Reviewerly/exHarmony huggingface dataset:
KONSEP DAN DATA MODEL APLIKASI LELANG KOMODITAS PERTANIAN BERBASIS ANDROID

Concept and data model of application can be implemented in the Auction sector, especially for agricultural commodities. The main benefit for farmers is to increase income because it expands market segmentation,for governments to monitor the pace and development of agriculture in an area. The application is designed in android system,that has high mobility so it is easy to use anywhere as long as it has an internet network, the addition of a price check facility from auction results in the application is expected to add information to farmers and buyers in order to reduce disinformation about the price of an agricultural commodity.
Randomized Sketching for Low-Memory Dynamic Optimization.

generated and shows how to use the compressed state to reliably solve the original dynamic optimization problem. Concretely, the compressed state is used to compute approximate gradients and to apply the Hessian to vectors. The approximation error in these quantities is controlled by the target rank of the sketch. This approximate first- and second-order information can readily be used in any optimization algorithm. As an example, we develop a sketched trust-region method that adaptively chooses the target rank using a posteriori error information and provably converges to a stationary point of the original problem. Numerical experiments with the sketched trust-region method show promising performance on challenging problems such as the optimal control of an advection-reaction-diffusion equation and the optimal control of fluid flow past a cylinder.
Investigating the Influence of YouTube on Speaking proficiency in English Major Students at Ho Chi Minh City University of Industry and Trade

YouTube has attracted a lot of interest from researchers and educators who specialize in the English language in the modern context of rapid technological development. The advent of YouTube videos as a resource has demonstrated the value and applicability of such movies in helping teachers and students participate in productive language instruction, particularly when it comes to speaking exercises. This study set out to critically examine how the YouTube platform affects students' ability to speak more when they practice. The poll included 100 third-year English majors at Ho Chi Minh City University of Industry and Trade (HUIT), with 65 males and 35 females. The participants ranged in age from 20 to 22 years old. The study employed a mixedmethods design, utilizing data collection instruments such as surveys and interviews. After looking at the questionnaire responses and interview recordings, it was found that the students in the experimental group liked the idea of using YouTube videos in speaking activities. The channels that participants like watching the most are Let's Talk and VOA Speaking English. Furthermore, the study identified some prevalent subjective and objective difficulties encountered by individuals during the consumption of YouTube content. Additionally, suggestions for educators and researchers are included at the conclusion of the text.
A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

Task scheduling poses a wide variety of challenges in the cloud computing paradigm, as heterogeneous tasks from a variety of resources come onto cloud platforms. The most important challenge in this paradigm is to avoid single points of failure, as tasks of various users are running at the cloud provider, and it is very important to improve fault tolerance and maintain negligible downtime in order to render services to a wide range of customers around the world. In this paper, to tackle this challenge, we precisely calculated priorities of tasks for virtual machines (VMs) based on unit electricity cost and these priorities are fed to the scheduler. This scheduler is modeled using a deep reinforcement learning technique which is known as the DQN model to make decisions and generate schedules optimally for VMs based on priorities fed to the scheduler. This research is extensively conducted on Cloudsim. In this research, a real-time dataset known as Google Cloud Jobs is used and is given as input to the algorithm. This research is carried out in two phases by categorizing the dataset as a regular or large dataset with real-time tasks with fixed and varied VMs in both datasets. Our proposed DRFTSA is compared to existing state-of-the-art approaches, i.e., PSO, ACO, and GA algorithms, and results reveal that the proposed DRFTSA minimizes makespan compared to PSO, GA, and ACO by 30.97%, 35.1%, and 37.12%, rates of failure by 39.4%, 44.13%, and 46.19%, and energy consumption by 18.81%, 23.07%, and 28.8%, respectively, for both regular and large datasets for both fixed and varied VMs.
A Study on Improving ALBERT with Additive Attention for Text Classification

The Transformer has made significant advances in various fields, but high computational costs and lengthy training times pose challenges for models based on this architecture. To address this issue, we propose an improved ALBERT-based model, which replaces ALBERT’s self-attention mechanism with an additive attention mechanism. This modification can reduce computational complexity and enhance the model’s flexibility. We compare our proposed model with other Transformer-based models, demonstrating that it achieves a lower parameter count and significantly reduces computational complexity. Through extensive evaluations on diverse datasets, we establish the superior efficiency of our proposed model over alternative ones. With its reduced parameter count, our proposed model emerges as a promising approach to enhance the efficiency and practicality of Transformer-based models. Notably, it enables practical training under resource and time limitations, highlighting its adaptability and versatility in real-world scenarios.
Staged Reinforcement Learning for Complex Tasks Through Decomposed Environments

Reinforcement Learning (RL) is an area of growing interest in the field of artificial intelligence due to its many notable applications in diverse fields. Particularly within the context of intelligent vehicle control, RL has made impressive progress. However, currently it is still in simulated controlled environments where RL can achieve its full super-human potential. Although how to apply simulation experience in real scenarios has been studied, how to approximate simulated problems to the real dynamic problems is still a challenge. In this paper, we discuss two methods that approximate RL problems to real problems. In the context of traffic junction simulations, we demonstrate that, if we can decompose a complex task into multiple sub-tasks, solving these tasks first can be advantageous to help minimising possible occurrences of catastrophic events in the complex task. From a multi-agent perspective, we introduce a training structuring mechanism that exploits the use of experience learned under the popular paradigm called Centralised Training Decentralised Execution (CTDE). This experience can then be leveraged in fully decentralised settings that are conceptually closer to real settings, where agents often do not have access to a central oracle and must be treated as isolated independent units. The results show that the proposed approaches improve agents performance in complex tasks related to traffic junctions, minimizing potential safety-critical problems that might happen in these scenarios. Although still in simulation, the investigated situations are conceptually closer to real scenarios and thus, with these results, we intend to motivate further research in the subject.
The research of the possibilities of gamification tools for the development of teacher's emotional intelligence

The problem and the aim of the study. The development of the teacher's emotional intelligence is both the direction of improving the teacher training system, and the condition for the social adaptation of young specialists, and the prevention of emotional burnout syndrome, and a factor in improving the quality of education in general. Gamification tools allow you to create additional conditions for the formation of both personal characteristics and professional competencies of digital school mentors. The purpose of the study is to identify the possibilities of using gamification tools to developing the emotional intelligence of future teachers as a professionally significant personality property. Research methods. The methodology is based on the analysis of the potential of digital technologies with the possibilities of gamification of learning both for the formation of skills and skills components of emotional intelligence, and the professional qualities of a teacher. The experiment involves 50 bachelors of the Institute of Mathematics and Information Systems of Vyatka State University (Russian Federation). Software implementation of game mechanics is performed by the tools of the gaming platform AhaSlides. To assess the formation of emotional intelligence, the questionnaire of D.V. Lyusin is used. Pearson's chisquared test (χ2 ) was used for statistical data processing. Results. Students of the experimental group in classes in the disciplines "Theory and methodology of teaching computer science," "Methodology of extracurricular activities in computer science" analyze the innovative experience of teachers to include game elements in training, study the tools of the game platform AhaSlides and use them to design an emotionally comfortable gamified environment. The functionality of AhaSlides for the development of emotional intelligence has been identified: QR code; time limit; hidden results; interactive presentations with surveys, a cloud of words, open questions. Statistically significant differences in qualitative changes were revealed as for the total integral indicator (χ2 = 10.445; p &lt; 0.05) and interpersonal (χ2 = 10.489; p &lt; 0.05), and intrapersonal (χ2 = 9.931; p &lt; 0.05) of emotional intelligence. In conclusion, factors affecting the effectiveness of the use of gamification tools to form the abilities that form the basis of the teacher's emotional intelligence are distinguished: temperament properties, information processing features, range of interests, information literacy, digital skills formation, experience in participating in gaming events.
Applying Ice Cream Stick Media To Improve Grade Eight Students’ Ability In Reading Comprehension Of Descriptive Text At SMP Negeri 2 Pematang Siantar

The research aims to find out the effect of Ice Cream Stick Media to improve students’ ability in reading comprehension of descriptive text at SMP Negeri 2 Pematang Siantar. This research is quantitative research with quasi experimental research design, according to Ary, et al (2010:426) quantitative research is research that gathers numeric data through control procedures and analyses to answer predetermined questions or test hypothesis. The population is the grade eight of SMP Negeri 2 Pematang Siantar with a total of 344 students. The sample of this research is divided into two classes namely experimental class (VIII-7) consist of 30 students that use Ice Cream Stick Media and control class (VIII-6) consist of 30 students that use conventional strategy. The data collection instrument uses a multiple choice for pre-test and post-test. The research finds that the mean of post-test in experimental class is 73.1 and the mean of post-test in control class is 65. After that the researcher find that the ttest is higher than ttable (2.066 &gt; 1.672) at the level significance is 5%. Based on the result finding Ha is accepted and Ho is rejected. Therefore, it can be concluded that use Ice Cream Stick Media significantly affects the students’ ability at SMP Negeri 2 Pematang Siantar in reading comprehension of descriptive text.
Policy Generation from Latent Embeddings for Reinforcement Learning

The human brain endows us with extraordinary capabilities that enable us to create, imagine, and generate anything we desire. Specifically, we have fascinating imaginative skills allowing us to generate fundamental knowledge from abstract concepts. Motivated by these traits, numerous areas of machine learning, notably unsupervised learning and reinforcement learning, have started using such ideas at their core. Nevertheless, these methods do not come without fault. A fundamental issue with reinforcement learning especially now when used with neural networks as function approximators is their limited achievable optimality compared to its uses from tabula rasa. Due to the nature of learning with neural networks, the behaviours achievable for each task are inconsistent and providing a unified approach that enables such optimal policies to exist within a parameter space would facilitate both the learning procedure and the behaviour outcomes. Consequently, we are interested in discovering whether reinforcement learning can be facilitated with unsupervised learning methods in a manner to alleviate this downfall. This work aims to provide an analysis of the feasibility of using generative models to extract learnt reinforcement learning policies (i.e. model parameters) with the intention of conditionally sampling the learnt policy-latent space to generate new policies. We demonstrate that under the current proposed architecture, these models are able to recreate policies on simple tasks whereas fail on more complex ones. We therefore provide a critical analysis of these failures and discuss further improvements which would aid the proliferation of this work.
Factors and mechanisms of mastering physical knowledge by technical university students

Introduction. Physics is one of the general professional disciplines at a technical university; its mastering is extremely important for future engineers. The process of formation of physical knowledge is accompanied by multiple difficulties, which actualises the problem of identifying the factors of assimilation thereof in students of a technical university as well as the mechanisms to influence these factors. The aim of the study is to identify and justify the factors of physical knowledge assimilation in students of a technical university as well as the mechanisms of influencing these factors. Materials and methods. The study involved 95 students of the National research university “MPEI” (Moscow Power Engineering Institute) (Russian Federation) majoring in the profiles: “Electric Power Engineering”, “Electrical Engineering”, “Biotechnical Systems”, “Electronics”. In order to assess the level of formation of students’ physical knowledge, a test comprising 15 questions was offered to them with a view to evaluate the students’ knowledge of the basic concepts and laws in the sections: “Mechanics”, “Molecular Physics” and “Electrodynamics”. The statistical analysis of test results was carried out by means of Mann-Whitney U test. Results. A number of factors influencing the formation of physical knowledge in students have been formulated, that were found to be in line with the objectives of studying physics at a technical university. The correctness of the revealed factors is confirmed by the statistics (Uemp. = 12 &lt; Ucr. = 16; p &lt; 0,01), i.e. the students who had poor performance at school or who failed the Unified State Examination are not motivated for studying physics at a university and have poor academic results in general physics. The success in test performance shown by students from weak Bachelor degree groups does not differ from the results of the same test performed by Master degree students (Uemp. = 47.5 &gt; Ucr. = 16). This result is explained by the influence of such factor as low relevance of physical knowledge for Master degree students at a given stage of study. A set of mechanisms directly aimed at students’ assimilation of basic physical concepts and laws was developed. They include: development of cognitive abilities due to the systemic nature of physical knowledge, students’ strengthened intrinsic motivation to study physics, discussion of mathematical and verbal formulation of physical concepts and laws, regular directed testing in physics at all stages of studying at the university. Conclusion. The practical significance of the study is determined by the possibility to use its results for improving the process of formation of physical knowledge in students of technical universities.
Model for assessing the formation of special (professional) competences of practical psychologists in the educational and information environment of higher education establishments under conditions of dynamic uncertainty

A comprehensive scientific and empirical analysis of the works of both domestic and foreign scientists regarding the construction of models for assessing the development of special (professional) competences in future practical psychologists within higher education technical establishments (HETE) is carried out in this article. The research aimed to create and justify an author's model for evaluating the development of special (professional) competencies among practical psychologists in the educational and informational environment of a higher technical education institution under conditions of dynamic uncertainty. The study's objectives included identifying theoretical approaches to examining the assessment of future practical psychologists' readiness for personal growth and professional advancement, delineating the specifics of cultivating special (professional) competencies in these individuals, substantiating the methodology for nurturing special (professional) competencies in future practical psychologists, and designing a theoretical model for assessing the development of these competencies. To achieve these goals, various research methods were employed, including scientific analysis, systematization, generalization, comparison, and pedagogical modeling. Consequently, the conducted research delineated the structural components of the model for evaluating the development of special (professional) competencies among practical psychologists, namely the goal-forming, content-technological, organizational-pedagogical, and effective components. Moreover, criteria for assessing the development of these special (professional) competences were established, encompassing motivational and valuable criteria, cognitive criteria, communicative criteria, organizational and operational criteria, and reflective criteria. Additionally, the research identified distinct levels of competence formation, which include the intensive-creative level, active-searching level, and reproductive-passive level. In conclusion, the model for assessing the development of special (professional) competencies was found to comprise five stages corresponding to the specialist training courses: the general education stage (first year), the subject orientation stage (second year), the operational and cognitive stage (third year), the control and assessment stage (fourth year), and the specialization stage (first year of master's degree). Furthermore, an algorithm for managing the cultivation of special (professional) competencies among future practical psychologists has been developed, and the conditions for its successful implementation have been identified.
On the $$(k,\ell )$$-anonymity of networks via their k-metric antidimension

This work focuses on the [Formula: see text]-anonymity of some networks as a measure of their privacy against active attacks. Two different types of networks are considered. The first one consists of graphs with a predetermined structure, namely cylinders, toruses, and 2-dimensional Hamming graphs, whereas the second one is formed by randomly generated graphs. In order to evaluate the [Formula: see text]-anonymity of the considered graphs, we have computed their k-metric antidimension. To this end, we have taken a combinatorial approach for the graphs with a predetermined structure, whereas for randomly generated graphs we have developed an integer programming formulation and computationally tested its implementation. The results of the combinatorial approach, as well as those from the implementations indicate that, according to the [Formula: see text]-anonymity measure, only the 2-dimensional Hamming graphs and some general random dense graphs are achieving some higher privacy properties.
Improved artificial intelligence discrimination of minor histological populations by supplementing with color-adjusted images

Abstract Despite the dedicated research of artificial intelligence (AI) for pathological images, the construction of AI applicable to histopathological tissue subtypes, is limited by insufficient dataset collection owing to disease infrequency. Here, we present a solution involving the addition of supplemental tissue array (TA) images that are adjusted to the tonality of the main data using a cycle-consistent generative adversarial network (CycleGAN) to the training data for rare tissue types. F1 scores of rare tissue types that constitute &lt; 1.2% of the training data were significantly increased by improving recall values after adding color-adjusted TA images constituting &lt; 0.65% of total training patches. The detector also enabled the equivalent discrimination of clinical images from two distinct hospitals and the capability was more increased following color-correction of test data before AI identification (F1 score from 45.2 ± 27.1 to 77.1 ± 10.3, p &lt; 0.01). These methods also classified intraoperative frozen sections, while excessive supplementation paradoxically decreased F1 scores. These results identify strategies for building an AI that preserves the imbalance between training data with large differences in actual disease frequencies, which is important for constructing AI for practical histopathological classification.
Soft Prompt Transfer for Zero-Shot and Few-Shot Learning in EHR Understanding

Electronic Health Records (EHRs) are a rich source of information that can be leveraged for various medical applications, such as disease inference, treatment recommendation, and outcome analysis. However, the complexity and heterogeneity of EHR data, along with the limited availability of well-labeled samples, present significant challenges to the development of efficient and adaptable models for EHR tasks (such as rare or novel disease prediction or inference). In this paper, we propose Soft prompt transfer for Electronic Health Records (SptEHR), a novel pipeline designed to address these challenges. Specifically, SptEHR consists of three main stages: (1) self-supervised pre-training on raw EHR data for an EHR-centric transformer-based foundation model, (2) supervised multi-task continual learning from existing well-labeled tasks to further refine the foundation model and learn transferable task-specific soft prompts, and (3) further improve zero-shot and few-shot ability via prompt transfer. Specifically, the transformer-based foundation model learned from stage one captures domain-specific knowledge. Then the multi-task continual training in stage two improves model adaptability and performance on EHR tasks. Finally, stage three leverages soft prompt transfer which is based on the similarity between the new and the existing tasks, to effectively address new tasks without requiring additional/extensive training. The effectiveness of the SptEHR has been validated on the benchmark dataset - MIMIC-III.
The effects of informal sector competition on innovative activities of firms

This study investigates the effects of informal sector competition on innovative activities of firms using a global context. The research results using Probit regression for a sample containing more than 97,000 observations in 119 countries suggest that informal sector competition has a positive impact on firm innovation. The finding is consistent when using alternatives measures of innovation and informal sector competition. In addition, the positive association between competition from informal sector and innovation is confirmed when using various alternative econometric techniques.