Reviewerly/exHarmony huggingface dataset:
Linearized Kernel Representation Learning from Video Tensors by Exploiting Manifold Geometry for Gesture Recognition

A video tensor is an organized multidimensional array of numerical values. In this paper, we explore the underlying manifold geometry of a video tensor by factorizing it using modified higher order singular value decomposition (HOSVD). Each factor (mode matrix) of a video tensor obtained after modified HOSVD can be thought of as a subspace and hence represents a point in Grassmann manifold (ℳ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GM</sub> ). These factors cumulatively represent a point in product Grassmann manifold (ℳ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PGM</sub> ). We propose a novel kernel for ℳ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PGM</sub> that measures the similarity between two points in ℳ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PGM</sub> and generates a kernel-gram matrix. For representation learning, we diagonalize the obtained kernel-gram matrix and generate a small fixed length representation corresponding to each point in ℳ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PGM</sub> . Classification is performed in sparse framework with minimum residual error as classifier. Experimentation is carried out over Cambridge hand gesture and UMD Keck body gesture databases for both static and dynamic settings. Experimental study shows that even with small length feature representation, there is a significant improvement in classification results as compared to state-of the-art techniques.
Mobile and direct-prospecting methods: the possibility of their application for areas of geothermal water accumulation searching and mapping

The results of application of the frequency-resonance technology of remote sensing data processing and geoelectric methods of forming a short-pulsed electromagnetic field (FSPEF) and vertical electric-resonance sounding (VERS) within prospecting areas in Turkey, Slovakia, Mongolia, Nevada (USA), England and Ukraine are analyzed. Anomalous zones of "thermal water" type have been found within all survey areas. The drilled geothermal wells in all surveyed areas in Turkey fell into the contours of the anomalous zones of "thermal water" type. In some areas, a detailed vertical sounding by VERS method and vertical scanning of satellite data were carried out; the depths and thicknesses of aquifers were determined, and the water temperatures in them were estimated. For a more confident search for zones of accumulation of fresh (mineralized, geothermal) water, it is necessary to process remote sensing data at a scale of 1:10 000 and larger. The results of frequency-resonance processing of satellite images can be verified and refined by FSPEF and VERS methods. The joint use of these methods allows accelerating and optimizing the geothermal sources prospecting and exploration of. Approved mobile geophysical methods are recommended for practical use during prospecting and exploration of geothermal waters within known zones of geothermal sources development.
What and where you have seen? Bag of Words-based local feature pooling for visual event detection

The security analysis of sensitive issues and medical diagnosis are immensely focus to determine exact location of event happening regions. In this paper we propose a model of clustering and pooling techniques to local features using Bag-of-Words (BoW) descriptor in SVM framework for event detection in video sequences. The proposed model extracts local features from six categories of Columbia Consumer Video (CCV) event detection benchmark. We developed the clusters of these features using KD-search tree and Lloyds algorithm. The clusters of features is pooled to vectors by using bag of words model. Introducing the inferring temporal instance labelling, the model performed fast for event detection. The significant performance of the research problem can thrilled out the social media by retrieving the best possible content. The proposed model can efficiently perform the experiments of event detection related to big data problem in visual media. Furthermore, the proposed approach in the model is invariant to rotation, translation and scale changes in the video sequence and robust to the illumination and viewpoints.
On UNESCO's contribution to restoring suffered funds of the Library of the Academy of Sciences

Article is devoted by the 30th anniversary from the day of bonfire in the Library of the Russian Academy of Sciences. Based on V. M. Montvilov's book, the author attempts to open his contribution to liquidate the bonfire consequences in the Academy of Sciences’ Library. Montvilov played a great role in organizing negotiations with leading libraries and rendering international aid. His work as a UNESCO official was connected with preservation of such cultural objects of Russia as the Bolshoi Theatre, Russian State Library, the Russian Academy of Sciences’ Library. In the book «International official …», the author opened the specific character of UNESCO and emphasized a special role of this organization in the cultural heritage development of preservation. The book reflected his recollections and first impression about the Academy of Sciences’ Library and revealed new facts of elimination of the bonfire consequences. Montvilov's book is of great interest to the reader due to a wide range of problems posed in it and great factual material.
A Comparative Study of Attitude towards Grading System of Teachers of Senior Secondary Schools in Rural and Urban areas of North-East Delhi

Evaluation of students about the success and failure of education shows, therefore, the better the evaluation system, the better the education and the shortcomings of education are found. The grading system is an evaluation system in which students do not score, But to know about its effectiveness, it is important to know the views of teachers, because one The evaluation system is not the same for all the students. The teachers know more about the best of the students.
Edward F. Mooney, Excursions with Thoreau: Philosophy, Poetry, Religion (New York: Bloomsbury, 2015, $25.00). Pp. xvi + 240.<scp>isbn</scp>978 1 5013 0566 5.

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Template-based Web AR service rapid generation platform

With the development of the mobile network and the improvement of computing capability of intelligent terminals, Augmented Reality (AR), as a new computer vision technology, is gradually changing people's lives. It has been widely used in entertainment, education, industrial maintenance, exhibition, and other fields. However, the current AR applications are created primarily by professional developers who are proficient in using open API, SDK, and 3D modeling techniques, rather than ordinary users. To make it easier for ordinary users to create AR activities, this paper designs, and implements a template-based Web AR service generation platform. Users can quickly generate Web AR activities through personalized configurations on the proposed platform, which reduces the threshold for users to customize Web AR services.
ANALYSIS OF DOMESTIC AVANT-GARDE MUSIC IN THE HIGHER SCHOOL COURSE “FUNDAMENTAL CONCEPTS OF THEORETICAL MUSICOLOGY”

The article is devoted to a relevant problem of the modern pedagogy of special and general musical education - insufficient attention to avant-garde music in the process of future music teachers’ professional training. The paper provides a survey of modern domestic music experts’ views on this issue. The author identifies the reasons for a relatively insignificant place of avant-garde music in the educational process of the musical faculties of pedagogical higher education establishments. The author argues that in the process of studying the discipline “Fundamental Concepts of Theoretical Musicology”, future music teachers should acquire competence in analysing domestic avant-garde compositions. As an example of the practical usage of a domestic avant-garde composition in educational process the author analyses the vocal cycle “Blätter” by E. Denisov. The paper examines the specificity of the composer’s interpretation of the poetical original source, the form of the composition, its pitch pattern, tone and texture peculiarities determined by the principles of serial technique, micro-chromatics and sonorism. The analysis of this composition allows identifying the original combination of traditional composition principles and modern composer’s technique.
Cryptanalysis of a hash function, and the modular subset sum problem

Abstract Recently, Shpilrain and Sosnovski proposed a hash function based on composition of affine maps. In this paper, we show that this hash function with its proposed parameters is not weak collision resistant, for plaintexts of size at least 1.9MB (about {2^{24}} bits). Our approach is to reduce the preimage problem to a (very) high density instance of the Random Modular Subset Sum Problem, for which we give an algorithm capable of solving instances of the resulting size. Specifically, given plaintexts of about 1.9MB, we were able to produce other plaintexts of the same size with the same hash value in about 13 hours each, on average.
Using Tablet Applications as Assistive Tools in Teaching English as a Foreign Language

A case study examined the contribution of tablet applications for English language acquisition and improvement of reading and reading comprehension skills among pupils diagnosed with learning disabilities and those not diagnosed with learning disabilities. Participants were Grade 4 pupils from a public school in central Israel in two groups. Group 1 included two boys and two girls diagnosed with learning disabilities; Group 2 included two boys and two girls not diagnosed with learning disabilities. English learning applications were used with the intent of improving the abilities of letter recognition, reading, reading comprehension, listening comprehension, and oral expression in English in a fun way relevant to the pupils' lives. Findings indicated a more pronounced improvement in English reading and reading comprehension skills among pupils who were diagnosed with learning disabilities. The research also confirms that the use of tablet applications resulted in increased motivation for learning.
LERI: Local Exploration for Rare-Category Identification

To identify the data examples of rare categories that form small compact clusters in large data sets, existing approaches mostly require enough labeled data examples as a training set to learn a classifier, assuming that the rare-category clusters are spherical or nearly spherical. Nonetheless, a large enough training set is usually difficult to obtain in practice, and rare categories in many real-world applications often form small compact clusters with arbitrary shapes. In this paper, we investigate how to identify all data examples of a rare category with an arbitrary shape based on only one seed (i.e., a labeled rare-category data example). Instead of finding a compact and spherical local region around the seed, we locally explore the data set from the seed by continuously searching and visiting the k-nearest neighbors of each newly visited data example. The local exploration connects the data examples in the objective rare category by the relationship of k-nearest neighbors, and meanwhile, suspected external data examples are filtered out if they are not close enough to any visited data example. Experimental results on both synthetic and real-world data sets are conducted, and the results verify the effectiveness and efficiency of our approach.
Piano Practice Evaluation and Visualization by HMM for Arbitrary Jumps and Mistakes

In this paper, we present a piano practice assisting system that tracks the user's piano performance with mistakes (note insertions, deletions and substitutions) and arbitrary jumps (repeats and skips) using a HMM with a fast Viterbi decoding algorithm. After tracking the user's free practice, the system provides feedback on various aspects of his/her performance, including information on missed and mistakenly played notes, jumps in the performance, and note overlap for evaluation of legato and staccato articulation. Additionally, the system provides information for comparing the user's practice performance with a teacher's exemplary performance with regards to tempo and dynamics. The practice support system's usefulness was evaluated in a subjective experiment, in which piano students of varying experience used the system and gave feedback on its features.
A Systematic Algorithm for Data Cluster Using Map-Reduce Approach

We have been studying the problem clustering data objects as we have implemented a new algorithm called algorithm of clustering data using map reduce approach. In cluster, main part is feature selection which involves in recognition of set of features of a subset, since feature selection is considered as a important process. They also produces the approximate and according requests with the original set of features used in this type of approach. The main concept beyond this paper is to give the outcome of the clustering features. This paper which also gives the knowledge about cluster and it's own process. To processing of large datasets the nature of clustering where some more concepts are more helpful and important in a clustering process. In a clustering methodology where more concepts are very useful. The feature selection algorithm which affects, the entire process of clustering is the map-reduce concept. since, feature selection or extraction which is also used in map-reduce approach. The most desirable component is time complexity where efficiency concerns in this criterion. Here time required to find the effective features, where features of quality subsets is equal to effectiveness. The complexity to find based on this criteria based map-reduce features selection approach, which is proposed and evaluated in this paper.
Relationships between Deep Learning and Linear Adaptive Systems

Linear adaptive systems are a well-known staple in numerous signal processing applications. Recently, significant activity and performance gains have been achieved in multilayer neural networks for deep learning applied to practical data processing applications. In this paper, we describe the important relationships and significant differences between the procedures and methods used in linear adaptive systems and those used in multilayer neural networks for deep learning tasks. Input-output structures, cost functions and training criteria, adaptive algorithms, and data processing and optimization strategies are considered. It is the hope of the authors that this discussion will spur further crossover between the two fields, and in particular allow knowledge to be shared and further progress to be made.
Non-negative Matrix Factorization Using Bregman Monotone Operator Splitting

A non-negative matrix factorization (NMF) algorithm based on Bregman monotone operator splitting (B-MOS) is proposed. Several commonly used NMF algorithms, such as the multiplicative update method, are often used in source separation for speech and image signals. To improve the convergence rate in the tail, applying the alternating direction method of multipliers (ADMM) is reported to be effective. However, a fixed step-size parameter has to be carefully chosen for fast and stable convergence. Our main idea to overcome this issue is to adaptively modify the variable space metric so that it matches the cost convexity. Besides this, selecting an appropriate MOS (e.g., Peaceman-Rachford splitting) instead of the Douglas-Rachford splitting used in the ADMM may effectively improve the convergence rate further. To realize these ideas w.r.t. adaptive metric modification and appropriate operator splitting selection, we apply B-MOS to the NMF problem and obtain a new NMF solver in this paper. Results of numerical experiments demonstrate that the proposed NMF solver with B-MOS improved the convergence rate in the tail.
Temporal Methods to Detect Content-Based Anomalies in Social Media

We develop a method for time-dependent topic tracking and meme trending in social media. Our objective is to identify time periods whose content differs significantly from normal, and we utilize two techniques to do so. The first is an information-theoretic analysis of the distributions of terms emitted during different periods of time. In the second, we cluster documents from each time period and analyze the tightness of each clustering. We also discuss a method of combining the scores created by each technique, and we provide ample empirical analysis of our methodology on various Twitter datasets.
A Study of Replicated Thai Benchmark Dataset

Nowadays, Semantic similarity has a huge impact in several applications including ontology learning, information extraction, document clustering, information retrieval, the business sector and so on. Most work in word sematic similarity has focused on English, other European languages and Asian languages including Chinese, Arabic and Japanese. There has been little study of word semantic similarity in Thai. As the language evolves over a period of time. This study focuses on finding out how language development will effect the Thai benchmark dataset over past sever or not by presenting a replicated version of Thai benchmark dataset as well as investigating the statistical difference between Replicated dataset and Original Thai benchmark dataset.
Automatic Cropping and Deskew of Multiple Objects

This chapter is devoted to a novel high-performance algorithm for automatic segmentation and skew correction of several objects on a scanned image. The complex multi-stage technique includes preprocessing, initial segmentation, classification of connected regions, merging of fragmented regions by a heuristic procedure, detection of bounding boxes and deskew of rectangular objects. Our method is highly effective due to the unification of most operations in one pass. The algorithm provides users with additional functionality and comfort. The method was evaluated by a proposed quantitative quality criterion.
On Role and Location of Normalization before Model-based Data Augmentation in Residual Blocks for Classification Tasks

Regularization is crucial to the success of many practical deep learning models, in particular in frequent scenarios where there are only a few to a moderate number of accessible training samples. In addition to weight decay, noise injection and dropout, regularization based on multi-branch architectures, such as Shake-Shake regularization, has been proven successful in many applications and attracted more and more attention. However, beyond model-based representation augmentation, it is unclear how Shake-Shake regularization helps to provide further improvement on classification tasks, let alone the baffling interaction between batch normalization and shaking. In this work, we present our investigation on Shake-Shake regularization. One of our findings illustrates the phenomenon that batch normalization in residual blocks is indispensable when shaking is applied to model branches, along with which we also empirically demonstrate the most effective location to place a batch normalization layer in a shaking regularized residual block. Based on these findings, we believe our work is beneficial to future studies on the research topic of refining control for model-based representation augmentation.
MATHEMATICAL MODEL AND MEASURING ALGORITHM OF CONTINUALLY DISTRIBUTED SEMICONDUCTOR SENSOR OF A TEMPERATURE PROFILE IN NON-ISOTHERMAL FLOWS

Abstract: This paper proposes a mathematical model and a measuring algorithm for a semiconductor one-dimensional distributed sensor designed to measure temperature fields in non- isothermal liquid and gas flows. A sensor device, its equivalent circuit and a circuit for connection to a measuring circuit are described. A step-by-step measurement algorithm is proposed, which includes the necessary electrical measuring operations and an algorithm for mathematical processing of the primary measurement information in order to determine the measured temperature field.
Time Series Analysis of Disease Outbreaks in Eastern Samar Province Philippines

Objective: To analyze the 5-year occurrence of different diseases based on hospital records in Eastern Samar Provincial Hospital; to present a time series analysis model for the occurrence of disease in Eastern Samar. Methods: On analyzing the data this study used the data mining model to process the activities and used Orange as the tool to show the result of the analysis. The study chooses 5 common diseases which are: Acute Gastroenteritis, Urinary Tract Infection, Pneumonia, Hypertensive Cardiovascular and Pulmonary Tuberculosis from the year 2007-2011 and through Orange widget the gathered data imported and used graph visualizer. The study gets the sum of all patients every month, year and by a quarter to get the different analysis of the occurrence of the disease. Findings: The result showed the time series analysis of the highest number of the patient from the year 2007-2011 was the Pneumonia followed by Acute Gastroenteritis, UTI, Hypertensive Cardiovascular and Pulmonary Tuberculosis, respectively. Borongan Eastern Samar has the highest number of occurrence of all the 5 diseases. Application: Consider common diseases that are affected by distinctive weather condition (rainy season and dry season). Keywords: Data Mining Tool, Disease Outbreak, Time Series Analysis
Temperature-aware workload management for sustainable datacenters powered by renewable energy

As large-scale datacenters become to be widely used in massive data processing and storage, the power consumption of these datacenters cannot be ignored any more, which leads to a significant carbon footprint. Renewable energy sources are used recently as the power supply for datacenters. In this paper, we focused on the sustainable datacenters using hybrid energy supply, and proposed a temperature-aware workload load management approach to maximize the utilization of renewable energy sources, considering the power consumption of both IT devices and cooling devices. In order to evaluate the effect of the proposed method, we perform simulation experiments using the Cloudsim tool. Results show that the proposed method can effectively reduce the brown energy consumption while maximizing the utilization of green energy.
Perfect, Hamming and Simplex Linear Error-Block Codes with Minimum $$\pi $$ -distance 3

Linear error-block codes were introduced in 2006 as a generalization of linear block codes. In this paper we construct two new families of perfect binary linear error-block codes of $$ \pi $$ -distance 3, namely, $$[n_1]\ldots [n_t][2]^s$$ (where $$ t\ge 1 $$ ), and $$[n_1][n_t][3]^s$$ (where $$ t= 1$$ or $$t=2$$ ), we also introduce the notions of Hamming and Simplex linear error-block codes, and we give a method to construct Hamming LEB codes from its parity check matrix. We also prove that Hamming LEB codes are perfect, and the constructed perfect codes are Hamming.