Reviewerly/exHarmony huggingface dataset:
Autonomous Learning System Towards Mobile Intelligence

How to efficiently deploy machine learning models on mobile devices has drawn a lot of attention in both academia and industries, among which the model training is a critical part.However, with increasingly public attention on data privacy and the recently adopted laws and regulations, it becomes harder for developers to collect training data from users and thus they cannot train high-quality models.Researchers have been exploring approaches to training neural networks on decentralized data.Those efforts will be summarized and their limitations be pointed out.To this end, this work presents a novel neural network training paradigm on mobile devices, which distributes all training computations associated with private data on local devices and requires no data to be uploaded in any form.Such training paradigm is named autonomous learning.To deal with two main challenges of autonomous learning, i.e., limited data volume and insufficient computing power available on mobile devices, this paper designs and implements the first autonomous learning system AutLearn.It incorporates the cloud (public data and pre-training)-client (private data and transfer learning) cooperation methodology and data augmentation techniques to ensure the model convergence on mobile devices.Furthermore, by optimization techniques such as model compression, neural network compiler, and runtime cache reuse, AutLearn can significantly reduce the on-client training cost.Two classical scenarios of autonomous learning are implemented based on AutLearn, with a set of experiments carried out.The results show that AutLearn can train the neural networks with comparable or even higher accuracy compared to traditional centralized/federated training mode with privacy preserved.AutLearn can also remarkably cut the computational and energy cost of neural network training on mobile devices.
A Survey on Applications of Deep Learning Techniques in Medical Diagnostics for Cancer Detection

One of the most important aspects of treating a cancer patient is how early a disease is diagnosed. The process involved in the diagnosis of cancerous disease is highly complicated, time taking, and costly. Even the delay in the detection is hazardous. Getting an accurate result of the diagnosis is also a persisting challenge. Under such critical scenarios, the use of AI -Artificial Intelligence and ML- Machine Learning and its related other technologies are playing a vital role in medical sciences nowadays. Particularly CNN- Convolutional Neural Networks   and DL- Deep Learning technologies are most promptly utilized for the analysis of medical images and detection of cancer. This paper comprises of brief review on such methods and the result obtained by applying in medical diagnosis with predicted accuracy to detect cancers (like Chest, Lung, Breast, Ovarian, Oral, Skin, Brain, Prostate, Pancreatic and Gastric cancer etc.) based on available medical data set.
AN ANALYSIS STUDENTS' WRITING OF USING GENERIC STRUCTURE OF HORTATORY EXPOSITION TEXT BY THE SECOND GRADE STUDENTS OF MAN 2 KEDIRI ACADEMIC YEAR 2019/2020

This research aimed 1) to know the generic structure written in the hortatory exposition text. 2) to know the quality of generic structure written in the hortatory exposition text, and 3) to know the problems are faced in using the generic structure of written hortatory exposition text. This research used the descriptive qualitative method and used students’ writing of Hortatory Exposition text to be analyzed. This research sample was 25 from 32 students of second-grade at MAN 2 Kediri, from XI-MIPA 3. Data collection was carried out by conducting writing test and interview. The data was analyzed by using contextual data which includes three procedures such as: reduction, data presentation, and drawing conclusions and verification. Furthermore, the researcher only used an external auditor to review the entire project's data validity. This result showed that there were five problems that are faced by students 1) developing the ideas into a paragraph, 2) translating the text into English, 3) determining the topic or title in accordance with the theme, 4) making recommendations, 5) making thesis and argumentation. The researcher suggested that the teacher should explain the materials step by step and intensively.
Pedestrian Behavior Recognition Based on Improved Dual-stream Network with Differential Feature in Surveillance Video

In order to improve the pedestrian behavior recognition accuracy of video sequences in complex background, an improved spatial-temporal two-stream network is proposed in this paper. Firstly, the deep differential network is used to replace the temporal-stream network so as to improve the representation ability and extraction efficiency of spatiotemporal features. Then, the improved Softmax loss function based on decision-making level feature fusion mechanism is used to train the model, which can retain the spatiotemporal characteristics of images between different network frames to a greater extent and reflect the action category of pedestrians more realistically. Simulation results show that the proposed improved network achieves 87% recognition accuracy on the self-built infrared dataset, and the computational efficiency is improved by 15.1%.
A Novel Spatial Image Steganalyzer with Adaptive Channel Attention

For imagesteganalysis, many studies have showed that the superiority of the convolutional neural network overconventional methods based on artificially designed features. Withthe trend of the fusion of traditional steganalysis methodsand some tricks used in classic computer vision tasks, such asSRNet equipped with residual modules and ZhuNet which usedspatial pyramid pooling, more and more CNN architecturesused for steganalysis are proposed. However, there still are somecharacteristics in most content-adaptive steganographic algorithms such as S-UNIWARD, HUGO, WOW, and tricks in designing network structure whichcan be used for steganalysis. Here, we propose a CNN network framework which can further improve theperformance of spatial imagesteganographic algorithms. First, we utilizemore SRM kernels to initialize the pre-processing layer than previous CNNs, and usean image padding method different from traditional modelsto preserve the integrity of image residuals as much as possible. Next, we use multiple channel attention layers which aim to discriminate the more informational features boosting the detection accuracy of network. Then, we deploy the spatial pyramid poolinglayer before features are fed into the fully-connected layers, aiming to extract more features from the last feature mapsin several scales. Several experiments under different steganographic algorithms show that, the proposed CNN outperforms the other CNN-based steganalyzerssuch as YeNet, XuNet, YedroudjNet,SRNet and ZhuNet.
WATERMARKING PADA CITRA DIGITAL MENGGUNAKAN KOMBINASI PIXEL VALUE INDICATOR DAN METODE MOST SIGNIFICANT BIT

One way to protect the copyright and authenticity of a digital image is to affix a watermark in the form of writing or a sign on the image. If the affixed watermark is visible, then its presence can interfere with the appearance of the image and it is possible that the watermark will be cut, discarded, or cropped by parties who are not entitled to do so. In this paper, an application is designed to affix a watermark in the form of an invisible message, or an invisible watermark by using a combination of pixel value indicator and the Most Significant Bit (MSB) method on the image, so that it is not detected visually and the visual appearance of the image is not disturbed. The message is scrambled using a key, then the scrambled bits are hidden in the 5th bit MSB and the 6th bit of the green channel or blue channel, depending on the number of bits 1 owned by the red channel which serves as an indicator. The process of hiding the watermark is carried out in the middle area of ​​the image, so it is difficult to cut to remove it. Applications designed can be used to affix an invisible watermark in the form of messages that are not easily removed on digital images, so that they can protect the copyright of a digital image.
ANALISIS PEMBELAJARAN JARAK JAUH (PJJ) PADA MAHASISWA PGSD UMSU

This study aims to analyze distance learning (PJJ) in UMSU PGSD students. This type of research is descriptive qualitative research subjects in semester III D. Data were collected using interview sheets. Data were analyzed by means of tianggulasi. The results of the study found that the impact of Distance Learning during the Covid 19 Pandemic on Umsu PGSD Class III D Students was effective enough to be carried out online where students were required to be more independent in obtaining their learning. The obstacles experienced when carrying out online-based distance learning activities experienced by UMSU PGSD Class III D students were the problem of a slow internet connection network so that they could miss the material from the lecturers. Thus Distance Learning (Online) becomes a solution as a substitute for face-to-face learning activities. Even though there are obstacles in implementing this Distance Learning, at least the learning is still running smoothly
Analysis on the New Development Direction Of Library Materials Based On Knowledge Informatization

Abstract With the development of information technology, the traditional paper knowledge information has been toward the trend of informatization. Therefore, the library materials information also gradually information zed. This paper first explains the development trend of library in China, and discusses the existing problems and optimization of library information, so as to provide readers with reference.
Detection and Recognition of Object for Image Captioning

As one of the most intelligent beings on the planet, we are equipped with the most powerful visual and language system as it is easy for us to extract visual information from a given image and transform it into proper linguistic description. Image Caption Generator deals with generating captions for a given image. The capturing mechanism involves a tiring task that collaborates both computer vision and image processing. The mechanism must detect and establish relationships between objects, people, and animals. The aim of this paper is to detect, recognize and generate worthwhile captions for a given image. We use transfer learning CNN on sentences, and extract image representation with Neural Networks.
"Yuk Cegah Virus Corona!" Edukasi Pencegahan Corona Melalui Buku Interaktif Anak-Anak

Corona Virus or COVID-19 was first discovered in December 2019 in Wuhan, China. On January 30, 2020, WHO announced that this epidemic was a global health emergency, spreading so quickly that it reached various countries including Indonesia. Covid-19 had weakened and paralyzed various sectors in Indonesia and even all countries. One of the impacts is in the world of education. Since the implementation of the online system, there have been many pros and cons that have occurred among the community as well as challenges that must be faced with the existence of this distance learning system (PJJ). Early childhood education is undeniably affected by COVID-19. The research method uses empirical methods by collecting data sources from libraries and journals that have been there before. The results of the study concluded that the making of an interactive book "Come on, Prevent Coronavirus!" is a type of busy book in which this book can provide various activities for children so that they will not be bored in digesting the learning material.
Innovative Training of Future Teachers of Higher Education Institutions in the Conditions of Distance Learning

The study examined the change in the attitude of students and teachers to the system of distance learning of future teachers of higher education institutions after passing the blitz course "Modern distance education". The experiment was conducted in Ukraine -a post-Soviet state, where part-time and part-time education still prevails, and distance learning still remains innovative and is gradually being implemented within the transitional credit transfer system. The article used sociological methods to study the acceptability of innovative forms of learning. In particular, after the participants of the blitz course "Modern Distance Learning" sociologically measured the attitude to innovative areas of distance learning, namely: work with electronic resources,counseling, modeling personal educational trajectory, organization and self-organization of independent forms of work and psychological training (motivation, training in self-regulation techniques, trainings and self-trainings on the practice of independent decision-making and responsibility). Particular attention was paid to diagnosis and self-diagnosis, as well as control and self-control. After a theoretical (sociological) discussion of innovative forms of distance learning, it was found that teachers are interested in innovative methods of work, but prefer a stationary form of educational services; during distance learning, emphasis is placed on consulting forms of work and multifaceted monitoring of competencies. Students, on the other hand, showed a high sensitivity to e-learning, providing maximum academic freedom and self-control. However, the dynamics of a positive attitude to distance innovation had a slight increase in students and moderate -in teachers. At an international level, the scientific novelty of the article involves subjective views of students and university teachers on distance learning in the post-Soviet countries, where until now such learning was implemented only by the top-down approach. Thus, the article can become a theoretical basis for the development of contact educational programs in the humanities for the implementation of innovative methods of distance learning in the studied region.
HARD WORK CHARACTER ON NOVEL RADEN MANDASIA SI PENCURI DAGING SAPI BY YUSI AVIANTO PAREANOM

Novel is one of literature which full of character education value. The aims of this research describe the character education value of hard work in the novel. The mental hard work of participants in educators have faded and much the mentality to get instant results. This research uses the descriptive qualitative method with source data from novel Raden Mandasia Si Pencuri Daging Sapi by Yusi Avianto Pareanom Banan publisher 2016. Close reading was used to collect the data, then the data was recorded in the form of hard work education data, processing data and analyzing data The results of this research that novel contains a lot of hard-working education values shown by the characters. Figures from this novel are (1) Sungu Lembu, (2) Raden Mandasia, (3) Prabu Watugunung, (4) Loki Tua, (5) Nyai Manggis, (6) Many Wetan, (7) Barja, (8) Wulu Many, (9) Jongkeng, (10) Resi Tama, and (11) Melur. The value of character education of hard work on this novel can be instilled to learners along with related learning. Thus it can assist the educator in determining the proper literary works for the material to grow the character of the hard work of the learners.
Penerapan Pembelajaran Think Pair Share Untuk Menaikkan Motivasi dan Hasil Belajar

This study aims to determine the application of the Think Pair Share (TPS) cooperative learning model as an effort to increase motivation and learning outcomes for PAI and BP prayer materials before and after meals. The type of research used is classroom action research (CAR). The subjects of this study were the second grade students of SDN Ciporos as many as 30 students. Data collection techniques using test, observation, and documentation techniques. Validation technique with data triangulation, member check, and audit trail. The data analysis technique used descriptive analysis. The results showed that learning motivation in the initial conditions was 33.33% or 10 students, increased to 56.67% or 17 students and 93.33% or 28 students in the second cycle. The average increase in learning outcomes from 56.00 in the initial conditions to 71.33 and 83.67 in the second cycle with learning mastery of 6 students or 20.00%, increased to 12 students or 40.00% and 90.00% or 27 students in the second cycle
ARTIFICIAL INTELLIGENCE BASED ON TENSORFLOW IN EDUCATIONAL PROCESS

This article discusses the problem of using the TensorFlow library for educational purposes. The main idea of the work is to analyze AI software solutions for students supporting based on the TensorFlow framework. Technological prerequisites are described; reviewed the fundamentals of TensorFlow-based AI systems. In this article, we will first illustrate the state of the art in the application of TensorFlow to educational problems. The paper analyzes the existing areas of application of software products based on the TensorFlow library. In conclusion, the classification of application areas of the software, which is based on TensorFlow, is carried out. The methodology for solving this problem is to identify the main types of problems in Artificial Intelligence based on TensorFlow and describing of a main directions TensorFlow-based solutions in Educational Process. Scientific novelty. The article shows that the use of the TensorFlow library for educational purposes, which is based on vectors and matrixes mathematics and Deep Learning, allows the implementation of artificial neural networks principles with Artificial Intelligence methodology.
A Self-Aggregated Hierarchical Topic Model for Short Texts

With the growth of the internet, short texts such as tweets from Twitter, news titles from the RSS, or comments from Amazon have become very prevalent. Many tasks need to retrieve information hidden from the content of short texts. So ontology learning methods are proposed for retrieving structured information. Topic hierarchy is a typical ontology that consists of concepts and taxonomy relations between concepts. Current hierarchical topic models are not specially designed for short texts. These methods use word co-occurrence to construct concepts and general-special word relations to construct taxonomy topics. But in short texts, word cooccurrence is sparse and lacking general-special word relations. To overcome this two problems and provide an interpretable result, we designed a hierarchical topic model which aggregates short texts into long documents and constructing topics and relations. Because long documents add additional semantic information, our model can avoid the sparsity of word cooccurrence. In experiments, we measured the quality of concepts by topic coherence metric on four real-world short texts corpus. The result showed that our topic hierarchy is more interpretable than other methods.
Revisiting Basaa verbal derivation

Basaa, a Narrow Bantu language, A 43, spoken in Cameroon in Central Africa holds a serious record of descriptive works in phonology, morphology, and syntax. Verb morphology has been studied in detail by Bitja’a Kody (2000), Dimmendaal (1988), Hyman (2003) among others. The present paper focusing on verb derivation raises two main issues: the paradigm of expansions that are recurrent in the language on the one hand, and extensions for which suggest the existence of additional suffixes to what is already reported on the other hand. Further extensions are the perfective, the associative, and the tentative. This paper ends up with an attempt to reconstruct Basaa extensions, mirroring the Proto-Bantu propositions from Schadeberg (2003).
Greeting Card app based on Augmented Reality

Abstract: Augmented Reality is a combination of a real and a computer-generated or virtual world. It is achieved by augmenting computer-generated images on real world. It is of four types namely marker based, marker less, projection based and superimposition based augmented reality. It has many applications in the real world. AR is used in various fields such as medical, education, manufacturing, robotics and entertainment. Augmented reality comes under the field of mixed reality. It can be considered as an inverse reflection of Virtual Reality. They both have certain similarities and differences. This paper gives information about Augmented Reality and how it started. It analyses various types of augmented reality, its applications and its advantages and disadvantages. This paper also gives us knowledge regarding those major threats that augmented reality will face in the near future and about its current and future applications. It gives us a comparison between the two related topics, Augmented reality and Virtual reality. The following paper also helps us know about the effect of Augmented Reality on the human life.
The Effects of Practical Problem-Based Home Economics Instruction (PPBHEI) on Middle School Students' Creativity: Focusing on the Content on “Clothing Management and Recycling”

The purpose of this study was to test the effects of Practical Problem-Based Home Economics Instruction (PPBHEI) on middle school students' creativity, focusing on the content on “clothing management and recycling.” This study carried out quasi-experimental research with an independent variable of PPBHEI and a dependent variable of creativity. The experiment was based on a sample of 62 (31 in each group) second grade students in the Cheongju area over four class hours. ANCOVA was performed to examine pre-test and post-test differences between the experimental group and the control group. The results showed that PPBHEI did not improve the level of creativity but improved the level of creative motivation among the sub-elements of creativity (creative thinking, creative tendency, creative motivation). In particular, PPBHEI improved the level of independence in creative tendency, intrinsic motivation, and diligence in creative motivation. From the results of this study, it can be concluded that PPBHEI is effective in improving independence, intrinsic motivation, and diligence in respect of middle school students’creativity.
Mining predicate rules without minimum support threshold

Association rule mining (ARM) is used for discovering frequent itemsets for interesting relationships of associative and correlative behaviors within the data. This gives new insights of great value, both commercial and academic. The traditional ARM techniques discover interesting association rules based on a predefined minimum support threshold. However, there is no known standard of an exact definition of minimum support and providing an inappropriate minimum support value may result in missing important rules. In addition, most of the rules discovered by these traditional ARM techniques refer to already known knowledge. To address these limitations of the minimum support threshold in ARM techniques, this study proposes an algorithm to mine interesting association rules without minimum support using predicate logic and a property of a proposed interestingness measure (g measure). The algorithm scans the database and uses g measure’s property to search for interesting combinations. The selected combinations are mapped to pseudo-implications and inference rules of logic are used on the pseudo-implications to produce and validate the predicate rules. Experimental results of the proposed technique show better performance against state-of-the-art classification techniques, and reliable predicate rules are discovered based on the reliability differences of the presence and absence of the rule’s consequence.