Reviewerly/exHarmony huggingface dataset:
Penguatan Pembekalan Kemampuan Pemrograman Dasar bagi Pelajar Sekolah Menengah Umum

Basic programming training that teaches problem-solving logic has developed at all ages, from kindergarten, elementary school to higher education age. This happens because at this time, the concept of computing or informatics thinking has been considered to be a basic requirement in everyday life. Various forms of learning the concept of informatics thinking, are designed according to the user's age group so that users can enjoy it. In the community service activities carried out this time, the target of basic programming training is Public High School students, which are open to all schools in Indonesia. This activity was implemented during the Covid-19 pandemic, so this activity was carried out online in the form of a bootcamp through the Zoom meeting application regularly in one month, which was led as a team by a lecturer and several teaching assistants. This training does not stop at delivering material and demonstrations from lecturers but also training assistance conducted by teaching assistants. The material in this training uses the Python programming language. Python was chosen to be used in the training, because currently Python is a programming language that is widely used for simple to complex programming such as data mining and machine learning. Participants can access training materials through the Learning Management System created by Universitas Surabaya to support the dissemination of knowledge to the wider community through the Ubaya Global Academy) platform. Based on the evaluation of the activities carried out before and after the activity was carried out, this activity is considered to provide benefits and can be carried out regularly every year and has follow-up classes.
Pengembangan Pembelajaran Matematika Diskrit Menggunakan Google Classroom pada Mahasiswa Pendidikan Matematika

Google Classroom is a platform in the form of an LMS (learning management system) which is provided free or unpaid by Google. Google classroom is integrated with email, so it's easy to use (user friendly). Google classroom makes it easy for teachers and lecturers to do distance learning, so that learning becomes efficient, effective and interactive. This study uses a quantitative descriptive research method (Maolani &amp; Cahyana, 2016). The quantitative approach used is in the form of questionnaires and interviews. The purpose of this study is to describe student learning using the Google Classroom Application in Discrete Mathematics courses during the Covid-19 pandemic. This research carried out from the odd semester of the 2021/2022 academic year to 10 students in the fifth semester of the Discrete Mathematics course, the Mathematics Education Study Program. useful for helping plan events, send surveys, give students or others quizzes, or gather simple information in an efficient way. Interviews were conducted at the end of online teaching and learning activities. Data analysis was performed using excel to calculate the percentage and average value. Based on the indicators of the application of the Google Classroom Application, it shows that the average student answer is 75.5% agree and 25.5% disagree. These results show that the application of Google Classroom gets good responses from students, so that it can be used for learning in Discrete Mathematics courses.
Implementasi Model Make a Match Berbantuan Media Pocket Book untuk Meningkatkan Aktivitas Pembelajaran IPA

This study aims to improve science learning activities in grade IV SDK St Ambrosius Ende 6. This research is a classroom action research. the research object consisted of 20 grade IV students. Data were collected by observation techniques, field notes and interviews. The data were analyzed by simple descriptive statistics to determine the percentage increase in student learning activities and their categories for each cycle. The results showed that after implementing the make a match model with the help of pocket book media, there was an increase in student learning activity from cycle I by 49% with the moderately active category to 91% in cycle II with the very active category. Thus, the make a match model with the help of pocket book media can increase the learning activities of class IV SDK St Ambrose Ende 6.
Self reward design with fine-grained interpretability

Abstract The black-box nature of deep neural networks (DNN) has brought to attention the issues of transparency and fairness. Deep Reinforcement Learning (Deep RL or DRL), which uses DNN to learn its policy, value functions etc, is thus also subject to similar concerns. This paper proposes a way to circumvent the issues through the bottom-up design of neural networks with detailed interpretability, where each neuron or layer has its own meaning and utility that corresponds to humanly understandable concept. The framework introduced in this paper is called the Self Reward Design (SRD), inspired by the Inverse Reward Design, and this interpretable design can (1) solve the problem by pure design (although imperfectly) and (2) be optimized like a standard DNN. With deliberate human designs, we show that some RL problems such as lavaland and MuJoCo can be solved using a model constructed with standard NN components with few parameters. Furthermore, with our fish sale auction example, we demonstrate how SRD is used to address situations that will not make sense if black-box models are used, where humanly-understandable semantic-based decision is required.
Perbandingan Model Pembelajaran Problem Based Learning dan Discovery Learning terhadap Hasil Belajar Siswa

The purpose of this study was to compare the effectiveness of Discovery Learning learning model and Problem Based Learning learning model on student learning outcomes. This research is a quasi-experimental research conducted on students of class VII SMP Negeri 1 Palopo, which uses two groups of students given different treatments. Experimental group A used Discovery learning model while Experimental group B used Problem Based Learning model. The results showed from data analysis that tvalue 2.03 and ttable 1.99, with testing criterion of Ha accepted if tvalue &gt; ttable. It was concluded that there is a significant difference between posttest learning results in Experimental Group A that uses Discovery learning model and Experimental Group B that uses Problem Based Learning model. According to average values of both classes, it indicates that Discovery Learning model is more effective on students' learning outcomes than Problem Based Learning model.
Problem Based Learning untuk Meningkatan Kreativitas Mahasiswa Berbasis Literasi Digital

Problem based learning is a model of learning that can be increased student creativity. This research is a classroom action research (CAR). Based on the results of research, it as found that 44% were in the reached category, while in cycle 2 there were 66% of students in the reached category. It was showed an increase the creativity of student in digital literacy through the application of PBL UDA FKIP’s student
Approaches to Constrained Quantum Approximate Optimization

We study the costs and benefits of different quantum approaches to finding approximate solutions of constrained combinatorial optimization problems with a focus on Maximum Independent Set. In the Lagrange multiplier approach we analyze the dependence of the output on graph density and circuit depth. The Quantum Alternating Ansatz Approach is then analyzed and we examine the dependence on different choices of initial states. The Quantum Alternating Ansatz Approach, although powerful, is expensive in terms of quantum resources. A new algorithm based on a "Dynamic Quantum Variational Ansatz" (DQVA) is proposed that dynamically changes to ensure the maximum utilization of a fixed allocation of quantum resources. Our analysis and the new proposed algorithm can also be generalized to other related constrained combinatorial optimization problems.
OMNI-CONV: Generalization of the Omnidirectional Distortion-Aware Convolutions

Omnidirectional images have drawn great research attention recently thanks to their great potential and performance in various computer vision tasks. However, processing such a type of image requires an adaptation to take into account spherical distortions. Therefore, it is not trivial to directly extend the conventional convolutional neural networks on omnidirectional images because CNNs were initially developed for perspective images. In this paper, we present a general method to adapt perspective convolutional networks to equirectangular images, forming a novel distortion-aware convolution. Our proposed solution can be regarded as a replacement for the existing convolutional network without requiring any additional training cost. To verify the generalization of our method, we conduct an analysis on three basic vision tasks, i.e., semantic segmentation, optical flow, and monocular depth. The experiments on both virtual and real outdoor scenarios show our adapted spherical models consistently outperform their counterparts.
Design of Quality Improvement Technique Through Ridgelet Transform on Watermarked Video

with the rapid spread of the use of video files cross the world before and during the pandemic, and the availability of capabilities and tools to create, store and transmit video files of high quality. The latest studies illustrate that video files are the highest data types that had been transmitted over the internet, during video transmission or broadcast from one point to the other or over the internet, videos may be damaged for several reasons, such as packet loss or jitter, in this paper, a proposed approach of Ridgelet transformation is presented to improve received video file quality, based on embedded watermark quality present in video frames. It is implemented by extracting three-color channels of video frames, i.e., Red (R), Green (G), and Blue (B). A selected watermark is included in blue channel (B) along with transmitting the video file from source to destination. However, the quality of watermark is measured with execution regarding watermark. Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) were adopted as measurement criteria. The evaluation results show interesting comparative values without noise, in comparison to applying salt, pepper, and Gaussian noise to video frames.
The Effect of “Carrot and Stick” Formal Control on Deviant Workplace Behaviour: A Proposed Framework

Some recent news media and studies reports indicate that deviant workplace behaviours, such as excessive absenteeism, extortion, and the recently publicised sexual harassment of female students, known as “sex for grades”, are prevalent on some campuses in Nigeria and other west African nations. To mitigate these behaviours, this study proposes a testable model of formal control with some theoretical, practical, and managerial implications. The article is a conceptual study that generates research propositions from an extensive literature review and the precepts of general deterrence and social exchange theories. The paper proposed a carrot-and-stick framework that shows that deterrence factors (punishment certainty, punishment severity and punishment celerity) and motivational elements (contingent reward) negatively influence deviant workplace behaviour and the relationship is stronger in an organisation controlled by transactional leadership behaviour. Practically, this paper attempts to help school administrators and policymakers to change the current strategies in fighting the menace of employee deviance by adopting an approach that uses well-validated negative and positive stimulants that mitigate unproductive behaviour at work. Proposing a well-tested approach, hitherto known only in information systems security, to mitigate deviant workplace behaviours in an educational context and a specific setting is one of the noble contributions of this paper. The paper is conceptual, though. However, the propositions made are theoretically grounded, but they need to be tested in the proposed context and setting. Future research can extend the conceptual framework by further validating the propositions and adding mediators in the proposed relationships. Other implications and recommendations were discussed.
Conclusion and Further Research Directions

Abstract The increasing complexity of real-world optimization problems demands fast, robust, and efficient meta-heuristic algorithms. The popularity of these intelligent techniques is gaining popularity day by day among researchers from various disciplines of science and engineering. The sine cosine algorithm is a simple population-based stochastic approach for handling different optimization problems. In this work, we have discussed the basic sine cosine algorithm for continuous optimization problems, the multi-objective sine cosine algorithm for handling multi-objective optimization problems, and the discrete (or binary) versions of sine cosine algorithm for discrete optimization problems. Sine cosine algorithm (SCA) has reportedly shown competitive results when compared to other meta-heuristic algorithms. The easy implementation and less number of parameters make the SCA algorithm, a recommended choice for performing various optimization tasks. In this present chapter, we have studied different modifications and strategies for the advancement of the sine cosine algorithm. The incorporation of concepts like opposition-based learning, quantum simulation, and hybridization with other meta-heuristic algorithms have increased the efficiency and robustness of the SCA algorithm, and meanwhile, these techniques have also increased the application spectrum of the sine cosine algorithm.
Sine Cosine Algorithm

Abstract Sine cosine algorithm (SCA) [1] is relatively a new algorithm, in the field of meta-heuristic algorithms. SCA is a population-based probabilistic search method that updates the position of search agents in the population using simple concept of trigonometric functions sine and cosine. SCA algorithm is inspired from the periodic property of the sine and cosine functions. The periodicity of the sine and cosine function in the range $$[-1,1]$$ provides great capacity to exploit the search space and helps in maintaining a fine balance between exploration and exploitation. In previous Chap. 1, we have already discussed about the criticality of the exploration and exploitation capabilities of any meta-heuristic algorithm.
Motif Seni Hias Koleksi Cutam Di Jabatan Muzium Malaysia

This writing is a report on the study of niello’s collection motif that has been carried out in five locations, namely the National Museum, the Perak Museum, the Malay World Ethnology Museum, the National Textile Museum and the repository of the Collection Management Division, Department of Museums Malaysia. Niello is a decorative art technique applied to gold, silver and copper carpentry. Niello is a black alloy that is used to fill in carved decorations with the aim of giving attractiveness and a contrasting color to silver items. The community recognizes the niello technique or translated into English as niello or nielloware based on the blue and dark blue colors that result on the surface of gold, silver and copper carpentry carvings. This study of motifs of decorative art was carried out in order to identify the motifs found in niello’s collection. As a result, a total of 127 of niello’s collections in the five locations have been identified and categorized into 18 types of material culture. From the 18 types of cultural equipment, the researcher has made observations and classified the samples into four motif themes, namely plant motifs, animal motifs, Hindu-Buddhist figure motifs and Arabic inscription motifs. The researcher's observations of each sample are made with references from book sources, field studies, journals and also interviews with the respondents. The results of the study show that motifs with Hindu-Buddhist influence such as mythical animal motifs (garuda, kinnari and kinnara, kochasri and others), 12 zodiac animal motifs, lotus-based plant motifs (kranok, dao klip bua and lai dok bua , lai bai bua, dok lai and kan bai bua), jasmine flowers and cotton flowers as well as 3 Hindu Buddhist religious figures (Rama, Hanuman and the deity Theppanom) were found in the collection of cutam at the five locations of the study. Also found is three niello pending with Arabic number inscription motif.
Proposing a Seamless Learning Experience Design (SLED) Framework Based on International Perspectives of Educators from Five Higher Education Institutions

Since seamless learning (SL) is still a rather unknown concept in higher education many educators classify it under the same categories as mobile, blended, online or hybrid learning. The purpose of this study is first to clarify the historical evolvement of the seamless learning concept over the past decades and, to position the seamless learning concept as it is understood today. Secondly, to find the most important concepts which can be proposed for a useful seamless learning experience design framework to assist educators with their course design. Considering this context, the research question for this study is formulated as follows: “Which concepts constitute a seamless learning experience design framework for students in higher education?” To answer this question, an inductive qualitative research analysis was conducted by collecting data from educators from countries on five continents on their views on this topic. Following a thematic coding approach of the combined dataset, five emerging themes crystallised, and are presented as part of a proposed Seamless Learning Experience Design (SLED) framework. They include core, positive, practical, human and design concepts – including sub-themes. The framework contributes to quality assurance processes in e-learning practices by providing a tool for developing seamless learning experiences for students.
Towards Single-Pass and Transferable Gan-Based Attacks Against Object Detectors

Object detection using deep learning is a computer vision task related to predictingbounding boxes and class labels for one or more objects in an image.Although deep neural networks have demonstrated huge success, recent studieshave shown that they are vulnerable to carefully crafted adversarial attacks,which can lead to misclassifications, object suppression, and object fabrication.This fact has led to works that have successfully generated adversarial examplesagainst object detectors using iterative optimization methods. However,such attacks need to query the target model to achieve the attack objectivesrepeatedly. In this paper, we design an adversarial attack against object detectionusing generative adversarial networks. To emphasize that such adversarialattacks are possible in practical conditions, we show that the attack works in asingle pass over the target image and does not need to query the target objectdetector to function. We demonstrate the transferability of our attack and itsresilience towards basic defenses by evaluating it on two different datasets andseveral defended black-box detectors, respectively.
SMALL LIPSCHITZ PERTURBATION OF SCALAR MAPS AND LYAPUNOV EXPONENT FOR LIPSCHITZ MAPS

In this paper we consider small Lipschitz perturbations for Lipschitz maps.We obtain conditions to ensure the permanence of fixed points (sink and source) for scalar Lipschitz maps without requiring differentiability, in a step norm weaker than the C 1 -norm and stronger than the C 0 -norm.Moreover, we also propose conditions in order to guarantee the permanence of periodic points.Additionally, we propose a new definition of Lyapunov exponent for Lipschitz maps which extends, in a natural way, the definition of Lyapunov exponent for differentiable maps.
SOLVING FREDHOLM INTEGRAL EQUATIONS USING BEES ALGORITHM BASED ON CHEBYSHEV POLYNOMIALS

An approximate solution of the Fredholm integral equations (FIEs) of the second kind is obtained.The equations are converted into an unconstrained optimization problem.In this paper, an algorithm (BAC) for solving the Fredholm integral equations (FIEs), with a combination of Bees algorithm and Chebyshev polynomials, is presented.Chebyshev polynomials are first formulated, with undetermined coefficients, as an approximate solution of FIEs.These polynomials are replaced by the unknown function in the given Fredholm equation.The algorithm is in turn calculating the coefficients.Numerical examples are employed to approve the validity and the applicability of the proposed algorithm and the results are compared to the exact solution.The results show the efficiency and accuracy of the proposed algorithm to solve Fredholm integral equations (FIEs).
Personalized Query Expansion

The rapid expansion of data on the web has largely increased the problems associated with efficient searching and retrieval of required data that fulfills the user’s needs. To resolve the ineffectiveness of information retrieval systems query expansion technology is employed. In this paper, the main focus is laid on the expansion of queries by using each user’s profile and search history.&#x0D; In this survey paper different personalized query expansion techniques are compared. This survey's main goal is to present all the ideas, technologies, and applications being employed for personalized query expansion.
Security and Energy Consumption Considerations of Electric Vehicles Integration in Smart Grids

Over the past years, electric vehicles (EVs) have attracted great attention worldwide. Using EVs helps reduce CO2 emissions and the use of fossil fuels and motivates the nations toward a more sustainable future where the consumed energy matters. EVs are the main part of E-mobility; therefore, the EV integration problem is very important. This paper discusses the importance of EV integration in smart grids, and challenges toward the electrification of future cars are investigated. The two main categories for challenges are security and utility-related challenges. Security challenges deal with the communication means in the e-mobility, while grid-related challenges correspond to the energy that must be supplied and delivered to the vehicles. Security challenges come from the devastating actions from outside threats such as hacks, but the grid-related challenges come from the system's nature and can be dealt with using physical and software-based methods. While the challenges of security and grid are investigated, since our future work focuses on energy and cost optimization, the solution of grid-related challenges will be covered.