Reviewerly/exHarmony huggingface dataset:
Lanskap Pembelajaran Bahasa Jawa Masa Pandemi Covid-19

&#x0D; &#x0D; Learning Javanese during the Covid-19 pandemic experienced several challenges. On the one hand, it is hoped that this lesson will remain strong as an entry point for the inheritance of noble character through language preservation. While on the other hand, learning Javanese, like a common phenomenon that is also faced by other subjects in schools in Indonesia, is experiencing difficulties due to online learning. This study reveals the phenomena, obstacles, and opportunities of learning Javanese, although in some cases it can also be generalized to other subjects. The design of this study was qualitative, using questionnaires and interviews with parents. This research shows that with the various difficulties faced in learning during the pandemic, face-to-face meetings and conventional learning such as the period before the Covid-19 pandemic were considered irreplaceable. However, our elaboration study shows something else. Learning that is more flexible and involves information technology should be paid more attention to equip students to develop more.
Keterbacaan Buku Teks Keterbacaan Buku Teks Bahasa Sunda Rancagé Diajar Kelas X Untuk SMA/SMK/MAK

&#x0D; &#x0D; Determining textbooks in legibility relates to a sentence or text form which when read is easy to understand, understand and remember the intent and meaning of the text. Readability is all elements of reading that affect the success achieved by a group of readers with the material. In Sundanese language textbooks at the SMA/SMK/MA/MAK level and equivalent. The level of readability of the discourse will affect students' understanding of the content (material) presented in a book. The purpose of this research is to determine whether the textbook can be read or used effectively by students. The method used in this research is descriptive qualitative by using literature study technique. The source of the research data is a Sundanese language textbook entitled Rancage Diajar Pikeun SMA/SMK/MA/MAK students grade X curriculum 2013 revision 2017. After being analyzed, the textbook has criteria that are very feasible and easy to understand.
An Integrated Single Framework for Text, Image and Voice for Sentiment Mining of Social Media Posts

The wide spread pandemic COVID-19 has propelled the entire world to rely on social media interaction digitally. Social media is thus a platform to express numerous kinds of direct and indirect sentiments by human beings. Psychologically, a person tends to share his/her feelings in terms of sentiments more openly over the social media. These sentiments, when intense may polarize oneself to commit severe mis-deeds. Here arises the role of the researchers to perform a real time identification of sentiments and classify them so that a prospective mishap can be averted. In this work, an integrated framework is proposed that does an early recognition of sentiments over social media in the digital domain. Along with sentiment categorization, another module has been integrated to the framework to perform a post-predictive analysis of the same. The proposed integrated framework involves combination of two distinct mechanisms. First, the proposed work channelizes the input data in line with its characteristics text, image, and voice. The text input is directly fed to our proposed ‘Lexicon based LSTM with sentiment word mapping’ mechanism. From the input image, both text and semantics are extracted through two different blocks. One block converts image-to-text and redirects the output to the above proposed model. We proposed a new generative model (GM) to extract the semantics of the image and the second block utilizes our generative model and redirects the outcome straight to the final output buffer of the framework. The voice-to-text module has been used for transforming voice input data to text data which is redirected to our proposed Lexicon based LSTM for further processing. A comparison of the proposed work has been made with state-of-the-art techniques. Our results indicate that the overall rate of accuracy of this framework is superior to the existing methods.
Query-Efficient and Imperceptible Attacks on Multivariate Time Series DNN Models

Many black-box adversarial attacks for deep neural network (DNN) models for two-dimensional image datasets have been proposed. Though there are many pervasive and computing application scenarios that need multivariate time-series data as DNN inputs, little research has been devoted to black-box adversarial attacks on multivariate time-series DNN models, which have higher requirements on efficiency and imperceptibility. To meet the requirements, in this paper, we propose three different black-box adversarial attacks based on an existing attack: a self-adaptive step technique to improve the query-efficiency and success rate; an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{0}$</tex> distance-based attack to improve imperceptibility; and an input coordinate importance oriented attack based on multiplicative weight update (MWU), which exploits the time-series structure and improves query-efficiency and imperceptibility. These proposed attacks are able to make trade-offs between successful rate, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{2}/L_{0}$</tex> distance, and the number of queries tailored for a particular targeted attack task. We conducted extensive experiments using ten different combinations of DNN models and datasets to test the effectiveness of the proposed black-box adversarial attacks for multivariate time-series based DNNs. The proposed attacks achieve up to 51% higher success rates with 25% fewer queries, and 84% fewer perturbation amounts over the existing black-box adversarial attacks.
Template matching between visible light and infrared images

In the field of computer vision, template matching technology is an important research direction. This technique compares the template image with the sample image to find out the position of the template image in the sample image. It has the characteristics of simple algorithm, small amount of calculation and high recognition rate, so it is usually used in other computer vision fields such as object detection and target tracking. In addition, with the popularity of infrared sensors, and infrared images can obtain additional information that is not included in visible light images, the integrated processing of visible light and infrared information has always been a research hotspot. The traditional template matching algorithm mainly focuses on the matching between visible light images. For the information difference between visible light and infrared images, the traditional template matching algorithms are difficult to achieve accurate matching between the two types of images, and the amount of calculation is large. In response to this problem, a template matching algorithm based on feature extraction of convolutional neural networks is proposed in this paper. Our method draws on the robust template matching using scale-adaptive deep convolutional features. We use a scaleadaptive method to extract the deep features of visible light and infrared images, and then uses the traditional NCC matching algorithm to obtain the matching position of the template on the feature map. Then the regression and optimization of the template position are performed to obtain the position of the template image on the sample image. The research results show that our method can achieve the matching of the infrared template on the visible light image, and the position error is not large.
Object Localization in Weakly Labeled Remote Sensing Images Based on Deep Convolutional Features

Object recognition, as one of the most fundamental and challenging problems in high-resolution remote sensing image interpretation, has received increasing attention in recent years. However, most conventional object recognition pipelines aim to recognize instances with bounding boxes in a supervised learning strategy, which require intensive and manual labor for instance annotation creation. In this paper, we propose a weakly supervised learning method to alleviate this problem. The core idea of our method is to recognize multiple objects in an image using only image-level semantic labels and indicate the recognized objects with location points instead of box extent. Specifically, a deep convolutional neural network is first trained to perform semantic scene classification, of which the result is employed for the categorical determination of objects in an image. Then, by back-propagating the categorical feature from the fully connected layer to the deep convolutional layer, the categorical and spatial information of an image are combined to obtain an object discriminative localization map, which can effectively indicate the salient regions of objects. Next, a dynamic updating method of local response extremum is proposed to further determine the locations of objects in an image. Finally, extensive experiments are conducted to localize aircraft and oiltanks in remote sensing images based on different convolutional neural networks. Experimental results show that the proposed method outperforms the-state-of-the-art methods, achieving the precision, recall, and F1-score at 94.50%, 88.79%, and 91.56% for aircraft localization and 89.12%, 83.04%, and 85.97% for oiltank localization, respectively. We hope that our work could serve as a basic reference for remote sensing object localization via a weakly supervised strategy and provide new opportunities for further research.
Digital Forensic Challenges in Internet of Things (IoT)

With emerging technology and the connection of electronic devices to the internet, Internet of Things (IoT) has become part of human life. From the development of smartphones to smartwatches and smart-homes, electronic devices now have the capability of performing human activities or aiding humans in performing activities such as turning hall lights on or off with their voice. Although a large number of people use these devices for the greater good, a few individuals or group of people hide behind these devices to perform malicious activities. In order to apprehend and prosecute perpetuators who hide behind smart devices for evil gains, forensic examinations or investigations must be conducted. This review aims to identify digital forensic challenges in IoT. The inclusion criteria for this paper were international journals, articles, conference papers and case studies published from 2019 to 2022. Thematic analysis was used to analyze and synthesis the literature. Three themes emerged from the analysis; automated compromised smart-home tracer; data volatility and reconstruction; IoT forensic investigation framework. This integrative review combines evidence of digital forensic challenges in diverse IoT devices. Keyword IoT forensics, IoT challenges, Digital forensics, Smart-home forensics.
Transformational Leadership and Teacher Work Motivation in Private Educational Institutions

Leadership in educational institution is essential for achieving high performance of teachers.There is hesitation about whether school administrators have the requisite management abilities, style of leadership, and techniques to ensure that schools operate efficiently.Teaching and development in transformational leadership style, which studies have evaluated shown to increase performance within organizations and institutional settings, may offer advantage to educational leaders.This research study aimed to provide school leader on the information related to transformational leadership to create an action plan in relation to teacher work motivation.An effective involvement and development strategy can be a contributing factor for the educational institutions to meet its common objective and improve its overall performance.The researcher conducted a survey questionnaire.Quantitative and descriptive method of research were utilized.The participants were teachers from Early Childhood Education to College Level from different renowned private educational institutions in Metro Manila via random sampling.A total of 134 teachers (35 male and 99 female) participated in the study.The results revealed that majority of the respondents were less than 30 years old, mostly female, single, bachelor degree holders, 64 of them are teaching in the Secondary level, and 37% of them have been in their institution for 5 to 10 years.The respondents mostly evaluated frequently on the dimensions of transformational leadership.Majority of the respondents in the educational institutions were Females which are composed of 73.9% of the population that shows a significant difference in transformational leadership and teacher work motivation in terms of sex.There is a significant difference between the dimensions of transformational leadership in terms of age.There is a significant difference between the dimensions of Idealized Influenced Attributes and Individualized Consideration in terms of Educational Attainment.There is a significant difference between the dimension of inspirational motivation in terms of level taught.There is a significant difference between the dimensions of intrinsic motivation and identified regulation in terms of civil status and age.There is no significant difference between transformational leadership in terms of civil status, subjects taught and length of service.The dimensions of transformational leadership show a significant relationship with motivation at work in terms of intrinsic motivation, identified regulation, introjected regulation and external regulation.The institutional management may organize teachers' development training workshops or programs to further improve the relationship between transformational leadership and teacher work motivation and to promote effectiveness and efficiency among the members.
Application of Image Processing Technology in the Visual Design of Motion Health under the Background of Wireless Communication: Take Swimming as an Example

Exercise is an active means to improve the body’s immune function, resist disease invasion, and prolong life. Research data shows that healthy people who exercise regularly have better cellular and humoral immunity than ordinary people. Although physical exercise can strengthen the body, unscientific physical exercise can also cause harm to the body. Contingencies in sports add to the complexity of medical care. In recent years, people pay more and more attention to sports health, and the development of information technology can enable experts to use sports health visualization technology to carry out scientific physical exercise, reduce medical accidents, and ensure sports health, which has become a consensus. As an important technology in the field of sports health visualization, image processing technology has attracted more and more attention. Image processing technology is realized with the help of computer vision technology. It uses cameras, computers, collectors, and other equipment instead of human eyes to monitor and identify the catalogue, collect images, and then process and analyze the images. Image processing technology has played a very important role in sports emergencies. This paper studies the image processing technology in the field of sports health visualization and takes swimming as an example to illustrate the role of image processing technology in sports health visual design. Using scientifically designed sensors, data analysis is performed on various images of athletes while swimming, and exercise recommendations are continued based on the principles of exercise science. According to the test results, the visual target recognition rate during swimming has reached a high level, which also greatly improves the scientificity of athletes during exercise.
CAPTION: Caption Analysis with Proposed Terms, Image of Objects, and Natural Language Processing

Abstract This paper proposes a novel algorithm, called CAPTION, for identifying and correcting errors in automatically generated image captions. The algorithm combines Deep Learning (DL) for object detection in images with Natural Language Processing techniques. CAPTION has been tested in the following three tasks: (1) classify a caption as correct or not; (2) detect wrong words in the caption, and (3) suggest text corrections. Results show that our method is superior with respect to others evaluated in the same data set in the error correction task. These other methods are generally based exclusively on DL models. This work shows that, although semantics still has not been used at its fullest in this type of task, a combination of DL with Natural Language Processing tools presents a better overall performance than using DL methods alone.
Penerapan Media E-Booklet dalam Pembelajaran Ikatan Kimia di SMA

This study aims to measure students' learning outcomes in class X SMA PAB 8 Saentis Percut Sei Tuan, taught between E-booklets and PPT media. The samples in this study were taken by random sampling technique by lottery. The type of research used is a quasi-experimental design (Quasi-Experimental Design. The data collection technique begins with the preparation stage and the implementation stage. As a prerequisite for hypothesis testing, the sample group data is tested for normality and homogeneity. Then hypothesis testing is carried out with SPSS Statistics 20 with criteria for a significant level of 5 %. Based on the results of data processing student learning outcomes, the average value of student learning outcomes in experimental class 1 using e-booklet media in the learning process is higher. Then the average value of student chemistry learning outcomes in practical class 2 using PPT media is reinforced in the second hypothesis test; the sig (2-tailed) score on SPSS is 0.015. This study also shows that the average value of student learning outcomes who are taught by the application of E-booklet media is higher than the KKM. This is reinforced by the first hypothesis test where the result of the sig (2-tailed) value of 0.000 using the one-sample t-test analysis on SPSS. According to the third hypothesis test, a significant value of 0.000 &lt; 0.05 obtained in 84% of students taught by e-booklet media reached the KKM.
Peran Chatbot Dalam Mendukung Smart Service pada Smart Library

Chatbot is a digital technology based on artificial intelligence that has been utilized by various libraries to support the efficiency of information services. The use of chatbots is considered capable of creating a smart library ecosystem so it is important to continue to be developed so that customers are satisfied with the information services provided. This study aims to provide an overview of the benefits that can be obtained by libraries from implementing chatbots to achieve the concept of a smart library. The method used in this study is a literature review from the Google Scholar and Scopus database. There are 5 articles as the main research data. The results show that there are at least 2 benefits that can be obtained from implementing chatbots in library information services, namely: personalized information services and integration of cyberspace with libraries. In personalized information services, customers can get information that suits their needs without having to be overwhelmed with unnecessary information. As for integrating cyber space with real life, customers are able to access services without having to come to the location. Efficiency is the key word in the use of technology to support library services. More in-depth empirical research on chatbots, especially in Indonesia, needs to be carried out to be able to develop and evaluate the use of this technology.
GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of  Transcribed Audio

This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable for speech recognition training, and to filter out segments with low-quality transcription. For system training, GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h. For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage, and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand, are re-processed by professional human transcribers to ensure high transcription quality. Baseline systems are provided for popular speech recognition toolkits, namely Athena, ESPnet, Kaldi and Pika.
Deep Learning for Weakly-Supervised Object Detection and Object  Localization: A Survey

Weakly-Supervised Object Detection (WSOD) and Localization (WSOL), i.e., detecting multiple and single instances with bounding boxes in an image using image-level labels, are long-standing and challenging tasks in the CV community. With the success of deep neural networks in object detection, both WSOD and WSOL have received unprecedented attention. Hundreds of WSOD and WSOL methods and numerous techniques have been proposed in the deep learning era. To this end, in this paper, we consider WSOL is a sub-task of WSOD and provide a comprehensive survey of the recent achievements of WSOD. Specifically, we firstly describe the formulation and setting of the WSOD, including the background, challenges, basic framework. Meanwhile, we summarize and analyze all advanced techniques and training tricks for improving detection performance. Then, we introduce the widely-used datasets and evaluation metrics of WSOD. Lastly, we discuss the future directions of WSOD. We believe that these summaries can help pave a way for future research on WSOD and WSOL.
BCNet: Searching for Network Width with Bilaterally Coupled Network

Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constraints. To fulfill the searching, a one-shot supernet is usually leveraged to efficiently evaluate the performance \wrt~different network widths. However, current methods mainly follow a \textit{unilaterally augmented} (UA) principle for the evaluation of each width, which induces the training unfairness of channels in supernet. In this paper, we introduce a new supernet called Bilaterally Coupled Network (BCNet) to address this issue. In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately. Besides, we leverage a stochastic complementary strategy for training the BCNet, and propose a prior initial population sampling method to boost the performance of the evolutionary search. Extensive experiments on benchmark CIFAR-10 and ImageNet datasets indicate that our method can achieve state-of-the-art or competing performance over other baseline methods. Moreover, our method turns out to further boost the performance of NAS models by refining their network widths. For example, with the same FLOPs budget, our obtained EfficientNet-B0 achieves 77.36\% Top-1 accuracy on ImageNet dataset, surpassing the performance of original setting by 0.48\%.