

The vision is the ultimate anchor for the entire enterprise – perhaps the one point on which everyone involved in that enterprise would agree. A functional vision for an enterprise is not centered around a single organization, and, crucially, it’s not a marketing pitch – although marketing may well be derived from it later. Instead, it’s a much simpler phrase or “mantra” that summarizes that enterprise’s “reason to be.” Good examples of valid visions include the Open Group’s vision about supporting “boundaryless information flow,” or the vision for the TED conferences, “ideas worth spreading.” We’ll see later here how we can derive a working vision for our ZapaMex worked example.
Breaking Barriers: How the Pandemic Bridged the Academic Achievement Gap in Rural and Urban Schools

&lt;p&gt;The article examines the impact of the COVID-19 pandemic on the dynamics of academic achievement in schools in the Novosibirsk region over the period of 2017&amp;ndash;2022. Using data on individual Basic State Examination results of grade 9 students, the study applies multilevel regression modelling to examine the differential impact of the COVID-19 pandemic on urban and rural schools. Contrary to previous studies which predicted that rural students would be most affected, the results were unexpected. When analysing the academic performance of students in the overall sample, no significant differences were found between the years: on average, the pandemic had no effect on academic performance in the region. However, assessing the differential effect according to the type of settlement in which the school was located revealed atypical trends: urban schools experienced a slight decrease in academic achievement after the pandemic, while rural schools experienced an increase. Notably, the year of the pandemic eliminated the achievement gap between urban and rural schools, making it statistically insignificant. Possible explanations for these results could be regional policies to support students or changes in examination procedures. These counterintuitive findings challenge the dominant educational research on COVID-19 and highlight the unexpected role of the pandemic in changing the trajectory of academic achievement.&lt;/p&gt;
Enhanced YOLOv7 integrated with small target enhancement for rapid detection of objects on water surfaces

Unmanned surface vessel (USV) target detection algorithms often face challenges such as misdetection and omission of small targets due to significant variations in target scales and susceptibility to interference from complex environments. To address these issues, we propose a small target enhanced YOLOv7 (STE-YOLO) approach. Firstly, we introduce a specialized detection branch designed to identify tiny targets. This enhancement aims to improve the multi-scale target detection capabilities and address difficulties in recognizing targets of different sizes. Secondly, we present the lite visual center (LVC) module, which effectively fuses data from different levels to give more attention to small targets. Additionally, we integrate the lite efficient layer aggregation networks (L-ELAN) into the backbone network to reduce redundant computations and enhance computational efficiency. Lastly, we use Wise-IOU to optimize the loss function definition, thereby improving the model robustness by dynamically optimizing gradient contributions from samples of varying quality. We conducted experiments on the WSODD dataset and the FIOW-Img dataset. The results on the comprehensive WSODD dataset demonstrate that STE-YOLO, when compared to YOLOv7, reduces network parameters by 14% while improving AP50 and APs scores by 2.1% and 1.6%, respectively. Furthermore, when compared to five other leading target detection algorithms, STE-YOLO demonstrates superior accuracy and efficiency.
In Search of the “First”, Community Language: Physicist Reflections

In view of the rapid development of science, there is a need to search for a common language. The article points out the existence of potential lines of correspondence and interrelation between seemingly disparate fields, such as the humanities and the natural sciences. It is surprising that the holistic feature of Nature resonates most strongly in the language of quanta, the most prophetic that science has at its disposal.
Exploring the surge of negativity during the COVID-19 pandemic: computational text and sentiment analysis across eight newsrooms’ tweets

The rise of Twitter as a news platform has radically changed the way we access, consume, and share news. Twitter becomes an important hub to quickly and easily access accurate information in times of crisis such as COVID-19 and is frequently used in journalism practices. This study examines how the COVID-19 pandemic is covered by news agencies in the Twitter ecosystem, the weight of the news about the pandemic in tweets and the sentiment analysis of the news. Within the scope of the study, the tweets related to COVID-19 shared between 2020 and 2021 by eight news agencies (BBC World, Reuters, CNN, Associated Press, TRT World, AL Jazeera English, DW English, Euronews) that broadcast on a global scale and have a high number of followers on Twitter are analyzed by using text mining methods. Firstly, the frequently used words in tweets were obtained by using the text analysis technique n-gram. Secondly, the sentiment values of all the tweets and the words are computed and later classified into certain categories. Lexicon based sentiment dictionaries such as VADER and NRC utilized in the sentiment analysis process. Findings reveal that messages containing fear, anxiety, sadness, and negative polarity are prevalent in the news during the pandemic.
Studying the Impact of Sampling in Highly Frequent Time Series

Nowadays, all kinds of sensors generate data, and more metrics are being measured. These large quantities of data are stored in large data centers and used to create datasets to train Machine Learning algorithms for most different areas. However, processing that data and training the Machine Learning algorithms require more time, and storing all the data requires more space, creating a Big Data problem. In this paper, we propose simple techniques for reducing large time series datasets into smaller versions without compromising the forecasting capability of the generated model and, simultaneously, reducing the time needed to train the models and the space required to store the reduced sets. We tested the proposed approach in three public and one private dataset containing time series with different characteristics. The results show, for the datasets studied that it is possible to use reduced sets to train the algorithms without affecting the forecasting capability of their models. This approach is more efficient for datasets with higher frequencies and larger seasonalities. With the reduced sets, we obtain decreases in the training time between 40 and 94% and between 46 and 65% for the memory needed to store the reduced sets.
Hybrid SkipAwareRec: A Streaming Music Recommendation System

In an automatic music playlist generator, such as an automated online radio channel, how should the system react when a user hits the skip button? Can we use this type of negative feedback to improve the list of songs we will playback for the user next? We propose SkipAwareRec, a next-item recommendation system based on reinforcement learning. SkipAwareRec recommends the best next music categories, considering positive feedback consisting of normal listening behaviour, and negative feedback in the form of song skips. Since SkipAwareRec recommends broad categories, it needs to be coupled with a model able to choose the best individual items. To do this, we propose Hybrid SkipAwareRec. This hybrid model combines the SkipAwareRec with an incremental Matrix Factorisation (MF) algorithm that selects specific songs within the recommended categories. Our experiments with Spotify’s Sequential Skip Prediction Challenge dataset show that Hybrid SkipAwareRec has the potential to improve recommendations by a considerable amount with respect to the skip-agnostic MF algorithm. This strongly suggests that reformulating the next recommendations based on skips improves the quality of automatic playlists. Although in this work we focus on sequential music recommendation, our proposal can be applied to other sequential content recommendation domains, such as health for user engagement.
Time-Series Pattern Verification in CNC Machining Data

Effective quality control is essential for efficient and successful manufacturing processes in the era of Industry 4.0. Artificial Intelligence solutions are increasingly employed to enhance the accuracy and efficiency of quality control methods. In Computer Numerical Control machining, challenges involve identifying and verifying specific patterns of interest or trends in a time-series dataset. However, this can be a challenge due to the extensive diversity. Therefore, this work aims to develop a methodology capable of verifying the presence of a specific pattern of interest in a given collection of time-series. This study mainly focuses on evaluating One-Class Classification techniques using Linear Frequency Cepstral Coefficients to describe the patterns on the time-series. A real-world dataset produced by turning machines was used, where a time-series with a certain pattern needed to be verified to monitor the wear offset. The initial findings reveal that the classifiers can accurately distinguish between the time-series’ target pattern and the remaining data. Specifically, the One-Class Support Vector Machine achieves a classification accuracy of 95.6 % ± 1.2 and an F1-score of 95.4 % ± 1.3.
Pre-training with Augmentations for Efficient Transfer in Model-Based Reinforcement Learning

This work explores pre-training as a strategy to allow reinforcement learning (RL) algorithms to efficiently adapt to new (albeit similar) tasks. We argue for introducing variability during the pre-training phase, in the form of augmentations to the observations of the agent, to improve the sample efficiency of the fine-tuning stage. We categorize such variability in the form of perceptual, dynamic and semantic augmentations, which can be easily employed in standard pre-training methods. We perform extensive evaluations of our proposed augmentation scheme in model-based algorithms, across multiple scenarios of increasing complexity. The results consistently show that our augmentation scheme significantly improves the efficiency of the fine-tuning to novel tasks, outperforming other state-of-the-art pre-training approaches.
Enhancing Arabic E-Commerce Review Sentiment Analysis Using a hybrid Deep Learning Model and FastText word embedding

The usage of NLP is shown in sentiment analysis (SA). SA extracts textual views. Arabic SA is challenging because of ambiguity, dialects, morphological variation, and the need for more resources available. The application of convolutional neural networks to Arabic SA has shown to be successful. Hybrid models improve single deep learning models. By layering many deep learning ensembles, earlier deep learning models should achieve higher accuracy. This research successfully predicted Arabic sentiment using CNN, LSTM, GRU, BiGRU, BiLSTM, CNN-BiGRU, CNN-GRU, CNN-LSTM, and CNN-biLSTM. Two enormous datasets, including the HARD and BRAD datasets, are used to evaluate the effectiveness of the proposed model. The findings demonstrated that the provided model could interpret the feelings conveyed in Arabic. The proposed procedure kicks off with the extraction of Arabert model features. After that, we developed and trained nine deep-learning models, including CNN, LSTM, GRU, BiGRU, BiLSTM, CNN-BiGRU, CNN-GRU, CNN-LSTM, and CNN-biLSTM. Concatenating the FastText and GLOVE as word embedding models. By a margin of 0.9112, our technique surpassed both standard forms of deep learning.
Evaluation of English Language Material for the 9th Grader Student at SMP Nurul Islam Ngemplak Boyolali

Teaching language in this global era demands mostly the creativity of the teachers, this is because in 21th century, students need to be critical thinkers, creative, communicative and collaborative. Therefore, the creativity of the teacher is manifested by evaluating the multiple resources and adapting or adopting them based on the contextualized learning situation including individual differences, styles and learners needs. It is also essential for the teacher to integrate the four macro skills in teaching language in order to make the students more communicative and collaborative. Hence, this paper presents the evaluation of the language teaching resources before and during applying them to the classroom contexts. The data were collected by document review and interview with the secondary school teacher. The data were analyzed qualitatively through interactive model. There is variation of the results based on the techniques of data collection. The interview shows that theoretically the teacher is creative to make the materials for students. Meanwhile, the document review findings show that there is a need of improvement on the way to evaluate and use the English materials based on the context. Finally, in evaluating the textbooks in order to adopt or adapt by adding or removing some materials to make English textbooks match with students’ contexts.&#x0D; Keywords evaluation, adapting, adopting, macro skills, English language teaching.
A dynamic obstacle avoidance method for collaborative robots based on trajectory optimization

<ns3:p>Background Collision detection is crucial in the design of robot planning algorithms. Efficient distance sensors can provide high-resolution environmental collision information to the robot's planning algorithm. However, this also leads to the robot obstacle avoidance performance being limited by the performance of the sensors. Therefore, it becomes a challenge to achieve efficient obstacle avoidance with low-resolution environmental information. Methods First, we use a self-developed capacitive array non-contact distance sensing flexible surface for sensing the proximity of colliding objects. Second, we designed an optimization-based dynamic obstacle avoidance planning algorithm, using only the minimum separation distance and penetration direction as obstacle avoidance information, and referring to the idea of stochastic gradient descent, using real-time collision avoidance information to do single-step optimization adjustment. Results We conducted the dynamic obstacle avoidance test experiment by connecting the electronic skin to the semi-physical prototype and the full physical prototype. The experiments show that efficient dynamic obstacle avoidance can be realized under the maximum effective range of only 5~7cm, and it has strong flexibility to avoid different shapes of dynamic obstacles in a non-contact manner, and finally arrive at the target position. Conclusions In this paper, an online obstacle avoidance planning algorithm designed based on an optimization method that is not limited to the shape of obstacles is proposed, and the effectiveness of the algorithm is verified by physical experiments in combination with a self-developed flexible distance sensing surface. It is of great significance for the safe operation of human-robot interaction in collaborative robots.</ns3:p>
Soft skills: definition, content, development (according to scientific literature)

The research examines the concept of soft skills, which include qualities, attitudes, problem-solving skills, leadership and interpersonal skills, communication skills and emotional intelligence. We consider social intelligence as the basis for the formation of soft skills. These skills are important for effective interaction and decision-making in the business process, communication in teams and teamwork. It also discusses such an important concept for the justification and development of soft skills as “social intelligence”, which includes knowledge and understanding of the main ways of communication, leadership, decision-making and teamwork, as well as the ability to evaluate the interaction between people and the ability to use these factors to improve work results. The development of social intelligence requires not only practice, experience and constant self-improvement, but also a special approach based on the basic principles of adult pedagogy.
A Web-based coordinated control platform for source-grid-load-storage of low-voltage stations

Abstract In order to improve the analysis and control ability of power consumption in the station area, a load storage analysis and control platform based on the Django Web framework is built. The platform takes real-time data acquisition, analysis, and optimal scheduling as the core to realize the comprehensive management and control of power supply, load, and energy storage equipment in the station area. The browser/server (B/S) mode is adopted to support a variety of interactive operations. The energy data, analysis results, and control status of the station area can be viewed intuitively. The Django Web framework is used to realize the extensible design. The analysis and control of the station area are realized by calling the optimization algorithm. Based on Bootstrap, the responsive layout is realized to improve the availability and accessibility of the platform. The graphics are drawn on the platform through Canvas technology, and the interaction and visualization effects are realized.
Preparing Maps of the Distribution of Organic Matter and Calcium Carbonate in Northern Iraq

Abstract The study area was chosen within the agricultural lands in the north of Kirkuk governorate to prepare a map of organic matter and calcium carbonate content. It identified three sites in Kalwor, Shwan, and Altun Kupri and collected 20 representative surface samples of the soil of agricultural lands in these sites. The maps were prepared in a software environment Arc GIS. 20 surface samples of soil from sites north of Kirkuk were chosen, representative of the nature of land uses, including field crops, rangeland, and barren soils, to study the spatial variations of some chemical soil characteristics of Kirkuk Governorate. The results revealed the nature of spatial variations in the content of organic matter, as it ranged between (0.58-19.54) g kg -1 , where the highest value was reached in ton coopers in lands exploited by growing field crops represented by corn, while the calcium carbonate content ranged between (208-365) g kg -1 . In conclusion, moderate organic matter content was dominant, specifically in exploited agricultural lands that follow a good soil management system, while the very abundant and plentiful variety was in lands that were not agriculturally exploited. The very high content of calcium carbonate is prevalent in most of the lands of the study area, which need agricultural land management, especially because they suffer from a specific factor, which is the high content of calcium carbonate, and in the future, it may lead to desertification of these lands.
Human Conditional Reasoning in Answer Set Programming

Abstract Given a conditional sentence “ ${\varphi}\Rightarrow \psi$ " (if ${\varphi}$ then $\psi$ ) and respective facts, four different types of inferences are observed in human reasoning: Affirming the antecedent (AA) (or modus ponens ) reasons $\psi$ from ${\varphi}$ ; affirming the consequent (AC) reasons ${\varphi}$ from $\psi$ ; denying the antecedent (DA) reasons $\neg\psi$ from $\neg{\varphi}$ ; and denying the consequent (DC) (or modus tollens ) reasons $\neg{\varphi}$ from $\neg\psi$ . Among them, AA and DC are logically valid, while AC and DA are logically invalid and often called logical fallacies . Nevertheless, humans often perform AC or DA as pragmatic inference in daily life. In this paper, we realize AC, DA and DC inferences in answer set programming . Eight different types of completion are introduced, and their semantics are given by answer sets. We investigate formal properties and characterize human reasoning tasks in cognitive psychology. Those completions are also applied to commonsense reasoning in AI.
A pluggable single-image super-resolution algorithm based on second-order gradient loss

Convolutional neural networks for single-image super-resolution have been widely used with great success. However, most of these methods use L1 loss to guide network optimization, resulting in blurry restored images with sharp edges smoothed. This is because L1 loss limits the optimization goal of the network to the statistical average of all solutions within the solution space of that task. To go beyond the L1 loss, this paper designs an image super-resolution algorithm based on second-order gradient loss. We imposes additional constraints by considering the high-order gradient level of the image, so that the network can focus on the recovery of fine details such as texture during the learning process. This helps to alleviate the problem of restored image texture over-smoothing to some extent. During network training, we extract the second-order gradient map of the generated image and the target image of the network by minimizing the distance between them, this guides the network to pay attention to the high-frequency detail information in the image, and generate a high-resolution image with clearer edge and texture. Besides, the proposed loss function has good embeddability and can be easily integrated with existing image super-resolution networks. Experimental results show that the second-order gradient loss can significantly improve both Learned Perceptual Image Patch Similarity(LPIPS) and Frechet Inception Distance score(FID) performance over other image super-resolution deep learning models.