Reviewerly/exHarmony huggingface dataset:
Channel Planting for Deep Neural Networks using Knowledge Distillation

In recent years, deeper and wider neural networks have shown excellent performance in computer vision tasks, while their enormous amount of parameters results in increased computational cost and overfitting. Several methods have been proposed to compress the size of the networks without reducing network performance. Network pruning can reduce redundant and unnecessary parameters from a network. Knowledge distillation can transfer the knowledge of deeper and wider networks to smaller networks. The performance of the smaller network obtained by these methods is bounded by the predefined network. Neural architecture search has been proposed, which can search automatically the architecture of the networks to break the structure limitation. Also, there is a dynamic configuration method to train networks incrementally as sub-networks. In this paper, we present a novel incremental training algorithm for deep neural networks called planting. Our planting can search the optimal network architecture with smaller number of parameters for improving the network performance by augmenting channels incrementally to layers of the initial networks while keeping the earlier trained parameters fixed. Also, we propose using the knowledge distillation method for training the channels planted. By transferring the knowledge of deeper and wider networks, we can grow the networks effectively and efficiently. We evaluate the effectiveness of the proposed method on different datasets such as CIFAR-10/100 and STL-10. For the STL-10 dataset, we show that we are able to achieve comparable performance with only 7% parameters compared to the larger network and reduce the overfitting caused by a small amount of the data.
Research on speech replay attack detection using approach of multi-feature and multi-classifier fusion

Replay attack detection is crucial to prevent security and reliability when the automatic speaker verification (ASV) systems encounter the spoofed attacks. In order to overcome the shortcomings of traditional cepstrum features can not effectively represent the difference between real speech and replay speech in low-frequency band. We proposed a novel low frequency cepstrum coefficient under singular value decomposition (LCSVD). In addition, an effective feature fusion method (LCSVD+CQCC) based GSV and RELIEF algorithm in the super vector space is studied to improve the performance of detection system. We also proposed to use an multi-classifier set, which includes Random Forest (RF) and LogitBoost classifiers. Experimental results have shown that the proposed multi-feature and multi-classifier fusion system can provide substantially better performance than baseline. EER of primary system on development and evaluation set are respectively 2.49% and 16.58%, compared to baseline 11.28% and 31.84%, which are also better than baseline 77.9% and 47.9%.
'It’s Not Going to Stop Till You Wise Up': Can Software and SaaS License Agreements be Renewed Automatically in New York?

Does New York’s “evergreen clauses” law apply to software and Software-as-a-Service license agreements? New York’s original “evergreen clauses” law was first passed in 1961, long before the software industry came into existence. Although New York is now a major technology hub, the case law does not offer a conclusive answer to this question. Software vendors should tread carefully.
Environmental education of preschool children in the media space of modern kindergarten

In this article, the authors draw the attention of the professional community to the need to organize innovative media space in the preschool educational organization. The phenomenon of environmental education of preschool children from the positions of the UN and UNESCO is revealed. The theoretical foundations of media education and the use of forms and methods of media education in the organization of environmental education of preschool children are presented. A brief analysis of partial environmental educational programs for preschool organizations is given. The foreign experience of media education in the system of preschool education is presented. The directions of the development of media education in environmental activities with preschool children are highlighted. The attention is focused on the phenomenon of gamification of the ecological space of a modern preschool educational organization.
Conditional Mixture Model and Its Application for Regression Model

Expectation maximization (EM) algorithm is a powerful mathematical tool for estimating statistical parameter when data sample contains hidden part and observed part. EM is applied to learn finite mixture model in which the whole distribution of observed variable is average sum of partial distributions. Coverage ratio of every partial distribution is specified by the probability of hidden variable. An application of mixture model is soft clustering in which cluster is modeled by hidden variable whereas each data point can be assigned to more than one cluster and degree of such assignment is represented by the probability of hidden variable. However, such probability in traditional mixture model is simplified as a parameter, which can cause loss of valuable information. Therefore, in this research I propose a so-called conditional mixture model (CMM) in which the probability of hidden variable is modeled as a full probabilistic density function (PDF) that owns individual parameter. CMM aims to extend mixture model. I also propose an application of CMM which is called adaptive regressive model (ARM). Traditional regression model is effective when data sample is scattered equally. If data points are grouped into clusters, regression model tries to learn a unified regression function which goes through all data points. Obviously, such unified function is not effective to evaluate response variable based on grouped data points. The concept &amp;ldquo;adaptive&amp;rdquo; of ARM means that ARM solves the ineffectiveness problem by selecting the best cluster of data points firstly and then evaluating response variable within such best cluster. In order words, ARM reduces estimation space of regression model so as to gain high accuracy in calculation.
C-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification

Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging task since pathologists must examine a huge number of histopathological images to detect infinitesimal abnormalities. In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The model incorporates multiple CNNs including Outer, Middle, and Inner. The first two parts of the architecture contain six networks that serve as feature extractors to feed into the Inner network to classify the images in terms of malignancy and benignancy. The C-Net is applied for histopathological image classification on two public datasets, including BreakHis and Osteosarcoma. To evaluate the performance, the model is tested using several evaluation metrics for its reliability. The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification. This approach has the potential to be extended to additional classification tasks, as experimental results demonstrate utilizing extensive evaluation metrics.
A Microblog Unbalanced Data Evolution Analysis Method Based on LDA Model

Abstract As a social network platform, Microblog has a large amount of information that can be mined. Microblog differs from static texts, which is time-sensitive, and the popularity of themes is changing with time. In addition, the popularity of Microblog varies from theme to theme, and the number of Microblogs varies greatly. Therefore, it has obvious unbalance characteristics. The traditional LDA model can not mine the theme intensity evolution process, and the theme mixing phenomenon will appear in the theme of unbalanced data mining. In view of the above problems, this paper proposes a method to analyze the theme evolution law of unbalanced Microblog data by introducing time variable. The experiment shows that the problem of theme words mixing is effectively solved after the balance treatment. The introduction of time variables can mine Microblog popularity and theme intensity evolution process.
S3-Net: A Fast and Lightweight Video Scene Understanding Network by Single-shot Segmentation

Real-time understanding in video is crucial in various AI applications such as autonomous driving. This work presents a fast single-shot segmentation strategy for video scene understanding. The proposed net, called S3-Net, quickly locates and segments target sub-scenes, meanwhile extracts structured time-series semantic features as inputs to an LSTM-based spatio-temporal model. Utilizing tensorization and quantization techniques, S3-Net is intended to be lightweight for edge computing. Experiments using CityScapes, UCF11, HMDB51 and MOMENTS datasets demonstrate that the proposed S3-Net achieves an accuracy improvement of 8.1% versus the 3D-CNN based approach on UCF11, a storage reduction of 6.9x and an inference speed of 22.8 FPS on CityScapes with a GTX1080Ti GPU.
Penguatan Literasi melalui Cakrawala Pers di SMK Ma’arif Walisongo Kajoran Magelang

At this time we are facing the era of industrial revolution 4.0 and Society 5.0 which demands good literacy skills. Literacy skills among high school students and equivalent are still very weak. This raises concerns for educators and managers of Ma’arif Walisongo Kajoran Vocational High School. In order to improve literacy skills among students, the school formed Cakrawala Press. This study aims to explain the concept and implementation of Cakrawala Press as reinforcement literacy, as well as supporting and inhibiting factors in journalistic-based literacy activities at Ma’arif Walisongo Kajoran Vocational High School. This research is a qualitative descriptive study. The process of collecting data is done by means of observation, interviews and documentation. Material data obtained is presented and analyzed in descriptive form. The results of this study indicate the existence of a student press institute can strengthen student literacy activities. Researchers hope that the results of this study can be used as a reference for other schools to develop reinforcement of literacy for their students.
Unsupervised Embedding of Hierarchical Structure in Euclidean Space

Deep embedding methods have influenced many areas of unsupervised learning. However, the best methods for learning hierarchical structure use non-Euclidean representations, whereas Euclidean geometry underlies the theory behind many hierarchical clustering algorithms. To bridge the gap between these two areas, we consider learning a non-linear embedding of data into Euclidean space as a way to improve the hierarchical clustering produced by agglomerative algorithms. To learn the embedding, we revisit using a variational autoencoder with a Gaussian mixture prior, and we show that rescaling the latent space embedding and then applying Ward's linkage-based algorithm leads to improved results for both dendrogram purity and the Moseley-Wang cost function. Finally, we complement our empirical results with a theoretical explanation of the success of this approach. We study a synthetic model of the embedded vectors and prove that Ward's method exactly recovers the planted hierarchical clustering with high probability.
SAR-NAS: Skeleton-based Action Recognition via Neural Architecture Searching

This paper presents a study of automatic design of neural network architectures for skeleton-based action recognition. Specifically, we encode a skeleton-based action instance into a tensor and carefully define a set of operations to build two types of network cells: normal cells and reduction cells. The recently developed DARTS (Differentiable Architecture Search) is adopted to search for an effective network architecture that is built upon the two types of cells. All operations are 2D based in order to reduce the overall computation and search space. Experiments on the challenging NTU RGB+D and Kinectics datasets have verified that most of the networks developed to date for skeleton-based action recognition are likely not compact and efficient. The proposed method provides an approach to search for such a compact network that is able to achieve comparative or even better performance than the state-of-the-art methods.
Participation in TREC 2020 COVID Track Using Continuous Active Learning

We describe our participation in all five rounds of the TREC 2020 COVID Track (TREC-COVID). The goal of TREC-COVID is to contribute to the response to the COVID-19 pandemic by identifying answers to many pressing questions and building infrastructure to improve search systems [8]. All five rounds of this Track challenged participants to perform a classic ad-hoc search task on the new data collection CORD-19. Our solution addressed this challenge by applying the Continuous Active Learning model (CAL) and its variations. Our results showed us to be amongst the top scoring manual runs and we remained competitive within all categories of submissions.
An Analysis of Future Education Research Trends : Focused on the 2011-2019 Papers

The purpose of this study is to examine the research trends of Korean future education by analyzing the article on 'Future Education Research' and to suggest future development directions. The subjects of the analysis are 121 papers published in the journal Future Education Research of ‘The Korean Association of Future Education Study’ from 2011 to December 2019. The analysis was largely composed of five parts. First, the current status of the papers published in the 'Future Education Research'. Second, summary of ressearcher’s characteristics. Third, as a result of analyzing the research method, the most used method was Mixed Research, and the unusual point was 'thought experiment'. Fourth, the main research subjects were students, which is predictable considering that most of the researchers are professors and teachers. Finally, in the analysis of 'research subject words', many theme words such as 'Erudition', 'future education', and 'multicultural' were repeated. Based on the results of this analysis, I would like to suggest the future development direction of future education research. First, education cannot have a single character, so it is necessary to try fusion and complex research between other fields and academic fields as well as researchers in the future. Second, it is necessary to diversify research methods, especially by utilizing the fact that there are many experts and researchers in the field of education, it is expected to study long-term and various methods. Last, it is necessary to maintain the originality of 'future education research'. In particular, I hope that we will be able to find original and professional researchers of ‘The Korean Association of Future Education Study’ through serious and steady research and practical activities on the 'Erudition paradigm'.
On Estimation of Parameters of a Three-Dimensional Uniform Regression Model

Typically, a researcher faces computational difficulties in assessing the parameters of a certain model when modeling the relationship between different indicators of analysis. A suitable model is generally obtained by a sequential refinement of the features included in its composition and, therefore, by performing multiple repetitions of computational algorithms. At the same time, the computational complexity of these algorithms begins to play a significant role in modeling. A certain set of indicators is used to reduce the number of iterations. These indicators are responsible for the quality of the constructed model and capable of “signaling" about the need to adjust the model. The model parameters and the quality functional value are such indicators in regression modeling. They are able to answer the question of the appropriateness of building a particular model and are indicators of the quality of the resulting functional dependence.&#x0D; In this paper, we study methods and algorithms for constructing and evaluating the main indicators of L∞-regression — a quality indicator and model parameters. The first part of the paper describes the most efficient computational procedures for determining parameters in the case of a three-dimensional uniform regression model, indicates the complexity of these algorithms, and gives a geometric interpretation. In the second part of the paper, a series of theorems on estimating the values of the parameters of threedimensional L∞-regression is presented, and a formula for calculation of an indicator of sample uniformity is provided.
Blockchain as a Disruptive Technology in Healthcare and Financial Services - a Review Based Analysis on Current Implementations

Blockchain is a time-stamped series of immutable records it is managed by a cluster of computers owned by different entities it allows only authorized participants to access data and log of all entries. Multiple industries are adopting blockchain technology to innovate the way they function. over two decades the hospitals function is the same. This space is rife with vertical innovation moreover, are lag with horizontal innovation patient identification and information blocking which is a huge problem in the healthcare industry. With blockchain the healthcare sector can achieve data efficiency, data access flexibility, interconnection, transparency, security, and more. Global economic slowdown has taken a toll on financial services. Forcing Indian companies to expose in the global economy. In the case of finance blockchain help reduce potential costs, labor savings, safe, secure, decentralized, transparent and more. The purpose of this paper is to review the development of blockchain and healthcare, financial services and to provide some references for the possible application of blockchain technology to the healthcare and financial services. secondly, several application scenarios of blockchain in healthcare and financial services are put forward, Finally, the challenges that still exist when applying blockchain technology based on its current implementations in healthcare and financial services.
Heterogeneous Identity Expression and Association Method Based on Attribute Aggregation

Existing identity expression methods are often limited in a single security domain, and this is inadequate to meet the cross-domain access requirements of heterogeneous networks. In view of this problem, we propose an index system for the ubiquitous expression of heterogeneous identities, and introduce the concept pair matching based attribute aggregation method by combining the characteristics of heterogeneous identity alliances. The selection of concept pairs considers the original meaning of attribute characteristics, including the lexical level, i.e., class, ontology, label, description, the structural level, i.e., position, distance between nodes, and the semantic level, i.e., formal concept analysis. As for the attribute aggregation, if multiple attributes from a heterogeneous network contain the same or similar concepts, they are considered the same attribute for the user identity in a heterogeneous network. Relevant domain knowledge or heuristic knowledge will adjust the result of attribute aggregation, and the constraint relationship between conceptual structures are used to adjust and optimize the attribute aggregation set. Based on the identity attribute index system of the heterogeneous identity alliance, the identity similarity evaluation results based on each attribute are generated. When the comprehensively considered identity similarity evaluation result is higher than the empirical threshold, the heterogeneous identity alliance has different trusts for the same user. The experimental results show that our scheme has a better overall aggregation effect on identity attribute aggregation.
The Many Faces of Gravity

We show that the number of different gravitational effects is significantly greater than previously thought.First of all, it turned out that the observed constancy of the speed of light relative to the surrounding masses is a special case of a previously unknown fundamental gravitational effect related to the action of gravitation on the speed of light.In other words, the constancy of the speed of light becomes an integral part of gravitation.Moreover, it turned out that the increase in inertial mass and the dilation of the proper time of particles that were accelerated relative to the surrounding masses are also consequences of this fundamental gravitational effect.All of these secondary effects are in the same row with such well-known effects as gravitational refraction and gravitational lensing, which are also a consequence of the action of gravitation on the speed of light.Their belonging to gravitation causes a number of unique features, for example, asymmetry in time dilation and anisotropy of the speed of light, which have been successfully confirmed experimentally.The research is based on a detailed analysis of a large set of experimental data using the classical axiomatic approach.
On the necessity of Chvátal’s Hamiltonian degree condition

Abstract In 1972 Chvátal gave a well-known sufficient condition for a graphical sequence to be forcibly Hamiltonian, and showed that in some sense his condition is best possible. In this paper, we conjecture that with probability 1 as n → ∞ , Chvátal’s sufficient condition is also necessary. In contrast, we essentially prove that for every k ≥ 1 , the sufficient condition of Bondy and Boesch for forcible k-connectedness is not necessary in the same way.
Ultimate limits of approximate unambiguous discrimination

Quantum hypothesis testing is an important tool for quantum information processing. Two main strategies have been widely adopted: in a minimum error discrimination strategy, the average error probability is minimized; while in an unambiguous discrimination strategy, an inconclusive decision (abstention) is allowed to vanish any possibility of errors when a conclusive result is obtained. In both scenarios, the testing between quantum states is relatively well understood, for example, the ultimate limits of the performance are established decades ago; however, the testing between quantum channels is less understood. Although the ultimate limit of minimum error discrimination between channels has been explored recently, the corresponding limit of unambiguous discrimination is unknown. In this paper, we formulate an approximate unambiguous discrimination scenario, and derive the ultimate limits of the performance for both states and channels. In particular, in the channel case, our lower bound of the inconclusive probability holds for arbitrary adaptive sensing protocols. For the special class of “teleportation-covariant” channels, the lower bound is achievable with maximum entangled inputs and no adaptive strategy is necessary.1 MoreReceived 29 July 2020Accepted 3 November 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043276Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum metrologyQuantum sensingQuantum Information