Reviewerly/exHarmony huggingface dataset:
Cheating with (Recursive) Models

To what extent can agents with misspecified subjective models predict false correlations? We study an who utilizes models that take the form of a recursive system of linear regression equations. The analyst fits each equation to minimize the sum of squared errors against an arbitrarily large sample. We characterize the maximal pairwise correlation that the analyst can predict given a generic objective covariance matrix, subject to the constraint that the estimated model does not distort the mean and variance of individual variables. We show that as the number of variables in the model grows, the false pairwise correlation can become arbitrarily close to one, regardless of the true correlation.
Accessing API Data

In the previous chapter, we learned about Previews and how we can see the UI and even interact with it. In this chapter, we shall have a look at downloading data from an API and decode the JSON data.
Estudio de la evolución gramatical

El objetivo general del presente trabajo es realizar un estudio de la evolucion gramatical acerca de su origen e inspiracion, las implementaciones que hay al momento para el uso de esta tecnica, asi como tambien las ventajas y desventajas de utilizarla. Para luego realizar pruebas con los problemas clasicos de programacion genetica y resolverlos mediante evolucion gramatical con una seleccion de sus implementaciones y comparar los resultados obtenidos.Abstract:The general objective of the present work is to carry out a study of the grammatical evolution about its origin and inspiration, the implementations that there are at the moment for the use of this technique, as well as the advantages and disadvantages of using it. To then perform tests with the classical problems of genetic programming and solve them by grammatical evolution with a selection of their implementations and compare the results obtained.
Accelerated Processing for Maximum Distance Separable Codes using Composite Extension Fields

This paper describes a new design of Reed-Solomon (RS) codes when using composite extension fields. Our ultimate goal is to provide codes that remain Maximum Distance Separable (MDS), but that can be processed at higher speeds in the encoder and decoder. This is possible by using coefficients in the generator matrix that belong to smaller (and faster) finite fields of the composite extension and limiting the use of the larger (and slower) finite fields to a minimum. We provide formulae and an algorithm to generate such constructions starting from a Vandermonde RS generator matrix and show that even the simplest constructions, e.g., using only processing in two finite fields, can speed up processing by as much as two-fold compared to a Vandermonde RS and Cauchy RS while using the same decoding algorithm, and more than two-fold compared to other RS Cauchy and FFT-based RS.
Research on blast furnace stability evaluation model based on neural networks using dynamic sampling approach

The blast furnace iron-making is a complicated physical and chemical reaction and heat exchange process in a closed shaft furnace. The blast furnace's operator must judge the condition of the furnace in time and accurately, and then take the adjustment measures based on the condition to ensure that the blast furnace is running smoothly. Not handling the fluctuation of the furnace condition in time, it may cause the failure of the furnace, which leads to the decrease of the blast furnace's output, the increase of fuel consumption, and a serious effect on the production and operation of the enterprises. In order to solve the above problem, the study implement several methods to try to judge the stability of the blast furnace in the iron-making process. Results shows that the Neural Networks with dynamic sampling method, which has a great superiority among the class imbalance problems, has the highest anomaly detection rate compared to the other methods.
STUDENTS’ RETICENT AND PARTICIPATORY BEHAVIOR IN EFL CLASSROOM (A CASE STUDY TO THE SECOND GRADE STUDENTS OF SMA NEGERI 1 PEUSANGAN)

Abstract Name:Salma Student No.:1409200080043 Study Program: Master Degree in English Education Department: English Education Title:Students’ Reticent and Participatory Behavior in EFL Classroom (A case Study to the Second Grade Students of SMA Negeri 1 Peusangan) Key words: Reticent behavior, participatory behavior, students’ verbal perception This research tries to identify the factors affecting students’ reticent behaviors and to find out how the teacher encourages the students’ participatory behavior in EFL classroom. The research focused on the students’ verbal participation. In collecting data, the writer used observation and interviewguided. Based on the result of the research findings, it was identified that the students’ reticence caused by variety of factors including: 1) Linguistic factors such as low English proficiency; 2) Individual factors, such as unfamiliarity with topic and lack of knowledge in the subject matter; 3) Social - psychological factors such as fear of making mistakes and fear of showing off; 4) Affective factors, such as laziness, anxiety, and lack of interest and motivation; 5) Personality factors, such as shyness and lack of confidence; 6) Environmental factors, such as uninteresting teaching method, media and teacher’s uneven attention. It was also identified some solutions to overcome reticence and to encourage students’ participationsuch as: 1) using various methods and activities in the classroom, 2) having good preparation, 3) making the students as active participants,4) creating conducive classroom atmosphere 5) using familiar and interesting topics, 6) using appropriate and interesting media, and 7) paying attention and learning to care about students. The results indicate that more research needs to be done to find solution to help students become more active and proficient EFL learners.
FACTORS OF STUDENTSâ€™ DEMOTIVATION IN ATTENDING BILINGUAL CLASS IN IAIN PURWOKERTO

This research was aimed at revealing factors of students demotivation in attending bilingual class in IAIN Purwokerto. This research was driven by the fact that some students were found proposing themselves to quit from the bilingual class. In addition, some others seemed reluctant to attend the lectures. This condition showed that they were demotivated in attending the bilingual class. This research is a survey study using a quantitative approach. The data of this research were gained through questionaire using total class sampling. Among 393 respondents of bilingual class students, 309 of them returning the questionaire. Respondents were asked two questions, one of which was a closed-ended question, asking whether they had ever experienced a feeling of demotivation. The other question is an open-ended question, asking the factors causing their feeling of demotivation. Students are allowed to give more than one answers. Data were analyzed using descriptive statistics analysis, counting the presentage of each factor. This research found that there are 8 factors causing studentsâ€™ demotivation in attending bilingual class, i.e. (1) lack of language ability (228 respondents, equals to 73.79%); (2) language experience (34 respondents, equals to 11.0%); (3) lecturer factor (21 respondents, equals to 6.80%); (4) task factor (18 respondents, equals to 5,83%); (5) lack of interest (17 respondents, equals to (5.50%); (6) environment factor (13 respondents, equals to 4.21%) (7) Friend factor (4 respondent, equals to 1.29%); and (8) facilities (2 respondents, equals to 0.65%).
Teacher’s Efforts to Overcome the Students’ Difficulties in Speaking Ability

This study aimed at finding out the teacherâ€™s efforts to overcome the studentsâ€™ difficulties in speaking ability at the eleventh grader of high school in the school year 2017-2018. In this research there are two research question formulated as follow: What are the difficulties of the studentsâ€™ in speaking ability? What are the teacherâ€™s efforts to overcome the studentsâ€™ difficulties in speaking ability? Since the study concerned on the studentsâ€™ difficulties of speaking ability for the eleventh grader of high school and the teacherâ€™s efforts to overcome the studentsâ€™ difficulties in speaking ability, this research was included as a descriptive qualitative research. The subject of the study was 15 students eleventh grader of high school in the school year 2017-2018. The data in this research were collected through observation, interview and questionnaire. The result of the analysis showed that there were three kinds of the studentsâ€™ difficulties in speaking ability at the eleventh grader of high school in the school year 2017-2018 that was lack of vocabulary, pronouncing the word and fluency. The teacherâ€™s efforts to overcome the studentsâ€™ difficulties in speaking ability were; asking the students to memorize the vocabulary, making English group, practicing speaking using English, and offering the studentsâ€™ motivation about the importance of English.
Investigation of hidden multipolar spin order in frustrated magnets using interpretable machine learning techniques

Frustration gives rise to a plethora of intricate phenomena, the most salient of which are spin liquids, both classical ones—such as the spin-ice phase which has been realized experimentally in rare-earth oxide pyrochlore materials—and their more elusive quantum counterparts. At low temperatures, classical frustrated spin systems may still order, despite their extensive ground-state degeneracy, due to the order-by-disorder phenomenon. The resulting orders are often of a multipolar type which defies conventional probes. Identifying and characterizing such “hidden” orders is thus a challenging endeavor.This thesis introduces a machine-learning framework for studying the phase diagram of classical frustrated spin models in an unbiased and automated way. The interpretability of the resulting classification was of paramount importance in the design of the method. It allows for the inference of both the order parameter tensors of the phases with broken symmetries as well as the constraints which are characteristic of classical spin liquids and signal their emergent gauge structure. On top of that, it establishes a hierarchical relationship among the various phases according to their degree of disorder.The framework is applied to three different models and spin configurations are harvested from classical Monte Carlo simulations of those. A gauge model is used to mimic the interactions between the mesogens of generalized nematics. These may possess arbitrary point group symmetry, resulting in benchmark models with a low-temperature phase that breaks the O(3) spin symmetry accordingly. In addition, two frustrated spin models are considered. The historically important case of the Heisenberg model on the kagome lattice gives rise to hidden triatic order which requires a description in terms of two tensors of different ranks; the machine is capable of finding both. Meanwhile, for the XXZ model on the pyrochlore lattice, the machine reconstructs the complex phase diagram which was only recently obtained and correctly identifies the spin nematic phase as well as three distinct types of classical spin liquids, including their crossovers.The method has the potential to accelerate the characterization of model Hamiltonians of frustrated magnets. It can scrutinize the whole parameter space at once and may thus help to identify interesting regimes, paving the way for the search of new orders and spin liquids.
Cloud Computing Awareness in Malaysia Construction industry

The construction industry has been treated byfragmentation, lack of integration and complexities in processes and activities which could ultimately bring a negative impact to the construction project success. Therefore, collaboration is very important with the purpose to improve the productivity and effectiveness of construction management and to ensure the efficient utilization of resources. Cloud computing is the latesttechnology which has acted as a platform to support access to applications, information and services by using only an internet connection. However, it is doubt that the construction stakeholders are aware and use the cloud computing. Thus, the purpose of this research is to determine the awareness of cloud computing and identify the reasons of engagement with cloud computing in Malaysia construction industry. This research was focused on the main contractors within Johore Bahru only. Inthis research, the research method used quantitative approach and descriptive analysis includes frequency analysis and mean score to interpret the data. From the finding of the research, the Malaysian construction professions have the awareness on cloud computing. However, their level of knowledge is at low level. Theconcept of cloud computing is still new and it is still in the infant stage of its development in Malaysia construction industry.
Influence of Image Pre-processing to Improve the Accuracy in a Convolutional Neural Network

Convolutional neural networks (CNN) have been applied in different fields including image recognition. A CNN requires a set of images that will be used to teach to classify it into specific categories. However, the question about how image pre-processing influences CNN accuracy has not yet been answered bluntly. This paper proposes the application of pre-processing methods for the images’ feed to a CNN in order to improve the accuracy of the classification. Two methods of pre-processing are evaluated, quantization and sharpness enhancement. Quantization carries out at 7 levels, and sharpness works with four levels using the discrete wavelet transform. The tests were implemented with two CNN models, LeNet-5 and ResNet-50. In the first part of this paper the methodology and description of the CNN models, as well as the data set are presented. Later the descriptions of the experiments are presented. Finally, it is shown how the proposed method achieved an improvement on the accuracy compared with the results obtained with the images with no modification. The proposed pre-processing methods had an improvement between 1.35 and 3.1% on the validation accuracy.
Conditional Response Generation Using Variational Alignment.

Generating relevant/conditioned responses in dialog is challenging, and requires not only proper modelling of context in the conversation, but also the ability to generate fluent sentences during inference. In this paper, we propose a two-step framework based on generative adversarial nets for generating conditioned responses. Our model first learns meaningful representations of sentences, and then uses a generator to \textit{match} the query with the response distribution. Latent codes from the latter are then used to generate responses. Both quantitative and qualitative evaluations show that our model generates more fluent, relevant and diverse responses than the existing state-of-the-art methods.
An Adversarial Learning Framework for Privacy Preserving Communications

We develop a machine learning-based approach that allows to achieve privacy in communications by exploiting an advantage at the physical layer. Our goal is to transmit useful data to the intended receiver while preventing sensitive data from leaking to an eavesdropper who has access to the channel. We adopt an adversarial approach involving two competing neural networks to learn efficient coding schemes that allow to regulate the tradeoff between quality and privacy.
On evaluating the collaborative research areas: A case study

The growth of social networks is ever-increasing. Many available scientific publications evidence the interest of researchers in this area. Within a time span of eight years from 2011 to 2018, approximately 2600, 230, 150, and 110 scientific articles were published from the USA, Iran, Saudi Arabia, and Turkey, respectively around this area of research. To comprehensively survey all the sub-fields and interests within this research area, the present paper proposes a novel density-based method for finding topic descriptors from academic articles. By employing a robust to noise fuzzy clustering algorithm, the terms are clustered, and by utilizing a modified Parzen window, k topic descriptors from each cluster are extracted. Besides, an optimization problem has been designed to detect the similarity between word pairs. By conducting the experiments, the research priorities for four countries within this time span have been found. Moreover, the closeness of the research in developing countries to the developed country have been measured. The experimental results show that for four years, the research topics in Turkey were close to the research topics in the USA on average, and the research topics in Saudi Arabia were close to the USA topics during the past two years. Additionally, the experimental comparison of the proposed method with two clustering baselines indicates the superiority of the proposed method in terms of precision, recall, and accuracy.
Adversarial Transformations for Semi-Supervised Learning

We propose a Regularization framework based on Adversarial Transformations (RAT) for semi-supervised learning. RAT is designed to enhance robustness of the output distribution of class prediction for a given data against input perturbation. RAT is an extension of Virtual Adversarial Training (VAT) in such a way that RAT adversarialy transforms data along the underlying data distribution by a rich set of data transformation functions that leave class label invariant, whereas VAT simply produces adversarial additive noises. In addition, we verified that a technique of gradually increasing of perturbation region further improve the robustness. In experiments, we show that RAT significantly improves classification performance on CIFAR-10 and SVHN compared to existing regularization methods under standard semi-supervised image classification settings.
The website deployment just happens, you do ...

The website deployment just happens, you do nothing else but start reading and writing. If you wanted to get a class sharing through webmentions and
Several classes of two-weight and three-weight linear codes.

Linear codes with few weights have been widely studied due to their applications in secret sharing, authentication codes, association schemes and strongly regular graphs. In this paper, we further construct several classes of new two-weight and three-weight linear codes from defining sets and determine their weight distributions by applications of the theory of quadratic forms and Weil sums over finite fields. Some of the linear codes obtained are optimal or almost optimal with respect to the Griesmer bound. This paper generalizes some results in~\cite{Jian2019,LiYueFu2016}.
Hybrid Differential Dynamic Programming for Planar Manipulation Primitives

We present a hybrid differential dynamic programming (DDP) algorithm for closed-loop execution of manipulation primitives with frictional contact switches. Planning and control of these primitives is challenging as they are hybrid, under-actuated, and stochastic. We address this by developing hybrid DDP both to plan finite horizon trajectories with a few contact switches and to create linear stabilizing controllers. We evaluate the performance and computational cost of our framework in ablations studies for two primitives: planar pushing and planar pivoting. We find that generating pose-to-pose closed-loop trajectories from most configurations requires only a couple (one to two) hybrid switches and can be done in reasonable time (one to five seconds). We further demonstrate that our controller stabilizes these hybrid trajectories on a real pushing system. A video describing our work can be found at this https URL.
The Concept of Machine Learning and Elliptic Curves United Approach in Solving of the Factorization Problem

The aim of this paper is to describe and analyze new approach in solving factorization problem based on the fuse of elliptic curves and machine learning theories. This idea is considered due to high importance of this question and great perspective of such approach.
The SKYNIVI Experience: Evoking Startle and Frustration in Dyads and Single Drivers.

To study naturalistic in-cabin emotion we developed SKYNIVI, a modified open source driving simulator, with scenarios designed to elicit startle and frustration. We target generating these emotions because we believe that by detecting these it will be possible for autonomous vehicles to learn to drive better. We show how to use SKYNIVI to develop datasets that capture naturalistic emotions in drivers and passengers for algorithmic development. We recruited 51 participants as dyads and single drivers to participate in two different scenarios. We show that we were able to evoke hundreds of instances of our target emotions in this cohort and present an analysis of factors we found to impact emotional expression including: scenario design $\mathbf{(\mathrm{p} , demographic factors, personality and baseline affect $\mathbf{(\mathrm{p} . We find that having a second person in the vehicle impacts observed expressions of emotion $\mathbf{(\mathrm{p} even when no difference in baseline affect is reported.
Seminal Divergence-based Generalization Bounds

In this chapter, we give a comprehensive description of different generalization bounds for domain adaptation that are based on divergence measures between source and target probability distributions. Before we proceed to the formal presentation of the mathematical results, we first explain why this particular type of result presents the vast majority of all available domain adaptation results. We further take a closer look at the very first steps that were established in order to provide a theoretical background for domain adaptation. Surprisingly, these first results reflected in a very direct way the general intuition behind domain adaptation and remained quite lose to the traditional generalization inequalities presented in Chapter 1. After this, we turn our attention to different strategies that were proposed in order to overcome the flaws of the seminal results as well as to strengthen the obtained theoretical guarantees.
Analysis of Message Generation Rules for Collective Perception in Connected and Automated Driving.

Collective Perception (CP) or cooperative sensing enables vehicles and infrastructure nodes to exchange sensor information to improve their perception of the driving environment. CP enables vehicles to detect objects (e.g. non-connected vehicles, pedestrians, obstacles, etc.) beyond their local sensing capabilities. ETSI is currently developing the European standards for collective perception or cooperative sensing. This includes defining which information should be exchanged about the detected objects, and how often it should be exchanged. To this aim, different CP generation rules for collective perception are currently under analysis, and this paper presents an in-depth analysis of their performance and efficiency. The conducted analysis highlights the existing tradeoffs between performance (capacity to detect surrounding objects) and efficiency (redundant detection and transmission of the same detected objects). It also demonstrates the need to design advanced policies that dynamically control the redundancy on the wireless channel while ensuring the capacity to reliably detect the driving environment.