Reviewerly/exHarmony huggingface dataset:
Sosialisasi Bahaya Penyalahgunaan Narkoba Bagi Peserta Didik SMP Negeri 4 Kwandang Kabupaten Gorontalo Utara

The purpose of the socialization activity on the dangers of drug abuse in Junior High School (SMP) Negeri 4 Kwandang, North Gorontalo Regency is so that participants can understand the impact of drug use, both physical, psychological and socio-economic impacts. Socialization activities use participatory methods, lectures and discussions. The socialization activity was carried out on October 12, 2021 with the number of participants who took part in the socialization activity consisting of 25 students and 10 teachers located in the hall of SMP Negeri 4 Kwandang. For this reason, through socialization activities on the dangers of drug abuse, it is able to increase the understanding of students of SMP Negeri 4 Kwandang about the types of narcotics, the impact of drug abuse from the physical, psychological, educational, social and economic aspects as well as ways to prevent and overcome drug abuse among children and teenager. For this reason, this socialization activity can fortify the younger generation as the nation's next generation, especially students at SMP Negeri 4 Kwandang, North Gorontalo Regency from the dangers of drug abuse.
Spatial Distribution Analysis of Novel Texture Feature Descriptors for Accurate Breast Density Classification

Breast density has been recognised as an important biomarker that indicates the risk of developing breast cancer. Accurate classification of breast density plays a crucial role in developing a computer-aided detection (CADe) system for mammogram interpretation. This paper proposes a novel texture descriptor, namely, rotation invariant uniform local quinary patterns (RIU4-LQP), to describe texture patterns in mammograms and to improve the robustness of image features. In conventional processing schemes, image features are obtained by computing histograms from texture patterns. However, such processes ignore very important spatial information related to the texture features. This study designs a new feature vector, namely, K-spectrum, by using Baddeley's K-inhom function to characterise the spatial distribution information of feature point sets. Texture features extracted by RIU4-LQP and K-spectrum are utilised to classify mammograms into BI-RADS density categories. Three feature selection methods are employed to optimise the feature set. In our experiment, two mammogram datasets, INbreast and MIAS, are used to test the proposed methods, and comparative analyses and statistical tests between different schemes are conducted. Experimental results show that our proposed method outperforms other approaches described in the literature, with the best classification accuracy of 92.76% (INbreast) and 86.96% (MIAS).
PENINGKATAN HASIL BELAJAR DENGAN METODE PICTURE AND PICTURE PADA MATERI PECAHAN KELAS II SD MUHAMMADIYAH 9 SURABAYA MASA PANDEMI COVID-19

This study aims to find out if there is an increase in student learning outcomes, to find out an increase in student activity, to find out an increase in teacher activity in managing learning using the picture and picture method in class II fraction material at SD Muhammadiyah 9 Surabaya during the covid-19 pandemic. This type of research is Classroom Action Research (CAR) which is carried out in cycle stages including planning, implementation, observation and reflection stages. The subject of this Classroom Action Research is the second grade students of SD Muhammadiyah 9 Surabaya, totaling 34 students. The object of this research is the result of learning in the matter of recognizing fractions. The instruments used in this study were observation sheets and test questions. The research data were analyzed descriptively qualitatively and quantitatively. The indicator of the success of this research is if at least 75% of students have reached the KKM, which is 75 and the average grade has reached the KKM. The results showed an increase. Based on student learning outcomes classically increased by 23%
Toward Exploiting Second-Order Feature Statistics for Arbitrary Image Style Transfer

Generating images of artistic style from input images, also known as image style transfer, has been improved in the quality of output style and the speed of image generation since deep neural networks have been applied in the field of computer vision research. However, the previous approaches used feature alignment techniques that were too simple in their transform layer to cover the characteristics of style features of images. In addition, they used an inconsistent combination of transform layers and loss functions in the training phase to embed arbitrary styles in a decoder network. To overcome these shortcomings, the second-order statistics of the encoded features are exploited to build an optimal arbitrary image style transfer technique. First, a new correlation-aware loss and a correlation-aware feature alignment technique are proposed. Using this consistent combination of loss and feature alignment methods strongly matches the second-order statistics of content features to those of the target-style features and, accordingly, the style capacity of the decoder network is increased. Secondly, a new component-wise style controlling method is proposed. This method can generate various styles from one or several style images by using style-specific components from second-order feature statistics. We experimentally prove that the proposed method achieves improvements in both the style capacity of the decoder network and the style variety without losing the ability of real-time processing (less than 200 ms) on Graphics Processing Unit (GPU) devices.
A Study of Colour Image Encryption with Chaos Logistic Map and DNA Encoding Techniques

Abstract: The encryption of image has really become an important task in today’s world as there is a continuous advancement of technology day-by-day. There is a need to protect the images like texts, encrypt them and then transfer them through a channel. As the fast-growing era is more inclined towards the usage of images during conversations, cryptography becomes an essential tool to cipher the information before transferring it over a medium. The transmission channel could be insecure therefore data that is getting transferred must be secured enough so that only the genuine receiver should decode the original meaning of the image. Moreover, the general compatibility of an image should be done before encrypting it and that image could be preprocessed using existing image processing techniques. However, the proposed system is a study of how cryptography tends to be an efficient tool for security and the proposed system is implementation of DNA sequence encoding which provides an additional layer to it. It is divided into four modules. The first module consists of generating a secret key with the help of chaotic logistic map and check the behaviour of the chaotic system. Next phase is to apply the DNA concepts thereby making the quality of image more complex and after that perform the overall encryption with the help of confusion and diffusion technique to get the result as encrypted image. The last phase would be to decode the image and get the original plain image. The performance metrics and evaluated and analysed with the existing methodologies as a comparison. Moreover, a good encrypted image should be able to deal with the different types of attacks that are happening while transmission. Keywords: Chaos logistic map, DNA, encryption, image processing, confusion, cryptography
Enhanced Multilevel Fuzzy Inference System for Risk Adaptive Hybrid RFID Access Control System

Risk-based access control systems are part of identity management systems used to accommodate environments with needs for dynamic access control decisions. The risk value is subjected to overestimation or underestimation since it is measured qualitatively, thus; causing uncertainty problems, which was apparent in a previously proposed hybrid risk adaptive (HRA) access control system. Conversely, Fuzzy Inference Systems can deal with the uncertainty of measures and control the outcomes more precisely; therefore, a multilevel fuzzy inference system (HRA-MFIS) was proposed to replace the risk assessment model in HRA. This paper continues to improve the previous model by introducing an enhanced multilevel fuzzy inference system (EHRA-MFIS), which utilizes user behaviour and time analysis to detect anomalous access behaviour. Moreover, it improves the hybrid adaptive risk calculation module by adding authentication, classification and the degree of user anomalous behaviour to the risk calculation algorithm. The results show that the proposed model has smoothed out the transition between the different risk levels and enhanced the system's overall security by considering the failed authorization attempts and failed authentication attempts, asset classification, and user behaviour when calculating the risk level.
Optimasi Sintesa Nanokoloid Human Serum Albumin sebagai Agen Limfoskintigrafi Menggunakan Central Composite Design-Response Surface Methodology

The prevalence of cancer in Indonesia is increasing and various cancer treatments still have shortcomings, cancer recurrence still occurs. Lymphoscintigraphy is a method for detecting disorders of the lymphatic system, which are closely related to cancer cells. Human serum albumin nanocolloid as a lymphoscintigraphic agent consists of particles measuring
Incorporating Global Sustainable Development Goals in University’s Strategic Development: The Case of the Northern Eastern Federal University in Yakutsk

Paying particular attention to global development problems during the last stages of its programs, the North-Eastern Federal University named after M. K. Ammosov, within the priorities until 2025, identified the northern territories and the territories of the Arctic zone as a key geostrategic priority. To take a more active stanсe and gradually bring its initiatives to support the socio-economic development of the northern territories to the level of international cooperation, the North-Eastern Federal University named after M. K. Ammosov needs broader support from the regional community, as well as knowledge of how SDGs are understood at the university level. Also, there is a need for help from external stakeholders. The study “The university is an intellectual, innovative, and spiritual platform for the sustainable development of the northern territories,” conducted on a sample of more than 2,000 people, allowed us to form an evidence base for integrating the UN SDGs global agenda into the strategic development of the university.
Design of Online Learning System of College Sports Gymnastics Based on BP Neural Network

With the rapid development of artificial intelligence technology and big data analysis technology, the use of online learning has become more technical premises. It has the advantages of convenient data update, simple real-time communication, and high stability and flexibility. These have laid a good technical cornerstone for the development of online learning platforms. In order to solve the problems such as the lack of teaching materials for physical gymnastics in colleges and the lack of technical support for teaching, this study aims to study the design of college sports gymnastics online learning system based on BP neural network. On the basis of analyzing the introduction of BP neural network, the status quo of gymnastics teaching in Chinese colleges and universities and the principles of system design and adopting the principles of reliability, stability, and maintainability as the method of system design, the gymnastics online learning system based on BP neural network is carried out. Finally, the performance of the constructed system is tested. The test results show that, as the number of concurrent users continues to increase, the response time is gradually increasing. When the number of concurrent users is 3000, the maximum response time is 1.832 s, the minimum response time is 1.473 s, and the average response time is 1.512 s, which can meet the needs of users when the amount of concurrency is large.
A Study on Visual Understanding Image Captioning using Different Word Embeddings and CNN-Based Feature Extractions

Image captioning is a task that can provide a description of an image in natural language. Image captioning can be used for a variety of applications, such as image indexing and virtual assistants. In this research, we compared the performance of three different word embeddings, namely, GloVe, Word2Vec, FastText and six CNN-based feature extraction architectures such as, Inception V3, InceptionResNet V2, ResNet152 V2, EfficientNet B3 V1, EfficientNet B7 V1, and NASNetLarge which then will be combined with LSTM as the decoder to perform image captioning. We used ten different household objects (bed, cell phone, chair, couch, oven, potted plant, refrigerator, sink, table, and tv) that were obtained from MSCOCO dataset to develop the model. Then, we created five new captions in Bahasa Indonesia for the selected images. The captions might contain details about the name, the location, the color, the size, and the characteristics of an object and its surrounding area. In our 18 experimental models, we used different combination of the word embedding and CNN-based feature extraction architecture, along with LSTM to train the model. As the result, models that used the combination of Word2Vec + NASNetLarge performed better in generating Indonesian captions than the other models based on BLEU-4 metric.
Android-Based Blended Learning Media for Computer Maintenance Lectures

Advances in science and technology that move so fast provide very significant changes to human life, especially in the field of education and learning. This study aims to design and develop a combination of Android-based learning media that can be used on communication devices such as smartphones in the hope of increasing students' understanding of computer maintenance. The type of research used is research and development (R&amp;D) using a 4D model, namely define, design, development and dissemination which produces android-based learning media products. The instrument used in this study was in the form of a questionnaire distributed to lecturers and students as well as experts to test the feasibility. based on the analysis, it was found that the feasibility test for this android-based learning media was 82.83%, with a valid interpretation of the level of validity of the use. The results of the overall practicality assessment from the practicality test were obtained at 86.34%, so that the practicality level could be interpreted as very practical to use. The results of the overall assessment of the effectiveness test for the effectiveness assessment are 100% so that it can be interpreted as being very effectively used. Thus, this learning media is valid to use, very practical to use and very effectively used in computer maintenance lectures.
PENGEMBANGAN MODEL QUANTUM READING YANG BERORIENTASI SELF REGULATED LEARNING (SRL) DALAM PEMBELAJARAN MEMBACA PEMAHAMAN

Reading is a skill that must be possessed by students. Ideally, every student has adequate reading skills according to their level of education. This is because in daily activities at school, all learning activities cannot be separated from reading skills. However, in reality, many students do not have adequate reading skills, both reading comprehension and creative reading. The learning model cannot be separated from the success of a lesson. The aims of this study were (1) to determine the condition of the reading comprehension learning model in SMP Negeri 1 Purbalingga; (2) to know how the profile of the Quantum Reading model is; (3) to determine the effectiveness of the Quantum Reading Oriented Self-Regulated Learning (SRL) learning model in learning reading comprehension. The research method used is the development method with the stages of preliminary studies, literature studies, and field surveys; limited model development phase and expanded trial; the model validation stage is in the form of an experiment. The research results obtained are (1) the conditions for using learning models in reading comprehension at SMP Negeri 1 Purbalingga before the research was carried out using quantum reading, only using the direct method and assignments; (2) Quantum Reading learning is considered effective in improving students' reading comprehension skills at SMP Negeri 1 Purbalingga (3) with Self-Regulated Learning (SRL) orientation, students understand reading better, as a basis for carrying out the teaching and learning process in any subject.
A Multi-Scale Image Enhancement Algorithm Based on Deep Learning and Illumination Compensation

There is a huge demand for high-quality images, whether in human visual perception or image applications.The existing methods for image enhancement ignore the influence of incident light on real color images.As a result, the output images are often not effectively enhanced or not natural, and the enhancement effect is not ideal for multi-scale images.To solve the problem, this paper presents a multi-scale image enhancement algorithm based on deep learning and illumination compensation.Firstly, a compensation algorithm was developed for image illumination drift based on the Hodge decomposition model.Then, low-illumination multi-scale images were enhanced by a lightweight multi-scale Retinex network, and the architecture of that network was explained in details.After that, a new joint network loss function was designed for the input and output forms, corresponding to the proposed deep learning network, and the low-illumination multi-scale image enhancement.Experimental results demonstrate that the proposed model can effectively enhance multiscale images.
Rancang Bangun Sistem Pengaman Kotak Dana Punia berbasis Mikrokontroler NodeMCU ESP32 dan Aplikasi Telegram

The Dana Punia box security system is a security that can be monitored with online chat media in order to minimize crimes that occur in temples. This tool will work after connecting to the internet via ESP32 which can detect nearby Wifi networks. The hardware consists of the NodeMCU ESP32 board as the main controller, the telegram application as a message/notification receiver, a magnetic sensor as a notification if there is an act of theft and a fingerprint as access to open the money box. Based on the results, it can be concluded that this tool can work according to the designed working principle. This is indicated by the operation of the telegram application as a recipient of messages notifications, and a magnetic sensor as a notification if there is an act of theft and a fingerprint which is a tool to open the money box.&#x0D; Keyword : Telegram application, NodeMCU ESP32, fingerprint, magnetic sensor.
Benefits and Difficulties of Integrating Mobile Technologies in the Academic Learning

Exploring students' perceptions of the advantages and disadvantages of mobile technologies (MT) is necessary in the dynamic context of their increasing use in learning. The data obtained from 532 students from a state university were analyzed. The students mentioned three advantages and three disadvantages of using mobile technology in the learning activity. Based on the content analysis, the main categories referring to the benefits and difficulties of using MT were identified. The main categories in terms of benefits are: technological advantages, information using mobile technologies, efficient communication, opportunities for educational process, personal development, facilities at the economic and environmental level. Referring to the difficulties of MT, there was identified: negative influence on personal development, technological difficulties, damaged information, diminishing communication and socialization abilities, the negative effects on the educational process, economic, ecological and ethical disadvantages. The novelty of the study consists in highlighting the importance of MT integration in achieving academic learning activity.
Concrete Application of Computer Virtual Image Technology in Modern Sports Training

The efficiency of basketball shooting training has always been an urgent problem to be solved in basketball sports. Applying computer virtual image technology to basketball sports training is the key to improve basketball shooting ability and improve the effect of basketball sports training. Therefore, based on the design of basketball shooting automatic recognition system based on the background difference method, this study puts forward the specific application of computer virtual image technology in modern sports training. By analyzing the techniques of image denoising, image detection, and image calibration, a goal detection algorithm for modern sports basketball shooting training is designed. Firstly, the camera is used for image capture, the RGB image is converted into gray image, and the median filter is used to suppress the noise in the image. Then, the background difference method is used to detect the moving region, and the background modeling is combined with the mean method. After obtaining the background reference model, the image is differentiated, the gray image after image difference is binarized, and then the binary image is postprocessed by morphological middle closure operation. Finally, the image calibration technology is used to extract the basketball feature information. Through the region segmentation algorithm, the basketball shooting part is segmented and judged, so as to realize the basketball shooting training goal detection. The experimental results show that the proposed method has a good effect on basketball shooting training goal detection and can effectively improve the detection accuracy and efficiency.
Seamless Copy–Move Replication in Digital Images

The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries. The core category in passive image forgery is copy-move image forgery that affects the originality of image by applying a different transformation. In this paper, a frequency-domain image-manipulation method is presented. The method exploits the localized nature of discrete wavelet transform (DWT) to attain the region of the host image to be manipulated. Both patch and host image are subjected to DWT at the same level l to obtain 3l+1 sub-bands, and each sub-band of the patch is pasted to the identified region in the corresponding sub-band of the host image. Resulting manipulated host sub-bands are then subjected to inverse DWT to obtain the final manipulated host image. The proposed method shows good resistance against detection by two frequency-domain forgery detection methods from the literature. The purpose of this research work is to create a forgery and highlight the need to produce forgery detection methods that are robust against malicious copy-move forgery.