Reviewerly/exHarmony huggingface dataset:
Effect of emoticon type on the problem solving process and discussion messaging according to problem type in online discussion

Purpose: This study aimed to ascertain implications for class design by comprehensively analyzingthe effects of emoticon types used as non-verbal communication in online discussions on theproblem-solving process and discussion messaging depending on the type of problem.&#x0D; Methods: In this study, a group of emoticon types (text type, graphic type, and flash type) wasformed, the online discussions were examined according to the problem (creativity problem, cognitiveconflict problem), and the content of the discussion messaging was classified. A two-way analysisof variance (ANOVA) was conducted with the type of emoticon and the type of problem as theindependent variable and the number of messages appearing by function of the problem-solving processas the dependent variable, and an interaction effect was confirmed. Contents of the following discussionmessaging contents were classified into semantic units according to a message type analysis tool (Henri,1992). A two-way analysis of variance (ANOVA) was then conducted with the emoticon and problemtype as the independent variable and the discussion messaging type as the dependent variable, andan interaction effect was confirmed.&#x0D; Results: It was found that emoticon types had different reactions depending on the emoticon typein the online discussion problem-solving process. The emoticon type influenced the discussionmessaging according to the problem type.&#x0D; Conclusion: This study suggests that an approach should be developed from the perspective ofinstructional design considering the types of problems and emoticons presented as learning tasks inonline discussions. The fact that the group by emoticon type showed differences in discussion messagingaccording to the problem type means that the emoticon type and problem type are the main factorsinfluencing the problem-solving process. The results indicate that if the problem is solved by consideringthe problem type and emoticon type suitable for online discussion, it can contribute to effective learningby facilitating the interaction of members and increasing their participation in learning.
Factors Affecting the Satisfaction of Sultan Qaboos University Students about Remote Academic Advising during the Covid-19 Pandemic

This study aimed to investigate the level of satisfaction of Sultan Qaboos University students with remote academic advising during the COVID-19 pandemic. It also aimed to explore the factors that might affect the extent of satisfaction, such as gender, academic year, and the nationality of the academic advisor. To achieve these aims, the researchers developed an academic advising satisfaction survey. The survey consisted of three sub-scales, namely, satisfaction with the advisor's competence and information, satisfaction with the advisor's interest and enthusiasm, and satisfaction with the ease of communication with the advisor. The survey was distributed to a sample of 490 undergraduate students (248 males and 242 females) during the COVID-19 period in the Spring 2021 semester. The study results showed that the students were highly satisfied with the competence and information of the academic advisors. At the same time, they expressed moderate levels of satisfaction with the advisors' interest and enthusiasm and the ease of communication with the advisors. The results also revealed that students supervised by Omani faculty members are more satisfied with academic advising than students supervised by non-Omani faculty members. In addition, the results showed that third-year students and above are more satisfied with academic advising than their first and second-year counterparts. Keywords: Academic advising, distance learning, Covid-19, Sultan Qaboos University
Media smart: lessons, tips and strategies for librarians, classroom instructors and other information professionals

"Media smart: lessons, tips and strategies for librarians, classroom instructors and other information professionals." Journal of the Australian Library and Information Association, 72(1), pp. 109–110
The Improving Students' English Speaking Skills by Using the Roll Play Method

This study is conducted to develop students speaking skills of eighth-grade students of Class VIII A SMP Muhammadiyah 2 Way Panji east Lampung through role-play activities. In this study, the researcher used a classroom action research (CAR) method using a Kurt Lewin design which consisted of planning, acting, observing and reflecting. In this classroom action research, the researcher uses two cycles where each cycle consists of two meetings. Based on the results of research conducted by researchers, the application of role play in the classroom is very popular with students, this is based on the results of the questionnaire 85,99% into very strong categories. In addition, students also experienced a significant increase from before the application of role play, this was proven by the results of the students' scores during classroom action research, namely pre-test 61.58, post-test 1 73.32, post-test 2 79.22.
The Increasing Students’ Vocabulary Through Spelling Bee Games

This research aimed to find out whether a spelling bee game can increase students’ vocabulary or not and to find out the effectiveness of a spelling bee game to increase students’ vocabulary. The research method was classroom action research which consisted of cycles. The research location was in SMPN 1 Banyuputih. The data collection instruments were observation, documentation, questionnaire, and trial questions. Furthermore, the data were analyzed by qualitative descriptive and comparative descriptive. The conclusions of this research were spelling bee games can increase students’ vocabulary with the comparison test values 57,8 (Pre-test), 68,9 (Post-test 1), and 84,0 (Post-test 2). This game was also effective, it showed by the students’ activity observations results of 73% (1st meeting), 75% (2nd meeting), 77% (3rd meeting), and 92% (4th meeting) in a series. The questionnaire of effectivity also showed a value of 84,4 with the predicate good.
AoCStream: All-on-Chip CNN Accelerator With Stream-Based Line-Buffer Architecture

Convolutional neural network (CNN) accelerators are being widely used for their efficiency, but they require a large amount of memory, leading to the use of slow and power consuming external memories. This paper exploits two schemes to reduce the required memory amount and ultimately to implement a CNN of reasonable performance only with on-chip memory of a practical device like a low-end FPGA. To reduce the memory amount of the intermediate data, a stream-based line-buffer architecture and a dataflow for the architecture are proposed instead of the conventional frame-based architecture, where the amount of the intermediate data memory is proportional to the square of the input image size. The architecture consists of layer-dedicated blocks operating in a pipelined way with the input and output streams. Each convolutional layer block has a line buffer storing just a few rows of input data. The sizes of the line buffers are proportional to the width of the input image, so the architecture requires less intermediate data storage, especially in the trend of getting larger input size in modern object detection CNNs. In addition, the weight memory is reduced by the accelerator-aware pruning. The experimental results show that a whole object detection CNN can be implemented even on a low-end FPGA without an external memory. Compared to previous accelerators with similar object detection accuracy, the proposed accelerator reaches higher throughput even with less FPGA resources of LUTs, registers, and DSPs, showing higher efficiency.
Adapting Skip Connections for Resource-Efficient FPGA Inference

Deep neural networks employ skip connections – identity functions that combine the outputs of different layers-to improve training convergence; however, these skip connections are costly to implement in hardware. In particular, for inference accelerators on resource-limited platforms, they require extra buffers, increasing not only on- and off-chip memory utilization but also memory bandwidth requirements. Thus, a network that has skip connections costs more to deploy in hardware than one that has none. We argue that, for certain classification tasks, a network's skip connections are needed for the network to learn but not necessary for inference after convergence. We thus explore removing skip connections from a fully-trained network to mitigate their hardware cost. From this investigation, we introduce a fine-tuning/retraining method that adapts a network's skip connections – by either removing or shortening them-to make them fit better in hardware with minimal to no loss in accuracy. With these changes, we decrease resource utilization by up to 34% for BRAMs, 7% for FFs, and 12% LUTs when implemented on an FPGA.
Design of improved road sign recognition system based on deep learning

As the basis of intelligent vehicle environment identification, street sign recognition is a necessary condition to assist safe driving. It is of great significance for realizing automatic driving, improving intelligent transportation system and promoting smart city construction. Traditional traffic sign recognition technology is limited for complex scenes, and the recognition effect is also affected by weather and light. The road sign recognition system based on deep learning includes street sign detection and street sign recognition. YOLOv3 deep learning object detection method is adopted for road sign detection, and CCTSDB standard data set is used as experimental data to perform operations such as region trimming, batch conversion of data format, BM3D noise reduction and image preprocessing and enhancement technology. 1800 images were selected to form the training set and test set, which ensured the high efficiency and timeliness of the road sign detection algorithm. The road sign recognition system adopts the image recognition method of Darknet-53, and the algorithm is optimized. The trained system is detected under the test of CCTSDB dataset, and the detection accuracy is over 97%, and the detection time reaches 0.021s. Therefore, the system meets the requirement of accurate real-time processing of traffic sign detection and recognition system.
A New Measure of Roughness Could Advance Earthquake Geophysics

Scientists recently developed an alternative way to measure a rock’s roughness. It might help them understand the physics of faults.
Robot Competition using ISO 29110

Robotics and Software Engineering are two important disciplines within Computer Science that are not separated. The robot competition workshop aims to motivate students to apply their knowledge in engineering and management to develop a project consisting of a robot's assembly and programming. The workshop uses a low-cost robot that should be programmed following the guidelines of the ISO/IEC 29110 standard, a set of established rules, limited resources, and time. The results obtained with the execution of the workshop highlighted the awareness achieved by students regarding the application of good management and engineering practices, as well as the importance of teamwork for the development of projects.
Software Product Evaluation - MyFEPS: A Past and Future Reflecive Analysis

MyFEPS was created in 2010 with the intention of being used in the certification of software products. In addition, it was used in the evaluation of own or third-party products to determine the level of quality and find points for improvement. Likewise, it was used in evaluations for the comparison of products of similar purpose and to select one for its acquisition. The different evaluation projects and the evolution of computer technologies have made it possible to identify improvements that have been implemented in MyFEPS in all these years. In the article, this evolution is described in 3 axes: the industry, the academy and the ISO standards in software product quality, and an analysis of the challenges that follow is presented.
DEVELOPMENT OF THE PROCESS OF ORGANIZING STEM-PROJECTS IN TEACHING PHYSICS IN SECONDARY SCHOOLS IN VIETNAM

STEM-education draws much attention not only in Vietnam but also in the world. The implementation of studying projects integrated with practical lessons is one of the effective forms of STEM-education. This article presents a brief definition of STEM-education, proposes the designing phases of a STEM-project in teaching physics and their application in developing the project «Production of electricity».
Double Prior Network for Multidegradation Remote Sensing Image Super-Resolution

Image super-resolution (SR) is widely used in remote sensing because it can effectively increase image details. Neural networks have shown remarkable performance in recent years, benefitting from their end-to-end training. However, remote sensing images contain a variety of degradation factors. Neural networks lack flexibility in dealing with these complex issues compared with reconstruction-based approaches. Traditional neural network methods can not take advantage of prior knowledge and lack interpretability. To develop a flexible, accurate, and interpretable algorithm for remote sensing SR, we proposed an effective SR network called YSRNet. It is performed by unfolding a traditional optimization process into a learnable network. Combining conventional reconstruction-based methods and neural networks can significantly improve the algorithm's performance. Since the gradient features of remote sensing images contain valuable information, the total variation (TV) constraints and the deep prior constraints are introduced into the objective function for image SR. Furthermore, we propose an enhanced version called YSRNet+, which can apply attention weights to different prior terms and channels. Compared with the YSRNet, the YSRNet+ enables networks to focus more on useful prior information and improve the interpretability of networks. Experiments on three remote sensing datasets are performed to evaluate the algorithm's effectiveness. The experimental results demonstrate that the proposed algorithm performs better than some state-of-the-art neural network algorithms, especially in the scenario of the multi-degradation factors.
Code Switching and Code Mixing on “Ngobrol Sore Semaunya” Podcast

This study focused on finding the types and the reasons behind the use of code switching and code mixing on “Ngobrol Sore Semaunya” podcast. This study is aimed to answer these following questions (1) What are the types of code switching and code mixing found on "Ngobrol Sore Semaunya" podcast? (2) What are the reasons for the use of code switching and code mixing during the “Ngobrol Sore Semaunya” podcast? It is qualitative study through document analysis to analyze the data by describing findings. The source of data was from “Ngobrol Sore Semaunya” podcast episode 2 and 13. While, the data were taken from the utterances of the host and the guests that contain code switching and code mixing. The result showed that there are 177 utterances on “Ngobrol Sore Semaunya” podcast indicating code switching; 53 data are considered as intra-sentential, 4 data are classified as tag switching, and 120 data belong to inter-sentential. Furthermore, the researcher found 59 data of code mixing include 45 insertion and 14 alternations. In both data 1 and data 2, the speakers did not utter congruent lexicalization type of code mixing. The speakers in “Ngobrol Sore Semaunya” podcast used code switching include talking about a particular topic, quoting somebody else, being emphatic about something, interjection, and repetition used for clarification. However, the speakers did not do code switching to express group identity. Last, the reasons that generated the speakers to utter code mixing are the social roles and relationship of the participants, situational factors, message-intrinsic consideration, and language attitude including social dominance and security. To sum up, there were three types of code switching and two types of code mixing appeared on “Ngobrol Sore Semaunya” podcast with varied reasons that lead the host and the guests uttered the code switching and the code mixing
Manufacturing and Economic Growth in Jordan (A Test of Kaldor’s Laws)

Purpose: The study aims to test the applicability of "the three Kaldor's hypotheses of growth" in the Jordanian economy, by measuring the impact of the growth of the manufacturing sector on both the real GDP and the productivity of the labour component in the industrial and non-industrial productive sectors.Design/methodology/approach: The three Kaldor model equations were estimated using ARDL bound test approach to test the long-term relationships between the variables, using annual time series data for the period 1990-2019, according to the reports issued by the Central Bank of Jordan and the Jordanian Department of Statistics.Findings: The study's findings show a positive and significant long-term effect of the growth of the manufacturing sector on the growth of both GDP and the productivity of the labour component in the industrial and non-industrial sectors.This confirmed the validity of applying Kaldor's hypotheses of growth to the Jordanian economy during the study period.Social implications: The study recommended developing a system of legislation for industrial investment to encourage the establishment of large-scale industries, and stimulate similar small-and medium-scale industries to merge taking advantage of the economies of scale.This raises the productivity of the production factors and then enhances the economic growth rates.Originality/value: The importance of the study emerges from the fact that it deals with a vital sector, that plays an important role in growth and economic development, due to its backwards and forwards linkages with other economic sectors, and being one of the few studies that dealt with the Caldor's three hypotheses comprehensively and in the modern analytical ARDL approach.
Bitcoin Does not Hedge Inflation

This paper examines the response of major cryptocurrencies to macroeconomic news announcements (MNA). While other cryptocurrencies exhibit no reaction to major MNA, Bitcoin responds negatively to inflation surprise. Price of Bitcoin decreases by 24 bps in response to a 1 standard deviation inflationary surprise. This reaction is inconsistent with widely-held beliefs of practitioners that Bitcoin can hedge inflation. I do not find support for hypothesis that the negative response of Bitcoin to inflation is due to its negative exposure to interest rates. Instead, I find support for the hypothesis that Bitcoin is strongly affected by the shift in consumption-savings decisions, driven by the rise in inflation. Consistent with this view, Bitcoin has negative exposure to a proxy for the consumption-savings ratio.
A Study on Measuring the Duration Effect of Recommender Systems for the Small E-commerce

Due to the development of ICT and expansion of the internet, users face information overload problem. To mitigate the issue of information overload problem, studies proposed the recommender systems and continued to improve the performance of the said model, however, which have the following problems. (1) Most of the studies are conducted using large-scale datasets. Studies verify performances using large-scale datasets such as MovieLens, Pinterest, etc. Such experiments may not perform well in cases that do not use large datasets such as small e-commerce. (2) It is difficult to measure a duration effect of the recommender systems because using only fixed datasets for measuring performance. Most of the studies measure the performance by using Cross Validation (CV) or by randomly dividing the datasets. However, these approaches make it difficult to measure the duration effect of performance between time when user recommended items and time user purchased items. In this study, the performance of various recommender systems for small e-commerce compared to the same performance of the recommender systems proposed in previous studies. In addition, to effectively measure the duration effect, we design a multi-period datasets constructed by extending collection period of the test datasets for one week. As a result, we show that the difference between the results of using recommender systems in the previous studies and the results of the systems implemented in small e-commerce exists. Moreover, we also manifest that the duration effect can exist in previous recommender systems proposed in other studies. Ultimately, we conclude the paper with ideas for novel applications in small e-commerce.
Equilibrium analysis of block withholding attack: an evolutionary game perspective

With the development of blockchain technology, blockchain-based digital cryptocurrencies, such as Bitcoin, have received broad interest. Due to the permissionless environment, the blockchain is vulnerable to different kinds of attacks, including the block withholding (BWH) attack. BWH attack is one of the common selfish mining attacks, by which the attacking pool infiltrates the attacked pool by sending some infiltrating miners. These infiltrating miners try to find (partial) proof-of-work solutions and share the reward of the attacked pool, but discard the solution that can actually create blocks. Therefore, the attacking pool benefits by withholding blocks from the infiltrating miners, while damaging the benefits of victim pools. In this paper, we introduce the reward reallocation mechanism by paying additional rewards to the miners who successfully mine blocks, and propose an evolutionary game model for BWH attack among pools to study the strategy selection of pools. By constructing the replicator dynamic equations, the evolutionary stable strategies of pools are explored based on different levels of additional rewards. Our results provide enlightening significance to mitigate the negative influence from BWH attacks in practice.
PELATIHAN PEMBUATAN DAN PEMANFAATAN MEDIA PEMBELAJARAN BERBASIS MOBILE LEARNING MENGGUNAKAN APLIKASI “RUMAH BELAJAR”

Technological developments have had a major impact on education so that educators must prepare themselves to develop processes and use of instructional media as the main means of supporting learning. The purpose of this study is to train teachers to be able to use Learning Houses as a technology-based platform that can be used as a learning medium. This service method uses the Assed Based Communities Development (ABCD) approach with community service steps, namely discovery, dream, design, define, destiny. Assisted subjects are IPPNU members. The assisted result is that the assisted subjects of IPPNU members are able to use, design and utilize the Learning House platform so that it can be used as an alternative free learning media.
Difficulty-Aware Mixup for Replay-based Continual Learning

Deep neural networks suffer from the issue of catastrophic forgetting in the scenario of continual learning, causing a sudden deterioration in performance when training on new tasks. Replay-based methods, which are one of the most effective solutions, alleviate catastrophic forgetting by replaying the subset of past data stored in memory buffer. However, due to the limited storage space, a small amount of past data can be stored, and will lead to a data imbalance situation between old and new tasks. Hence, in this work, we tried to increase the diversity of past samples by mixup. In addition, we propose a difficulty-aware mixup approach that modifies the mixing coefficient according to the distance between output logits and ground truth labels to reduce the ambiguity of hard examples. We implement our method on ER, DER, and DER++, and test it on split-CIFAR10, split-CIFAR100, and split-miniImagenet. The experimental result shows that the proposed method can effectively improve the average accuracy and reduce the forgetting without adding too many computing resources.