Reviewerly/exHarmony huggingface dataset:
The effectiveness of an investigative educational program in the achievement of female students of the Department of Educational and Psychological Sciences in educational planning

The current research aims to identify (the effectiveness of an educational program based on the directed investigative approach in the achievement of female students of Department of Educational and Psychological Sciences in the subject of educational planning).The researchers adopted the experimental design with partial control for the two equal experimental and control groups with the post-test, to suit the nature of the research, as the research sample consisted of (30) female students who were deliberately selected from the Department of Educational and Psychological Sciences in the College of Education for Girls, Tikrit University.( 15) female students studied the first experimental group according to the educational program, and the second group studied according to the usual method, and by (15) students, the researchers prepared the model teaching plans for both groups.An achievement test was also prepared for the experiment, which consisted of (30 items), and the test was verified by appearance and content.Post-achievement, and in light of the results reached by the researchers, they recommend a number of recommendations, including: The necessity of adopting educational programs in teaching educational subjects in universities.Teaching methods curricula include investigative learning strategies in teaching planning in educational sciences departments, and the need to train their students on modern programs in teaching.To complement the results of the study, the researchers suggested some suggestions, including: the effect of the educational program in developing skills of acquiring educational concepts in the subject of educational planning and developing their tendency towards it, and building an educational program according to the strategies of the investigation in the collection of teaching methods and knowing its impact on their performance efficiency among third-year students.
Implementasi Algoritma Fisher-Yates Shuffle Pada Game Edukasi Aksara Jawa Menggunakan Godot Engine

Game is one of the implementations of the computer science field which has developed very rapidly. Game is also a form of educational application, meaning that it can be used as a learning media where the process can be carried out with the 'learning while playing' concept for children.The utilization and use of 'Aksara Jawa' educational game can support the learning process of the children when they want to learn while playing to recognize 'Aksara Jawa'. This 'Aksara Jawa' educational game is a game that is designed to improve children's ability to memorize and understand 'Aksara Jawa'. This game contain questions that need to be answered by players by arranging the scrambled 'Aksara Jawa' to form the correct words or sentences. The used algorithm is Shuffle Random which players can't memorize the position of 'Aksara Jawa' and make the game not boring, and also able to apply 'learning and playing' system and become a good learning method among the children. In this study, Shuffle Random Algorithm was used to randomize the 'Aksara Jawa' contained in every questions which were packaged in the form of a game. The making of this 'Aksara Jawa' educational game was using Godot engine software and GDscript programming language. The final result of this research is that this game can help to learn to memorize, so that it can train reasoning and memory power, and also can be used as a means of entertainment.
Hybrid Renewable Energy Tariff Selection Using Providers and Consumers Lexicographic Preferences Similarity

Due to the importance of hybrid renewable energies for green power plants, strategies are required to make the market competitive and encourage consumers to admit to using such still less available electricity compared to the power generated from fossil fuels.Promotion-based group-buying tariffs are a selling marketing tool that can be adapted for this purpose.Energy producers and consumers can express their preferences regarding hybrid renewable energies through multiple attributes and values in a conditional manner, a lexicographic representation.In this paradigm, "what to buy" and "who else might incline to buy this," is a challenging issue for a group of consumers to make a single purchase decision.To this end, an HRECS and a PLPSim method are proposed to group consumers having the most similar lexicographic preferences for purchasing the most appropriate supplier tariff.The evaluation results demonstrate that HRECS using PLPSim outperforms the existing PLPDis method regarding Normal Discounted Cumulative Gain (nDCG) as well as intra-and inter-group Davies-Bouldin dispersion.
A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract)

We present a novel parallel algorithm for drawing balanced samples from large populations. When auxiliary variables about the population units are known, balanced sampling improves the quality of the estimations obtained from the sample. Available algorithms, e.g., the cube method, are inherently sequential, and do not scale to large populations. Our parallel algorithm is based on a variant of the cube method for stratified populations. It has the same sample quality as sequential algorithms, and almost ideal parallel speedup.
Correction

This article refers to:If You Quit Smoking, This Could Happen to You: Investigating Framing and Modeling Effects in an Anti-Smoking Serious Game
Proof of Learning: Towards a Practical Blockchain Consensus Mechanism Using Directed Guiding Gradients (Student Abstract)

Since Bitcoin, blockchain has attracted the attention of researchers. The consensus mechanism at the center of blockchain is often criticized for wasting a large amount of computing power for meaningless hashing. At the same time, state-of-the-art models in deep learning require increasing computing power to be trained. Proof of Learning (PoL) is dedicated to using the originally wasted computing power to train neural networks. Most of the previous PoL consensus mechanisms are based on two methods, recomputation or performance metrics. However, in practical scenarios, these methods both do not satisfy all properties necessary to build a large-scale blockchain, such as certainty, constant verification, therefore are still far away from being practical. In this paper, we observe that the opacity of deep learning models is similar to the pre-image resistance of hash functions and can naturally be used to build PoL. Based on our observation, we propose a method called Directed Guiding Gradient. Using this method, our proposed PoL consensus mechanism has a similar structure to the widely used Proof of Work (PoW), allowing us to build practical blockchain on it and train neutral networks simultaneously. In experiments, we build a blockchain on top of our proposed PoL consensus mechanism and results show that our PoL works well.
Experience prototyping through virtual reality head-mounted displays: Design appraisals of automotive user interfaces

This paper investigates virtual reality head-mounted displays (VR-HMDs) as immersive and interactive media to simulate how new automotive user interfaces will contribute to passengers’ user experience (UX) through experience prototyping of a conceptual infotainment system for front-seat passengers. It first demonstrates the integration of VR-HMDs into experience prototypes through (1) identification of what to prototype by deconstructing the system into its aspects; (2) definition of the scope and fidelity of the prototype for each design aspect; and (3) elimination of challenges brought by VR-HMD (e.g. simulation sickness) through strategies like static presentation of car surroundings. Secondly, it confirms the strategies to exploit immersion safely with the results of the simulation sickness and presence questionnaires. Thirdly, it probes how using VR-HMD affects UX appraisals with content analysis of the follow-up interviews. The paper concludes with a discussion of how VR-HMDs enrich digital appraisals of (automotive) user interfaces at early design stages.
The meaning of the rock-painting “Human Face Figures” at the Mesolithic caves in the karts region of Southeast Meratus, South Kalimantan: Charles S. Peirce's semiotic perspective

Rock-paintings in the karts region of Southeast Meratus, South Kalimantan, among others, were found in the Liang Bangkai kart area, which was in the Mesolithic caves of Liang Bangkai 11 and 12.The findings of rock-paintings in the two Mesolithic caves included paintings of the "Human Face Figures".The findings of this rock-pantings are new, but research on the meaning of the rock-paintings of "Human Face Figures" has not received attention, so we further researched the rock-paintings.Further research was done using the "Semiotics Charles.S. Peirce" perspective.In "Charles S. Peirce semiotics", there are three main elements of the study, that is sign, interpretant, and referent.The sign element is identified as rock-paintings, the interpretant is a human supporting rock-paintings, and the referent element can be in the form of religious ceremonial activities, such as burial ceremonies.Between the sign and the referent there is a formal, contiguous, and arbitrary relationship pattern.A formal relationship produces an icon, contiguous produces an index and an arbiter produces a symbol.In this article, we examine the patterns of the relationship between the sign and the referent to reveal the meaning of the rock-painting of "Human Face Figures" at the Mesolithic caves of the Liang Bangkai 11 and 12.The novelty of the research that is the "Semiotics of Charles S. Perce" approach can be used as a reference to examine the meaning of the rock-painting of "Human Face Figures" as a symbol of the spirit of the human face of the dead who has lived in the afterlife.
Verbal and Non-Verbal Elements Found in Chatime Advertisement and Their Meaning

This paper focused on verbal and non-verbal elements that were found in Chatime advertisements and the meaning of these elements. The data used in this research were taken from Chatime advertisement on the internet. The data were collected by reading and finding the Chatime advertisement. Then, the data collected were classified based on kinds of visual and verbal elements and their meaning found in the advertisement. The method in analyzing the data was descriptive method. There were two types of method to present the result of analysis - they were formal method and informal method. The formal method was used commonly with detail illustration data by using symbol, picture or image of the advertisement. The informal method was the description data in paragraph sentence, or the text found on the advertisements. The result of this research showed that the visual elements found in the Chatime advertisements are in the form of pictures and colors. The types of meaning found in visual elements were conceptual meaning and affective meaning- in this case, those visual elements are intended to be able to affect the customers to buy the product. The verbal elements found in the Chatime advertisements were in the form of phrases and sentences. Those found in headline, body copy, signature line and standing details. Types of meaning found in verbal elements were conceptual meaning, affective meaning and connotative meaning.&#x0D; Keywords: verbal and non-verbal, elements, advertisement, discourse analysis
Formal Semantics and Formally Verified Validation for Temporal Planning

We present a simple and concise semantics for temporal planning. Our semantics are developed and formalised in the logic of the interactive theorem prover Isabelle/HOL. We derive from those semantics a validation algorithm for temporal planning and show, using a formal proof in Isabelle/HOL, that this validation algorithm implements our semantics. We experimentally evaluate our verified validation algorithm and show that it is practical.
Collaborative Measurement System of Dual Mobile Robots That Integrates Visual Tracking and 3D Measurement

The manufacturing accuracy of large-scale complex components determines the performance and quality of aircraft, ships, high-speed rail, and other equipment. High-precision 3D measurement plays a crucial role in ensuring manufacturing accuracy. At present, the existing measurement methods rely heavily on manual labor, which cannot satisfy the requirements of industry quality and efficiency. This paper introduces an integrated mobile robotic measurement system for the accurate and automatic 3D measurement of large-scale components with complex curved surfaces. The system consists of the mobile optical scanning measurement device, visual tracking device, and software platform, which can realize comprehensive and accurate data acquisition and stitching of large-scale complex components. The combination of visual tracking and 3D measurement based on the coordinated motion of the dual robot achieved the automatic data acquisition of large-scale complex components without sticking coded targets. Additionally, this paper also introduces a DeepMerge algorithm that combines local and global features of the point cloud, which effectively corrects the initial stitching error of the visual tracking system. The validity of the measurement system and method was shown by the measurement and stitching experiments on the surface of the vehicle nose, ensuring the accurate measurement of the robot’s wide range of motion.
Pemanfaatan Teknologi Informasi untuk Peningkatan Pelayanan pada UPT TIK Politeknik Negeri Cilacap

UPT Teknologi Informasi dan Komunikasi (UPT TIK) is a technical implementing unit that provides information and communication technology services for the academic community at the Cilacap State Polytechnic. At UPT TIK has services such as email submission, subdomain and website submission, installation submission and network disturbance submission. In the management of UPT TIK, there are problems, including the lack of information about standard operating procedures, the head of UPT TIK finds it difficult to collect data because users are still doing it via telephone, users have difficulty monitoring submissions, forms that are paper based so that data loss occurs every year approximately 5 sheets and storage takes up a lot of space. At UPT TIK there are problems, so there is a need for a system that overcomes these problems, so the UPT TIK information system was developed which aims to assist the submission process, manage data submissions, and monitor submissions. This web-based system was developed using the RAD method because this method is very short work and is designed from the results of user responses with existing prototypes. In addition, the RAD method consists of the requirements planning stage, RAD design workshop, and implementation. The output of this system is the submission process is better controlled, users can monitor submissions and file management can be properly archived. From the results of the questionnaire data analysis with 20 respondents, it was found that 1% disagreed, 8% disagreed, 81% agreed and 10% stated strongly agree that this system is said to be successful in helping improve services at UPT TIK
Chaining Value Functions for Off-Policy Learning

To accumulate knowledge and improve its policy of behaviour, a reinforcement learning agent can learn `off-policy' about policies that differ from the policy used to generate its experience. This is important to learn counterfactuals, or because the experience was generated out of its own control. However, off-policy learning is non-trivial, and standard reinforcement-learning algorithms can be unstable and divergent. In this paper we discuss a novel family of off-policy prediction algorithms which are convergent by construction. The idea is to first learn on-policy about the data-generating behaviour, and then bootstrap an off-policy value estimate on this on-policy estimate, thereby constructing a value estimate that is partially off-policy. This process can be repeated to build a chain of value functions, each time bootstrapping a new estimate on the previous estimate in the chain. Each step in the chain is stable and hence the complete algorithm is guaranteed to be stable. Under mild conditions this comes arbitrarily close to the off-policy TD solution when we increase the length of the chain. Hence it can compute the solution even in cases where off-policy TD diverges. We prove that the proposed scheme is convergent and corresponds to an iterative decomposition of the inverse key matrix. Furthermore it can be interpreted as estimating a novel objective -- that we call a `k-step expedition' -- of following the target policy for finitely many steps before continuing indefinitely with the behaviour policy. Empirically we evaluate the idea on challenging MDPs such as Baird's counter example and observe favourable results.
Sampling and Counting Acyclic Orientations in Chordal Graphs (Student Abstract)

Sampling of chordal graphs and various types of acyclic orientations over chordal graphs plays a central role in several AI applications such as causal structure learning. For a given undirected graph, an acyclic orientation is an assignment of directions to all of its edges which makes the resulting directed graph cycle-free. Sampling is often closely related to the corresponding counting problem. Counting of acyclic orientations of a given chordal graph can be done in polynomial time, but the previously known techniques do not seem to lead to a corresponding (efficient) sampler. In this work, we propose a dynamic programming framework which yields a counter and a uniform sampler, both of which run in (essentially) linear time. An interesting feature of our sampler is that it is a stand-alone algorithm that, unlike other DP-based samplers, does not need any preprocessing which determines the corresponding counts.
Relational Data Model on The University Website with Search Engine Optimization

The visibility of a university’s website on the search engine becomes an essential factor to reach a wider audience. One way to improve the visibility of a website is through Search Engine Optimization (SEO). University’s website development with SEO is inseparable from the data model because SEO supporting factors are parts of the consideration in the components and structure of the data model. This study aims to build a data model for a university website accompanied by SEO. The relational data model is used in this study based on the performance and maturity in defining schema-based design. This study was conducted through four sequential stages: literature review, planning, implementation, and evaluation. The resulting relational data model is one that has accommodated four supporting factors for SEO, namely Meta description, Meta keywords, URL structure, and image description. This study has succeeded in building a relational data model at the abstraction level of conceptual and logical. In the conceptual data model, one entity and 11 attributes are formed. The logical data model was implemented in independent work environments using RelaX and operational requirements can be fulfilled by representing each table or relationship in the schema using relational algebra.
Learning Parameterized Task Structure for Generalization to Unseen Entities

Real world tasks are hierarchical and compositional. Tasks can be composed of multiple subtasks (or sub-goals) that are dependent on each other. These subtasks are defined in terms of entities (e.g., "apple", "pear") that can be recombined to form new subtasks (e.g., "pickup apple", and "pickup pear"). To solve these tasks efficiently, an agent must infer subtask dependencies (e.g. an agent must execute "pickup apple" before "place apple in pot"), and generalize the inferred dependencies to new subtasks (e.g. "place apple in pot" is similar to "place apple in pan"). Moreover, an agent may also need to solve unseen tasks, which can involve unseen entities. To this end, we formulate parameterized subtask graph inference (PSGI), a method for modeling subtask dependencies using first-order logic with factored entities. To facilitate this, we learn parameter attributes in a zero-shot manner, which are used as quantifiers (e.g. is_pickable(X)) for the factored subtask graph. We show this approach accurately learns the latent structure on hierarchical and compositional tasks more efficiently than prior work, and show PSGI can generalize by modelling structure on subtasks unseen during adaptation.
Formation of future teachers’ intercultural tolerance through digital learning resource at professional education institutions

Introduction. The worldwide globalisation processes lead to academic mobility of students, which is expressed in the possibility of entering any educational institutions, including those located abroad. As a consequence, educational institutions are developing a special intercultural learning environment, where peaceful coexistence is largely determined by the presence of such quality as intercultural tolerance. This quality is especially important for students of pedagogical specialties since the quality of educational process and the psychological climate within the involved learners’ team will depend on them. Therefore the problem of formation of intercultural tolerance comes to the fore. Materials and methods. The research involved 81 pedagogical-profile students of the 1st-4th years of study at the State budget-funded educational institution of secondary vocational education of Kaluga region “A.P. Churilin Kirov Industrial and Pedagogical College”. The teaching at experimental groups involved the use of specially developed technology for foreign-language teaching and the digital educational resource. The control groups worked under a teaching technology described in the programme for the discipline “Foreign language”. In order to assess the level of formation of intercultural tolerance, A. Kyveralg’s method was used, which allows the researcher to evaluate the general level of formation of the explored phenomenon proceeding from each component’s indicators. The mathematical and statistical processing of the results was carried out using Pearson’s chi-squared test (χ2). Results. A number of statistically significant differences were revealed in the distribution between the control and experimental groups under the following indicators: motivational χ2=16.98 and χ2=31.369), cognitive (χ2=21.123 and χ2=38.956), activity-based (χ2=12.588 and χ2=8.75) and reflexive (χ2=7.892 and χ2=7.317). Conclusion. The diagnostic indicators made it possible to conclude that the students in the experimental groups used the methods of intercultural interaction that are based on the knowledge of cultural diversity of educational process participants, the ability to apply different interaction methods from the positions of the explored phenomenon, to seek ways out of conflict situations independently, to plan educational and professional activities.
Quantum Computing to Enhance Performance of Machine Learning Algorithms

The field of quantum computing aims to speed up the solution for certain computational problems using quantum computers. The key milestone in this field is the claim that quantum supremacy can be achieved using quantum computers which shows that the quantum speedup is achievable in a real time system. Quantum computing can improve the performance of machine learning algorithms based on the quantum algorithms by producing better solutions for machine learning tasks than classical machine learning approaches. This chapter provides an overview on quantum computing, quantum supremacy and recent research and inventions in the field. The paper additionally illustrates the research conducted with respect to impact of quantum computing on machine learning.