Reviewerly/exHarmony huggingface dataset:
MEDIA KOMIK PADA MATERI CERITA DONGENG UNTUK KETERAMPILAN MEMBACA SISWA KELAS III

The process of communication in literature appreciation learning willbe more effective if supported by the media. One of the development media for students is comics. The objection of this study is to develop and produce a fairy tale comic and to know the results of comic product so it proper to be used as a medium of literature appreciationlearning. This type of research is Research and Development (R & D), adapt from the Warsito's development model. Determination of proper level of comic media is based on validation test of experts and student test through questionnaire and questioning test. The result of the teaching materials done by material experts showed the properity of 85% and the media expert of 87,5%. Both experts indicate that the media is valid and ready to be implemented. The results of the test to the students show the properity of 92,50% and stated as valid. Student learning outcomes after showing the use of teaching materials has a value of 85,22 and can support the properity of teaching materials after tested to students without the need to be revised . Keywords: comic media ,fairy tale, and reading skills. The process of communication in literature appreciation learning willbe more effective if supported by the media. One of the development media for students is comics. The objection of this study is to develop and produce a fairy tale comic and to know the results of comic product so it proper to be used as a medium of literature appreciationlearning. This type of research is Research and Development (R & D), adapt from the Warsito's development model. Determination of proper level of comic media is based on validation test of experts and student test through questionnaire and questioning test. The result of the teaching materials done by material experts showed the properity of 85% and the media expert of 87,5%. Both experts indicate that the media is valid and ready to be implemented. The results of the test to the students show the properity of 92,50% and stated as valid. Student learning outcomes after showing the use of teaching materials has a value of 85,22 and can support the properity of teaching materials after tested to students without the need to be revised .
PENERAPAN ALGORITMA NAÏVE BAYES PADA PENILAIAN KINERJA PEMERINTAH DESA DALAM PENGELOLAAN DANA DESA

The purpose of this study is to classify which village government is good and bad in managing village funds in Tolitoli regency to be a reference for local government to control the performance of village government in managing village funds. The method used in this research is naïve bayes classification based on PHP programming computerization is expected to help assess village government performance in village fund management so that it is faster and more accurate in classifying village government performance. Based on the analysis of calculations that have been done, the application of the naïve Bayes method of classification in village government performance appraisal has been done against 2 (two) village with criteria used: budget amount, government budget, development budget, empowerment budget, guidance field budget, unexpected budget and remaining budget so that the final result of village government performance assessment in management village funds namely villages with the name Teluk Jaya village are declared good because they have a good likelihood value of 3.57921E ^ (- 58)&gt; bad likelihood value of 3.39635E ^ (- 60) while the village with the name Salumpaga village is declared bad because it has a good likelihood value of 2.421E ^ (-139) &lt;bad likelihood value 3.20355E ^ (- 83)
Intelligence to Artificial Creativity

Assuming creativity is not the same as intelligence and we know that intelligence can be generated by computing (AI), can we generate creativity with computing and call it Artificial Creativity (AC)?What is the connection between AI and AC?This article is attempting to answer these questions and to is propose a system to generate creativity.Human intelligence has been researched for years by psychologists and biologists.Then, based on working mechanism of human brain, computer scientists developed neural network algorithm and other machine learning algorithms to generate intelligence.Creativity is a unique characteristic of living creatures.Recently more work has been on generating creativity through combining creativity theories and artificial intelligence algorithms.
Promoting inclusive school culture through leadership - Case study in Romanian schools

One of the greatest challenges for 21st century school is to rethinking school culture for human diversity. Among the factors contributing to the ethos of the inclusive school, most specialists emphasise the importance of leadership, of the managerial team that both through conception and through its own behaviour promotes cooperation, human diversity, tolerance, open mind-set and help offering. The purpose of this research was to identify the factors of the leadership, which are involved in promoting a culture of inclusion. The research in question was embedded in a constructivist - interpretivist paradigm. The study included the analysis of school culture and interviews with school managers and teachers. The institutional analysis of schools addressed the physical/material aspects of the school and the social dimension (of human resources). One of the main conclusions of the research is that the leader and his team have a decisive role to play in promoting values of diversity. Keywords: Inclusive school, leadership, school culture, inclusive education.
Implementasi Fuzzy Logic dalam Pembuatan Kontrol Navigasi Mobile Robot

Artificial Intelligence is defined as intelligence exhibited by an artificial entity. Intelligence was created and put into a machine that can do the job as do humans. Some kinds of fields that use artificial intelligence include fuzzy logic, expert systems, computer games, neural networks and robotics. The purpose of research is Applying the theory of fuzzy logic in the manufacture of robot control and create teaching materials for the subjects of physics, especially on the concept of a microcontroller or other allied subjects of the research conducted. In this study apply fuzzy logic on the movement of wheeled robot control where in the controlling controlled via a smartphone which is then processed by a microcontroller Arduino that has grown the program by applying the method of fuzzy logic rule. Then by the microcontroller is used as a command to drive the DC motor as a navigation system that course in accordance with the rules created. Every step of the control system provides a signal in the form of a large steering angle (θ) which guides the robot to the target of a starting position. The results showed that the application of a system of fuzzy logic can be implemented in a navigation system for a robot control and the results of testing the robot's movement, then obtained some data points specified coordinates with the coordinates of the control robot acquired a 96% accuracy rate.
Assessing and managing business risks for artificial intelligence based business process automation

Purpose – the number of projects and amount of investment into Artificial Intelligence (AI) based business process au-tomation is increasing. To utilize the power of AI, business organizations shall achieve a certain level of digital maturity that enables of handling the risks arising from AI. AI brings new risk factors to their life that has to be reduced to an ac-ceptable level. If risk mitigation procedures are not in place, then AI might cause a greater headache than a market ad-vantage resulting in expensive implementation with no business benefit. Research methodology – the objective is to analyze what risk factors can AI bring with itself to the life of corporations by analyzing general IT risk assessment processes and the stages of AI development. Findings – We observed that current IT risk assessment methodologies don’t detail possible risk scenarios regarding in-telligent applications and don’t extend their threat catalogs to help organizations consider threats related to AI. Research limitations – the research work details possible risks for general AI development that might differ across in-dustries, business cases, specific algorithms etc. Practical implications – the research contributes to organizations to assess possible risks arising from the use of AI. Originality/Value – since AI based automation is the result of recent research work, analyzing risk management aspects of its use can be considered as a new field for further research.
Pembuatan Alat Tracking Artefak pada Museum Menggunakan Modul GSM Berbasis Mikrokontroler dan Short Massage System.

The museum is in charge of providing facilities for these research activities for anyone, in addition to the museum in charge of carrying out the research activities themselves and disseminating the results of the research to the development of general science. However, in reality there is still the fact that cases of missing historical objects have a value of up to millions of rupiah. This study aims to create a tracking system that is able to track artifacts in the museum using the BTS system. Therefore, the author will make a tool that can track the position of the artifact with the Tracking method. The design of the study that the author will adopt from the above problem is "Making an Artifact Tracking Tool at the Museum Using a Microcontroller and Short Massage System-Based GSM Module".
A Novel Regularizer for Temporally Stable Learning with an Application to Twitter Topic Classification

Supervised topic classifiers for Twitter and other media sources are important in a variety of long-term topic tracking tasks. Unfortunately, over long periods of time, features that are predictive during the training period may prove ephemeral and fail to generalize to prediction at future times. For example, if we trained a classifier to identify tweets concerning the topic of “Celebrity Death”, individual celebrity names and terms associated with these celebrities such as “Nelson Mandela” or “South Africa” would prove to be temporally unstable since they would not generalize over long periods of time; in contrast, terms like “RIP” (rest in peace) would prove to be temporally stable predictors of this topic over long periods of time. In this paper, we aim to design supervised learning methods for Twitter topic classifiers that are capable of automatically downweighting temporally unstable features to improve future generalization. To do this, we first begin with an oracular approach that chooses temporally stable features based on knowledge of both train and test data labels. We then search for feature metrics evaluated on only the training data that are capable of recovering the temporally stable features identified by our oracular definition. We next embed the top-performing metric as a temporal stability regularizer in logistic regression with the important property that the overall training objective retains convexity, hence enabling a globally optimal solution. Finally, we train our topic classifiers on 6 Twitter topics over roughly one year of data and evaluate on the following year of data, showing that logistic regression with our temporal stability regularizer generally outperforms logistic regression without such regularization across the full precision-recall continuum. Overall, these results establish a novel regularizer for training long-term temporally stable topic classifiers for Twitter and beyond.
EFFECT OF GROUP COUNSELLING IN REDUCING EXAMINATION MALPRACTICE TENDENCY AMONG SECONDARY SCHOOL STUDENTS IN IMO STATE, NIGERIA.

This study investigated the effect of group counselling in reducing examination malpractice tendency among secondary school students in Imo state, Nigeria.Two research questions were answered and two null hypotheses tested at 0.05 level of significance guided the study.The pre-test post-test control group quasi-experimental design was adopted for the study.The target population of the study was 2326 (1096male and 1230female) SS2 students that had examination malpractice tendency in all public secondary schools in Ngor-Opkala Local Government Area of Imo State as identified through their pre-test scores.The sample of this study consisted of eighty-four (84) students from two co-educational secondary schools selected through purposive sampling technique.Students Examination Malpractice Tendency Questionnaire (SEMTQ) was the instrument used for data collection.Data collected from the study were analysed using mean and ANCOVA.Results obtained from the study revealed that group counselling was significantly effective in reducing examination malpractice tendency among the participants.The results also indicated that group counselling was more effective in reducing examination malpractice tendency among female students than their male counterpart.The researchers recommended among others, that Guidance Counsellors should adopt group counselling as an effective intervention strategy in fighting the menace of examination malpractice in secondary schools, and that the government should provide school counsellors with proper in-service training programmes to enable them acquire adequate group counselling kills in other to discharge their duties more effectively.Copy Right, IJAR,
Images Steganography Approach Supporting Chaotic Map Technique for the Security of Online Transfer

One of the most important issue in this domain is the security concern of the transfer data.The online transfer data may access illegally through attack the communication gate between the servers and the users.The main aim of this study is to enhance the security level of the online data transfer using two integrated methods; images steganography to hide the transfer data in image media, and chaotic map to remap the original format of the transfer data.The integration between these two methods is effective to secure the data in several format such as text, audio, and images.The proposed algorithm is the prototyped using JAVA programming, 20 images and text messages of usable sizes (plain data) were tested on the dataset using the developed programming.The simulation using local server is accomplished to analyze the security performance based on two factors; the plain data size and the data transfer distance.Many attacking attempts are performed on the simulation test using known attacking techniques such as observe the stego images quality.The experiment results show that about 85% of the attacking attempts fail to catch the stego images.95% of the attacks fail in remap meaningful parts of the chaotic data.The results indicate the very good level of the propose security methods to secure the online transfer data.The contribution of this study is the effective integration between the steganography and chaotic map approaches to assure high security level of online data transfer.
Dynamic Access Control of Encrypted Data in Cloud Computing Environment

The confidentiality of data is a difficult problem in a cloud computing environment.The DAC technique (Dynamic Access Control) based on encrypted data is an important way to solve this problem.In the current access control techniques based on encrypted data, the highsecurity requirement of data and frequent policy updates lead to the high cost of owner right update which seriously restricts the flexibility of access control.A DCA method based on CACDP encrypted data is proposed.The selective encryption model is built.In the model, a key derivation diagram is generated to distribute the key.In the case of ensuring the confidentiality of cloud computing access control, the key is less in the system.The proposed CACDP scheme includes the key management mechanism based on the binary Trie tree.Based on this, the ELGamal-based proxy re-encryption mechanism and double layer encryption strategy are used to transfer the partial spending of the key and data update to the cloud to ease the DO authority management burden and increase the efficiency of DO.Then, the DCA method of encrypted data in cloud computing environment is researched.Experimental results show that our proposed method can effectively improve the flexibility of encrypted data access control.
BSP-Based Strongly Connected Component Algorithm in Joint Cloud Computing

There is a trend that many applications are deployed among multiple public or private clouds. Due to the data asset protection and network communication cost, it is not feasible to gather all the data from different clouds to a single cloud. Inter-cloud graph data mining is becoming a new challenge. The strongly connected component algorithm is a basic graph algorithm that plays an important role in many important areas such as social network analysis, web search and even biomedical areas. However, only few distributed processing frameworks provide this algorithm, which is currently unavailable in joint cloud computing frameworks. To this end, this paper proposes a BSP (Bulk Synchronous Parallel) service over joint cloud computing and a BSP-based strongly connected component algorithm which can be easily realized on any distributed platform as long as it provides BSP service. Many graph algorithms can be easily developed in the cross-cloud computation environment by using this BSP service. Moreover, this BSP-based strongly connected component algorithm not only fills the gaps in the domain of distributed graph processing frameworks, but also extends its scalability to the level of cross-cloud computing by using the BSP service.
The Heart of the Cycle: How Can Metadata 2020 Improve Serials Metadata for Scholarly Communications and Research?

Metadata 2020 is a collaboration that aims to facilitate the work of all parties in scholarly communications to consistently improve metadata to enhance discoverability, facilitate new services, and create efficiencies with the ultimate goal of accelerating scholarly discovery. Almost a year after launch, Metadata 2020 has gained considerable ground in gathering information from multiple community groups. These community groups focus on metadata challenges and opportunities – finding ways to enhance metadata and finding new solutions. Now that Metadata 2020 has received feedback and stories from all sectors of the research and scholarly communication cycle, the group is targeting crucial points where metadata needs to work across all groups in order for a mature metadata model to become a useful reality. Juliane Schneider, Lead Data Curator at Harvard Catalyst, Clinical and Translational Science Center, provided an update on the Metadata 2020 project and led a roundtable discussion format where audience participants talked about challenges and offered recommendations for improvement to the Metadata 2020 project.
An Application of Support States from Speech Emotions in Consensus Building

People propose various ideas and opinions in public organization and conferences. In order to reach an agreement among the participants, discussion is essential. During a discussion, difference in the processing of forming an agreement will affect the conclusion. So major statements should be analyzed for building a consensus. However, in the process of forming consensus, a proper understanding of the statements acting as the basis among various discussants, is necessary. In this study, the relationship between the consciousness of the conversation and the state of support is discovered through the speech emotional perception of the conversation. Also, the state of support—supportive, negative and unknown, are inferred. In addition to listening experiments on the emotions and support states, the application of the analysis of the speech emotion recognition process is discussed on the basis of verifying the emotion of speech and the dependence of the support state. The accuracy of the average objective recognition rate can be increased to 75% in the formation of the consensus during speech conversation.
Ambiguity in Writing Story Text of Experience of Grade IV Students of the Champion School Denpasar-Bali

This research aims is to describe the lexical ambiguity, gramatical ambiguity and what factors that influence the writing skills of primary IV students of The Champion Denpasar-Bali. The method of this research is a qualitative approach. The data is obtained by the written story text from the student’s experience. The results showed that the type of ambiguity contained is consisted of lexical ambiguity and grammatical ambiguity. Lexical ambiguity is divided into two parts, namely polysemy and homonymy, while grammatical ambiguity is divided into four parts including: grammatical ambiguity caused by grammatical word formation events, grammatical ambiguity in similar phrases, grammatical vocabulary because of sentence context or lack of grammatical context and ambiguity because of the inaccuracy of the grammatical structure. The factors that influence the writing skills of primary IV students of The Champion Denpasar - Bali. First, the use of a foreign language (English) as a daily communication language of students in schools that intervene in Indonesian, which causes the lack of vocabulary that students have so that students find it difficult to write the text of the Indonesian experience stories correctly. Second, family, the assumption that the task of giving lessons to students is only the duty of the teacher to make the family environment rarely train their children to write at home. Third is, intelligence which is related to students' ability to absorb the material taught to them. The fourth is interest related to encouragement, motives, and emotional responses.
Automatic morphological analysis on the material of Russian social media texts

Automatic morphological analysis is one of the fundamental and significant tasks of NLP (Natural Language Processing). Due to special features of Internet texts, as they can be both normative texts (news, fiction, nonfiction) and less formal texts (such as blogs and texts from social networks), the morphological tagging has become non-trivial and an actual task. In this paper we describe our experiments in tagging of Internet texts presenting our approach based on deep learning. The new social media test set was created, that allows to compare our system with state-of-the-art open source analyzers on the social media texts material.
Emotional Analysis of Public Opinions in Colleges and Universities:Based on Naive Bayesian Classification Method

In this paper, we use the emotion dictionary to process and express the public opinion texts of colleges and universities. We construct the public opinion texts of colleges and universities sentiment emotional classifier based on Naive Bayesian theory, and then judge the emotional tendency of public opinion texts of colleges and universities. Experiments show that the method has the characteristics of fast classification and high accuracy, and is suitable for the public opinion system of colleges and universities. The method facilitates the university management's interpretation and mastery of the public opinions related to the school in the network, promotes and guides the positive energy in the communication network, corrects the vulgar and biased content, prevents the forwarding and dissemination of inappropriate speech, and establishes a positive campus environment among the teachers and students of the whole school.