Reviewerly/exHarmony huggingface dataset:
The Power of Adaptivity for Stochastic Submodular Cover

In the stochastic submodular cover problem, the goal is to select a subset of stochastic items of minimum expected cost to cover a submodular function. Solutions in this setting correspond to sequential decision processes that select items one by one "adaptively" (depending on prior observations). While such adaptive solutions achieve the best objective, the inherently sequential nature makes them undesirable in many applications. We ask: how well can solutions with only a few adaptive rounds approximate fully-adaptive solutions? We give nearly tight answers for both independent and correlated settings, proving smooth tradeoffs between the number of adaptive rounds and the solution quality, relative to fully adaptive solutions. Experiments on synthetic and real datasets show qualitative improvements in the solutions as we allow more rounds of adaptivity; in practice, solutions with a few rounds of adaptivity are nearly as good as fully adaptive solutions.
A new family of Polyak and Ribiere conjugate gradient method for impulse noise removal

Abstract In this paper, impulse noise removal problem is formulated as an unconstrained optimization problem with smooth objective function. It can be solved by conjugate gradient methods with desired properties (low memory and strong global convergence) in high dimensions. Accordingly, a family of the Polyak-Ribi`ere conjugate gradient directions is constructed for which the descent condition holds. Using several images, it is shown that the new methods are very robust and efficient in comparison with other competitive methods for impulse noise removal, especially in terms of the peak signal to noise ratio (PSNR). Mathematics Subject Classification (2000) 90C30a · 90C25 · 90C90 · 68U10 · 03D15
Imagining a More Inclusive World

Fiction can stimulate empathy toward others by deepening sensitivity to difference. Pragmatist philosophers like Richard Rorty and John Dewey consider imaginative capacity the root of empathy and promote fiction as a source of ethical insight. Understanding others who are different is fundamental to designing more inclusive spaces. We explore how The Shape of Water illuminates the current discourse on inclusive design, particularly how design practices influence encounters between and among diverse bodies and environments. The Shape of Water is a non-trivial, award-winning, commercial film about the inter-species romance between a mute janitor, Elisa Esposito, who works in a top-secret, 1960s military research facility, and her love interest, the captive Amphibian Man, a tall, green, biped, bi-respiratory humanoid. We explore the film’s ethos as a speculative (even magic) ‘realm’ in which excluded individuals find agency and power in their very marginalisation and exclusion. This is most evident in its depictions of mutism and non-normative communication, which inspire fundamental reformulations of the nature of otherness and disability inrelation to the material environment. We address both the film’s narrative action and its scenography, with interest in how production design embodies the protagonists’ exclusion and agency. The Shape of Water illuminates the discourse on inclusive design in multiple respects. First, through narrative depiction of individuals’ interactions and lives, it demonstrates how divisions between ‘us’ and ‘them’ materialise in objects and spaces, and how differences considered a deficit in one context may manifest as an advantage in another. Second, through the agency of magical realism, the film invites us to explore the potential of spaces that accommodate such a/symmetry through environmental hybridity. Finally, while social and material environments are are known to disable or enable, The Shape of Water represents how they interact, highlighting the role of everyday design(ers) in making spaces more inclusive.
AREA OF JUNIOR HIGH SCHOOL CLASSROOM FROM THE VIEWPOINT OF ACTIVE LEARNING PROMOTION

M junior high school practices original group learning called “the unit learning” for active learning. We clarified the actual situation of the class development in the school and analyzed it about the area of the classroom. As a result, we showed it is necessary to extend the area of the classroom for the round of inspection between the desks and the storage space, and the expectation of teachers to expansion of the width of the classroom suggests that the flexible classroom design that is different from the conventional dimensions is necessary for planning.
EDUCATIONAL EFFECT OF SMALL SPACES CREATION BY STUDENTS DESIGN-BUILD THROUGH INDUSTRY-ACADEMIA COLLABORATION

At Niigata Institute of Technology, two small spaces were created inside and outside the university in 2019 and 2020 through industry-academia collaboration and students’ ideas, design, construction work. Through these efforts, students have gained deep learning and experience that cannot be obtained in conventional classes. A comparison of the results of the student review sheets analyzed by text mining and the results of the learning questionnaire in the conventional lessons revealed that there was much deeper learning than in the conventional lessons.
The Role of Cultural Content in Teaching Reading Comprehension

This study investigates the role of cultural content in teaching reading comprehension. This research is (1) to analyze the teachers’ way to teach the cultural content in the classroom in teaching reading comprehension; (2) to find out whether teachers have an awareness of the importance of cultural learning in the form of cultural content in learning English; and (3) to find out the teachers’ responses to teach cultural content in reading comprehension. The reseach used qualitative research. The instruments used in this research were documentation, observation, and interviews. This research involves three English teachers as participants of SMKN 02 Rangkasbitung. The research findings show that cultural content on teaching reading comprehension was found to have been applied by the three English teachers. In class X, it was found that the teacher represented international culture in descriptive texts with material on the history of Hagia Sophia. Then, in class XI, it was found that the teacher represented target culture in the procedure text with the material How to Make Sandwich. In class XII, it was found that the teacher represented source culture in descriptive texts with Baduy Culture material. Next, the English teachers have realized the importance of learning cultural content on teaching reading comprehension. The last, the teachers gave a positive response and argued that cultural content can improve student’s learning processes in reading comprehension.
Developing Supplementary Speaking Material for Eighth Grade Students

The objective of this study is focusing on developing supplementary speaking materials for the eighth grade students of junior high school. It is according on students’ need and their difficulties in mastering their speaking ability. This problem found in SMP Plus Al-Mashduqiah. Students felt very difficult to speak because they did not know how to pronounce the word. Moreover, the speaking material in the existing book that used by school are too limited in scope of speaking. In fact, textbook and workbook used by the eighth grade students SMP Plus Al-Mashduqiah only have little portion, making a supplementary speaking material is needed and crucial. This design of the development is research and development with the adaption of the development proposed by Borg and Gall (1983). The procedures employed in this study were need analysis, stating instructional goals, developing instructional material, expert validation, try-out and final product. The results indicate that speaking materials developed by the researchers are appropriate to use for eight-grade students in the school and the teachers, therefore, strongly recommend that it be used by the teachers in other school settings as well.
A Study on the Influence of Teacher's Transformational Leadership and Students' Followership toward Student Engagement Perceived by Elementary School Students

Objectives The purpose of this study is to figure out how much teacher’s transformational leadership and students’ followership effect elementary school students’ engagement. Methods The subjects of this study were 250 elementary school students. These subjects completed a 5-point scale offline questionnaire. Except for 12 responses that were invalid, 238 responses were used for the actual analysis. The responses were analyzed using the SPSS statistic program. Results The results of this study are as follows: First, student’s perception of teacher’s transformational leadership showed positive influence on the student engagement. Visioning and empowerment, which are variables of teacher’s transformational leadership, showed positive influence on the student engagement. Second, students’ followership showed positive influence on the student engagement. Independent, critical thinking and active behaviour, which are variables of students’ followership, showed positive influence on the student engagement. Conclusions The conclusions of this study are as follows: First, as elementary school teacher’s transformational leadership is an important factor to increase student engagement, a teacher should strive to strengthen their transformational leadership skills. Second, as students’ followership is another important factor to increase student engagement, a teacher should help students foster followership through activities that promote independent, critical thinking and provide an atmosphere of an active participation.
FSCLBF: Feature Selection based on Correlation Label and B-R Belief Function in Multi-label Data

Abstract In many real-world problems, each sample has over one label, which may also have semantic correlations between labels. However, in multi-label datasets, there are many irrelevant features that impact the accuracy of multi-label learning. Using feature selection to improve accuracy is an effective method. Even though numerous papers have presented novel methods to feature selection, more effort has to be done to improve the accuracy of such methods. This paper presents a novel feature selection method based on correlation labels using a belief function called FSCLBF. Our proposed method increases the accuracy of multi-label learning by considering the correlation labels and the data uncertainty. The proposed method's first step is to select the essential features for each label using a belief function based on a rough set theory (B-R belief function). Then, we identify the equivalent labels according to the overlapping based on their essential features. In the next step, the tag of samples is determined in the set of equivalent labels. Finally, we select the essential features for each equivalent label set, using the B-R belief function by considering the sample differentiation to label. The experimental results show the superiority of FSCLBF to state-of-the-art methods in terms of Hamming loss, One-error, and Accuracy measures.
Stability analysis of chaotic systems from data

Abstract The prediction of the temporal dynamics of chaotic systems is challenging because infinitesimal perturbations grow exponentially. The analysis of the dynamics of infinitesimal perturbations is the subject of stability analysis. In stability analysis, we linearize the equations of the dynamical system around a reference point, and compute the properties of the tangent space (i.e., the Jacobian). The main goal of this paper is to propose a method that learns the Jacobian, thus, the stability properties, from observables (data). First, we propose the Echo State Network (ESN) with the Recycle Validation as a tool to accurately infer the chaotic dynamics from data. Second, we mathematically derive the Jacobian of the Echo State Network, which provides the evolution of infinitesimal perturbations. Third, we analyse the stability properties of the Jacobian inferred from the ESN, and compare them with the benchmark results obtained by linearizing the equations. The ESN correctly infers the nonlinear solution, and its tangent space with negligible numerical errors. In detail, (i) the long-term statistics of the chaotic state; (ii) the covariant Lyapunov vectors; (ii) the Lyapunov spectrum; (iii) the finite-time Lyapunov exponents; (iv) and the angles between the stable, neutral, and unstable splittings of the tangent space (the degree of hyperbolicity of the attractor). This work opens up new opportunities for the computation of stability properties of nonlinear systems from data, instead of equations. Mathematics Subject Classification (2020) 68T07 · 34D08 · 37D45 · 37M22
Improved marine predators algorithm with refracted opposition-based learning

This paper aims at the shortcomings of marine predators algorithm, such as low initial population quality and easy to fall into local optimum. In this paper, an improved marine predator algorithm is proposed to solve the above problems. Firstly, the algorithm uses opposition-based learning to generate high quality initial population. Secondly, the algorithm applies refracted opposition-based learning and opposition-based learning according to individual fitness. This method can generate high quality populations for local and global scope to improve population diversity obviously. Then, the adaptive mechanism is used to prevent the function from falling into local optimum at the later stage of iteration. Finally, the algorithm selects 14 test functions to verify performance.
Pedestrian detection and ranging based on edge computing

For the realization of low-power intelligent assisted driving systems, we design and implement a real-time pedestrian detection and ranging system for the embedded edge device Raspberry Pi using deep learning and binocular stereo vision. It has been verified by experiments that the system is feasible and can meet the real-time and precise requirements within a certain range.
Breast Cancer Prediction Using Gated Attentive Multimodal Deep Learning

Abstract Women are prone to breast cancer, which is a major cause of death. One out of every eight women has a lifetime risk of developing this cancer. Early diagnosis of this disease is critical and enhances the success rate of cure. It is extremely important to determine which genes are associated with the disease. However, too many features make studies on gene data challenging. In this study, an attention-based multimodal deep learning model was created by combining data from clinical, copy number alteration and gene expression sources. Based on our findings, it was observed that the proposed method produced successful results and will be helpful in breast cancer prediction tasks.
Transformation of Classical to Quantum Image, Representation, Processing and Noise Mitigation

Quantum and classical computers have drastically different image representations.In a classical computer, bits are used.However, in a quantum computer, qubits are used.In this paper, the quantum image representation is the similar as the classical image representation.To represent quantum images, qubits and their associated properties have been used.Quantum imaging has previously been done via superposition.As a result, quantum imaging implemented using the superposition feature.Unitary matrices are then used to represent quantum circuits.For the quantum representation, we've gone with a modest image.To create quantum circuits, IBM's Qiskit software and Anaconda Python was used.On an IBM real time computer and an Aer simulator, a quantum circuit with 10,000 shots runs.Noise has been reduced more in the IBM real time computer than in the IBM Aer simulator.As a result, the Aer simulator's noise and qubit errors are higher than the IBM real time computer's.Quantum circuit design and image processing are both done with Qiskit programming, which is an appendix at the end of the paper.As the number of shots raise, the noise level decreases even further.Noise and qubit errors increase when the image operates at a low number of shots.Quantum image processing, noise reduction, and error correction done by circuit computation shots increase.Quantum image processing, representation, noise reduction, and error correction all make use of the quantum superposition concept.
Sliding Window Based High Utility Item-Sets Mining over Data Stream Using Extended Global Utility Item-Sets Tree

High utility item-sets mining(HUIM)is a special topic in frequent item-sets mining(FIM).It gives better insights for business growth by focusing on the utility of items in a transaction.HUIM is evolving as a powerful research area due to its vast applications in many fields.Data stream processing, meanwhile, is an interesting and challenging problem since, processing very fast generating a huge amount of data with limited resources strongly demands high-performance algorithms.This paper presents an innovative idea to extract the high utility item-sets (HUIs) from the dynamic data stream by applying sliding window control.Even though certain algorithms exist to solve the same problem, they allow redundant processing or reprocessing of data.To overcome this, the proposed algorithm used a trie like structure called Extended Global Utility Item-sets tree (EGUI-tree), which is flexible to store and retrieve the mined information instead of reprocessing.An experimental study on real-world datasets proved that EGUI-tree algorithm is faster than the state-of-the-art algorithms.
Predicting Politicians' Misconduct: Evidence from Colombia

Corruption has pervasive effects on economic development and the well-being of the population. Despite being crucial and necessary, fighting corruption is not an easy task because it is a difficult phenomenon to measure and detect. However, recent advances in the field of artificial intelligence may help in this quest. In this article, we propose the use of machine learning models to predict municipality-level corruption in a developing country. Using data from disciplinary prosecutions conducted by an anti-corruption agency in Colombia, we trained four canonical models (Random Forests, Gradient Boosting Machine, Lasso, and Neural Networks), and ensemble their predictions, to predict whether or not a mayor will commit acts of corruption. Our models achieve acceptable levels of performance, based on metrics such as the precision and the area under the ROC curve, demonstrating that these tools are useful in predicting where misbehavior is most likely to occur. Moreover, our feature-importance analysis shows us which groups of variables are most important upon predicting corruption.
Pengaruh Motivasi Berprestasi dan Gaya Mengajar Guru Terhadap Prestasi Belajar Dalam Pembelajaran Ekonomi

This study aims to determine the effect of achievement motivation and teacher teaching style on learning achievement, determine the effect of achievement motivation on learning achievement, and determine the effect of teacher teaching style on learning achievement. The sample of this study were 92 students of class XI IPS SMAN 2 Pariaman. data collection using questionnaires and documentation. The data analysis technique used multiple regression analysis, f test, t test and R2. Based on the hypothesis t test, it can be concluded that there is a significant effect between achievement motivation and teacher teaching style on learning achievement. there is an influence of achievement motivation on learning achievement and there is an influence of teacher teaching style on student achievement. The variable of achievement motivation and teaching style of teachers has an influence of 51%, while 49% is influenced by other variables. Keywords : achievement motivation, teacher teaching style
Massovization and Professionalization of Universities in the Middle of the 20th Century: Sociocultural Effects

The article analyzes the socio-cultural consequences of massovization and professionalization universities processes launched in the middle of the 20th century under the influence of the idea of profitability of education. It is giving some rationales for understanding why these processes disadvantages have become the decline in the value and prestige of intellectual labour (including a scientific one), which was shifted to the shoulders of ordinary people, whereas previously, intellectual labour and creativity were mainly the duties of wealthy people and, in general, social elites. It is concluded that as a result of the massification and professionalization of universities, a trend has been formed for the commercialization of higher education, which destroys the traditional models of university education.
Identifying factors impacting the overall accuracy in image classification problems: a statistical approach

Image classification is a subject of pattern recognition that can be applied in several areas. Obtaining highly-accurate classification involves choosing optimal set-ups from which images will be classified. In this process, controllable variables can affect the overall classification accuracy, such as the image’s spatial resolution and the classification method. In this sense, we have designed a factorial experiment where the classification accuracy of an image (from Curitiba, Paraná, Brazil) was obtained from three satellites and three classification methods. The Kruskal-Wallis test was applied to evaluate if the variability across factor levels supports the hypothesis that the experimental factors’ effects are statistically significant. Then, we evaluated which factor levels differed from each other using post-hoc tests. Our findings suggest that the image’s spatial resolution and the interaction between Satellite and Classification Method are determinants in obtaining accurate image classifications in a geographical context.
Methodology System for Training University Staff in Foreign Languages to Ensure Education Export

The paper presents Tomsk polytechnic university experience in the field of forming and realization the methodology system of in-house training of university staff in foreign languages for education export. Under discussion are the formed by foreign languages professional competences of the main categories of university staff —research and teaching staff; administrative staff including heads of departments and divisions and university top-managers. These groups of competences serve for sustaining professional performance — teaching, research, managerial work, verbal and written communication in foreign language. The complete cycle of in-house training of university staff in foreign languages is presented; it embraces the university demand in trained staff up to integration of training results into professional performance and feedback provision. Didactic principles of teaching and learning process organization are formulated. There is described the portfolio of programs for various staff categories taking into consideration the levels of foreign language proficiency and goals of training. The characteristics of the professional enhancement programs portfolio are discussed; they allow for continuous training and free access to the required level of proficiency. Among the main characteristics of the system is the outcome-based approach which is discussed taking the example of final attestation paper and language portfolio of university staff — the participants of programs. There are formulated the perspective goals for development of the system of professional enhancement in foreign languages.