Reviewerly/exHarmony huggingface dataset:
Close Category Generalization for Out-of-Distribution Classification

Out-of-distribution generalization is a core challenge in machine learning. We introduce and propose a solution to a new type of out-of-distribution evaluation, which we call close category generalization. This task specifies how a classifier should extrapolate to unseen classes by considering a bi-criteria objective: (i) on in-distribution examples, output the correct label, and (ii) on out-of-distribution examples, output the label of the nearest neighbor in the training set. In addition to formalizing this problem, we present a new training algorithm to improve the close category generalization of neural networks. We compare to many baselines, including robust algorithms and out-of-distribution detection methods, and we show that our method has better or comparable close category generalization. Then, we investigate a related representation learning task, and we find that performing well on close category generalization correlates with learning a good representation of an unseen class and with finding a good initialization for few-shot learning. The code is available at this https URL
RSP-BT:An Advanced Parallel Method for Depth Map Motion Estimation

Aiming at the high computational complexity and long encoding time caused by insufficient consideration of the homogeneous area in depth map motion estimation of 3D High Efficiency Video Coding(3D-HEVC), An optimization method to reduce search points based on block type (RSP-BT) is proposed. Firstly, according to the pixel distribution features of coding blocks, the coding blocks are classified into homogeneous or edge areas. And then the different search strategies are selected adaptively in the light of center-biased characteristic of the motion vectors for different types of area. Thereby, the overall calculation overhead is reduced. Experimental results show that, compared with the full-search algorithm, the proposed algorithm has an average Peak Signal to Noise Ratio (PSNR) loss of only 0. 0519dB; Compared with the TZSearch (Test Zone Search) algorithm, the search points is reduced by 11.25% on average. In order to further improve the coding speed, a parallel implementation is given on the video array processor of the optimized algorithm, in the view of there is no data correlation among the matching operates for different coding blocks. The experimental results show that the average speedup of the designed parallel scheme is 2.8940.
First-Order Optimization Algorithms via Discretization of Finite-Time Convergent Flows

In this paper, we investigate the performance of several discretization algorithms for two first-order finite-time optimization flows. These flows are, namely, the rescaled-gradient flow (RGF) and the signed-gradient flow (SGF), and consist of non-Lipscthiz or discontinuous dynamical systems that converge locally in finite time to the minima of gradient-dominated functions. We introduce three discretization methods for these first-order finite-time flows, and provide convergence guarantees. We then apply the proposed algorithms in training neural networks and empirically test their performances on three standard datasets, namely, CIFAR10, SVHN, and MNIST. Our results show that our schemes demonstrate faster convergences against standard optimization alternatives, while achieving equivalent or better accuracy.
ДИСТАНЦИОННЫЙ УЧЕБНЫЙ КУРС КАК РАЗВИТИЕ ТРАДИЦИОННЫХ МЕТОДОВ ОБУЧЕНИЯ С ИСПОЛЬЗОВАНИЕМ ИНТЕРНЕТ-ТЕХНОЛОГИЙ

A promising direction of the current stage of the development of education is the interactive communication of the teacher with students through information and communication networks. Distance learning is the development of the traditional part-time form of education. Methodological support for this type of training is a distance learning course. The paper considers the use of modern Internet technologies for the creation of a distance learning course on the example of the authors’ development “Methods of solving the USE problems in mathematics of an increased level of complexity” intended for implementation under the supplementary education program.
Multi-label Classification Based on Domain Analysis in Fixed Point Method

Nowadays a multi-label classification problem arises in different areas for which the significant amount of data has been gained. This problem can be viewed as the one comprising two steps: training some ranking function sorting instances in each class and defining the optimal number of predictions for it. This paper is devoted to the second step of the optimal threshold selection while maximizing the F-macro measure. To do so, we reduce the multi-dimensional problem to the two-dimensional problem of finding a fixed point of a specifically introduced transformation defined on a unit square. We suggest the algorithm of finding the vector of optimal thresholds based on the domain analysis of the introduced transformation. Moreover, we provide the complexity estimations of the proposed algorithm. We evaluate the algorithm on the extreme classification benchmark WikiLSHTC-325K comparing its performance with some baseline results.
LibGuides: Spring 2021 Library Services: Laptops and Equipment

The library has laptops, whiteboard markers, scissors, calculators, and more at our main service desk. At this desk you can also pick up Digital Lab equipment that you have reserved, such as cameras, microphones, and tripods.
Pretrain-to-Finetune Adversarial Training via Sample-wise Randomized Smoothing

Developing certified models that can provably defense adversarial perturbations is important in machine learning security. Recently, randomized smoothing, combined with other techniques (Cohen et al., 2019; Salman et al., 2019), has been shown to be an effective method to certify models under l2 perturbations. Existing work for certifying l2 perturbations added the same level of Gaussian noise to each sample. The noise level determines the trade-off between the test accuracy and the average certified robust radius. We propose to further improve the defense via sample-wise randomized smoothing, which assigns different noise levels to different samples. Specifically, we propose a pretrain-to-finetune framework that first pretrains a model and then adjusts the noise levels for higher performance based on the model’s outputs. For certification, we carefully allocate specific robust regions for each test sample. We perform extensive experiments on CIFAR-10 and MNIST datasets and the experimental results demonstrate that our method can achieve better accuracy-robustness trade-off in the transductive setting.
Name-based Approach to Identify Suspicious Return Statements

Abstract Identifier names account for 70 percent of source code in characters. Meaningful identifier names convey valuable natural language information of the corresponding software entities, and thus they can be exploited to analyse the program and comprehend the semantics of it. However, existing bug detection approaches often ignore such natural language information and semantics encoded in identifier names. To this end, we propose a name-based approach to identify suspicious return statements. The recent study suggests that source code elements are often lexically similar to their corresponding semantic related elements. Based on this assumption, we design a sequence of heuristic rules to determine whether the return statement of a given method matches the signature of it. Evaluations on 100 open-source Java projects suggest that the proposed approach detects 16 out of 60 real return bugs. The precision and recall of the proposed approach are 31% and 27%, respectively.
Amateur Radio Operators Help Fill Earthquake Donut Holes

Ham radio networks gear up to provide real-time, on-the-ground information about earthquake shaking and damage when other communication pathways are knocked out of commission.
Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion

Aerobics is one of the main contents of physical education, which has a positive role in promoting the health of young people. This paper mainly studies the parallel processing method of inertial aerobics multisensor data fusion. In this paper, an aerobics exercise system is designed, which uses digital filter to remove the noise generated in the process of exercise. In this paper, Kalman filter is used to filter the pulse error of accelerometer, and the data structure of unidirectional link is used to achieve the effect of sliding window, which can reduce the memory cost to the greatest extent. In this paper, the region of moving object is determined by horizontal and vertical projection of binary symmetric difference image. At the same time, the optimal feature combination is selected from the reduced features by feature subset selection, and the classification algorithm is used as the evaluation function in the optimization process. Finally, the collected data are tested, analyzed, and sorted out. The experimental data show that, after calibrating the sensor data, the static x-axis and y-axis data are about 0 g, and the z-axis data are about 1 g, which is closer to the real value. The results show that the attitude data collected by the inertial sensor can be stably transmitted to the software of the computer wirelessly for attitude reconstruction, and the recognition of each attitude and parameter has reached a high accuracy.
The Potential of Integrating User Experience (UX) and Aesthetic Experience (AX) in Augmented Reality Comic (AR Comic)

The purpose of this paper is to explore the potential of synergising of two major theories that sprang from two entirely different disciplines, namely the human-computer interaction (HCI) and the arts. Indeed, there are vast and diverse gaps when two different theories, such as technology and art, are to be combined to develop a new element that complements to both disciplines. In this paper, the proposition is to measure the user experience when dealing with an art object that infuses with digital technology. Augmented reality (AR) derived from the HCI discipline and customarily to UX as a measurement tool. On the other hand, a comic is an aesthetic object that requires an aesthetic-friendly method as its measurement tool. Ultimately, this paper proposes an integration of the UX and AX theories to evaluate an AR comic.
Designing a Relational Database Model for Harvest Management Applications Using the Life Cycle Database Method

Agriculture is one of the dominant sectors in people's income in Indonesia because the majority ofIndonesians work as farmers. However, Indonesian people who depend on agriculture for their income arerelatively mediocre because the price of the food commodity produced is not in line with expectations. One ofthe factors that cause this is the poor delivery system from the place of production (agricultural location) to themarket. Therefore, an application is needed that can be a bridge for farmers in increasing the progress of theeconomy in Indonesia. This paper focuses more on designing a model from a database that is built with theconcept of a life cycle database design. Database design methodology using DBLC (database life cycle).Relational database design includes Conceptual Database Design, Logical Database Design, and PhysicalDatabase Design. The final result of this study resulted in 6 tables, namely farmers, product types, products,customers, transactions, and transaction details.
Scaling methodology and scale reporting in the TREE2 panel survey. Documentation of scales implemented in the baseline survey (2016)

This paper documents the questionnaire-based scales and item-based composites that have been collected on the occasion of the baseline survey administered to the second TREE cohort (TREE2) in 2016. First, the paper focuses on the methods and the estimation procedures that we have adopted for the calculation of the student scores published in the scientific use data files. Second, we describe the calculation of scale-specific statistics and quality measures (reported in the technical appendix) and provide some clues for their interpretation.
LibGuides: Current Library Services: Other library services

Find out what services are currently available from the Library and how they are being delivered.
Implementation of an image analysis agent within the framework of an intelligent multi-agent system for searching for illegal materials on the Internet

The article describes the development process of an analyzer agent that uses deep machine learning methods and is part of a multi-agent system for searching for illegal information on the Internet. The task of the analyzer is to extract text information from images, where the image and text parameters cannot be predefined.
Cleansing and separation procedures reflect resource concerns

Abstract We propose that procedures of separation have two functions, namely first, to establish the integrity of individual parts, and second, to make previously joint entities discreet and therefore countable. This allows taking stock of available resources, including evaluating the use of individual objects, a process that is especially adaptive under conditions of threat of contagious disease and resource scarcity.
Latent Space Conditioning on Generative Adversarial Networks

Generative adversarial networks are the state of the art approach towards learned synthetic image generation. Although early successes were mostly unsupervised, bit by bit, this trend has been superseded by approaches based on labelled data. These supervised methods allow a much finer-grained control of the output image, offering more flexibility and stability. Nevertheless, the main drawback of such models is the necessity of annotated data. In this work, we introduce an novel framework that benefits from two popular learning techniques, adversarial training and representation learning, and takes a step towards unsupervised conditional GANs. In particular, our approach exploits the structure of a latent space (learned by the representation learning) and employs it to condition the generative model. In this way, we break the traditional dependency between condition and label, substituting the latter by unsupervised features coming from the latent space. Finally, we show that this new technique is able to produce samples on demand keeping the quality of its supervised counterpart.
A SURVEY STUDY OF THE STUDENTS RESPONSES ON THE USE SQ3R TECHNIQUE IN TEACHING READING COMPREHENSION OF TENTH GRADERS IN VOCATIONAL HIGH SCHOOL AL – FATTAH KALITIDU IN ACADEMIC YEAR 2019/2020

Abstract: This study is to describe the teaching readings conducted by teachers for the tenth grade of Al Fattah Kalitidu in the academic year 2019/2020. This is done to find out how to learn and teach especially reading using SQ3R techniques implemented by the teacher such as the material used by the teacher, the techniques used by the teacher, the teaching methods used by the teacher, the problems and solutions encountered in the implementation of teaching and learning process. Researchers used descriptive qualitative to analyze data. The researcher collected data by conducting interviews, asking questions and documenting some important data that support this research. Data was collected from interview scripts and questionnaire results. Data analysis techniques are data reduction, data analysis, data presentation, and drawing conclusions. The subject of this research focuses on the teaching process of reading using the SQ3R technique (survey, question, read, recite and review) to class XI students of Al-Fattah Kalitidu Vocational School. Based on data collection, the researchers concluded that the material used by English teachers was the SQ3R technique which could improve the effectiveness of reading.Keywords: Reading, Reading Comprehension, SQ3R Technique
Accelerate Randomized Low Dimensional Manifold Method for Image Reconstruction

In this paper, an image processing method based on low-dimensional manifold model is improved by a structured stochastic method. In the original low-dimensional manifold image processing method, the key step is to solve a large linear system involving the matrix of image patch similarity, but the computation of the similarity matrix and large linear equations solving will bring great computational complexity. In this paper, by using the structural information of the image data, the similarity of image patches is obtained by structured local random computation, so as to quickly calculate the similarity matrix and solve the related linear equations. The experimental results show that the computational efficiency of the proposed method is greatly improved, and results similar to the original method can be obtained.
LibGuides: Database Navigation 101: Search Symbols

Learn the components of academic databases and how to efficiently search for scholarly sources
PENGENALAN AUTOMASI PERPUSTAKAAN TERINTEGRASI INLISLITE

This paper describes the basic concepts of library automation based on INLISLite. Although the digital era is the topic of digital libraries, there are also types of libraries that have not been automated. INLISLite as the software was developed by the National Library to make it easier for visitors to get information quickly,precisely, and accurately. INLISLite is an integrated library system software automation application to support the implementation of a digital library network. There are nine modules provided in the INLISLite version 3 application
Quantifying Task Complexity Through Generalized Information Measures

How can we measure the “complexity” of a learning task so that we can compare one task to another? From classical information theory, we know that entropy is a useful measure of the complexity of a random variable and provides a lower bound on the minimum expected number of bits needed for transmitting its state. In this paper, we propose to measure the complexity of a learning task by the minimum expected number of questions that need to be answered to solve the task. For example, the minimum expected number of patches that need to be observed to classify FashionMNIST images. We prove several properties of the proposed complexity measure, including connections with classical entropy and sub-additivity for multiple tasks. As the computation of the minimum expected number of questions is generally intractable, we propose a greedy procedure called “information pursuit” (IP), which selects one question at a time depending on previous questions and their answers. This requires learning a probabilistic generative model relating data and questions to the task, for which we employ variational autoencoders and normalizing flows. We illustrate the usefulness of the proposed measure on various binary image classification tasks using image patches as the query set. Our results indicate that the complexity of a classification task increases as signal-to-noise ratio decreases, and that classification of the KMNIST dataset is more complex than classification of the FashionMNIST dataset. As a byproduct of choosing patches as queries, our approach also provides a principled way of determining which pixels in an image are most informative for a task.
A Technical and Normative Investigation of Social Bias Amplification

The conversation around the fairness of machine learning models is growing and evolving. In this work, we focus on the issue of bias amplification: the tendency of models trained from data containing social biases to further amplify these biases. This problem is brought about by the algorithm, on top of the level of bias already present in the data. We make two main contributions regarding its measurement. First, building off of Zhao et al. (2017), we introduce and analyze a new, decoupled metric for measuring bias amplification, $\text{BiasAmp}_{\rightarrow}, which possesses a number of attractive properties, including the ability to pinpoint the cause of bias amplification. Second, we thoroughly analyze and discuss the normative implications of this metric. We provide suggestions about its measurement by cautioning against predicting sensitive attributes, encouraging the use of confidence intervals due to fluctuations in the fairness of models across runs, and discussing what bias amplification means in the context of domains where labels either don't exist at test time or correspond to uncertain future events. Throughout this paper, we work to provide a deeply interrogative look at the technical measurement of bias amplification, guided by our normative ideas of what we want it to encompass.
IMPLICATIONS OF HANNAH ARENDT’S AGONISM FOR MODERN DEMOCRACY: A CASE OF NIGERIA’S ELECTORAL PROCESSES

This essay discusses the implications of Hanna Arendt’s agonistic spirit for modern democracy in the light of Nigeria’s electoral processes. It claims that while Modern democratic experience of the Twenty-first Century in most nation states is tremendously driven by public spheres, the contest is characterized by agonal spirit. The impacts of the public spares and agonal spirit accomplished transformation of the social and political lives of the citizenry through good leaders. Generally conceived, a public sphere is an arena in social life where individuals can assemble to discuss issues and to identify societal problems and through that discussion influence political action. In modern society, it can be seen as a theatre in which political participation is enacted through the medium of talk and social participation in which public opinion can be formed. While the public sphere in the contemporary Nigerian society is heightened, consolidated and complemented by the use of the social media where public opinion could be expressed to a global coverage, the agonal spirit is characterized by fraudulent electoral practices. The paper argues and upholds with Benhabib that whereas the public spere is inclusive, and hence democratic, candidacy and contest for public offices should be agonistic. It maintains that agonism is at odds with the culture of fraudulent electoral practices in Nigeria. It recommends measures that will checkmate the problem of electioneering and electoral fraud thereby strengthening the nascent democracy though, not without the political will of the Independent Electoral Commission that must make itself credible to gain the confidence of the electorates and the competitors.
PAC-Bayesian Randomized Value Function with Informative Prior

Randomized value function has been shown as an effective exploration strategy for reinforcement learning (RL), which samples from a learned estimation of the distribution over the randomized Q-value function and then selects the optimal action. However, value function methods are known to suffer from value estimation error. Overfitting of value function is one of the main reasons to estimation error. To address this, in this paper, we propose a Bayesian linear regression with informative prior (IP-BLR) operator to leverage the data-dependent prior in the learning process of randomized value function, which can leverage the statistics of training results from previous iterations. We theoretically derive a generalization error bound for the proposed IP-BLR operation at each learning iteration based on PAC-Bayesian theory, showing a trade-off between the distribution obtained by IP-BLR and the informative prior. Since the optimal posterior that minimizes this generalization error bound is intractable, we alternatively develop an adaptive noise parameter update algorithm to balance this trade-off. The performance of the proposed IP-BLR deep Q-network (DQN) with adaptive noise parameter update is validated through some classical control tasks. It demonstrates that compared to existing methods using non-informative prior, the proposed IP-BLR DQN can achieve higher accumulated rewards in fewer interactions with the environment, due to the capabilities of more accurate value function approximation and better generalization.