Reviewerly/exHarmony huggingface dataset:
PENGARUH PENDEKATAN CLIL DENGAN MEDIA BLOG TERHADAP KETERAMPILAN MENULIS ARTIKEL ILMIAH SEBAGAI PENGEMBANGAN BAHASA

Abstrak. Penelitian ini bertujuan untuk mengetahui Pengaruh Pendekatan CLIL dengan Menggunakan Media Blog Terhadap Keterampilan Menulis Artikel Ilmiah Siswa Kelas XI SMA Negeri 113 Jakarta. Penelitian ini menggunakan metode penelitian pre experimental design atau quasi experiment karena hanya menggunakan kelas eksperimen dalam penelitian. Populasi penelitian yaitu siswa kelas XI sejumlah 30 siswa. Pengambilan data dengan pretest dan posttest yaitu sebelum dan sesudah diberikan perlakuan. Analisis data menggunakan analisis kuantitatif dengan menggunakan uji persyaratan analisis pada kelas eksperimen. Hasil penelitian didapatkan bahwa penggunaan pendekatan CLIL dengan menggunakan media blog dalam menulis artikel ilmiah siswa kelas XI SMAN 113 Jakarta mengalami keberhasilan. Pada kriteria penilaian kesesuaian judul dengan isi didapatkan sejumlah 70 %. Kemudian pada kriteria penilaian struktur teks didapatkan sejumlah 83,33%. Selanjutnya pada kriteria penilaian aspek kebahasaan didapatkan sejumlah 43,33%. Sementara itu, pada kriteria penilaian aspek menulis didapatkan sejumlah 53,33%. Dengan demikian, dapat disimpulkan bahwa pendekatan CLIL dengan menggunakan media blog terhadap keterampilan menulis teks artikel ilmiah memilikipengaruh yang signifikan.Kata Kunci: pendekatan CLIL, media blog, teks artikel ilmiah, pengembangan bahasa Abstract. The objective of this research is to identify the impact of CLIL aproach using blog as the media to students’ capability in scientific article writing. The subject was eleventh grade students of 113 Senior High School Jakarta. This research applied pre experimental designorquasi experimentas this research only used experiment class in the research. The population of the research was the eleventh gradestudents totaling 30students. Data collection was through pretest and posttest or before and after the treatment. This research applied quantitative analysis by fulfilling research requirement in the experiment classroom. The research showed that CLIL approach succeded in improving students capability in writing scientific article. Regarding title and content conformity there was 70% of the articles have met the criteria.83,33% of the articles have met the criteria of text structure. 43,33% of the articles prepared by the students have met the requirement of language aspects. Furtermore, there are 53,33% of articles prepared have met the writing aspects requirements. It can be concluded that CLIL approach using blog as media has significant impact to the improvement of students’ capability in writitng scietific article.Keywords: CLIL aproach, blog, scientific article writing, language development.
The Identity in Everyone's Pocket

Newer phones use security features in many different ways and combinations. As with any security technology, however, using a feature incorrectly can create a false sense of security. As such, many app developers and service providers today do not use any of the secure identity-management facilities that modern phones offer. For those of you who fall into this camp, this article is meant to leave you with ideas about how to bring a hardware-backed and biometrics-based concept of user identity into your ecosystem.
Una propuesta didáctica mediante el Robot Nao para la enseñanza del idioma inglés como una herramienta de apoyo para la educación 4.0

NAO is a humanoid robot developed by SoftBank Robotics (formerly Aldebaran Robotics). NAO allows interactivity with its components, making it a versatile platform for the development of applications in different sectors, such as health, education and research. The present work entitled “A didactic proposal through the Nao Robot for teaching English as a support tool for education 4.0.” Its main objective is to design, develop and implement learning situations that increase the level of expression and understanding of the English language, through the humanoid robot Nao, who will help to carry out activities that allow people to interact with the robot first so that people do not shy away from speaking this language. The project is designed to teach English to children and young people of school age belonging to an educational institution, with the help of the Nao Humanoid Robot belonging to the area of educational robotics of the Polytechnic University of Juventino Rosas. For the design of the activities to be carried out, the grammar topics that are taught in the levels of schooling from primary to university were investigated to be able to achieve the design that is needed at each educational level and so the Nao Humanoid Robot can perform its task of Correctly, these topics belong to the curriculum designed by the SEP (Secretary of Public Education).
An exponent one-fifth algorithm for deterministic integer factorisation

Hittmeir recently presented a deterministic algorithm that provably computes the prime factorisation of a positive integer $N$ in $N^{2/9+o(1)}$ bit operations. Prior to this breakthrough, the best known complexity bound for this problem was $N^{1/4+o(1)}$, a result going back to the 1970s. In this paper we push Hittmeir's techniques further, obtaining a rigorous, deterministic factoring algorithm with complexity $N^{1/5+o(1)}$.
Data-Driven Learning of Geometric Scattering Networks

We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations. The incorporation of our LEGS-module in GNNs enables the learning of longer-range graph relations compared to many popular GNNs, which often rely on encoding graph structure via smoothness or similarity between neighbors. Further, its wavelet priors result in simplified architectures with significantly fewer learned parameters compared to competing GNNs. We demonstrate the predictive performance of LEGS-based networks on graph classification benchmarks, as well as the descriptive quality of their learned features in biochemical graph data exploration tasks.
A Method for Dynamically Assessing Credit Risk of Internet Financial Service Platform

This paper comprehensively uses the fuzzy cognitive graph method and entropy weight method to dynamically assess the credit risk of Internet financial service platforms. First, a platform credit risk indicator system was constructed. Secondly, the genetic algorithm is used to learn the fuzzy cognitive graph weight matrix. Then, the fuzzy cognitive graph inference mechanism is used to predict the dynamic changes of the credit risk index and the stable state finally achieved. Finally, combined with the index weights calculated by the entropy weight method, the credit risks of the three online lending platforms are dynamically assessed. This paper realizes the dynamic assessment and prediction of Internet financial service platforms, and provides theoretical support for the credit risk of government supervision platforms.
Higher Public School as Space for Third-Age Person’s Self-Actualization

The paper aims to identify psychological and pedagogical conditions promoting a third-age person’s self-actualization in Higher Public School educational space. The authors describe the basic approaches to defining the concepts “self-actualization”, “third age”, clarify the essence of the notion “Higher Public School”, reveal socio-psychological, pedagogical and social effects of andragogic interaction between subjects of educational space. Scientific originality of the study includes developing an organizational and methodological basis of third-age education focused on self-actualization, taking into account modern trends in adult education. The research findings are as follows: relying on empirical research results, the authors justify importance of third-age education for a personality’s self-actualization, reveal psychological and pedagogical conditions of educational process organization and substantiate its optimal model - Higher Public School.
An Effective Integrated Metaheuristic Algorithm For Solving Engineering Problems

To tackle a specific class of engineering problems, in this paper, we propose an effectively integrated bat algorithm with simulated annealing for solving constrained optimization problems.Our proposed method (I-BASA) involves simulated annealing, Gaussian distribution, and a new mutation operator into the simple Bat algorithm to accelerate the search performance as well as to additionally improve the diversification of the whole space.The proposed method performs balancing between the grave exploitation of the Bat algorithm and global exploration of the Simulated annealing.The standard engineering benchmark problems from the literature were considered in the competition between our integrated method and the latest swarm intelligence algorithms in the area of design optimization.The simulations results show that I-BASA produces high-quality solutions as well as a low number of function evaluations.
Difficulties of Summarizing and Paraphrasing in English as a Foreign Language (EFL): Saudi Graduate Students’ Perspectives

This study examined the major challenges that graduate students ordinarily encounter when it comes to summarizing and paraphrasing source texts. Drawing upon a qualitative methodology, nine Saudi students who were pursuing their graduate degrees in various disciplines have been interviewed. The study findings have shown that various sources of impediments could aggravate students' difficulties in the skills under investigation. These were: Students' inadequate level of English proficiency as the first area to cause difficulties in summarizing and paraphrasing source texts, problems pertaining to students' writing styles as the second, and third, poor reading comprehension skills. However, multiple listening to the interviews and careful analysis of the transcripts produced for these revealed that the challenges faced by students in summarizing and paraphrasing could be enhanced by a further important factor that feed into students struggles with these skills, that is, fear of committing plagiarism. Constantly being in doubt whether the paraphrases that they produce are legitimized or not makes the process of paraphrasing even harder for them. Possible strategies to cope with these difficulties are also provided in this study. The findings presented in this study add to our understanding of the challenges that writing in an academic genre poses for graduate students. Furthermore, these findings send important practical implications for decision-makers in higher studies institutions.
Learning Not to Learn: Nature versus Nurture In Silico

Animals are equipped with a rich innate repertoire of sensory, behavioral and motor skills, which allows them to interact with the world immediately after birth. At the same time, many behaviors are highly adaptive and can be tailored to specific environments by means of learning. In this work, we use mathematical analysis and the framework of memory-based meta-learning (or ’learning to learn’) to answer when it is beneficial to learn such an adaptive strategy and when to hard-code a heuristic behavior. We find that the interplay of ecological uncertainty, task complexity and the agents’ lifetime has crucial effects on the meta-learned amortized Bayesian inference performed by an agent. There exist two regimes: One in which meta-learning yields a learning algorithm that implements task-dependent information-integration and a second regime in which meta-learning imprints a heuristic or ’hard-coded’ behavior. Further analysis reveals that non-adaptive behaviors are not only optimal for aspects of the environment that are stable across individuals, but also in situations where an adaptation to the environment would in fact be highly beneficial, but could not be done quickly enough to be exploited within the remaining lifetime. Hard-coded behaviors should hence not only be those that always work, but also those that are too complex to be learned within a reasonable time frame.
Virtual Interaction between Teacher and Parents in Modern Educational Institution

The research objective includes theoretical and empirical justification of efficiency of virtual teacher-parents interaction in digital educational environment. The authors classify virtual forms of family-school interaction, analyse the existing digital tools and reveal relevant problems of distance communication between teachers and parents. Scientific originality of the study lies in the fact that relying on the conducted theoretical and applied research, the authors have identified the most efficient forms of virtual teacher-parents interaction considered as an additional communication channel, revealed the existing problems of virtual family-school communication. The conducted empirical research justifies importance of interaction with parents, including virtual interaction, which allows improving information competence of a teacher and parents and helps to overcome typical problems of distance learning.
Pemanfaatan Metode Association Rules dan Holt-Winter Multiplicative untuk Meningkatkan Peluang Penjualan Obat Pertanian

The competition level between companies on executing product marketing is rapidly increasing, so the companies have to understand the importance of correlation between external environments of company with consumer’s needs. One of the efforts that can be done is by utilizing data warehouse and the application of infrastructure in information and technology field. This research combined Association Rules method to extracting pattern and finding every possibility that potential to increase sales and Holt-Winter Multiplicative method to estimate the alteration of trend on the seasonal data. After passed through data processing process by using RapidMiner tools, information that consists of correlation pattern between rule that describe the comparison of product and the sales working area and season that affects the product sale. The pattern used by company to know which product is often purchased by customer. Besides that, this research produces changing trend data of PT ABC’s product that generated by result of previous data comparison with forecast data. Based on value of error rate Mean Absolute Percentage Error (MAPE) in estimating forecast result on the PT ABC’s sales transaction data during 3 years, it shows good level of accuracy. Result of data test, by considering rule that formed and forecast result so the company can control and manage product in order to avoid incorrect sales. This thing will effect on repression of operational cost and PT ABC can identify available opportunities to increase sale of agricultural medicine.
Dense Relational Image Captioning via Multi-task Triple-Stream Networks

We introduce dense relational captioning, a novel image captioning task which aims to generate multiple captions with respect to relational information between objects in a visual scene. Relational captioning provides explicit descriptions for each relationship between object combinations. This framework is advantageous in both diversity and amount of information, leading to a comprehensive image understanding based on relationships, e.g., relational proposal generation. For relational understanding between objects, the part-of-speech (POS; i.e., subject-object-predicate categories) can be a valuable prior information to guide the causal sequence of words in a caption. We enforce our framework to learn not only to generate captions but also to understand the POS of each word. To this end, we propose the multi-task triple-stream network (MTTSNet) which consists of three recurrent units responsible for each POS which is trained by jointly predicting the correct captions and POS for each word. In addition, we found that the performance of MTTSNet can be improved by modulating the object embeddings with an explicit relational module. We demonstrate that our proposed model can generate more diverse and richer captions, via extensive experimental analysis on large scale datasets and several metrics. Then, we present applications of our framework to holistic image captioning, scene graph generation, and retrieval tasks.
Health-saving technologies in teacher's activity

The state, society and the labor market are making ever higher demands on teaching staff. Compliance with the established requirements forces students to increase energy consumption, the rate of development of the data, which entails a violation of biological rhythms, poor health and poor performance. Therefore, there is a need to improve the competencies of teachers related to mastering ways to improve the functional state and human health. The relevance of health-saving technologies implementation in vocational education is growing. Since there is no special course for their study, additional training courses on this topic become popular. Purpose of the article: designing the content of the course for additional professional training of teaching staff, ensuring the achievement of the development of health-saving technologies and their use in professional training. The aim of the course is to promote development of the competencies of teaching staff in the field of health-saving technologies application. The article reveals the possibilities of health-saving technologies, the role in preparation of an efficient, highly qualified specialist who meets modern requirements and is able to carry out professional activities at a high level. The study, in which experts selected from among the teachers of a higher educational institution, contributed to the solution of the problem of selecting the content of the course of additional professional training. When selecting the course content, an expert opinion on the established criteria was taken into account. As the planned results of the course, we determined a list of competencies that ensure the ability and willingness of teachers to systematically implement health-saving technologies.
Personally Oriented Methodology of Teaching a Foreign Language to Primary School Pupils

The author proposes principles of early foreign-language education of 1st-grade pupils in the period of transition from pre-school to primary school. The article analyses the existing principles of foreign language teaching in primary school. The paper focuses on developing efficient principles of teaching foreign-language speech activity to 6-7-year-old children. Scientific originality of the study involves developing personally oriented methodology of teaching a foreign language to primary school pupils. The conducted research allows concluding that early foreign language learning should be based on taking into account individual, psychophysiological peculiarities of junior schoolchildren.
Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data

The process of collecting and annotating training data may introduce distribution artifacts which may limit the ability of models to learn correct generalization behavior. We identify failure modes of SOTA relation extraction (RE) models trained on TACRED, which we attribute to limitations in the data annotation process. We collect and annotate a challenge-set we call Challenging RE (CRE), based on naturally occurring corpus examples, to benchmark this behavior. Our experiments with four state-of-the-art RE models show that they have indeed adopted shallow heuristics that do not generalize to the challenge-set data. Further, we find that alternative question answering modeling performs significantly better than the SOTA models on the challenge-set, despite worse overall TACRED performance. By adding some of the challenge data as training examples, the performance of the model improves. Finally, we provide concrete suggestion on how to improve RE data collection to alleviate this behavior.
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI

Trust is a central component of the interaction between people and AI, in that 'incorrect' levels of trust may cause misuse, abuse or disuse of the technology. But what, precisely, is the nature of trust in AI? What are the prerequisites and goals of the cognitive mechanism of trust, and how can we promote them, or assess whether they are being satisfied in a given interaction? This work aims to answer these questions. We discuss a model of trust inspired by, but not identical to, sociology's interpersonal trust (i.e., trust between people). This model rests on two key properties of the vulnerability of the user and the ability to anticipate the impact of the AI model's decisions. We incorporate a formalization of 'contractual trust', such that trust between a user and an AI is trust that some implicit or explicit contract will hold, and a formalization of 'trustworthiness' (which detaches from the notion of trustworthiness in sociology), and with it concepts of 'warranted' and 'unwarranted' trust. We then present the possible causes of warranted trust as intrinsic reasoning and extrinsic behavior, and discuss how to design trustworthy AI, how to evaluate whether trust has manifested, and whether it is warranted. Finally, we elucidate the connection between trust and XAI using our formalization.
Evolutionary Algorithms and Efficient Data Analytics for Image Processing

Steganography algorithms facilitate communication between a source and a destination in a secret manner. This is done by embedding messages/text/data into images without impacting the appearance of the resultant images/videos. Steganalysis is the science of determining if an image has secret messages embedded/hidden in it. Because there are numerous steganography algorithms, and since each one of them requires a different type of steganalysis, the steganalysis process is extremely challenging. Thus, researchers aim to develop one universal steganalysis to detect all known and unknown steganography algorithms, ideally in real-time. Universal steganalysis extracts a large number of features to distinguish stego images from cover images. However, the increase in features leads to the problem of the curse of dimensionality (CoD), which is considered to be an NP-hard problem. This COD problem additionally makes real-time steganalysis hard. A large number of features generates large datasets for which machine learning cannot generate an optimal model. Generating a machine learning based model also takes a long time which makes real-time processing appear impossible in any optimization for time-intensive fields such as visual computing. Possible solutions for CoD are deep learning and evolutionary algorithms that overcome the machine learning limitations. In this study, we investigate previously developed evolutionary algorithms for boosting real-time image processing and argue that they provide the most promising solutions for the CoD problem.
The Protoworld as a Neutron-Like Object

Here we show the similarity between the neutron and the virtual neutral pion inside it on the one hand, and the Protoworld and the very early Universe inside it on the other hand. Such similarity leads to the correct abundances of different states (i.e. baryon matter, dark matter, photons and neutrinos, and dark energy) in the cosmic structure at the beginning of the expansion of the Universe and in the Universe today.