Reviewerly/exHarmony huggingface dataset:
Reading Related Activities in English Textbook and How the Texts are Exploited in the Classroom

Reading comprehension skill is an important requirement that must be possessed by students in learning English. To support students’ reading skill development, the role of the teacher and the learning materials are crucial. This study is an attempt to describe the reading related activities in English text book for Grade 9 and how the texts are exploited in the classroom by teacher. This study uses a descriptive qualitative which analyze the textbook used by the students and observe an English teacher who teach the ninth grade. The data of this research collected through document analysis, classroom observation, and interviews. The data were analyzed through four processes of data analysis which involved data collection, data reduction, data display and conclusion. The study found that, there are five types of reading related activities contain in English textbook namely: skimming/scanning activities, lexical items activity, linking words activity, activating activity, and questioning activity. Besides that, the most frequent exploited text done by the teacher in the class are completing activity in the form of fill the blank, grammatical words activity, and identifying order of event activity. This research was limited in time, number of subjects and data source so further research is suggested for more generable findings.
Efficient Residual Bottleneck for Object Detection on CPU

Object detection is the most fundamental and important task in computer vision. With the development of hardware such as computing power of GPUs and cameras, object detection technology is gradually improving. However, there are many difficulties in using GPUs in industrial fields. Therefore, it is very important to use efficient deep learning technology in the CPU environment. In this paper, we propose a deep learning model that can detect objects in real-time from images and videos using CPU. By modifying the CSP [1] bottleneck, which corresponds to the backbone of YOLOv5 [2], an experiment was conducted to reduce the amount of computation and improve the FPS. The model was trained using the MS COCO dataset, and compared with the original YOLOv5, the number of parameters was reduced by about 2.4%, and compared with RefineDetLite, the mAP value was measured to be 0.367 mAP, which is 0.071 higher than that of RefineDetLite. The FPS was 23.010, which was sufficient for real-time object detection.
A-TD3: An Adaptive Asynchronous Twin Delayed Deep Deterministic for Continuous Action Spaces

Twin delayed deep deterministic (TD3) policy gradient is an effective algorithm for continuous action spaces. However, it cannot efficiently explore the spatial space and suffers from slow convergence, which is mainly due to the serial mode strategy in learning policies. On the other hand, asynchronous reinforcement learning algorithms, e.g., asynchronous advantageous actor-critic (A3C), are effective in exploring the environment, but they ignore the gradient information of different local agents, resulting in limiting their performance. To solve the above problems, in this study, we propose an asynchronous twin delayed deep deterministic, denoted as A-TD3, algorithm with an adaptive update strategy for continuous action spaces. Specifically, a parallel mechanism is used to improve the convergence speed, along with two adaptive weight functions based on Off-policy learning to dynamically adjust the weights of the local agents. The experimental results show that the proposed A-TD3 algorithm is able to produce comparable results in terms of training execution time and convergence speed as compared with that of conventional TD3 and other state-of-the-art algorithms.
Designing Procedurally Generated Levels

There is an increasing demand to improve the procedural generation of game levels. Our approach empowers game designers to author and control level generators, by expressing gameplay-related design constraints. Graph grammars, resulting from these designer-expressed constraints, can generate sequences of desired player actions as well as their associated target content. These action graphs are used to determine layouts and content for game levels. We showcase this approach with a case study on a dungeon crawler game. Results allow us to conclude that our control mechanisms are both expressive and powerful, effectively supporting designers to procedurally generate levels.
Designing Gamified Application: An Effective Integration of Augmented Reality to Support Learning

Early childhood learning is an important concern for the concept building of children.Students have been taught with traditional methods from the beginning.However, these methods lack kids' attention.Application of Augmented Reality (AR) in a traditional way of teaching can help students in better engagement and better learning.In this research, an ABCD-AR mobile based application is designed to overcome the flaws in the traditional education system.While designing AR mobile applications, certain guidelines need to be considered that cause problems for kids in their learning.These may include but are not limited to small screen size and irregularity.Considering these problems, this study proposes 23 usability principles for AR based learning applications for kids.These identified usability principles are validated by human experts.Furthermore, experiments are carried out to evaluate the application in terms of efficiency, effectiveness, learn-ability, user satisfaction engagement.The results implies that the developed game-based application is highly effective and efficient because the mean of marks obtained by students before using ABCD-AR application is lower (M = 4.97, SD = 1.650) than the marks obtained after using ABCD-AR application (M = 7.97, SD = 1.608).Similarly, results indicate that mean of time taken to perform tests without using ABCD-AR application (M = 7.97, SD = 1.650) is higher as compared to with using it (M = 4.37, SD = 1.608).Majority of the students were entertained by playing the game while learning, and felt motivated to continue based on the game's scenario due to the variety of activities included.Teachers and parents involved in these experiments have shown satisfaction towards the use of ABCD-AR application in learning process of preschool kids.
Too Drained from Being Agile? The Self-Regulatory Effects of the Use of Agile ISD Practices and their Consequences on Turnover Intention

While much is known about the beneficial effects of agile information systems development (ISD), scholars have largely neglected to address its potential downsides. Specifically, research on this topic has thus far overlooked the ambivalent implications of the specific demands placed on developers working in agile ISD teams, including potentially depleting effects. Drawing on ego depletion theory and the associated literature, we provide a more balanced view and introduce self-regulatory resource depletion triggered by using agile ISD practices—encompassing software development (SD) and project management (PM) practices—as a theoretical perspective on why agile developers experience different levels of work-related fatigue that lead to stronger or weaker turnover intentions. Furthermore, we propose that due to the specific way in which agile ISD methods organize ISD project work, developers’ perceived workload influences the intensity by which agile ISD practices affect self-regulatory resources and developers’ feelings of fatigue. We examined our research model using a multimethod approach including quantitative and qualitative data. We found that the use of agile SD practices enhances developers’ self-regulatory resources and reduces fatigue and turnover intention. Our results also show that perceived workload strengthens the energizing effects of the use of agile SD practices and reveals a depleting effect of the use of agile PM practices, with countervailing implications for turnover intention. This study contributes to agile ISD literature by drawing a more nuanced and balanced picture with both resource-enhancing and resource-draining effects of the use of agile ISD practices. Finally, we give managerial advice regarding factors to consider when designing and managing agile ISD projects.
Corrections to “An Implementation of Real-Time Traffic Signs and Road Objects Detection Based on Mobile GPU Platforms”

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , two references <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> , <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> were missing. The first sentence of Section 3.1 should read “The datasets of this work include various class images from the dataset used in <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> , our dataset <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> and <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> , and GTSRB datasets.” The authors requested this correction to give detailed information about the Dataset section.
An Action Plan for Diamond Open Access

This conference on open science is covering many aspects of strategic relevance to build the future of Europe, a future based on knowledge leading to more opportunities for younger generations to express their talent in attractive research careers, be it in universities, research organisations or industry.Some sessions will be dedicated to discussing the assessment of research and researchers' performance, which affects their behaviours.This debate is timely and justified.Most researchers are used to measuring in their labs when collecting and processing data.Scientists have to measure with accuracy to produce reliable and verifiable results.The Nobel laureate Richard Feynman started one of his famous lectures challenging his students about the complexity introduced over the centuries by using different measurement units.Measuring research and researchers' performance is of a different nature and more complex in many ways.It has direct impact on people and a far-reaching policy dimension for the European Research Area.We therefore agree on the need to ensure objectiveness and fairness as guiding principles and we can use assessment methodologies to support broader policy objectives, considering that we live in times of change, including in the scientific methods and the public perception of the role of science.Research processes are in constant evolution, accelerated by the current pandemic and the transition to a resilient, green and digital economy and an inclusive society.The variety of activities making increased use of advanced technologies puts us in a situation where research deliverables are richer and not limited to delivering and counting publications.Modern science delivers other outputs, mostly digital, like datasets, software, algorithms or protocols, and it delivers highly skilled people through specialised education and mentoring.Multidisciplinary collaborations are required to address new scientific questions arising from increased complexity and that is another aspect of this evolution.More and more researchers from different domains, from biology and physics to economics, anthropology or humanities, work together in the boundaries of areas of knowledge.One of the actions included in the first Policy Agenda for the European Research Area, for the next three years 2022-2024, is to advance towards the reform of the assessment system for research, researchers and institutions to improve their quality, performance and impact.We consulted a variety of stakeholders in 2021, including organisations representing researchers, public funders, private funders, universities, research centres, academies, national evaluation agencies and policymakers, and we received strong support for the objective of ensuring a combination of qualitative and quantitative indicators to evaluate research and researchers without compromising on objectivity and peer review.
Aspect based Sentiment &amp; Emotion Analysis with ROBERTa, LSTM

Internet usage has increased social media over the past few years, significantly impacting public opinion on online social networks.Nowadays, these websites are considered the most appropriate place to express feelings and opinions.The popular social media site Twitter offers valuable insight into people's thoughts.Throughout the conflict between Russia and Ukraine, people from all over the world have expressed their opinions.In this study, "machine-learning" and "deep-learning" techniques are used to understand people's emotions and their views about this war are revealed.This study unveils a novel deeplearning approach that merges the best features of the sequence and transformer models while fixing their respective flaws.The model combines Roberta with ABSA (Aspect based sentiment analysis) and Long Short-Term Memory for sentiment analysis.A large dataset of geographically tagged tweets related to the Ukraine-Russia war was collected from Twitter.We analyzed this dataset using the Roberta-based sentiment model.In contrast, the Long Short-Term Memory model can effectively capture longdistance contextual semantics.The Robustly optimized BERT with ABSA approach maps words into a compact, meaningful word embedding space.The accuracy of the suggested hybrid model is 94.7%, which is higher than the accuracy of the stateof-the-art techniques.
Twitter Sentiment Analysis Using BERT

Sentiment analysis (SA) in social networks is an important research area. In Twitter, popular information that is either facts or opinions is propagated throughout the network. Unlike normal documents, the restricted length of the messages along with constant changing internet slangs on Twitter had become a challenge for researchers. Bidirectional Encoder Representations from Transformers (BERT) represents the latest technology of pre-trained language models which have recently advanced a wide range of natural language processing (NLP) tasks. This paper aims to develop a proposed model using BERT for sentiment analysis tasks. This paper fine-tunes BERT with a single layer added on the top for the SA task. With fully connected neural network reaching state of the art results using simple tips preventing it from over-fitting and enabling it to be fine-tuned easily on such downstream task. The proposed method is implemented and few sets of features are tested. The model model showed that usage of stop-words inside the dataset does hinder the performance of the classifier and that by separating the positive tweets and negative tweets to extract the topics and the keywords, it achieves a better result than combining the positive and negative tweets together. Experimental results also showed the change in model performance when k random topics are selected out of a total of t topics.
Vehicle Completion in Traffic Scene Using 3D LiDAR Point Cloud Data

Modern autonomous vehicles perceive their surroundings with the help of 3D computer vision technologies (e.g., object detection, vehicle recognition). The 3D point cloud obtained by vehicle-mounted mobile LiDAR is the primary data for 3D visual tasks in the driving system. Due to partial observations, a complete point cloud of the surrounding vehicle cannot be obtained. In this paper, we proposed Point Voxel Completion Network (PVCNet), an end-to-end learning-based model for 3D vehicle completion. Unlike existing point cloud completion methods, PVCNet only predicts the missing parts of the input and preserves the original spatial structure of the point cloud. PVCNet consists of two branches, the voxel branch and the point cloud branch. The voxel branch extracts the local features of the input and predicts the position of the missing point cloud in voxel form. The point cloud branch extracts the global features of the input point cloud. Combined with features from two branches, PVCNet can generate the missing points. The proposed PVCNet is validated on our hybrid dataset and KITTI dataset and performed competitive results in the task of urban vehicle point cloud completion.
Influence of Micro-Controller-Based Single Axis Solar Tracker System on Solar Panel’s Performance: Case Study

There is governmental, academic, and business interest in new emerging renewable energy sources, such as solar energy. In particular, how and how much it produces energy and the environmental impact of these sources. To highly benefit from renewable energies, new methods should be utilized. In this paper, the Microcontroller Arduino Uno board has been used and programmed by Arduino software to build a system as a photo-sensor (Active) Single Axis Solar Tracker System (SASTS). The system was created using a solar panel, two Light Dependent Resistance (LDR) have been used on the two sides (north/south) of the photovoltaic (PV), and a servo motor is connected to the Uno board. The tracking system is constructed to procure the data using the Microcontroller Uno board and specially designed mechanical base. Based on the extracted results, the influence of the SASTS on increasing the solar panel performance is more evident than the outcome Maximum Power Point (MPP) of MATLAB Simulink. Continuously, the SASTS searches for the sun and its position in the sky and puts the PV direct to the sun. Significantly, the tracker system raises the power ratio of the PV. As a result, it significantly increases the solar panel's performance and increases by 55.2% on average during the daytime.
Dynamic Secure Access Control and Data Sharing Through Trusted Delegation and Revocation in a Blockchain-Enabled Cloud-IoT Environment

The Internet of Things (IoT) is vulnerable to leakage of private information during data sharing.To avoid this problem, access control and secure data sharing have been introduced in IoT; however, many challenges are faced because of centralized access control and single delegator selection.Additionally, blockchain is integrated into IoT to enhance the security of the environment.For that purpose, this research proposes dynamic secure access control using the blockchain (DSA-Block) model, which performs secure access control and data sharing.Initially, the IoT device attributes and user attributes are registered at a local domain authority (LDA) for generating private and public keys using the hyperelliptic curve cryptography (HECC) algorithm, which ensures the legitimacy of the users and devices.Then, the IoT devices send a request message to the edge nodes (ENs) via a gateway, which performs request filtration by validating the user's authenticity.The filtered requests are sent to the edge server to perform access delegation using rock hyraxes swarm optimization (RHSO), which selects a set of delegator nodes.The access control decision is made by using the Trusted practical Byzantine fault tolerance (PBFT) consensus algorithm.The IoT data are stored in the cloud server for secure storage, in which the data are secured using a differential privacy mechanism.Finally, dual revocations, such as user attribute revocation and user revocation, are used to maintain security.The performance of DSA-Block is evaluated and the results demonstrate that the proposed DSA-Block model achieves superior performance compared to previous works.
Peranan Orang Tua Siswa dalam Melaksanakan Pendampingan Pembelajaran dari Rumah Secara Online

The purpose of this research is to improve students' learning outcomes in science subjects grade VI SD Negeri 02 Ujan Mas by using lung respiratory media. This research uses a type of Class Action (PTK) research conducted in 2 cycles, each cycle consists of 4 stages, namely the stage of planning, implementation of actions, observation and reflection. This research subsection numbered 19 people. Data collection techniques are carried out through tests, and observations. Data analysis is done through observation data management. The results showed before learning using respiratory media (Pre-Test) learning results can be categorized as medium with a high category of 2 people, a moderate category of 15 people, and a low category of 2 people. Learning results when using respiratory media (Pre-Test) can be categorized as moderate or sufficient, this is evidenced by the results of learning in a high category of 0 people, a moderate category of 19 people and a low category of 0 people. The results of the analysis showed no significant influence of lung respiratory media on the results of science study grade VI SD N 02 this is evidenced by the results of rxy value data known correlation between variable X and variable Y of 0.061279 located at intervals of 0.00-0.20. In conclusion, the use of pulmonary respiratory media in grade 6 science lessons at SDN 02 Ujan Mas showed moderate or sufficient correlation in improving student learning outcomes, although statically it did not show significant influence.&#x0D; Keywords: Lung Respiratory Media, Learning Outcomes, Science Lessons&#x0D;
Word-Level and Pinyin-Level Based Chinese Short Text Classification

Short text classification is an important branch of Natural Language Processing.Although CNN and RNN have achieved satisfactory results in the text classification tasks, they are difficult to apply to the Chinese short text classification because of the data sparsity and the homophonic typos problems of them.To solve the above problems, word-level and Pinyin-level based Chinese short text classification model is constructed.Since homophones have the same Pinyin, the addition of Pinyin-level features can solve the homophonic typos problem.In addition, due to the introduction of more features, the data sparsity problem of short text can be solved.In order to fully extract the deep hidden features of the short text, a deep learning model based on BiLSTM, Attention and CNN is constructed, and the residual network is used to solve the gradient disappearance problem with the increase of network layers.Additionally, considering that the complex deep learning network structure will increase the text classification time, the Text Center is constructed.When there is a new text input, the text classification task can be quickly realized by calculating the Manhattan distance between the embedding vector of it and the vectors stored in the Text Center.The Accuracy, Precision, Recall and F1 of the proposed model on the simplifyweibo_4_moods dataset are 0.9713, 0.9627, 0.9765 and 0.9696 respectively, and those on the online_shopping_10_cats dataset are 0.9533, 0.9416, 0.9608 and 0.9511 respectively, which are better than that of the baseline method.In addition, the classification time of the proposed model on simplifyweibo_4_moods and online_shopping_10_cats is 0.0042 and 0.0033 respectively, which is far lower than that of the baseline method.