Reviewerly/exHarmony huggingface dataset:
Indirect Supervision to Mitigate Perturbations

Vulnerability of state-of-the-art computer vision models to image perturbations has drawn considerable attention recently. Often these perturbations are imperceptible to humans because they target the perception of deep neural networks (DNNs) employed in the corresponding computer vision task. Recent studies have revealed that DNNs, which are unable to handle targeted perturbation often fail to handle untargeted perturbations as well such as Gaussian noise. Various techniques in past have been explored to mitigate both these types of perturbations ranging from classical preprocessing to current supervised and self-supervised deep discriminative and generative models. However, a common challenge with most of these techniques is that they approach the problem from a quality enhancement point of view, which is primarily driven by human perception. In addition, the supervised models require a large volume of gold standard unperturbed data, whereas others fail to take into account the feedback of the targeted downstream DNN. We propose to model this problem in indirect supervision framework, where we assume that the gold standard data is missing, however, a variable dependent on it is available and the dependency of the observed variable is stated by the considered downstream DNN. The proposed method maintains the advantages of supervised models while relaxing the requirement of gold standard unperturbed data. To prove its utility, we conduct several experiments on various network architectures for downstream tasks of classification and medical image segmentation. We used MNIST, CIFAR-10-C and ISIC skin lesion dataset in our experiments. In all the experiments, a considerable restoration in the performance of the considered downstream model is observed.
Use of A3 Report by Industrial Engineering Students As a Tool to Analyze and Interpret a Case Study

Abstract Lean Thinking is a philosophy which principles were redesigned from Toyota Production System (TPS) by Womack and Jones of MIT. Currently, Lean Thinking principles are taught in the academy and are applied in all sectors, from production to services. Services is what is provided to students in a university. Teachers provide a service to them, and they are the clients of this service. As so, teachers want to provide the best service, adding value to the “client” product. In order to do so, they search for new methods that create flow in the way students learn what they need to learn. Lean Thinking have been providing tools to the classroom to obtain such flow. This paper intends to present a tool, an A3 report, which was used by engineering students to analyze, interpret and report a published case study. This was a team assignment task among others. This task was assessed as a component for the final grade of a course of third year of Master Integrated of Industrial Engineering and Management (IEM). The presentations, A3 reports and discussions results were analyzed and compared using Bloom taxonomy levels and 3H taxonomy to infer about students learning. Main findings obtained were very positive.
The Card Shuffling Hypotheses: Building a Time and Memory Efficient Graph Convolutional Network

This paper investigates the design of time and memory efficient graph convolutional networks (GCNs). State-of-the-art GCNs adopt K-nearest neighbor (KNN) searches for local feature aggregation and feature extraction operations from layer to layer. Based on the mathematical analysis of existing graph convolution operations, we articulate the following two card shuffling hypotheses. (1) Shuffling the nearest neighbor selection for KNN searches in a multi-layered GCN approximately preserves the local geometric structures of 3D representations. (2) Shuffling the order of local feature aggregation and feature extraction leads to equivalent or similar composite operations for GCNs. The two hypotheses shed light on two possible directions of accelerating modern GCNs. That is, reasonable shuffling of the cards (neighbor selection or local feature operations) can significantly improve time and memory efficiency. A series of experiments show that the network architectures designed based on the proposed card shuffling hypotheses decrease both the time and memory consumption significantly (e.g., about 50% for point cloud classification and semantic segmentation), while maintaining comparable accuracy, on several important tasks in 3D deep learning, i.e., 3D classification, part segmentation, semantic segmentation, and mesh generation.
A Study of the Effect on Personality and Adjustment of Maturity in Math Subject of Intermediate Level Adolescent Students

The student has central position in whole educational process. Teacher only helps to develop their personality and situational adjustment. Physical, mental and social virtues are spread on the basis of learning experiences of the student. Maturity is the more effective clause inculcated to provide shape and size of any work and behavior; in which self confidence, self respect and safe feeling are developed in the students and his personality glooms. The basic foundation of future personality developments depending on maturity level of the students. So maturity level in adolescent science students of mathematics subject would be the foundation of personality development.
UPAYA MENINGKATAN PRESTASI BELAJAR SISWA MELALUI MODEL PEMBELAJARAN EXAMPLES NON EXAMPLES PADA MATA PELAJARAN BAHASA INDONESIA SISWA KELAS IX SMP NEGERI 2 LUBUK PAKAM

This classroom action research aims: 1) To determine the application of the Examples Non Examples Model in improving students’ skills in compiling the text structure reports; 2) To determine the increase in compiling the text structure of the report in improving the students' skills in compiling the text structure of the report through the application of Model Examples Non Examples This research was conducted using an interactive qualitative model and a class action research approach to PTK (class action research) which was conducted in 2 cycles. Each cycle included planning, implementing, observing and reflecting. The subjects of this classroom action research were students of class IX.8 odd semester SMP Negeri 2 Lubuk Pakam Kab. Deli Serdang. The results indicated that there was an increasing in the learning outcomes achieved by students in mastering the material. Based on the data analysis, it can be concluded that in cycle 1 the students' learning completeness in this material was only 63.00, while in cycle 2 by applying the Examples Non-Examples Model learning strategy increased in cycle 2 the average value increased to 77.67.
IMPROVED THE ABILITY TO WRITE EXPLANATION TEXT COMPLEX THROUGH STAD MODELS IN IT AL FATTAH DESA LAMA PRIVATE HIGH SCHOOL STUDENTS

ABSTRACT&#x0D; Many activities are closely related to writing skills, for example making summaries, taking notes, writing minutes, writing various kinds of letters, writing activity plans, to the ability to write scientific papers. Writing complex explanatory text is writing that tells of an event that is arranged in an orderly manner. Meanwhile, the Student Teams Achievement Division (STAD) is a method that prioritizes good teamwork. The ability to write complex explanatory texts of class XI SMA SWASTA IT AL FATTAH Lma Village in cycle I was (52%) complete learning and (48%) students have not finished learning. Then after taking the action in cycle II, the learning outcomes are (95%) complete and (5%) have not finished learning
Research on the road using the ExperiVan

Background: The BMS Lab is the faculty lab of the faculty of behaviour, management and social sciences (BMS) of the University of Twente. It consists of over 450 m2 lab facilities. The majority of its 240 annual research projects physically takes place at fixed lab spaces at the university. Although having clear advantages (e.g. high control, fixed set-up), university locations also come with substantial restrictions. The most important drawback is that the research tends to focus on high-educated, and Western student samples from relatively high socio-economic status. These samples are arguably limited representative. Moreover, inclusion of elderly, patients and healthcare professionals is challenging. Methods: To tackle the abovementioned restrictions, BMS commissioned a mobile laboratory in 2018. The goal was to allow research on the road, with participants and professionals from the street or at specific locations. Based on an extensive design process and input from different stakeholders and experts, it was constructed and first used for field measurements in June 2019. Findings: Three studies have been carried out with the mobile laboratory called ExperiVan since then. This included an eye tracking experiment using participants from the street, in which participants were asked about their opinion on an informative website about infection diseases. Secondly, on-site evaluations with healthcare professionals were carried out, to gather expert input on a serious game about dilemmas regarding zoonosis. Lastly, the ExperiVan was successfully used in a virtual reality calibration study. Discussion: The mobile laboratory is used in different studies with participant from. Usage so far shows that it is a fitting facility to conduct field research in naturalistic settings, using technology such as eye tracking or VR.
HUBUNGAN ANTARA PEMAHAMAN KOMPETENSI PEDAGOGIS GURU DENGAN KESIAPAN MENGAJAR MICROTEACHING MAHASISWA PIAUD IAIN SURAKARTA

ABSTRACT The problem in this research is that students' readiness to teach microteaching is still lacking. This study aims to determine the relationship between understanding pedagogical competence and students' readiness to teach microteaching. The method used is quantitative correlational. The population in this study were all students of PIAUD IAIN Surakarta class 2017 a total of 103 students, while the sample used was 68 students who were taken using cluster random sampling technique. Data collection obtained by conducting tests and questionnaires. Before the data is analyzed, first the prerequisite test is carried out with the normality test and hypothesis testing. The results of the study using the spearman rank correlation formula and converted to the t-test yielded t count 2.3695 consulted with t table dk = n-2 = 68-2 = 66, 5% significance obtained a value of 1.6683. Based on the results of t_count (2.3695)> ttable (1.6683), it can be concluded that there is a positive relationship between understanding pedagogical competence and the readiness to teach microteaching students of PIAUD IAIN Surakarta for the 2019/2020 academic year. Keywords: Pedagogical Competence, Teaching Readiness
Improving Students’ Writing Ability based on Higher Order Thinking Skills (HOTS) Questions at 8th Grade in SMP Swasta Kartika 1-4 Pematangsiantar

The objective of the research was designed to find out the students‟ ability in writing descriptive text by using HOTS questions at 8th grade in SMP Swasta Kartika 1-4 Pematangsiantar. It deals with content, organization, vocabulary, language use, and mechanics. The problem of this research was “to what extent does the students writing ability based on the application of HOTS questions? This research was designed as a quantitative research. The sample of this research was taken from two classes that consist of 60 students (30 students in experimental group in class VIII A and 30 students in control group in class VIII B. Then quantitative data were obtained from the students‟ writing score of pre-test and post-test. The research finding of this research showed that there was an improvement on the students‟ writing ability in descriptive text based on HOTS questions. It can be seen from the mean score of pre-test was 57.7 and the mean score of post-test was 85. And the researcher also found that t-test is higher than t-table it can be seen from df: 58 (30+30 -2) with the t-table is 1.67 and the level of significance 0.05. It shows that t-test is higher than t-table (3.28 > 1.67). After analyzing the data, the researchers concluded that using HOTS questions could develop the students‟ writing ability in descriptive text at 8th grade in SMP Swasta Kartika 1-14 Pematangsiantar.
Adversarially Robust Low Dimensional Representations

Many machine learning systems are vulnerable to small perturbations made to inputs either at test time or at training time. This has received much recent interest on the empirical front due to applications where reliability and security are critical. However, theoretical understanding of algorithms that are robust to adversarial perturbations is limited.In this work we focus on Principal Component Analysis (PCA), a ubiquitous algorithmic primitive in machine learning. We formulate a natural robust variant of PCA where the goal is to find a low dimensional subspace to represent the given data with minimum projection error, that is in addition robust to small perturbations measured in $\ell_q$ norm (say $q=\infty$). Unlike PCA which is solvable in polynomial time, our formulation is computationally intractable to optimize as it captures a variant of the well-studied sparse PCA objective as a special case. We show the following results:1. Polynomial time algorithm that is constant factor competitive in the worst-case with respect to the best subspace, in terms of the projection error and the robustness criterion. 2. We show that our algorithmic techniques can also be made robust to adversarial training-time perturbations, in addition to yielding representations that are robust to adversarial perturbations at test time. Specifically, we design algorithms for a strong notion of training-time perturbations, where every point is adversarially perturbed up to a specified amount. 3. We illustrate the broad applicability of our algorithmic techniques in addressing robustness to adversarial perturbations, both at training time and test time. In particular, our adversarially robust PCA primitive leads to computationally efficient and robust algorithms for both unsupervised and supervised learning problems such as clustering and learning adversarially robust classifiers.
Independent work of bachelor students in the e-learning course on chemistry

The article discusses the creation of an electronic educational course in chemistry for students of technical specialties.The role of students' independent work in the study of a chemistry course is discussed.
System-synergetic approach in professional training of the future teacher of natural sciences

Ткаченко І.А., Краснобокий Ю.М
Libraries: Collection Development for Education in the Boston College Libraries: Scope and Collection Emphases

Criteria for collecting and maintaining materials in the subject area of Education, with links to general library collection policies.
Dense Global Context Aware RCNN for Object Detection

RoIPool/RoIAlign is an indispensable process for the typical two-stage object detection algorithm, it is used to rescale the object proposal cropped from the feature pyramid to generate a fixed size feature map. However, these cropped feature maps of local receptive fields will heavily lose global context information. To tackle this problem, in this paper, we propose a novel end-to-end trainable framework, called Dense Global Context Aware (DGCA) RCNN, aiming at assisting the neural network in strengthening the spatial correlation between the background and the foreground by fusing global context information. The core component of our DGCA framework is a context aware mechanism, in which both global feature pyramid and attention strategies are used for feature extraction and feature refinement, respectively. Specifically, we leverage the dense connection to fuse the global context information of different stages in the top-down process of FPN, and further leverage the attention mechanism to perform global context aware. Thus, the implicit relationship between object proposal and global features can be captured by neural networks to improve detection performance. Experimental results on COCO benchmark dataset demonstrate the significant advantages of our approach.
A Survey on Machine Learning Based Medical Assistive Systems in Current Oncological Sciences

Background: Cancer is one of the life threatening disease which is affecting a large number of population worldwide. The cancer cells multiply inside the body without showing much symptoms on the surface of the skin thereby making it difficult to predict and detect at the onset of disease. Many organizations are working towards automating the process of cancer detection with minimal false detection rates. Introduction: The machine learning algorithms serve to be a promising alternative to support health care practitioners to rule out the disease and predict the growth with various imaging and statistical analysis tools. The medical practitioners are utilizing the output of these algorithms to diagnose and design the course of treatment. These algorithms are capable of finding out the risk level of the patient and can reduce the mortality rate concerning to cancer disease. Method: This article presents the existing state of art techniques for identifying cancer affecting human organs based on machine learning models. The supported set of imaging operations are also elaborated for each type of Cancer. Conclusion: The CAD tools are the aid for the diagnostic radiologists for preliminary investigations and detecting the nature of tumor cells.
PENGARUH PENGGUNAAN MEDIA E-LEARNING, MOTIVASI BELAJAR DAN LINGKUNGAN BELAJAR TERHADAP KEMAMPUAN INTELEKTUAL (STUDI: MAHASISWA MAHASISWI DI UNIVERSITAS SARJANAWIYATA TAMANSISWA YOGYAKARTA)

Campus academic life lies in the learning media and its environment. The role of the lecturer as a facilitator should be able to facilitate students and their students to find their own way of learning, which can be done without being limited by space and time. It is hoped that with this E-learning method, students and female students will be able to be more active and more creative in learning with the motivation to learn from themselves. This study aims to determine whether E-learning, Learning Motivation, and the Learning Environment affect Intellectual Ability. The research method used is a quantitative method and the data collection using a questionnaire. The results of the analysis show that the independent variable has a positive and significant effect on the independent variable.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS GAME UNTUK PENGUASAAN VOCABULARY DALAM PEMBELAJARAN BAHASA INGGRIS DI SMP/Mts

Development of Game-Based Learning Media is done as an effort to solve student learning problems. This learning media, in addition to helping students understand the material, can also add students' English vocabularies. This study aims to produce products as well as alternative learning media that are in accordance with the eligibility criteria of media and material in English subjects in class VII MTs S Kampung Dadok. This typeiof research isi development research known as Research and Development (R&D).iThe resultsiof the validity test of Game-based Learning Media products in English class VII subjects developed have an average validity of the material experts at a scale of 4.08 in the very valid category, from media experts at a scale of 4.03 in the very valid category. While the practicality test is on a scale of 3.97 in the valid category. Keywords: Game-based learning media, English Learning
Embedding a random graph via GNN: mean-field inference theory and RL applications to NP-Hard multi-robot/machine scheduling

We develop a theory for embedding a random graph using graph neural networks (GNN) and illustrate its capability to solve NP-hard scheduling problems. We apply the theory to address the challenge of developing a near-optimal learning algorithm to solve the NP-hard problem of scheduling multiple robots/machines with time-varying rewards. In particular, we consider a class of reward collection problems called Multi-Robot Reward Collection (MRRC). Such MRRC problems well model ride-sharing, pickup-and-delivery, and a variety of related problems. We consider the classic identical parallel machine scheduling problem (IPMS) in the Appendix. For the theory, we first observe that MRRC system state can be represented as an extension of probabilistic graphical models (PGMs), which we refer to as random PGMs. We then develop a mean-field inference method for random PGMs. We prove that a simple modification of a typical GNN embedding is sufficient to embed a random graph even when the edge presence probabilities are interdependent. Our theory enables a two-step hierarchical inference for precise and transferable Q-function estimation for MRRC and IPMS. For scalable computation, we show that the transferability of Q-function estimation enables us to design a polynomial-time algorithm with 1-1/e optimality bound. Experimental results on solving NP-hard MRRC problems (and IMPS in the Appendix) highlight the near-optimality and transferability of the proposed methods.
EFEKTIVITAS SISTEM ADMINISTRASI MELALUI SISTEM INFORMASI MANAJEMEN KEPEGAWAIAN (SIMPEG): STUDI DI BADAN KEPEGAWAIAN PENDIDIKAN DAN PELATIHAN DAERAH KABUPATEN KAPUAS

This study aims to describe and analyze the effectiveness of the administration system through a Management Information System (SIMPEG) in Education and Training Board of Civil Service Agency, Kapuas regency in Civil Service Administration Division. The focus of this research study is to determine the effectiveness of the administration system using indicator of the effectiveness of Richard M. Steers which is the optimization objectives, systematic perspective and behavior of employees in the organization as well as in the use of Management Information System which are the input, process and output.
The impacts of known and unknown demonstrator irrationality on reward inference

Algorithms inferring rewards from human behavior typically assume that people are (approximately) rational. In reality, people exhibit a wide array of irrationalities. Motivated by understanding the benefits of modeling these irrationalities, we analyze the effects that demonstrator irrationality has on reward inference. We propose operationalizing several forms of irrationality in the language of MDPs, by altering the Bellman optimality equation, and use this framework to study how these alterations affect inference. We find that incorrectly assuming noisy-rationality for an irrational demonstrator can lead to remarkably poor reward inference accuracy, even in situations where inference with the correct model leads to good inference. This suggests a need to either model irrationalities or find reward inference algorithms that are more robust to misspecification of the demonstrator model. Surprisingly, we find that if we give the learner access to the correct model of the demonstrator's irrationality, these irrationalities can actually help reward inference. In other words, if we could choose between a world where humans were perfectly rational and the current world where humans have systematic biases, the current world might counter-intuitively be preferable for reward inference. We reproduce this effect in several domains. While this finding is mainly conceptual, it is perhaps actionable as well: we might ask human demonstrators for myopic demonstrations instead of optimal ones, as they are more informative for the learner and might be easier for a human to generate.
Scientiﬁc progress in AGI from the perspective of contemporary behavioral psychology

While the ﬁeld of Artiﬁcial General Intelligence (AGI) continues to develop, it seems to be a need for continued progress in terms of developing AGI as a science. In this paper, we discuss scientiﬁ ...