Reviewerly/exHarmony huggingface dataset:
ChatGPT ile işbirliği yapmak: Alman Dili Eğitimi Üzerine Keşfedici Bir Çalışma

The continuous advancements in artificial intelligence technology bring about changes in foreign language learning. One of the artificial intelligence applications, ChatGPT, developed by OpenAI, draws attention for its applicability in foreign language education. This study explores the opportunities provided by ChatGPT for the learning of four fundamental language skills (listening, reading, speaking, writing) in the context of German language education. The advantages and disadvantages of ChatGPT in foreign language education are examined. Recommendations are provided regarding the points to be considered when utilizing artificial intelligence for the acquisition of the four language skills. Additionally, the disadvantages and limitations of artificial intelligence are discussed.&#x0D; In conclusion, artificial intelligence offers new opportunities in foreign language education. Artificial intelligence applications like ChatGPT can assist students in developing communication skills in a foreign language. Particularly, ChatGPT is believed to have a significant role in enhancing reading and writing skills. However, the disadvantages and limitations of artificial intelligence should not be overlooked. Careful utilization of artificial intelligence in foreign language learning can play a crucial role in students' skill development and improvement of their communication abilities.
The Effectiveness of Cooperative Learning Model with Group Investigation (Gi) Using Comic Media Towards Mathematical Literacy Ability of Viii Students at SMP Negeri 1 Punggelan

This study aims to determine the effectiveness of the GI-type cooperative learning model assisted by comic media on increasing the mathematical literacy skills of class VIII students of SMP Negeri 1 Punggelan. This research is an experimental research with a quantitative approach. The population in this study were students of class VIII SMP Negeri 1 Punggelan and the samples used were taken by simple random sampling technique and the sample results obtained were students of class VIII G and class VIII H, totaling 58 students. The instrument used in this study was a description test consisting of a pretest and a posttest. The results of this study are that the average mathematical literacy ability of students who are subjected to the GI type cooperative learning model assisted by comic media is better than those who are not subjected to it, as seen from the results of the N-Gain test, namely the experimental class obtained an average N-Gain value of 0 .77 with high criteria, while in the control class the average N-Gain value for the control class was 0.47 with moderate criteria. In the t-test, the two independent samples showed a significance result of 0.000 &lt;0.05, meaning that there was a significant difference in students' mathematical literacy skills between the experimental and control classes. The GI type cooperative learning model assisted by comic media was declared effective for increasing mathematical literacy skills with the average N-Gain test results obtained in the experimental class by 77% with effective criteria and in the control class by 47% with less effective criteria.
Forecasting Simcard Demand Using Linear Regression Method

The purpose of this research is to get a prediction of how package card growth will be based on sales data from SINAR E-XIX CELL. The method used for this forecasting is linear regression, based on the number of card packs sold which is the causal variable. The accuracy of predictions is carried out using Python based on the results of research conducted with data on simcard sales over a period of two years, it was found that in the following year the number of growth in demand for sim cards in the coming year has decreased, but there is one card that has experienced an increase in the number of growth in the following year. which will come. Forecasting using Linear Regression can be said to be classified as very well based on using python. After doing the forecasting it can be concluded that in the next few years the demand for cards will be less.
Learning to Adapt Adversarial Perturbation Consistency for Domain Adaptive Semantic Segmentation of Remote Sensing Images

Semantic segmentation techniques for remote sensing images (RSIs) have been widely developed and applied. However, most segmentation methods depend on sufficiently annotated data for specific scenarios. When a large change occurs in the target scenes, model performance drops significantly. Therefore, unsupervised domain adaptation (UDA) for semantic segmentation is proposed to alleviate the reliance on expensive per-pixel densely labeled data. In this paper, two key issues of existing domain adaptive (DA) methods are considered: (1) the factors that cause data distribution shifts in RSIs may be complex and diverse, and existing DA approaches cannot adaptively optimize for different domain discrepancy scenarios; (2) domain-invariant feature alignment, based on adversarial training (AT), is prone to excessive feature perturbation, leading to over robust models. To address these issues, we propose an AdvCDA method that guides the model to adapt adversarial perturbation consistency. We combine consistency regularization to consider interdomain feature alignment as perturbation information in the feature space, and thus propose a joint AT and self-training (ST) DA method to further promote the generalization performance of the model. Additionally, we propose a confidence estimation mechanism that determines network stream training weights so that the model can adaptively adjust the optimization direction. Extensive experiments have been conducted on Potsdam, Vaihingen, and LoveDA remote sensing datasets, and the results demonstrate that the proposed method can significantly improve the UDA performance in various cross-domain scenarios.
Students’ Challenges in EFL Speaking Classrooms

Speaking is considered one of the main skills of communication for English language learners. Students tend to face challenges through the process of learning and improving speaking skills. The current paper aims to study the difficulties students encounter in EFL-speaking classrooms and the major causes of these challenges. The sample for this study included six students studying the Listening and Speaking module as first-year undergraduate students for two semesters in the English department at Knowledge University. The study employed a qualitative approach using a semi-structured interview. Furthermore, it uses thematic analysis to analyze the raw data. This research paper examines the issues of speaking from students' perspectives to understand the demands and challenges of students in EFL classrooms that focus on speaking which have not been studied yet. The results of analyzing the data show that students have obstacles in EFL-speaking classrooms, like fear of making mistakes, shyness, limited vocabulary knowledge, and large class sizes. Finally, the implications of these results for both teachers and students and the limitations of the study are discussed.
Importance analysis of evaluation items in memorial space planning: enhancing tourism experiences through phenomenological spatial perspectives

This study introduces a novel spatial planning evaluation method that employs a phenomenological spatial perspective to enhance the dark tourism experience in memorial spaces. The phenomenological view recognizes space as a comprehensive entity and emphasizes the impact of perception and behavior on the experiential aspect of space. First, a theoretical exploration was conducted to establish the connections among memorial spaces, dark tourism, and phenomenology. Subsequently, a set of spatial evaluation elements and detailed indicators were developed. Finally, the analytic hierarchy process was applied to determine each hierarchical element's importance and assign weights to the evaluation indicators. The findings highlight the significance of perceptual and behavioral factors in the dark tourism experience within memorial spaces. Thus, these elements should be considered when designing new projects or undertaking functional renovations in such spaces.
NDARTS: A Differentiable Architecture Search Based on the Neumann Series

Neural architecture search (NAS) has shown great potential in discovering powerful and flexible network models, becoming an important branch of automatic machine learning (AutoML). Although search methods based on reinforcement learning and evolutionary algorithms can find high-performance architectures, these search methods typically require hundreds of GPU days. Unlike searching in a discrete search space based on reinforcement learning and evolutionary algorithms, the differentiable neural architecture search (DARTS) continuously relaxes the search space, allowing for optimization using gradient-based methods. Based on DARTS, we propose NDARTS in this article. The new algorithm uses the Implicit Function Theorem and the Neumann series to approximate the hyper-gradient, which obtains better results than DARTS. In the simulation experiment, an ablation experiment was carried out to study the influence of the different parameters on the NDARTS algorithm and to determine the optimal weight, then the best performance of the NDARTS algorithm was searched for in the DARTS search space and the NAS-BENCH-201 search space. Compared with other NAS algorithms, the results showed that NDARTS achieved excellent results on the CIFAR-10, CIFAR-100, and ImageNet datasets, and was an effective neural architecture search algorithm.
Addressing the class imbalance in tabular datasets from a generative adversarial network approach in supervised machine learning

One common issue with datasets used for supervised classification tasks is data imbalance or the unequal distribution of classes within a dataset. The class imbalance may cause biased machine learning models to favor the dominant class, misclassifying the minority class. Specific techniques can be employed to deal with the issue of class imbalance, including resampling by oversampling or undersampling and ensemble approaches. Besides, generative adversarial networks, a deep learning technique for building generative models, offer an alternative machine learning technique that is particularly well suited to address the class imbalance problem. This work introduces a machine learning-based approach to deal with the class imbalance in a cancer intracellular signaling dataset produced by a verified and validated computer simulation. Specifically, we use synthetic data generation to increase and balance the dataset generated by the computational simulation. The used approach simulates the oversampling method by employing a generative adversarial network to produce new examples for the minority class. Subsequently, we applied supervised machine learning methods, such as the K-NN algorithm, to assess whether or not the classification accuracy improved relative to the unbalanced dataset. The results presented in this work have shown an accuracy increase in the classification of patterns belonging to the minority class, with an improvement of [Formula: see text].
EFFECTIVE INNOVATIVE TECHNOLOGIES OF HIGH-QUALITY EDUCATION BY CHOOSING A COMPONENT OF TECHNICAL EDUCATIONAL PROGRAMS OF MODERN UNIVERSITIES

Осы мақалада жоғары оқу орнының техникалық мамандығының таңдау пәнінен өтілген сабақта қолданылған инновациялық оқыту әдістерінің сапалы білім беру мен командада немесе ұжымда жұмыс істей алу қабілетіне ие кадрларды даярлаудағы тиімділігі қарастырылған. Атап айтқанда, Қорқыт Ата атындағы Қызылорда университетінің 6В07260-«Азық-түлік өндірісінің технологиясы» білім беру бағдарламасы бойынша 3-курстың ТПП-20-1 оқу тобы студенттеріне, бекітілген сабақ кестесі бойынша өткізілген, «Қоғамдық тамақтану өнеркәсіптерінде сервисті ұйымдастыру» атты таңдау пәнінен «Мейрамхана залында тұтынушыларға қызмет көрсету» тақырыбындағы дәрістік сабақ нәтижелері арқылы техникалық мамандықтың таңдау пәнінен өтілген сабақта қолданылған инновациялық оқыту әдістерінің сапалы білім беру мен командада немесе ұжымда жұмыс істей алу қабілетіне ие кадрларды даярлаудағы тиімділігі дәлелденген. 6В07260 - «Азық-түлік өндірісінің технологиясы» білім беру бағдарламасы бойынша білім алушыларды болашақта бір командада немесе ұжымда жұмыс істей алу қабілетіне ие кадрлар ретінде қалыптастыру үшін дәрістік сабақта «Шағын топтардағы жұмыс» атты интербелсенді оқыту технологиясын қолданудың маңыздылығы сабақ нәтижесі арқылы дәлелденді. Сабақта қолданылған «Шағын топтардағы жұмыс» атты интербелсенді оқыту технологиясы білім алушылардың болашақта бір командада жұмыс істеу қабілетіне ие болуларына ықпал етеді. Дәрістік сабақта қолданылған «Төңкерілген сынып (сабақ)» атты инновациялық оқыту әдісі білім алушылардың сабақ тақырыбын сапалы әрі нәтижелі меңгеруіне септігін тигізетіндігі анықталынды. Техникалық мамандықтардың таңдау пәндерінен дәрістік сабақтарда «Ассоциация» оқыту әдісін сабақты қорытындылауда қолдану аса тиімді екендігі сабақ нәтижесі арқылы дәлелденді.
Decision Tasks and Basic Algorithms

Recommender systems are decision support systems helping users to identify one or more items (solutions) that fit their wishes and needs. The most frequent application of recommender systems nowadays is to propose items to individual users. However, there are many scenarios where a group of users should receive a recommendation. For example, think of a group decision regarding the next holiday destination or a group decision regarding a restaurant to visit for a joint dinner. The goal of this book is to provide an introduction to group recommender systems, i.e., recommender systems that determine recommendations for groups. In this chapter, we provide an introduction to basic types of recommendation algorithms for individual users and characterize related decision tasks. This introduction serves as a basis for the introduction of group recommendation algorithms in Chap. 2 .
Further Choice Scenarios

Until now, we have focused on group recommendation techniques for choice scenarios, related to explicitly defined items. However, further choice scenarios exist that differ in the way alternatives are represented and recommendations are determined. We introduce a categorization of these scenarios and discuss knowledge representation and group recommendation aspects on the basis of examples.
Explanations for Groups

Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the item domain. Consequently, explanations are designed in order to achieve specific goals such as increasing the transparency of a recommendation or increasing a user’s trust in the recommender system. In this chapter, we provide an overview of existing research related to explanations in recommender systems and specifically discuss aspects relevant to group recommendation scenarios. In this context, we present different ways of explaining and visualizing recommendations determined on the basis of aggregated predictions and aggregated models strategies.
Optimization of solar tree performance in Egypt: a simulation-based investigation

Abstract The paper discusses the orientation and positions of solar panels in a solar tree. Solar energy contributes to the most available and abundant energy source in Egypt. Hence, The PV system is considered a promising solution to generate power from this source. However, its energy density is low. Therefore, the solar tree is considered in this paper as it has higher density and fits the urban cities of Egypt. The design of the solar tree is divided into two sections: the study of the solar panel’s orientation and positions. For the solar tree orientation, a single-objective genetic algorithm is used to maximize the total incident irradiance on a tilted solar panel. The output of this optimization problem is 20 optimal angles for the tilt angle ( $$\beta$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>β</mml:mi> </mml:math> ) and surface azimuth angle of the solar panel ( $$\gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>γ</mml:mi> </mml:math> ). These angles are inputted to the second methodology to determine their positions. In this methodology, a multi-objective genetic algorithm is used to trade between the area occupied by the solar tree representing the cost and the power loss due to the shading of the solar panels on each other; the output is the coordinates of the center of each solar panel. The proposed solar tree design increased the incident irradiance from 4427.389 to 4496.584 watts/m 2 /year using the optimal angles and the output power increased as the shadow loss decreased from 904.685 watts to 300 watts. The final design of the solar tree is 5 m in height and 1.13 m wide.
Handling Preferences

This chapter presents an overview of approaches related to the handling of preferences in (group) recommendation scenarios. We first introduce the concept of preferences and then discuss how preferences can be handled for different recommendation approaches. Furthermore, we sketch how to deal with inconsistencies such as contradicting preferences of individual users.
Trademarks in the Age of Automated Commerce: Consumer Choice and Autonomy

Abstract In the age of automated commerce, powered by artificial intelligence and machine learning (AI/ML) tools, the role of trademarks in shopping may be diminishing. For instance, the Alexa voice assistant will announce only a couple of purchase options under specific trademarks, concealing the plenitude of other products on its interface. Another example is that Amazon will pursue the shipping-then-shopping scenario, with trademarks being perceived by consumers only upon delivery. Moreover, it has been predicted that, in some cases, the new AI/ML tools will search for products irrespective of trademarks and will do so faster than any human being. Under those circumstances, consumer search costs will be lower, not because of trademarks but because of the new shopping architecture based on those tools. While lowering consumer search costs has traditionally been the role of trademarks, the availability of other tools for the same purpose may be a positive development. However, another trend is that the new AI/ML tools are constantly taking part in consumer decision-making, possibly reducing consumers’ freedom of choice and personal autonomy. In attempting to tackle this issue, it is worth approaching trademarks from the perspective of choice and autonomy. Seen from this angle, it may be possible to learn specific lessons from European Union (EU) trademark law that will assist in reshaping the automated – and autonomous – shopping architecture for the benefit of consumer well-being. In this exercise, close attention will be paid to the case-law of the Court of Justice of the European Union, with its discourse on product alternatives, while due regard will be shown to the new legislation on the platform economy in the EU. In the end, this paper will demonstrate that trademarks are valuable not only because they help reduce search costs but also because they promote alternatives and, thus, improve consumers’ general autonomy.
Shared perceived causes of suicide among young men and violence against young women offer potential for co-designed solutions: intervention soft-modelling with fuzzy cognitive mapping

Violence against young women (VAW) and suicide among young men are serious concerns in Botswana and elsewhere. We examined the overlap in locally perceived causes of these two forms of violence in Botswana using the results from separate studies that used fuzzy cognitive mapping (FCM) to explore perceived causes of the two outcomes. FCM depicts perceived causes of an outcome and their links to the outcome and each other, with weights denoting the perceived strength of each link. The two studies engaged groups of young women, young men, older women, and older men in rural communities. We grouped related concepts into broader categories, then combined category maps for each outcome into a single map including both forms of violence. Based on social network analysis, we calculated the out-degree centrality of each category indicating its influence within the network. Intervention soft-modelling explored the effects of removing individual categories on suicide and VAW. Of 24 causal categories in the combined map, six were shared between both outcomes, 10 were for suicide only, and seven were for VAW only. The six shared categories accounted for 60% of cumulative influence of all categories in the combined map. The three most influential shared categories were financial difficulties, conflict in relationships, and parenting and family issues. Based on local perceptions, avoiding conflict in relationships could reduce suicide by 4.8% and VAW by 18.5%. Eliminating parenting and family issues could reduce suicide by 3% and VAW by 5.4%. Preventing financial difficulties could reduce suicide by 9.3% and VAW by 2.9%. The findings support the idea that some interventions might reduce both personal and interpersonal violence among youth. Analysis of stakeholder perceived causes and soft-modelling of potential interventions could inform community-led co-design of strategies to reduce youth suicide and violence against young women in Botswana.
Will the Metaverse Revolutionize the Narrative?

The metaverse narrative “inverts” the relationship between human beings and the story. It uses the whole human body as “feeling node” and creates hallucinatory immersion consciousness and interactive somatosensory through the closed way of “seeing, hearing, touching and knowing”, ultimately forming a whole new story situation. This is a “subject bodilyization” situation, presenting a large collection of human desires. It allows the will and desires of different people to interweave and collide, and it is a place where all kinds of people’s desires are transformed and reborn in virtual reality. The metaverse creates a “story forever reality” that does not require endings and narrators. It has changed the situation in real life where the jungle rule is the first logic, and has become a “non-social” society. It covers up the existence of market monopoly, profit deprivation, and value manipulation cleverly. It points to the empirical production of castrated “Something”.
An analysis of adult learners’ needs for college English education

With the increase in the number of adult learners pursuing higher education, there is a growing necessity to identify their needs for college English education. In this study, a survey was conducted among adult learners enrolled in a lifelong education college at a university in Seoul. The collected responses underwent both quantitative and qualitative analyses. The findings indicate that a larger number of respondents considered learning English to be essential. The primary reason is that English is now a requirement, and some respondents also needed it for everyday use, employment, or job changes. Speaking and listening were chosen as the most necessary areas for learning. Most of the respondents expressed agreement with the necessity of level-differentiated classes, and over half of them supported the administration of a diagnostic test. More adult learners showed a preference for Korean instructors and instruction in Korean in contrast to native English-speaking instructors and instruction in English. The courses the respondents requested mainly centered around fundamental or practical subjects, reflecting their English proficiency levels and characteristics as adult learners. Based on the discussion of the findings, this study offers suggestions to foster effective English learning for adult college students.