Reviewerly/exHarmony huggingface dataset:
ANALYSIS OF POTENTIAL APPLICATION OF BLOCKCHAIN TECHNOLOGY IN RENEWABLE ENERGY SECTOR

The need for secure and reliable applications will increase due to the growing necessity for sustainably and independently produced energy. For example, the amount of energy generated by individuals at their homes will probably grow fast in the near future so requiring for easy-to-use and precise solutions. To transform and facilitate the processes related to energy sector it is possible to use blockchain technology. The aim of this paper is to evaluate and analyse different areas of blockchain application in the renewable energy sector. Using content, SWOT and PESTLE analysis, the results show that the main strengths and opportunities are confidentiality, immutability of recordings and potential involvement of each individual, while weaknesses and threats � scalability, compatibility and legal framework. Latvia is already using more (42.1%) renewable energy than in the European Union on average (22.1%) but in order to meet the binding targets for the Member States by 2030, the blockchain technology could promote development in this sector. The results of the study improves the scientific opinion on blockchain technology in sustainable energy field by identifying potential implementation benefits and challenges; thus, gathering the knowledge for easier moving towards real-life implementations and also highlighting the areas with the need for specific further research.
On the Design of Privacy-Aware Cameras: A Study on Deep Neural Networks

In spite of the legal advances in personal data protection, the issue of private data being misused by unauthorized entities is still of utmost importance. To prevent this, Privacy by Design is often proposed as a solution for data protection. In this paper, the effect of camera distortions is studied using Deep Learning techniques commonly used to extract sensitive data. To do so, we simulate out-of-focus images corresponding to a realistic conventional camera with fixed focal length, aperture, and focus, as well as grayscale images coming from a monochrome camera. We then prove, through an experimental study, that we can build a privacy-aware camera that cannot extract personal information such as license plate numbers. At the same time, we ensure that useful non-sensitive data can still be extracted from distorted images. Code is available on https://github.com/upciti/privacy-by-design-semseg .
Peer Assessment-based Digital Literacy, EFL Students’ Reading Competency, and Engagement

In the 21st century, students must master digital literacy. During the COVID-19 pandemic, many students could still not use online learning support facilities. It has an impact on decreasing reading competence and student engagement. This study aims to determine the effect of peer assessment-based digital literacy on reading competency and engagement of EFL students during the post-COVID-19 pandemic. This research utilized a quasi-experimental design. A total of 10 groups of students participated in this study. Two groups of students were selected using cluster random sampling; 30 participants were assigned to the experiment, and 30 were assigned to the control group. The data instruments used two post-tests and a questionnaire. The dataset was statistically analyzed using One-way ANOVA and MANOVA, with assistance from SPSS version 26.0. The findings of this study implied that there was a significant effect of peer assessment-based digital literacy on the reading competency of EFL students; and there was a significant effect of peer assessment-based digital literacy on the EFL students' engagement. There is a simultaneous effect of peer assessment-based digital literacy on EFL students' reading competency and engagement. This study found that peer assessment-based digital literacy is one of the best ways to improve EFL students' reading competency and engagement. It is suggested that more research be done on this topic, not just on how well students read, but also on how motivated students are to learn, how students and parents see each other, how involved parents are, and what factors help and hinder students.
Single dendritic neural classification with an effective spherical search-based whale learning algorithm

&lt;abstract&gt;&lt;p&gt;McCulloch-Pitts neuron-based neural networks have been the mainstream deep learning methods, achieving breakthrough in various real-world applications. However, McCulloch-Pitts neuron is also under longtime criticism of being overly simplistic. To alleviate this issue, the dendritic neuron model (DNM), which employs non-linear information processing capabilities of dendrites, has been widely used for prediction and classification tasks. In this study, we innovatively propose a hybrid approach to co-evolve DNM in contrast to back propagation (BP) techniques, which are sensitive to initial circumstances and readily fall into local minima. The whale optimization algorithm is improved by spherical search learning to perform co-evolution through dynamic hybridizing. Eleven classification datasets were selected from the well-known UCI Machine Learning Repository. Its efficiency in our model was verified by statistical analysis of convergence speed and Wilcoxon sign-rank tests, with receiver operating characteristic curves and the calculation of area under the curve. In terms of classification accuracy, the proposed co-evolution method beats 10 existing cutting-edge non-BP methods and BP, suggesting that well-learned DNMs are computationally significantly more potent than conventional McCulloch-Pitts types and can be employed as the building blocks for the next-generation deep learning methods.&lt;/p&gt;&lt;/abstract&gt;
CRT-700.1 Multi-Center Compassionate use Early Feasibility Evaluation of J-Valve Transcatheter Treatment for Severe Aortic Valve Regurgitation: Preliminary Results

The coastal tracts of India consist of beach sands showing good concentration of strategic heavy minerals. In the present study, the mineralogy of important coastal placer deposits in India like Chavara deposits in Kerala and Manavalakurichi deposits in Tamilnadu were studied using Landsat multispectral satellite data for deriving potential targets of these strategic minerals. The ilmenite mineral predominantly exists in these areas, followed by monazite, sillimanite, rutile, zircon, garnet, leucoxene, and kyanite. The distribution of minerals was studied using advanced hyperspectral techniques like minimum noise fraction transformation, pixel purity index, and n-dimensional visualizer followed by spectral angle mapper (SAM) classification. Two end members of ilmenite mineral and quartz were identified from the Landsat imagery and successfully mapped using SAM algorithms. Detailed investigation of the mineralogy of beach sediments using advanced hyperspectral remote sensing techniques helps to derive potential targets for the ecofriendly exploration of minerals.
Toward Interpretable Graph Tensor Convolution Neural Network for Code Semantics Embedding

Intelligent deep learning-based models have made significant progress for automated source code semantics embedding, and current research works mainly leverage natural language-based methods and graph-based methods. However, natural language-based methods do not capture the rich semantic structural information of source code, and graph-based methods do not utilize rich distant information of source code due to the high cost of message-passing steps. In this article, we propose a novel interpretable model, called graph tensor convolution neural network (GTCN), to generate accurate code embedding, which is capable of comprehensively capturing the distant information of code sequences and rich code semantics structural information. First, we propose to utilize a high-dimensional tensor to integrate various heterogeneous code graphs with node sequence features, such as control flow, data flow. Second, inspired by the current advantages of graph-based deep learning and efficient tensor computations, we propose a novel interpretable graph tensor convolution neural network for learning accurate code semantic embedding from the code graph tensor. Finally, we evaluate three popular applications on the GTCN model: variable misuse detection, source code prediction, and vulnerability detection. Compared with current state-of-the-art methods, our model achieves higher scores with respect to the top-1 accuracy while costing less training time.
Efficient Algorithms and Architectures for DSP Applications

In the new era of digital revolution, the digital sensors and embedded designs become cheaper and more present [...]
Decentralization of E voting based on Blind Signature and Smart Contract

Traditional voting systems like ballot voting or voting using EVMs (electronic voting machines) basically rely on paper and other human resources during the voting phase, which is time consuming as well as costly. The elections generally have a central system, where an organization looks after the election and manages it. The problem here is that the election is handled by a single organization that has full control. In this work-in-progress paper, we suggest a decentralized application based on blockchain technology and smart contracts that supports e-voting systems. To implement distributed electronic voting systems we propose using a blockchain technology. The paper proposes a unique electronic voting system backed by blockchain that addresses restrictions in existing systems and recommends the favoured blockchain frameworks for building a decentralized e-voting web application. The E-voting system is secure, transparent, protects the privacy of the voters and also, does not allow duplicate votes.
Optical flow correction network for repairing video

Some areas in the video may be defective due to storage medium damage or data transmission loss. The defective area can be repaired utilizing the optical flow since the optical flow represents the motion information of the pixel and contains the information redundancy properties of the video. However, it is a challenge for current algorithms to directly calculate the correct optical flow in the defective area. Therefore, an optical flow correction network (OFC-Net) is proposed to solve this problem. Firstly, as the basic framework, OFC-Net employs a lightweight U-shaped structure to gradually “repair” the broken optical flow map based on the known information in the defective neighborhood. And then, the activation function that conforms to the property of optical flow is used to accelerate the model’s convergence during training, while the simplified loss function is adopted to evaluate the similarity of the optical flow map from a global perspective to improve the prediction accuracy. As a result, the corrected optical flow output at the masked position can be obtained. Secondly, the optical flow corrected by OFC-Net can guide locating the pixels in nearby frames to repair the defective region. Optical flow correction and video repair experiments were conducted on MPI Sintel, DAVIS datasets, and real movie clips. The experimental results show that the corrected optical flow map is accurate without bringing artifacts under a wide range of scenarios and the arbitrary irregular mask shapes, which demonstrate the network’s strong generalization capabilities. The average PSNR and SSIM of repaired video frames are greater than 33.67 and 0.986, respectively, outperforming the results from the traditional method PatchMatch and deep learning-based synthetic pixel methods.
Supplemental Material: Redefinition of the Petersburg batholith and implications for crustal inheritance in the Dinwiddie terrane, Virginia, USA

&lt;p&gt;Table S1: Major- and trace-element geochemical data for 40 samples of Petersburg Granite (sensu lato). Table S2: Isotopic data for all analyses by secondary ionization mass spectrometry on the U.S. Geological Survey/Stanford sensitive high-resolution ion microprobe-reverse geometry (SHRIMP-RG). &lt;/p&gt;
Pancreas Instance Segmentation Using Deep Learning Techniques

The segmentation of pancreatic ductal adenocarcinoma (PDAC) images facilitates computer vision applications. This paper proposes using UNet, a fully convolutional neural network based on a residual neural network (ResNet34), as an encoder and turning point for boundary class separation, designed for biomedical applications. Image segmentation separates one image into a number of smaller pieces. When doing PDAC image segmentation tasks, these resulting fragments or numerous segments are helpful. Another important requirement for image segmentation tasks is masks and edge classes. TensorFlow and Keras are deep learning frameworks used to build U-Net architectures. UNet aims to capture the characteristics of both context and localisation. The main idea of the implementation is to use a continuous shrink layer and immediately use the upsampling operator to get a higher resolution output on the input image. A total of 800 images were used, 0.2 for testing and 0.8 for training. When using the original UNet model, the accuracy was 0.86. With the help of masking and boundary classes, binary images consisting of null or non-zero values, we got an accuracy of 0.89 from the segmentation tasks.
A study on pre-service teachers’ perceptions on English teaching practice

본 연구에서는 교육실습 기간 중 초등영어 수업실습에 대한 예비교사들의 인식에 대하여 알아보고자 하였다. 본 연구를 위해 2022년 10월에 총 4 주간의 수업실습에 참여한 3학년 예비교사들 중, 3~6학년에 배정된 총 53명의 예비교사들(영어과 25명, 국어과 11명, 사회과 17명)이 설문조사에 참여하였다. 연구의 결과는 다음과 같다. 첫째, 예비교사들은 3학년 수업실습 기간 중 평균 1.6회의 영어수업을 하였다. 하지만 예비교사들이 1~2학년에 배정이 되면 영어수업을 할 기회를 한 번도 갖지 못하고 졸업을 할 수도 있다. 둘째, 예비교사들의 78%가 본인의 영어수업에 만족한 것으로 보인다. 셋째, 예비교사의 75%는 실습기간 중 영어수업에서 수업의 60%이상을 영어로 진행하였으며 80%이상의 영어를 사용하여 수업을 진행한 학생들도 이 중 47%나 되었다. 본 연구에서 예비교사들의 영어수업에서 영어사용의 정도는 학과별 차이보다 실습학교 별로 차이를 보였다. 대전지역 실습학교의 경우, 대부분의 예비교사들이 60%이상의 영어를 사용하여 영어수업을 하였으나 충남지역에서 수업실습을 한 예비교사 중 영어수업에서 60%이하의 영어를 사용하였다고 답한 경우가 다수 발견되었다. 넷째, 예비교사들의 91%가 영어수업 참관의 기회를 가질 수 있었다. 하지만 1~2학년에 배정된 예비교사들과 3~6학년에 배정된 학생 중에서도 일부(9%)의 학생들은 영어수업 참관의 기회를 갖지 못하였다. 다섯 째, 89%의 실습생들은 실습기간 중 지도교사로부터 받은 도움이 매우 적절하거나 적절하였다고 응답하였다. 하지만 11%의 실습생은 지도교사의 도움이 적절하지 않았다고 응답하였다. 여섯째, 설문에 응답한 예비교사의 87%가 교육대학의 수업이 도움이 되었다는 긍정적인 답변을 하였고, 13%의 학생만이 교육대학의 수업이 도움이 되지 않았다고 응답을 하였다. 응답한 예비교사의 87%가 졸업 후 영어수업을 할 자신이 있다는 응답을 하였고, 나머지 13%의 예비교사들은 그렇지 않다는 응답을 하였다. 설문 결과를 바탕으로 현장 지도교사의 교생지도 전문성 제고와 교생지도 표준화의 필요성, 모든 예비교사들이 교육대학 교육실습 기간 중 적어도 1회의 영어수업 참관 및 시연 기회를 가질 수 있도록 실습학교 배정 시 고려를 해야 할 것 등의 개선점을 제안하였다.This study aims to investigate pre-service teachers' perceptions on primary English teaching practice. For this study, a survey questionnaire was administered to 53 pre-service teachers who had teaching practice for four weeks in 2022. The results of the study are as follows. First, pre-service teachers had an average of 1.6 English teaching opportunities. However, if pre-service teachers were assigned to 1st or 2nd grade, they may graduate without ever having an opportunity to teach English during teaching practicum. Second, 78% of pre-service teachers seem to be satisfied with their English teaching. Third, 75% of the pre-service teachers taught more than 60% of the English classes in English, and 47% of the students used English for more than 80% of the classes. In this study, the degree of English use in pre-service teachers' English classes showed differences by schools rather than by their majors. In the case of elementary schools in Daejeon, most pre-service teachers used more than 60% of English to teach English. Fourth, 91% of pre-service teachers had the opportunity to observe English classes. However, the pre-service teachers assigned to the 1st and 2nd grades did not have the opportunity to observe English classes. Fifth, 89% of pre-service teachers responded that the feedback they received from the guidance teacher during the practicum was helpful. Sixth, 87% of the pre-service teachers answered positively that the English education-related courses of the pre-service teacher program were helpful, and only 13% of the students answered otherwise. 87% of pre-service teachers responded that they were confident in teaching English after graduation. The remaining 13% of pre-service teachers responded that they were not. Based on the results of the study, some suggestions were made for better pre-service English teaching practice.
Cyberbullying Awareness Through Sentiment Analysis Based on Twitter

Nowadays, cyberbullying issues have rapidly arisen with various type of negatives impacts have been affected millions of users. Students’ academic performance might be affected due to being a cyberbullying victim. A social networking service, Twitter is one of the most used platforms worldwide has significantly increase on the activity of cyberbullying even though this platform limits the words characters up to 280 only. Due to this activity which involving online bullying, this paper aims to develop an awareness with some potential detection platform where users can take an advance action to block or report any Twitter account that involving with cyberbullying activity. Consequently, it could help them to have a positive as well as better online environment.
Decision tree using ant colony for classification of health data

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Arief Kelik Nugroho, Ipung Permadi, Yogiek Indra Kurniawan, Aini Hanifa, Nofiyati; Decision tree using ant colony for classification of health data. AIP Conf. Proc. 21 February 2023; 2482 (1): 020002. https://doi.org/10.1063/5.0128787 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Analysis of the performance of SLIC super-pixel toward pre-segmentation of soil-transmitted helminth

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Siew Wen Loke, Chee Chin Lim, Aimi Salihah Binti Abdul Nasir, Norhanis Ayunie Binti Ahmad Khairudin, Yen Fook Chong, Mohd Yusoff Mashor, Zeehaida Mohamed; Analysis of the performance of SLIC super-pixel toward pre-segmentation of soil-transmitted helminth. AIP Conf. Proc. 21 February 2023; 2562 (1): 060004. https://doi.org/10.1063/5.0111563 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Development of life cycle classification system for Plasmodium knowlesi malaria species using deep learning

In this paper, the performance of deep learning model for GoogleNet and AlexNet are analysed to classify plasmodium knowlesi life cycle stages. Plasmodium knowlesi images are taken from department of microbiology and parasitology in Hospital Universiti Sains Malaysia (HUSM) in this research work. The data images are enhanced using contrast stretching method. The enhanced image undergoes process of segmentation to extract parasite inside the effected red blood cells. The segmented images go through bounding box process according to their size input image for both deep learning models. There are 5940 data which it represents for four classes: artifact, trophozoite, schizont and gametocyte stage. These datasets are trained using GoogleNet and AlexNet to classify the life cycle stages of plasmodium knowlesi. The analysed performance of both models includes training, validation, and testing process. According to the result, both model able to reach 100% for training accuracy. For validation accuracy, AlexNet has higher accuracy with 93.4% compared to GoogleNet with 92.2%. For testing accuracy, Google has higher accuracy with 91.1% where AlexNet with 89.1%.
Pengembangan Media Pembelajaran Menggunakan Aplikasi Prezi pada Bidang Studi IPS Materi Kedatangan Bangsa Eropa Ke Indonesia Kelas VIII SMP

This study aims to produce a product in the form of Prezi application learning media. This type of research is development research known as Research and Development (R&amp;D), the development model used is the ADDIE model. The development procedures in this study consisted of 5 stages, namely analysis, design, development, implementation and evaluation. The research instruments used were questionnaires, documentation, and assessment formats. Based on the results of the material validation, a value of 4.78 was obtained "Very Eligible" so that the material was declared fit for use, the results of the media validation I obtained a value of 4.5 "Very Eligible" and the validation of media experts II obtained a value of 4.65 "Very Eligible", so media is categorized as feasible to try. Furthermore, students' practicality trials obtained a value of 4.38 "Practical". Thus it can be concluded that Learning Media Using the Prezi Application in Social Science Subjects in Class VIII SMP is feasible and practical to use.
Hubungan Persepsi Siswa tentang Proses Pembelajaran dengan Hasil Belajar di Sekolah Menengah Kejuruan

This study aims to look at the problem of student perceptions about the Learning Process with Student Learning Outcomes in the Subject of Electrical Lighting Installation. The research used was quantitative research which was correlational in nature, the population in this study were 88 students from class XI TITL SMK N 5 Padang. Sampling technique with probability sampling techniques. Instrument trials were conducted on 45 students of class XI TITL at SMK N 5 Padang. The results of the description of the perception data about the learning process, the data obtained shows that the distribution of the answer scores spreads from the lowest score on variable x 54 and the highest score is 84. Based on the distribution of these scores, the average (mean) is 68.11, the middle score (median) is 67 , the number that occurs frequently (mode) 67, and the standard deviation (standard deviation) 7. the results of calculating the value of the correlation coefficient (r) obtained rcount greater than rtable it can be stated that there is a relationship between perceptions of the learning process and learning outcomes of the Information Installation Electricity. From the data analysis it is also known that this relationship is in the strong and significant category.
A Semantic Search Framework for Similar Audit Issue Recommendation in Financial Industry

Audit issues summarize the findings during audit reviews and provide valuable insights of risks and control gaps in a financial institute. Despite the wide use of data analytics and NLP in financial services, due to the diverse coverage and lack of annotations, there are very few use cases that analyze audit issue writing and derive insights from it. In this paper, we propose a deep learning based semantic search framework to search, rank and recommend similar past issues based on new findings. We adopt a two-step approach. First, a TF-IDF based search algorithm and a Bi-Encoder are used to shortlist a set of issue candidates based on the input query. Then a Cross-Encoder will re-rank the candidates and provide the final recommendation. We will also demonstrate how the models are deployed and integrated with the existing workbench to benefit auditors in their daily work.