Reviewerly/exHarmony huggingface dataset:
Performance analysis of cache replacement algorithm in information center network and construction of electronic music composition system

The Internet has become an increasingly indispensable existence. It not only affects people's daily life and work, but also continuously promotes the development of human economy. But now that the Internet has become popular, people are not only concerned about where the content appears, whether there is traffic or not, the shortcomings of the current network structure are also constantly showing up. In order to solve these existing problems, some researchers have proposed the information center network structure, which is a content-centric structure that can highlight the main characteristics of the network cache, that is, there will be a cache in every router. Function, but also can issue a request function for the user. In the era of digitization of a large amount of information, traditional music has begun to undergo major changes. Not only the mode of music creation, but also some music with electronic music as the main body adds more color to traditional music. Therefore, the emergence of electronic music may create a new stage for music in the future, and traditional music is currently the most understood art. It is a main body of traditional music and occupies most of the position of music, especially by the older generation. With the continuous expansion of the attraction of traditional music, young people are beginning to join in the pursuit of traditional music. Therefore, traditional music has gained a lot of fresh blood. Many young people have invested in the pace of creating traditional music. Young people are unique. The idea of combining these traditional music with electronic music has produced a very wonderful artistic effect, which has promoted the development of traditional Chinese music, and has also brought new challenges to electronic music.
Space-Partition-Driven Learning Network for Efficient MIMO Detection

With the development of communication systems, the MIMO order of low-cost communication equipments have also increased. However, classical MIMO algorithms either have too high computational complexity or have poor performance. Some existing deep learning-based MIMO detection networks have achieved excellent results in high-order MIMO, but they have unbearable detection performance in low-order MIMO. Based on the idea of dividing the sample space, we propose MLNet. Compared with Det Net and MMNet, MLNet has made a huge leap in time-varying channels. At the same time, MLNet has four orders of magnitude less computation than existing neural network algorithms. We also propose a training method, which is not only useful for MLNet, but also improves the detection performance of DetNet.
Examining Daily Activities and Propaganda on Government Social Media in China:

This study systematically examined the Beijing Police Department (BPD) daily use of social media. We collected six-month daily posts, the corresponding interactions (likes, shares, and comments), and the most active followers' profiles from the Weibo account of the BPD to answer these questions: what content the BPD posted on a daily basis on its Weibo account; what the daily work schedule of the BPD's Weibo account administrators was like; whether the content was customized for different groups of citizens; what media types they used to engage with followers; and the impact of fake followers on engagement rates. We found that besides dealing with crime and enforcing law, the Weibo administrators of the BPD also act as educators especially for the city's newcomers. The fake accounts do not affect the overall engagement rate significantly. This study also uncovers the dilemma faced by the BPD in trying to juggle the promotion of national ideological propaganda, efforts to experiment with engagement strategies, and building trusting relationships with citizens.
Performance Analysis of Deep Learning Models for Biomedical Image Segmentation

Image segmentation is the process of partitioning an image into multiple meaningful segments, which can be easily analyzed. Image segmentation is one of the most common tasks in computer vision. Cancer is termed as abnormal growth of cells and it can be either malignant or benign. Since the cancer rates in the world are increasing at an alarming rate and if it is found at earlier stages, chances of getting the cancer cured are very high. Since manual annotation of these cancer regions by doctors from medical imaging like X-Ray, MRI, etc., is highly time-consuming and can be prone to human errors, automatic and accurate tumor segmentation is of great interest. In recent years, deep learning is showing great progress in most of the applications and thus motivated us to use in the biomedical image segmentation task. The data that we have considered in this chapter are brain tumor (preprocessed BraTs 2015) and skin tumor (International Skin Imaging Collaboration (ISIC) 2018), as it is dangerous and as well as the rate of people getting affected is also increasing around the world. For our biomedical image segmentation, we proposed to use three architectures: (a) SegAN, (b) SegNet, and (c) U-Net. Segmentor adversarial network (SegAN) belongs to generative adversarial networks (GAN) family and other two architectures are commonly used for image segmentation task. Initially, performance analysis of SegAN was done using BraTs 2017 and ISIC 2018 data set. Based on the standard evaluation parameters such as Jaccard index and Dice score, the segmented/predicted output is evaluated. Then to increase the segmentation accuracy, U-Net and SegNet are used for skin and brain tumor image segmentation. In terms of Jaccard index, our proposed work achieved comparable performances, and in terms of Dice score, our proposed work outperformed the existing approaches in skin lesion image segmentation by achieving a maximum Dice score of 87.56% with U-Net architecture. For brain tumor segmentation, our proposed work was able to outperform the existing approaches by achieving a maximum Dice score of 85.01% with U-Net architecture. Lastly, segmentation output by all three architectures was compared for skin lesion segmentation and found that U-Net gave the best segmentation output followed by SegAN. In case of SegNet, the segmentation output is smooth-edged. In terms of learnable parameters, SegAN has the highest and U-Net has the least, when compared with all the three architectures. Thus, the proposed chapter concludes that U-Net achieved the best parameter results for both skin and brain tumor image segmentation task when compared with SegAN and SegNet.
A General Framework For Detecting Anomalous Inputs to DNN Classifiers

Detecting anomalous inputs, such as adversarial and out-of-distribution (OOD) inputs, is critical for classifiers (including deep neural networks or DNNs) deployed in real-world applications. While prior works have proposed various methods to detect such anomalous samples using information from the internal layer representations of a DNN, there is a lack of consensus on a principled approach for the different components of such a detection method. As a result, often heuristic and one-off methods are applied for different aspects of this problem. We propose an unsupervised anomaly detection framework based on the internal DNN layer representations in the form of a meta-algorithm with configurable components. We proceed to propose specific instantiations for each component of the meta-algorithm based on ideas grounded in statistical testing and anomaly detection. We evaluate the proposed methods on well-known image classification datasets with strong adversarial attacks and OOD inputs, including an adaptive attack that uses the internal layer representations of the DNN (often not considered in prior work). Comparisons with five recently-proposed competing detection methods demonstrates the effectiveness of our method in detecting adversarial and OOD inputs.
Efficient Bayesian phase estimation using mixed priors

We describe an efficient implementation of Bayesian quantum phase estimation in the presence of noise and multiple eigenstates. The main contribution of this work is the dynamic switching between different representations of the phase distributions, namely truncated Fourier series and normal distributions. The Fourier-series representation has the advantage of being exact in many cases, but suffers from increasing complexity with each update of the prior. This necessitates truncation of the series, which eventually causes the distribution to become unstable. We derive bounds on the error in representing normal distributions with a truncated Fourier series, and use these to decide when to switch to the normal-distribution representation. This representation is much simpler, and was proposed in conjunction with rejection filtering for approximate Bayesian updates. We show that, in many cases, the update can be done exactly using analytic expressions, thereby greatly reducing the time complexity of the updates. Finally, when dealing with a superposition of several eigenstates, we need to estimate the relative weights. This can be formulated as a convex optimization problem, which we solve using a gradient-projection algorithm. By updating the weights at exponentially scaled iterations we greatly reduce the computational complexity without affecting the overall accuracy.
MoET: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning.

Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their adaption into safety-critical settings, such as healthcare or self-driving cars is hindered by their inability to provide safety guarantees or to analyze the inner workings of the model. We present MoET, a novel model based on Mixture of Experts, consisting of decision tree experts and a generalized linear model gating function. While decision boundaries of decision trees (used in an existing verifiable approach), are axis-perpendicular hyperplanes, MoET supports hyperplanes of arbitrary orientation as the boundaries. To support non-differentiable decision trees as experts we formulate a novel training procedure. In addition, we introduce a hard thresholding version, MoET_h, in which predictions are made solely by a single expert chosen via the gating function. Thanks to that property, MoET_h allows each prediction to be easily decomposed into a set of logical rules. Such rules can be translated into a manageable SMT formula providing rich means for verification. While MoET is a general use model, we illustrate its power in the reinforcement learning setting. By training MoET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models.
Linear-Time Erasure List-Decoding of Expander Codes

We give a linear-time erasure list-decoding algorithm for expander codes. More precisely, let r > 0 be any integer. Given an inner code <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> of length d, and a d-regular bipartite expander graph G with n vertices on each side, we give an algorithm to list-decode the code <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> = <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> (G, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) of length nd from approximately δδ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> nd erasures in time n·poly (d2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sup> /δ), where δ and δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> are the relative distance and the r'th generalized relative distance of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , respectively. To the best of our knowledge, this is the first linear-time algorithm that can list-decode expander codes from erasures beyond their (designed) distance of approximately δ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> nd. To obtain our results, we show that an approach similar to that of (Hemenway and Wootters, Information and Computation, 2018) can be used to obtain such an erasure-list-decoding algorithm with an exponentially worse dependence of the running time on r and δ; then we show how to improve the dependence of the running time on these parameters.
Personalized News Recommendation: A Survey.

Personalized news recommendation is an important technique to help users find their interested news information and alleviate their information overload. It has been extensively studied over decades and has achieved notable success in improving users' news reading experience. However, there are still many unsolved problems and challenges that need to be further studied. To help researchers master the advances in personalized news recommendation over the past years, in this paper we present a comprehensive overview of personalized news recommendation. Instead of following the conventional taxonomy of news recommendation methods, in this paper we propose a novel perspective to understand personalized news recommendation based on its core problems and the associated techniques and challenges. We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face. Next, we introduce the public datasets and evaluation methods for personalized news recommendation. We then discuss the key points on improving the responsibility of personalized news recommender systems. Finally, we raise several research directions that are worth investigating in the future. This paper can provide up-to-date and comprehensive views to help readers understand the personalized news recommendation field. We hope this paper can facilitate research on personalized news recommendation and as well as related fields in natural language processing and data mining.
Calibration of solar radiation ensemble forecasts using convolutional neural network

&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;E&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;nsemble &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;forecast &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;approaches have become state-of-the-art for the quantification of weather forecast uncertainty. &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;However&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;, ensemble forecasts &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;from&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt; numerical weather prediction models (NWPs) still tend to be biased and underdispersed, &amp;lt;/span&amp;gt;hence justifying the use of statistical post-processing techniques &amp;lt;span&amp;gt;to improve forecast skill. &amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, ensemble forecasts are post-processed using a convolutional neural network (CNN). CNNs are the most popular machine learning tool to deal with images. In our case, CNNs allow to integrate information from spatial patterns contained in NWP outputs.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We focus on solar radiation forecasts for 48 hours ahead over Europe from the 35-members ARPEGE (M&amp;amp;#233;t&amp;amp;#233;o-France global NWP) and a 512-members WRF (Weather Research and Forecasting) ensembles. We used a U-Net (a special kind of CNN) designed to produce a probabilistic forecast (quantiles) using as ground truth the CAMS (Copernicus Atmosphere Monitoring System) radiation service dataset with a spatial resolution of 0.2&amp;amp;#176;.&amp;lt;/p&amp;gt;
Protecting Sensitive Data

In this chapter, we will discuss some principles for protecting sensitive data. We will consider sensitive data to be information that are not secrets but might still result in damage when putting multiple pieces of data together. For example, driver’s licenses, birthdays, medical history, and so on are sensitive data. We will learn how to apply these principles in the cloud provider services, the software used to build the application, and the application configuration.
Changing the Quality of Education in a University Using Digital Technologies and Artificial Intelligence in the Context of the COVID-19 Pandemic

the purpose of the article was to identify the nature of the impact of the pandemic on the quality of education at a university using distance learning and artificial intelligence. The research methodology was based on a complex of general scientific and special methods. The data obtained during the survey and in-depth interviews were summarized and analyzed in a focus group. Stable connections and tendencies in the change in the quality of teaching at the university are revealed. Traps for students are attributed to stable connections: lack of a valid system of control over the authorship of completed works; the possibility of unauthorized use of various electronic sources when responding; coronavirus quarantine leads to the erasure of students’ boundaries between study and life, personal space and social environment; an increase in students’ desire to have high grades in subjects with a decrease in interest in learning. Trends: increased workload on teachers and supporting (technical) personnel; growing dissatisfaction with distance learning; reduction of responsibility on the part of students for mastering knowledge in the course of distance learning.
One-Shot Learning for Facial Sketch Recognition using the Siamese Convolutional Neural Network

Deep Convolutional Neural Networks have been widely used in computer vision tasks like classifying an image and detecting an object within an image. To archive the state-of-the-art performance, it normally requires a huge number of labeled samples. However, in the facial sketch recognition tasks, collecting this amount of samples is not feasible. Each subject will only have one sketch and one photo. To address this, a One-shot Learning method with Siamese Network is proposed in this paper due to the fact that it only requires one training sample per class. The network comprises two identical model instances that share the same architecture and weights to be trained to learn the similarity between the two images. The similarity score is computed by using Euclidean distance. Some four different activation functions are evaluated in this research to see how feasible those functions to be used in this recognition task. The results demonstrate that the most suitable activation function for this task is sigmoid, with an accuracy of 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> after about 300 learning iterations for 10- way One-shot Learning. The evaluation is extended to the CUHK dataset and the results indicate the same accuracy pattern.
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients

We propose a lower bound on the log marginal likelihood of Gaussian process regression models that can be computed without matrix factorisation of the full kernel matrix. We show that approximate maximum likelihood learning of model parameters by maximising our lower bound retains many of the sparse variational approach benefits while reducing the bias introduced into parameter learning. The basis of our bound is a more careful analysis of the log-determinant term appearing in the log marginal likelihood, as well as using the method of conjugate gradients to derive tight lower bounds on the term involving a quadratic form. Our approach is a step forward in unifying methods relying on lower bound maximisation (e.g. variational methods) and iterative approaches based on conjugate gradients for training Gaussian processes. In experiments, we show improved predictive performance with our model for a comparable amount of training time compared to other conjugate gradient based approaches.
Model Evaluation Approaches for Human Activity Recognition from Time-Series Data

There are many evaluation metrics and methods that can be used to quantify and predict a model’s future performance on previously unknown data. In the area of Human Activity Recognition (HAR), the methodology used to determine the training, validation, and test data can have a significant impact on the reported accuracy. HAR data sets typically contain few test subjects with the data from each subject separated into fixed-length segments. Due to the potential leakage of subject-specific information into the training set, cross-validation techniques can yield erroneously high classification accuracy. In this work (Source code available at: https://github.com/imics-lab/model_evaluation_for_HAR.), we examine how variations in evaluation methods impact the reported classification accuracy of a 1D-CNN using two popular HAR data sets.
Penerapan Face Recognition Pada Sistem Presensi

The development of digital image technology is increasing nowadays. However, the use of image technology on surveillance cameras has not been optimally utilized. On the other hand, the various presence data monitoring systems that currently exist have their respective advantages and disadvantages, and need to be continuously developed so as to facilitate the data processing. The student attendance system at STT Bandung is basically good but it is still not optimal. The process of collecting student attendance data is still quite time-consuming and still allows human errors to occur in the data input process. Therefore, the author intends to help overcome this by utilizing face recognition technology in an integrated presence process. LBPH (Local Binary Pattern Histogram) is currently the best method in face recognition technology because the detection and recognition process is relatively fast and has the highest level of accuracy when compared to other methods. After testing the resilience of the system from the results of the prototyping that was built, the results obtained with a success rate of 86.85%.
CLUSTER AS INNOVATIVE APPROACH TO PEDAGOGICAL EDUCATION

The process of globalization observed all over the world, like other industries, requires clustering of the education sector. The process of globalization has also led to a sharp increase in competition in the market of educational services. In such competitive conditions, the cluster is a means of using the power of the globalization process against itself. Combining the subjects of education, science and production around a common goal increases their potential. And the cluster of pedagogical education provides for such cooperation. The article is devoted to the scientific point of view of ensuring the competitiveness of the subjects of the market of educational services through clusters. The concept of a pedagogical educational cluster is described, its necessity, implementation processes, principles and directions are shown. The author made comments on the goals, functions, principles and directions of development of the cluster of pedagogical education. Organizational aspects, practical significance, and theoretical bases for implementing a pedagogical educational cluster in practice are highlighted. The author tried to base his views on the views of Western scientists. The research of Western scientists on the educational cluster is analyzed and the author's attitude to them is expressed. Scientific conclusions about the social, economic, legal, marketing and pedagogical consequences of education clustering are presented.