Reviewerly/exHarmony huggingface dataset:
Augmented Reality-Based Mobile Application for Knowledge Transferring in The System of Rice Intensification

Currently, the System of Rice Intensification (SRI) is one method that can be applied and used to produce seeds of local rice species in highland areas. However, it can currently be seen that the traditional methods to transfer knowledge about SRI from the experts still have limitations in many aspects due to the number of farmers that have experienced success still being low. Some farmers do not have their own fields and it takes a period for farmers to test the rice growing. Currently, mobile devices are widely used in peoples’ lives. Many scholars focus on the application of mobile devices and the augmented reality (AR) technology for the simulation games in many issues while the use of AR-based mobile applications as the learning tools is not widespread. There-fore, in this study, we propose the development of an application on mobile devices with augmented reality technology in order to use it as media in sharing knowledge related to the methods of the System of Rice Intensification by virtual farms simulation. To examine the efficiency of this developed application. A total amount of participants were 512 farmers from 5 regions of Thailand. The experimental results demonstrated that the newly developed AR-based mobile application is effective for improved knowledge on the participants who used the developed application and it can help them to practice their SRI farming skill in the virtual farm simulation. This indicates that the developed AR-based mobile application is the benefits tool for the new knowledge transferring meth-od in the system of rice intensification. For future work, it is necessary to evaluate the farmer success after learning from this AR-based mobile application in order to study the impact of the new method of SRI knowledge transferring.&#x0D; Keywords—augmented reality, knowledge transferring, system of rice intensification.
Animation of VBOC2(a1,a2) GMGM Special Cases Waveforms ACF

The main purpose of this research is the animation of futuristic generalized multidimensional geolocation modulation (GMGM) waveforms.Animation is defined as the technique, or the execution of an algorithm by means of a computer simulation, of photographing successive drawings, pictures, illustrations, representation, portrayal, delineation, depiction, composition, diagrams, study, outline, graphs, design, plans, plots, charts, or snap shots, or models to create an illusion of movement when the movie is shown as a sequence.The focus on this paper is the description of the animation itself more than the analysis that results by utilizing the animation.I believe that the case can be made that animation may be seen as a very high qualitative and quantitative tool to bring into light insights, understanding, knowledge, verification of the ACF of the VBOC2 GMGM that a researcher, scientist, scholar, developer, engineer, expert may not be able to derive by other means.Nevertheless, as seen in the content of this paper, development of animation in MATLAB requires significant amount of work, creativity, originality, and novelty to produce the execution of animation via a '*.avi', '*.mp4', or '*.mj2' files of the special cases on the VBOC2 GMGM.
Evaluation of Collaborative Filtering Based Recommender Systems against Segment-Based Shilling Attacks

Collaborative filtering (CF) is a successful and hence most widely used technique for recommender systems. However, it is vulnerable to shilling attack due to its open nature, which results in generating biased or false recommendations for users. In literature, segment attack (push attack) has been widely studied and investigated while rare studies have been performed on nuke attack, to the best of our knowledge. Further, the robustness of binary collaborative filtering and hybrid approach has not been investigated against segment-focused attack. In this paper, from the perspective of robustness, binary collaborative filtering, hybrid approach, stand-alone rating user-based, and stand-alone rating item- based recommendation have been evaluated against segment attack on a large dataset (100K ratings) which is found to be more successful as it attacks target set of items. With an aim to find an approach which reflects a higher accuracy in recommending items and is less vulnerable to segment-based attack, the possibility of any relationship between accuracy and vulnerability of six CF approaches were studied. Such an approach needs to be re-examined by the researchers marking the future of recommender system (RS). Experimental results show negligible positive correlation between accuracy and vulnerability of techniques. Publicly available dataset namely MovieLens was used for conducting experiments. Robustness and accuracy of CF techniques were calculated using prediction shift and F-measure, respectively.
Personalized Review-Oriented Explanations for Recommender Systems

Explainable recommender systems aim to provide clear interpretations to a user regarding the recommended list of items. The explanations present different formats to justify the recommended list of items such as images, graphs or text. We propose to use review-oriented explanations to help users in their decision since we can find crucial detailed feature in the reviews given by users. The model uses advances of natural language processing and incorporates the helpfulness score given in previous reviews to explain the recommended list of items provided by a latent factor model prediction. We conducted empirical experiments in the Yelp and Amazon datasets, proving that our model improves the quality of the explanations. The model outperforms baselines models by $$13\%$$ for NDCG@5, $$83\%$$ for HitRatio@5, $$13\%$$ for NDCG@10, and $$55\%$$ for HitRatio@10 in the Yelp dataset. For the Amazon dataset, the observed improvement was $$9\%$$ for NDCG@5, $$83\%$$ for HitRatio@5, $$9\%$$ for NDCG@10, and $$22\%$$ for HitRatio@10.
Recommendations for participatory design in low-resource settings

Participatory design is a widely recognised approach in Design for Development projects. It supports collaborative, community-based practices and it empowers users to take ownership. Despite the importance of participatory design in solving global challenges, the majority of research has focused its application in the Global North. Recently, some studies have explored participatory design methods in more low-resource settings. Still there is a gap between the existence of these methods, and designers being able to use them successfully because of the complex realities they face in low-resource settings. Existing knowledge is fragmented and there is a lack of best practice guidance for practitioners using participatory design in low-resource settings. We address this problem by reporting the experiences of Simprints, a technology company based in the UK, providing biometric identification solutions in the Global South. Our study reveals key recommendations for participatory design in low-resource settings, providing useful insights for practitioners and design researchers.
AN EXTRACTIVE BASED MULTI-DOCUMENT SUMMARIZATION USING WEIGHTED TF-IDF AND CENTROID BASED K-MEANS CLUSTERING (TF-IDF: CBC) FOR LARGE TEXT DATA

In research problems associated with text mining and classification, many factors have to be considered as on what basis the classification needs to be done. These factor variables are termed as features. The hardness of the visualization of training data is directly based on the number of features. Most of the times, the features are found to have high correlation and redundant. Dimensionality reduction helps to reduce the number of these features under the task by accumulating a group of principle variables. In the previous work an automated feature extraction technique using the weighted TF-IDF was proposed. Although the proposed method performed well, there was a drawback that some of the features generated are correlated to each other which resulted in high dimensionality resulting in more time complexity and memory usage. This paper proposes an Automatic text summarization method using the weighted TF-IDF model and K-means clustering for reducing the dimensionality of the extracted features. The various similarity measures are utilized in order to identify the similarity between the sentences of the document and then they are grouped in cluster on the basis of their term frequency and inverse document frequency (tf-idf) values of the words. The experiments were carried out on the student text data from the US educational data hub and the results were compared with other dimensionality reduction methods in terms of co-selection, content based, weight based and term significance parameters. The proposed method found to be efficient in terms of memory usage and time complexity.
Robust Secret Sharing for Image Encryption in Fractional Fourier Transform Domain

The protection of real-time data such as image, audio etc., plays a huge role in the digital era. In the literature, the encryption of these data by using the share generation matrix has been revealed in considerable depth. The thought of sharing matrix constitutes a vast potential to encrypt the image. The statistical investigation for S(k,n) matrix reveals the very much strength and protection of the image. However, recently, the majority of the works were accomplished in the spatial domain. Here, we expose the idea of fractional Fourier transform with two keys [α 1 , α 2 ]. For the demonstration, we consider the simplest case with two orders. To increase the keyspace, we can choose many blocks of the image. For different blocks, we may consider distinct orders. We also show how our encryption technique, along with the shares, can make the decryption process more robust. With the help of an example, we have performed and listed all the steps in our proposed technique. A slight or significant change in α 1 and α 2 produces a large discrepancy in the decrypted output. Comparative analysis with state-of-the-art techniques shows the feasibility of the proposed technique.
16.75 Dhur land on sale at Bhim Nagar,Dharma Bhakari Tole, Bharatpur M

Land For sale In Dharma Bhakari Tole, Bhimnagar, Ward No. 20, Bharatpur Metropolitan City, Chitwan, Pradesh 3 Nepal
PENENTUAN POLA SEKUENSIAL DATA TRANSAKSI PENJUALAN MENGGUNAKAN ALGORITMA SEQUENTIAL PATTREN DISCOVERY USING EQUIVALENT CLASSES (SPADE)

Transaction data is customer or customer data at a commercial or non-commercial institution that contains the consumer id, transaction time, and transaction items. From transaction data such as supermarket transactions, sequential patterns can be found to determine the interrelationship between items or items. Data if further processed or analyzed will produce information or knowledge that is important and valuable as a support in decision making. This study aims to determine the consumption patterns owned by customers and provide information that can be used in determining the layout of new store shelves. The SPADE algorithm is an algorithm for finding sequential patterns to break down the main problem into sub-problems that can be solved separately. Based on the result obtained it can be concluded that the application of the SPADE algorithm has the highest minimum support value that can still form maximal frequent sequences is 29%. The highest minimum support value of the SPADE algorithm is 0.2% with a maximum minimum confidence value of 81% and the number of rules formed is 1,118 Rule, but confidence is taken 60% up so that there are only 15 Rule. Whereas the Apriori algorithm has the highest minimum support value that can still form the maximum frequent sequences is 25%. The highest minimum support value of the apriori algorithm which can still form a rule is 0.3% with a maximum value of 88% minimum confidence and the number of rules formed as many as 494 Rule, but confidence is taken 60% up so that there are only 29 Rule.
Advantages of using computer graphics in the field of design

The use of computer graphics in the design area will be a great deal of convenience for the owners of this field. Graphics play a decisive role in design and advertising. This article discusses some of the uses of computer graphics in design.
Characterizing and detecting livestreaming chatbots

Livestreaming platforms enable content producers, or streamers, to broadcast creative content to a potentially large viewer base. Chatrooms form an integral part of such platforms, enabling viewers to interact both with the streamer, and amongst themselves. Streams with high engagement (many viewers and active chatters) are typically considered engaging, and often promoted to end users by means of recommendation algorithms, and exposed to better monetization opportunities via revenue share from platform advertising, viewer donations, and third-party sponsorships. Given such incentives, some streamers make use of fraudulent means to increase perceived engagement by simulating chatter via fake "chatbots" which can be purchased from shady online marketplaces. This inauthentic engagement can negatively influence recommendation, hurt streamer and viewer trust in the platform, and harm monetization for honest streamers. In this paper, we tackle the novel problem of automating detection of chatbots on livestreaming platforms. To this end, we first formalize the livestreaming chatbot detection problem and characterize differences between botted and genuine chatter behavior observed from a real-world livestreaming chatter dataset collected from Twitch.tv. We then propose SHERLOCK, which posits a two-stage approach of detecting chatbotted streams, and subsequently detecting the constituent chatbots. Finally, we demonstrate effectiveness on both real and synthetic data: to this end, we propose a novel strategy for collecting labeled, synthetic chatter dataset (typically unavailable) from such platforms, enabling evaluation of proposed detection approaches against chatbot behaviors with varying signatures. Our approach achieves .97 precision/recall on the real-world dataset, and .80+ F1 scores across most simulated attack settings.
Guidelines for an Effective Network Forensic System

Network forensics is a process of investigating cybercrimes to identify source of the attack. Paper discusses guidelines and recommendations to build an effective network forensic system. This helps the organizations to strategically establish their logging infrastructure so that the identified evidences are reliable, protected, and admissible in the court. The guideline also helps the forensic investigator while investigating a cyber attack.
READER'S REACTIONS ON THE INDONESIAN ONLINE NEWS TRANSLATION

Nowadays online news articles might be said to be the basis of communication as they are the main source which delivers most up-to-date news on social life, culture, politics, etc. to the audience. Since cultural diversity and identity are the issues of modern cultures and societies, online media texts need to be analyzed, especially from the perspective of translation. This paper discusses the reader’s reaction in relation to their responses to reading translated news to Indonesia. This study uses qualitative approach to describe the results of the analysis of reader’s reaction in reading translated news to Indonesia. From the analysis conducted by the researchers, it can be seen that reader’s reaction indicated that the text is translated
New approaches towards chemosensing

Sensing and information processing can be considered as two sides of a coin. They are strictly bound together at the formal and functional level. This contribution presents novel semiconducting materials - cadmium sulphidealizarin complexone hybrids, which at the same time perform chemosensing and post-processing of analytical signal according to reservoir computing paradigms and basic rules of fuzzy logic.
Pola Distribusi Keong Air Tawar Melanoides Tuberculata (Muller, 1774) Di Danau Lindu, Sigi, Sulawesi Tengah

This research was carried out in the littoral zone of Lake Lindu, Sigi Regency, Central Sulawesi with the aim to determine the distribution pattern of freshwater snail Melanoides tuberculata (Muller, 1774) whose area was divided into 5 sample sampling stations. Collecting samples using the transect line method drawn from the edge of the lake to the middle part of the 17 m long lake. Lines at each station are placed with a 19x15 cm reducing plot whose size is in accordance with the collection tool. The equipment for which the plot is placed is spaced 1.5 m, with the transect line collected three and having a distance of 2 m. Melanoides tuberculata (Muller, 1774) which is located in Lake Lindu, Sigi, Central Sulawesi, is distributed randomly and in groups.
Global Change Information System Provenance Evaluator

Tool to evaluate provenance for Climate metadata.This presentation was given at the Earth Science Information Partners (ESIP) Winter Meeting held in Bethesda, MD in January 2020.
The Status and Perception of the 5th Rank Deputy Director Promotion System

교육행정 환경이 변화함에 따라 공무원의 승진제도 방식도 바뀌었다. 교육행정공무원 사무관 승진제도의 경우도 시험제도에서 역량평가를 포함한 심사승진제도로 전환되고 있다. 이 연구는 문헌조사와 설문조사를 통해 얻은 자료를 종합하여, 지방공무원 사무관 심사제도의 변화 과정과 주요 요소를 파악하고 제도 운영에 대한 인식을 분석하였다. 교육청별 심사승진제도 운영 계획을 분석하여 제도변화와 구성요소를 파악하였다. 설문조사는 전국 17개 시도교육청 지방교육행정공무원을 대상으로 5급 사무관 심사승진제도에 대한 인식 조사를 실시하고, 그 중 3,485명이 응답한 자료를 분석하였다. 승진제도에 대한 전반적인 인식(문제점과 적합성)과 집단 간(지역, 근무기관, 연령, 경력) 인식 차이를 분석하였다. 연구의 결과는 교육청의 인재상과 비전에 따른 공무원의 필수 역량 구축 등을 통한 역량평가의 수용성 개선, 생애주기별 역량 모델링 및 교육훈련, 역량평가에 대한 체계적인 학습 기회 제공 등 다양한 노력의 필요성을 제시하였다.The civil officers’ promotion system has changed as educational administration environment changed. The promotion system for deputy directors(5th level) of local education agencies changed from requiring promotion exams to having a promotion system with capabilities approach. This research examined the process and main factors of the promotion system development by synthesizing the related literature as well as conducing a survey on the perceptions of the promotion system management. It analyzed the deputy director promotion plans of 17 local agencies in order to analyze the promotion system’s current status and components. A survey was conducted targeting 67,132 local education agency officers on their perceptions of the promotion system and procedures. The result, based on the 3,485 participants’ responses, was analyzed for general perceptions (problems and appropriateness) and groups differences (based on regions, organizations, age, and career experiences) in terms of their perceptions. The results indicated local education agencies need to develop their own concept of talent and core competencies which accord with their organizational vision and purpose in order to increase the officers’ receptivity. They also may create life cycle of capability modeling and educational programs in order to provide systematic learning opportunities.
An Automatic Face Hiding System based on the Deep Learning Technology

As social network service platforms grow and one-person media market expands, people upload their own photos and/or videos through multiple open platforms. However, it can be illegal to upload the digital contents containing the faces of others on the public sites without their permission. Therefore, many people are spending much time and effort in editing such digital contents so that the faces of others should not be exposed to the public. In this paper, we propose an automatic face hiding system called ‘autoblur’, which detects all the unregistered faces and mosaic them automatically. The system has been implemented using the GitHub MIT open-source ‘Face Recognition’ which is based on deep learning technology. In this system, two dozens of face images of the user are taken from different angles to register his/her own face. Once the face of the user is learned and registered, the system detects all the other faces for the given photo or video and then blurs them out. Our experiments show that it produces quick and correct results for the sample photos.
Context-Aware Adaptive M-Learning: Implicit Indicators of Learning Performance, Perceived Usefulness, and Willingness to Use

Mobile Learning tools suffer from low engagement, and lack of content personalization and adaptation. This imperial study investigates the possible effect on learning performance; perceive usefulness, and willingness to use of undergraduate students when they use an adaptive m-learning tool that personalizes learning material format to the user and device contexts. Assessment was done using implicit measure (dwell time), explicit measure (satisfaction questionnaire), and learning assessment (post test). An experiment was conducted with 31 Egyptian students using the traditional school e-learning system, while half of them were asked to use the context-aware adaptive learning tool. Results suggest that context-based adaptive mobile-learning tools can significantly improved perceived usefulness of learning materials, as well as learners’ willing to use the tools. Practical and theoretical implications were suggested.
Operation and Development Plan of the Education Program s for Welfare for the Aged in the Buddhist Community

Operation and Development Plan of the Education Programs for Welfare for the Aged in the Buddhist Community - welfare for the aged in the Buddhist community;education program for the aged;convenience sampling;t-test
Predicting Target Feature Configuration of Non-stationary Objects for Grasping with Image-Based Visual Servoing

This paper considers the final approach phase of visual-closed-loop grasping where the RGB-D camera is no longer able to provide valid depth information. Many current robotic grasping controllers are not closed-loop and therefore fail for moving objects. Closed-loop grasp controllers based on RGB-D imagery can track a moving object, but fail when the sensor's minimum object distance is violated just before grasping. To overcome this we propose the use of image-based visual servoing (IBVS) to guide the robot to the object-relative grasp pose using camera RGB information. IBVS robustly moves the camera to a goal pose defined implicitly in terms of an image-plane feature configuration. In this work, the goal image feature coordinates are predicted from RGB-D data to enable RGB-only tracking once depth data becomes unavailable -- this enables more reliable grasping of previously unseen moving objects. Experimental results are provided.
Rethinking Class Relations: Absolute-relative Few-shot Learning.

The majority of existing few-shot learning describe image relations with {0,1} binary labels. However, such binary relations are insufficient to teach the network complicated real-world relations, due to the lack of decision smoothness. Furthermore, current few-shot learning models capture only the similarity via relation labels, but they are not exposed to class concepts associated with objects, which is likely detrimental to the classification performance due to underutilization of the available class labels. To paraphrase, while children learn the concept of tiger from a few of examples with ease, and while they learn from comparisons of tiger to other animals, they are also taught the actual concept names. Thus, we hypothesize that in fact both similarity and class concept learning must be occurring simultaneously. With these observations at hand, we study the fundamental problem of simplistic class modeling in current few-shot learning, we rethink the relations between class concepts, and propose a novel absolute-relative learning paradigm to fully take advantage of label information to refine the image representations and correct the relation understanding. Our proposed absolute-relative learning paradigm improves the performance of several the state-of-the-art models on publicly available datasets.
Character counting in message bodies

This document describes how to correctly count characters in message bodies. This is required when referencing a position in the body.