Reviewerly/exHarmony huggingface dataset:
Cyber Crime: Child as a Perpetrator, Child as a Victim

The web of cyber world is moving rapidly like a galloping horse with the introduction of novel technologies every second, penetrating its roots amongst masses and particularly in teenagers. At times, this penetration takes us to the dark side of this cyber web which entraps people into a pool of cyber-crimes, often affected by socio-economic as well as psychological factors. One of the most affected classes belongs to the group of children who often tend to fall on both sides of the curtain- at one side remains a child in the face of a perpetrator, on the other side is a child who becomes the victim. Both facets can be seen within offences like cheating by personation (often referred as cyber fraud), violation of privacy, publication or transmission of obscene materials, child pornography, cyber stalking, cyber bullying in educational institutions, etc. punishable under various legislations. Complexity in this field arises because of the fragile age of the perpetrator and need for diverse protection mechanism for victims. This has increased our burden, as apart from legislations and judicial decisions, there has to be sufficient mechanism for prevention and increasing awareness at a very early age so as to sensitize them of severity of these offences.
Multi-pose Face Recognition Based on TP-GAN

In recent years, a large number of smart products have been widely used and become an indispensable part of people's life. At the same time, the identity authentication by means of face recognition has become familiar. Although the traditional face recognition system can achieve a high recognition rate in most environments, as the external factors change, such as lighting, blocking, and posing, the performance of the system degrades. In this paper, we propose a new deep network method based on TP-GAN and investigate the behavior of the proposed system when there is a change in the angle pose of the face. In the generation part, we propose a deeper convolutional neural network to extract the pose-invariant face features and synthesize the virtual pose, simultaneously. The deeper network is divided into multiple overlapping local networks, each of which was trained to synthesize a small pose change; the joint training local network synthesizes the front face from the non-positive pose in a progressive manner. By stacking multiple local networks, we can extract more robust pose-invariant features and generate multiple virtual poses in front of the synthetic front. Face recognition with different postures is achieved by combining pose-invariant features and virtual postures. Experimental results demonstrate that our method has achieved superb results in pose-invariant face recognition.
The Importance of an Articulated Personal Theory of Professional Development

People become teachers for a variety of reasons but mainly because teaching is viewed as a career, and a profession which is personally satisfying. Many have found the pace and the nature of change too much, they have become disillusioned, found new careers or have taken early retirement. A professional’s life is overridden by the need to make quick decisions intuitively in practice. Professional decisions made in the practical busy milieu of practice are influenced by personal factors more than we would like to admit. Professionals dealing with personal problems may be less effective, or respond differently in their professional context because of their own paramount personal needs. Jo is a young teacher who has readily accepted the professional theories passed on to her in her recent training. However, the personal account is much more revealing and real and details more explicitly the thoughts ideas and emotions embodied in the transformation of the situation.
Media Pembelajaran Berbasis Mobile Tajwid Pada Lembaga Bimbingan Masuk Gontor dengan Metode Demonstrasi

The Darussalam Gontor Islamic Boarding School graduates established a tutoring institution for prospective students who wish to enter Gontor in their respective regions. The institution is called the Gontor Entrance Guidance Institute. Prospective students who want to enter Gontor do not all pass the oral exam, one of the factors is the difficulty in tajwid lessons. At the Gontor entrance guidance institution, tajwid learning currently uses a limited number of printed books, making it difficult for those who want to learn it. Among them are Gontor entrance guidance agencies, branch cottages and alumni lodges. For that we need other learning media that can solve these problems, one of which is the mobile smartphone media. The purpose of this research is to make it easier for prospective students who want to learn the Gontor version of Tajwid. The implementation of this application uses a waterfall approach and applies a demonstration method. Testing the Tajwid 2 application is done in 6 ways. With the blackbox method, it shows the application is running smoothly. The use of hardware with six smartphones of various brands and screen sizes indicates the application is running well. A learning media expert gave an average rating of 94%. Tajwid teachers about learning materials by distributing questionnaires to 6 respondents who gave good feedback with a value of 87%. Prospective students about learning materials by distributing questionnaires to 15 respondents who gave good feedback with a value of 88.5%. General Google Playstore users scored 4.9 out of 5 (highest total rating) totaling 112 reviews (https://play.google.com/store/apps/details?id=com.konu.tajwid). The results of the questionnaire show that this application is very helpful, it can be an effective learning tool, but the application is only helpful, it cannot replace the role of the teacher and the interaction between teachers and real students.
ON THE ISSUE OF CONSTRUCTING AN AGENT MODEL OF TECHNICAL SUPPORT FOR A SPECIAL-PURPOSE COMMUNICATION SYSTEM

To study complex systems, which include communication systems, both analytical and simulation modeling are used. Recently, simulation modeling has become widespread, which is becoming one of the most powerful analysis and synthesis tools used by specialists in the study and design of complex systems. However, traditional simulation modeling usually has a number of difficulties that arise from trying to represent processes in real systems in greater detail. At the same time, there is currently a tendency in modeling to improve the accuracy and adequacy of the created models. The answer to this requirement is the emergence of agent-based simulation. The aim of the article is to show the possibility and feasibility of using this rather new direction of modeling for the study of a special purpose communication system (SPCS). SPCS as an object of modeling is a rather complex system characterized by many parameters, and an attempt to improve some characteristics can and often leads to deterioration of other characteristics of this system. The work discusses the construction of a model of technical support for the SPCS based on the agent-based approach. The proposed approach to the construction of such a model, in contrast to the well-known approaches to the construction of simulation models, allows you to study the system "bottom-up". The developed model provides an opportunity to assess the quality of functioning of the SPCS taking into account the performance indicators of its elements, presented in the form of individual interacting agents, as well as a variety of random factors. The article deals with the application of the agent-based approach to modeling the technical support of the SPCS. The formulations of the problems of analysis and synthesis of SPCS are formulated. The composition of the components and the structure of the agent model are discussed. The aspects of the implementation of the agent-based model in the AnyLogic tool environment and the possibility of conducting experiments using this model are demonstrated. It is shown that the agent-based model of the technical support of the SPCS is universal for systems that implement similar functions. It allows simulating, setting and changing many network characteristics in a wide range, as well as determining the coefficient of technical availability of this system.
A Classroom Listening Teaching Practice

The aims of this study were (1) to find out how the teachers of SMP Negeri 5 Banda Aceh prepare teaching and learning process on listening instruction. (2) to find out how the teachers of SMP Negeri 5 Banda Aceh conduct teaching and learning process of listening instruction. And (3) to find out how the teachers of SMP Negeri 5 Banda Aceh evaluate the result on teaching and learning process of listening instruction. The subject of this study was the teachers of SMP Negeri 5 Banda Aceh who teach English. The number subjects are two teachers. The method used was descriptive analysis. Required data were collected through observation and interview. The data were analyzed by narration procedure and discussing. The result shows that before teaching and learning process was conducted, the teachers prepared teaching administrative requirement such as lesson plan based on the syllabus. The schedule and time allocation were suited with annual program, terms, and duration of effective week. During the teaching process, the teachers used four techniques of teaching listening skills namely: listening to dialogue, listening to a song, listening to the story that the teacher read and discussed. In teaching and listening process of listening instruction, the teacher applied Learner-Centered Approach. Next, the teachers evaluated students in two kinds of tests namely ongoing process test and final test.
EFFECT OF USING ANAGRAM GAME IN IMPROVING STUDENTS’ VOCABULARY MASTERY AT THE ELEVENTH GRADE OF SMA NEGERI 4 BAUBAU

This study is aimed to find out the effect of using anagram game to improve students’ vocabulary mastery at the eleventh grade of SMA Negeri 4 Baubau. The research used quantitative approach and applied quasi experimental design. The population of this research was the eleventh grade students of SMA Negeri 4 Baubau. The number of population was 185 students. The sample was class XI MIA 5 as the experimental class and class XI MIA 1 as the control class in which the total of both classes was 60 students. The sampling technique in this research was cluster random sampling. The instrument of the research was tests in the form of multiple choices. The data obtained were analized by using SPSS 22.0 program. The result of the research shows that the mean score of experimental class in pre-test is 55.17 and the mean score in pre-test of control class is 47.50, while the mean score of experimental class in post-test is 72.83 and the mean score of control class in post-test is 56.83. The data shows that the mean score of experimental class is higher than the control class. The result of hypotesis testing is found that tcount 57.44 &gt; ttable 2.021 and the probability value (sig.(2-tailed)) 0.000 &lt; 0.05 which means that Ha is accepted and H0 is rejected. Thus, there is a significant effect of using anagram game in improving students’ vocabulary mastery at eleventh grade of SMA Negeri 4 Baubau.
Hybrid convolutional neural networks-support vector machine classifier with dropout for Javanese character recognition

This research paper explores the hybrid models for Javanese character recognition using 15600 characters gathered from digital and handwritten sources. The hybrid model combines the merit of deep learning using convolutional neural networks (CNN) to involve feature extraction and a machine learning classifier using support vector machine (SVM). The dropout layer also manages overfitting problems and enhances training accuracy. For evaluation purposes, we also compared CNN models with three different architectures with multilayer perceptron (MLP) models with one and two hidden layer(s). In this research, we evaluated three variants of CNN architectures and the hybrid CNN-SVM models on both the accuracy of classification and training time. The experimental outcomes showed that the classification performances of all CNN models outperform the classification performances of both MLP models. The highest testing accuracy for basic CNN is 94.2% when using model 3 CNN. The increment of hidden layers to the MLP model just slightly enhances the accuracy. Furthermore, the hybrid model gained the highest accuracy result of 98.35% for classifying the testing data when combining model 3 CNN with the SVM classifier. We get that the hybrid CNN-SVM model can enhance the accuracy results in the Javanese characters recognition.
we matched on Tinder and they seemed alright so we met in a bar and after a couple of hours I said are you God?

we matched on Tinder and they seemed alright so we met in a bar and after a couple of hours I said are you God? Paula Harris (bio) and they said hahaha, no, why would you say that? and I said you seem a bit translucent and your feet don't touch the ground—nice shoes by the way—so you're either God or a ghost and they looked awkward and said haha, God! whoever heard of such a thing?! and I said every time you swirl your drink there's a tsunami warning and every time you move in your chair there's an earthquake and when you scratch your scalp there's thunder and also the white robes and the angels trying to blend into the crowd is all a bit OTT and they squirmed a little and the building rocked and I said see! and they sighed in concession and I said I bet you're a tap and gap kinda god and they looked offended and said no way, I'm looking for long term, baby doll and I said what would Mary have to say about that and they said haha and I said do you even take human form very often? at least Zeus is used to fucking humans; you seem super awkward in this body and I suspect you might be bad in bed and do you even go down? and God said ahh. ha. [End Page 320] Paula Harris Paula Harris lives in Aotearoa/New Zealand, where she writes and sleeps in a lot, because that's what depression makes you do. She won the 2018 Janet B. McCabe Poetry Prize and the 2017 Lilian Ida Smith Award. Her writing has been published in various journals, including The Sun, Gulf Coast, Passages North, New Ohio Review and Aotearotica. She is extremely fond of dark chocolate, shoes and hoarding fabric. website: www.paulaharris.co.nz | Twitter: @paulaoffkilter | Instagram: @paulaharris_ poet | Facebook: @paulaharrispoet. Copyright © 2022-2023 Pleiades and Pleiades Press
REMOTE LEARNING IN THE TIME OF POST - COVID-19: INTERACTIVE PDPR SYSTEM FOR PRIMARY SCHOOL VIA VISUAL BASIC PROGRAMMING

Abstract between 150-250 words only. It has been more than a year since the implementation of movement order control (MCO) on March 18, 2020, in Malaysia. In March 2020, PdPR 1.0 was initially implemented. Through this e-learning distance education, technology plays a key role in designing online learning environments that meaningfully engage students. To learn online via these platforms, a student would need to have access to the platform and guidance from parents. Some parents have faced various challenges since the introduction of PdPR. they cannot afford to provide sufficient gadgets for their children, and most of the students faced internet problem and did not have the knowledge to use technology and find it difficult to participate in online learning. Teachers are also impacted by this problem when students learn online.The system development of this project is using a waterfall model that consists of requirements, design, and development phases. Hence, to create an application that would aid teachers and students in the process of online teaching and learning, this study introduces the application system using Visual Basic programming 2010 known as "VB," and it is designed to make software creation quick and simple while still being strong enough to construct complex programs. This study only focuses on the problem faced by primary school students (Year 4, 5, and 6) and on the curriculum's emphasis on four core subjects: Bahasa Melayu, English, Mathematics, and Sciences. Hopefully, using this application will give some excitement to teachers and students for their online teaching and learning.
Complementary Reward Function Based Learning Enhancement for Deep Reinforcement Learning

Several complex sequential decision-making problems have been successfully implemented using reinforcement learning (RL) for continuous optimal control. However, the sample efficiency of data collection process during the learning phase is still not well addressed. The convergence rate to the optimal policy as well as the time of the learning process are strongly influenced by the efficiency of the data collected by the agent during the learning phase. This paper proposes a method to generate efficient sample data which allows the agent to collect high reward trajectories more frequently, decreasing the learning phase time. The proposed method consists of Complementary reward (CR) function augmented to the traditional reward function. The CR tends to infinity when the control input leads to the system performance that meets the given requirements very accurately. Consequently, the control policy which maximizes the reward function can render the system to optimal performance. The main contributions of this study include the following aspects: (1) a new proposed Complementary reward that is augmented to the reward function which improves performance of the reinforcement learning based controller in terms of system requirements; (2) speed-up of training phase via generation of more efficient data resulting in a better learned policy.
ForGen: Autoregressive Generation of Sparse Graphs with Preferential Forest

Graph is a natural way to model interactions between objects, such as in the field of biology and social science. Over the past decades, modeling and generating graphs have been a popular research topic, largely inspired by observed properties of real-world graphs. Since traditional approaches relying on hand-crafted mechanisms are only capable of capturing some specific graph properties, recent focus has been shifted to deep neural methods. However, the task is still challenging in terms of efficiency. To address this issue, we observe that the connectivity (i.e., degree) of nodes follows scale-free distribution for most real-world graphs, which can be utilized to accelerate the generation process. We propose ForGen, a Forest-based Generation model that contains a graph-level and an edge-level autoregressive generator. Specifically, for the edge-level model, motivated by the skewed distribution of node degree and the Huffman tree, we design a forest-like data structure to accelerate edge connection via shallow tree searches and better parallelism. Experiments on both synthetic and real-world graph datasets show that ForGen is two times faster than the current state-of-the-art method for graph generation, and guarantees better generated graph quality.
Analisis Kesulitan Belajar Matematika Materi Pecahan pada Siswa Kelas IV SD Negeri 53 Prabumulih

Many influences as it happens, including lack of interest or lack of pleasure students in learning mathematics, students are less active in the learning process takes place sometimesteachers just explain the material is not interesting and just monotonous. This bias is because at the time of learning the material given is only monotonous should be by using or utilizingexisting media so that students are less active in learning, lack of teachers in mastery of thematerial so that the learning process is less interesting and this becomes one of the mainproblems that cause a lack of interest in students in the learning process Mathematics lastsand causes low and causes students to have difficulty in learning mathematics.
A Bayesian Network Approach to Explainable Reinforcement Learning with Distal Information

Nowadays, Artificial Intelligence systems have expanded their competence field from research to industry and daily life, so understanding how they make decisions is becoming fundamental to reducing the lack of trust between users and machines and increasing the transparency of the model. This paper aims to automate the generation of explanations for model-free Reinforcement Learning algorithms by answering “why” and “why not” questions. To this end, we use Bayesian Networks in combination with the NOTEARS algorithm for automatic structure learning. This approach complements an existing framework very well and demonstrates thus a step towards generating explanations with as little user input as possible. This approach is computationally evaluated in three benchmarks using different Reinforcement Learning methods to highlight that it is independent of the type of model used and the explanations are then rated through a human study. The results obtained are compared to other baseline explanation models to underline the satisfying performance of the framework presented in terms of increasing the understanding, transparency and trust in the action chosen by the agent.
Data Security Techniques Using Square Block Keys in Text Format

The text format is a character format that can be read directly with any software. This format is used in sending data to facilitate sending and speed up sending data to message recipients. The text format is the message storage format with he least security because it can be directly read by the person who gets the message. The author wants to build a cryptographic application that can secure text formats using the character transposition technique. Character transposition is used based on the formation of a square matrix which is used as a key in the encryption and decryption process. The size of the matrix used is 4 x 4. The results of the study found that the text format was successfully transformed into ciphertext so as to avoid the possibility of data theft. Keywords: square, security, block, encryption
Study on Epithermal Gold Mineralization System at Shwebontha Prospect, Monywa Copper-Gold Ore Field, Central Myanmar

The Shwebontha prospect area is situated in the Central Volcanic Belt, central Myanmar, where the well-known Sagaing Fault serves as its eastern boundary. This study aims to document key the mineralogy, host rock geochemistry and ore mineralizing fluids. The mineralization, hosted by Upper Oligocene to Middle Miocene rhyolites, displays a strong lithological control. Mineralization is characterized by gold-bearing silicified massive ore and chalcedonic quartz veins in which sulfides are clustered and disseminated not only in quartz gangue but also in rhyolite host rocks. The significant ore minerals in the mineralized veins include pyrite, sphalerite, galena, chalcopyrite, and gold. Common hydrothermal alterations such as silicic, argillic and propylitic alteration types are recognized. According to the fluid inclusion data and interpretation, ore mineralizing fluids in the research area are characterized by formation temperatures of 260-280 °C and salinity of 0.35-2.41 % wt. NaCl eq. respectively. Mixing of hydrothermal fluids was generally considered to be an effective mechanism for ore transport and deposition.