Reviewerly/exHarmony huggingface dataset:
What the Appearance Channel from Two-Stream Architectures for Activity Recognition Is Learning?

The automatic recognition of human activities from video data is being led by spatio-temporal Convolutional Neural Networks (3D CNNs), in particular two-stream architectures such as I3D that reports the best accuracy so far. Despite the high performance in accuracy of this kind of architectures, very little is known about what they are really learning from data, resulting therefore in a lack of robustness and explainability. In this work we select the appearance channel from the I3D architecture and create a set of experiments aimed at explaining what this model is learning. Throughout the proposed experiments we provide evidence that this particular model is learning the texture of the largest area (which can be the activity or the background, depending on the distance from the camera to the action performed). In addition, we state several considerations to take into account when selecting the training data to achieve a better generalization of the model for human activity recognition.
Simulation and operation control strategy of ground source thermal energy management system by cold and heat auxiliary technology

To explore the performance of the ground source thermal energy management system under the cold and heat sources, based on the cold and heat auxiliary technology, a ground source thermal energy composite management system is constructed and simulated. The constructed ground source heat pump-refrigeration unit-hybrid heating management system of urban heating networks, as well as the simple system, are analyzed and investigated in terms of power consumption and underground temperature control. The research results show that the constructed ground source heat pump-refrigeration unit-hybrid heating management system of the urban heating network has lower power and energy consumption than a simple system during the same period, which meets the economic requirements and guarantees the system with relatively low energy consumption. For underground temperature control, the constructed system is more stable than a simple system without excessive temperature fluctuations. The operation control strategy of the constructed system is mainly for chiller units, heat pump units, cooling towers, source side, and side circulation water pump modules. In summary, the constructed ground source heat pump-refrigeration unit-hybrid heating management system of an urban heating network based on the ground source heat pump meets the requirements for energy consumption and temperature control and can operate the control strategy normally. The results are significant for subsequent researches on the ground source thermal energy management system based on cold and heat auxiliary technology.
Syntactic and Semantic Bias Detection and Countermeasures

Applied Artificial Intelligence (AAI) and, especially Machine Learning (ML), both had recently a breakthrough with high-performant hardware for Deep Learning [1]. Additionally, big companies like Huawei and Google are adapting their product philosophy to AAI and ML [2–4]. Using ML-based systems require always a training data set to achieve a usable, i.e. trained, AAI system. The quality of the training data set determines the quality of the predictions. One important quality factor is that the training data are unbiased. Bias may lead in the worst case to incorrect and unusable predictions. This paper investigates the most important types of bias, namely syntactic and semantic bias. Countermeasures and methods to detect these biases are provided to diminish the deficiencies.
A discernible criterion for 3D point cloud based on multifractal spectrum

The traditional discernible criteria for a 2D target are mostly based on Johnson criterion, to overcome the limitations of the Johnson criterion and fill the gap in a 3D point cloud, a novel discernible criterion has been proposed for the 3D point cloud. Based on the multifractal spectrum, the spatial distribution of the 3D point cloud is described. By analyzing the multifractal spectra at different resolutions, feature trend and the final discernible resolution are concluded. The experimental results show that the limiting resolution of T90, F15C is 585mm, the limiting resolution of T90 and Rexton is 517mm, and the limiting resolution of F15C and Rexton is 541mm. The proposed discernible criteria can provide theoretical support for limit identification resolution of 3D point cloud target.
Balancing Straight-Line Programs for Strings and Trees

The talk will explain a recent balancing result according to which a context-free grammar in Chomsky normal form of size m that produces a single string w of length n (such a grammar is also called a straight-line program) can be transformed in linear time into a context-free grammar in Chomsky normal form for w of size $$\mathcal {O}(m)$$ , whose unique derivation tree has depth $$\mathcal {O}(\log n)$$ . This solves an open problem in the area of grammar-based compression, improves many results in this area and greatly simplifies many existing constructions. Similar balancing results can be formulated for various grammar-based tree compression formalism like top DAGs and forest straight-line programs. The talk is based on joint work with Moses Ganardi and Artur Jeż. An extended abstract appeared in [11]; a long version of the paper can be found in [12].
Optimal Skeleton Huffman Trees Revisited

A skeleton Huffman tree is a Huffman tree in which all disjoint maximal perfect subtrees are shrunk into leaves. Skeleton Huffman trees, besides saving storage space, are also used for faster decoding and for speeding up Huffman-shaped wavelet trees. In 2017 Klein et al. introduced an optimal skeleton tree: for given symbol frequencies, it has the least number of nodes among all optimal prefix-free code trees (not necessarily Huffman’s) with shrunk perfect subtrees. Klein et al. described a simple algorithm that, for fixed codeword lengths, finds a skeleton tree with the least number of nodes; with this algorithm one can process each set of optimal codeword lengths to find an optimal skeleton tree. However, there are exponentially many such sets in the worst case. We describe an $$\mathcal {O}(n^2\log n)$$-time algorithm that, given n symbol frequencies, constructs an optimal skeleton tree and its corresponding optimal code.
FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network

Significant progress has been made for estimating optical flow using deep neural networks. Advanced deep models achieve accurate flow estimation often with a considerable computation complexity and time-consuming training processes. In this work, we present a lightweight yet effective model for real-time optical flow estimation, termed FDFlowNet (fast deep flownet). We achieve better or similar accuracy on the challenging KITTI and Sintel benchmarks while being about 2 times faster than PWC-Net. This is achieved by a carefully-designed structure and newly proposed components. We first introduce an U-shape network for constructing multi-scale feature which benefits upper levels with global receptive field compared with pyramid network. In each scale, a partial fully connected structure with dilated convolution is proposed for flow estimation that obtains a good balance among speed, accuracy and number of parameters compared with sequential connected and dense connected structures. Experiments demonstrate that our model achieves state-of-the-art performance while being fast and lightweight.
Semi-supervised Parsing with a Variational Autoencoding Parser

We propose an end-to-end variational autoencoding parsing (VAP) model for semisupervised graph-based projective dependency parsing.It encodes the input using continuous latent variables in a sequential manner by deep neural networks (DNN) that can utilize the contextual information, and reconstruct the input using a generative model.The VAP model admits a unified structure with different loss functions for labeled and unlabeled data with shared parameters.We conducted experiments on the WSJ data sets, showing the proposed model can use the unlabeled data to increase the performance on a limited amount of labeled data, on a par with a recently proposed semi-supervised parser with faster inference.
A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication

Matrix factorization is a very common machine learning technique in recommender systems. Bayesian Matrix Factorization (BMF) algorithms would be attractive because of their ability to quantify uncertainty in their predictions and avoid over-fitting, combined with high prediction accuracy. However, they have not been widely used on large-scale data because of their prohibitive computational cost. In recent work, efforts have been made to reduce the cost, both by improving the scalability of the BMF algorithm as well as its implementation, but so far mainly separately. In this paper we show that the state-of-the-art of both approaches to scalability can be combined. We combine the recent highly-scalable Posterior Propagation algorithm for BMF, which parallelizes computation of blocks of the matrix, with a distributed BMF implementation that users asynchronous communication within each block. We show that the combination of the two methods gives substantial improvements in the scalability of BMF on web-scale datasets, when the goal is to reduce the wall-clock time.
An Anti-Power Attack Circuit Design for Block Cipher

To improve the Anti-Power Attack capability of the Block Cipher algorithm circuit, we briefly summarized the research status of Anti-Power Attack technology and the advantages and disadvantages in each thesis. On the basis of Masking Technique, the concept of Reverse Interleaving was put forward and its key property was proved. A Reverse Interleaving circuit structure was designed then, whose timing alignment is disturbed by adding a register. On this basis, an optimized circuit structure was further designed and the generation of essential signal sequences was analyzed. Then, the protecting mechanism and the timing of the optimized structure were discussed: The Anti-Power Attack capability of the algorithm circuit is improved by realizing Power Randomization. Finally, the structure was applied to the AES algorithm circuit and was verified in terms of encryption function and protection performance. The results show that AES algorithm circuit with the protective structure designed in this paper can correctly realize encryption in two different timings in 12 clock cycles and the addition of the Reverse Interleaving circuit structure can greatly improve the Anti-Power Attack capability: Before protection, the attack sample size is 600, and the obtained correlation coefficient is 0.225; after protection, the sample size rises to 10,000, and the correlation coefficient decreases to 0.016. The circuit structure designed in this paper has a high reference value.
Transmediale’s Postdigital Proposition

Book Review| July 01 2020 Transmediale’s Postdigital Proposition Across and Beyond: A Transmediale Reader on Post-digital Practices, Concepts, and Institutions, edited by Bishop, Ryan; Gansing, Kristoffer; Parikka, Jussi; Wilk, Elvia, Berlin: Sternberg, 2016, 350 pages, €15.00 (paperback), ISBN 978-3-95679-289-2 Scott Wark Scott Wark Scott Wark is a research fellow for the Wellcome-funded project “People like You: Contemporary Figures of Personalisation.” He is based at the Centre for Interdisciplinary Methodologies at the University of Warwick. He researches online culture, among other things. Search for other works by this author on: This Site Google Cultural Politics (2020) 16 (2): 274–276. https://doi.org/10.1215/17432197-8233462 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Scott Wark; Transmediale’s Postdigital Proposition. Cultural Politics 1 July 2020; 16 (2): 274–276. doi: https://doi.org/10.1215/17432197-8233462 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter Books & JournalsAll JournalsCultural Politics Search Advanced Search Across and Beyond: A Transmediale Reader on Post-digital Practices was ostensibly published to mark thirty years of Transmediale, the art and digital culture festival that happens in Berlin each winter. Summing up just one iteration of this sprawling festival is hard enough; doing justice to thirty years of art and theory is something else again. Should the editors of this kind of book document its history? Serve up its greatest hits? Try to influence its future? Over its fifteen essays and ten profiles of artworks, Across and Beyond does a little of each. The result is a book that uses Transmediale as a platform for a series of propositions about how we might think media now—and imagine its futures differently.This book’s editors include Transmediale’s outgoing director, Kristoffer Gansing; Winchester School of Art professors Ryan Bishop and Jussi Parikka; and the writer and editor, Elvia Wilk. To lend it coherence,... Issue Section: Book Reviews You do not currently have access to this content.
Bayesian Inference on the Generalized Gamma Distribution using Conjugate Priors

This paper focuses on the three-parameter generalized gamma distribution and uses Bayesian techniques to estimate its parameters. Many authors con-sidered estimating the parameters of the generalized gamma distribution in a Bayesian framework using Jeﬀrey’s priors. Others used diﬀerent loss functions and the least squares approach. This study uses Bayesian techniques to estimate the three-parameter generalized gamma distribution by using conjugate priors. The random Metropolis algorithm is used to simulate the Bayesian estimates of the three parameters. Then these estimates are compared to the maximum like-lihood estimates using the mean error through simulation. It has been shown in this paper that the obtained estimates using this approach is more accurate than the traditional methods of estimation such as the Maximum likelihood method. The same approach is then used to estimate the parameters of mixtures of the generalized gamma parameters using conjugate priors.
LIBRARY IN THE MEDIA ENVIRONMENT

Media environment, emerged from the traditional system of mass communications and enriched with new opportunities for the community, is intensively developing in the modern society. Scientists’ research shows that media defines modern existence, consciousness and values of anindividual and social groups. Media environment acts as the place of human existence and means of influencing his consciousness. The author characterizes the media environment as the social space for the library institution, reveals the possibilities of the public library as the leader of public opinion and identifies technological features of introducing information influence into the library media communications. With the development of the Internet technologies, mass communication has become open and accessible to the library community. Modern library simultaneously acts as an active user ofmedia content and as an actor creating its own media products for a wide audience. However, these opportunities are mainly used for internal purposes: to reflect the life of the institution and to disclose its information resources. The article justifies the possibility of strengthening the social position of the library by assuming the role of the local community information leader. The author comes to the conclusion, that social effectiveness of the library actor behavior in the media environment will be determined by two factors: the correspondence of the communication product to social demands and the power of information influence of the message and social consequences (actions).
INTRODUCTION OF DIGITAL TECHNOLOGIES IN THE RESEARCH WORK OF SSUGT STUDENTS

The article discusses the current trend of digitalization of research activities of students. A model of digitalization of the research work of students of the Siberian State University of Geosystems and Technologies is presented. The conceptual directions of the development of a portal for research work of students are shown. Existing and planned developments, of a digital portal for the scientific work of student, are shown.
IOT WEATHER DATA ANALYSIS METHDS INBIGDATA

In present day years, huge statistics and Internet of Things (IoT) implementations started out gettingmore hobby. Researchers focused on growing large statistics analytics answers the use of device gaining knowledgeof models. Machine mastering is a developing style in this subject because of its ability to extract hidden featuresand patterns even in fairly complicated datasets. Systems supporting financial dealings at an organization scaleought to be able to deal with large volumes of transactions in a cozy manner while retaining excessive stage ofoperational availability. These structures must also have the electricity to interface with many 0.33-party programswithout problems. In this paper we discover those architectural issues for systems helping monetary transactions theusage of the block chain technology. We perceive the strengths of this era in reaching the functions related to thosearchitectural concerns and recommend architectural tactics that can be used to triumph over its weaknesses. Areference structure is presented collectively with a prototype that implements this structure using Iroha, acommercially to be had economic ledger for block chain systems.
New collision paths for round-reduced SKINNY-Hash

In recent years, with the rapid development of the Internet of Things (IoT), RFID tags, industrial controllers, sensor nodes, smart cards and other small computing devices are increasingly widely deployed. In order to help protect low-power, low-cost Internet of things devices, lightweight cryptography came into being. In order to launch the standard of cryptographic algorithm suitable for constrained environment, NIST started the process of lightweight cryptography standardization in 2016, and published the second round of candidate cryptographic algorithms in August 2019. SKINNY-Hash in the sponge construction is one of the second round candidates, as well as SKINNY-AEAD. The tweakable block cipher SKINNY is the basic component for both of them. Although cryptanalysts have proposed several cryptanalysis results on SKINNY and SKINNY-AEAD, there is no cryptanalysis results on SKINNY-Hash. Based on the differential cryptanalysis and the method of mixed integer programming (MILP), we perform differential cryptanalysis on SKINNY-Hash. The core is to set up the inequations of the MILP model. Actually, it is hard to obtain the inequations of the substitution (i.e. S-box) obeying the previous method. By a careful study of the permutation, we partition the substitution into a nonlinear part and a linear part, then a series of inequations in the MILP model is obtained to describe the differentials with high possibilities. As a result, we propose a differential hash collision path of 3-round SKINNY-tk3-Hash. By adjusting the bit rate of SKINNY-tk3-Hash, we propose a 7-round collision path for the simplified algorithm. The cryptanalysis in this paper will help to promote the NIST Lightweight Crypto Standardization process.
“V.V. Bessel” Collection in the Russian National Museum of Music

Vasily Vasilyevich Bessel (1843—1907) entered the history of Russian and world music culture as one of the largest music publishers. His company was occupying one of the leading positions in terms of production volume in the Russian music printing market in the late 19th — early 20th century. It was the company that first published many of works by Russian classical composers — A.G. Rubinstein, A.P. Borodin, P.I. Tchaikovsky, M.P. Mussorgsky, N.A. Rimsky-Korsakov, and A.K. Lyadov. V.V. Bessel’s music publishing activities were connected with his works on the history of music printing in Russia and copyright. He left an extensive legacy in the form of numerous handwritten materials, now dispersed in various archives (mainly in Moscow and Saint Petersburg). The Russian National Museum of Music, Collection 42, holds one of the largest archives associated with V.V. Bessel. Major part of it makes up a separate collection called “V.V. Bessel”, which includes unofficial documents, responding mail, as well as literary manuscripts and photographic materials. Due to lack of comprehensive research of that documentary collection, this article provides a brief overview of its content, and the history of formation of V.V. Bessel’s collection. The main purpose of the research is to characterize both published and unknown sources. The article meets the relevant task of modern musicology: disclosure of Moscow and St. Petersburg archival collections. Many of the documents reviewed by the author are an important addition to the only monograph on V.V. Bessel, which belongs to the pen of N.F. Findzein. The article discusses, in more detail, the documents related to the literary weekly “Muzykal’nyi Listok [Musical Sheet]” (1872—1877), the first periodical published by “V. Bessel and Co.”, as well as the correspondence of December 1886 between V.V. Bessel and P.I. Tchaikovsky, which, at the latter’s initiative, ended all the composer’s personal and business contacts with his Petersburg publisher. This study expands the researchers’ understanding of the body of documents stored in the collection under consideration, the problems associated with them, and their prospects.
Research on Coal and Rock Image Recognition Based on 2DPCA Algorithm

Abstract The PCA algorithm in coal and rock recognition had some fundamental flaws such as large computation and low efficiency. In this paper, a coal and rock image recognition method based on 2DPCA algorithm was proposed. The experimental results showed that this method could not only reduce the dimension of the original feature, but also reduce the complicated calculation and improve the recognition accuracy effectively.
An Intelligent Bi-Directional Parallel B* Routing Algorithm

Pathfinding is a kind of problem widely used in daily life.It is widely used in network games, map navigation and other fields.However, the traditional A* algorithm has some shortcomings, such as heuristic function needs to be designed according to different problems, path has many inflection points, and algorithm stability is poor.B* algorithm also has the shortcoming of inaccurate pathfinding.In order to solve the problems existing in A* and B* algorithms, obstacle avoidance regeneration mechanism, pre-exploration mechanism and equivalent waiting strategy are proposed.It adds a bidirectional parallel search mechanism to form an IBP-B* algorithm (Intelligent bi-directional parallel B* routing algorithm).The simulation results show that the speed of IBP-B* algorithm is 182% higher than that of A* algorithm and 366% higher than that of BFS algorithm.Meanwhile, compared with B* algorithm, IBP-B* algorithm improves the pathfinding accuracy of the algorithm.
DEVELOPING A DIGITAL PLATFORM FOR REGISTRATION AND SUPPORT OF STUDENT SCIENTIFIC CONFERENCE AT SSUGT

At present, the student scientific conference of SSUGT includes a large number of sections, master classes and other events, and the number of participants of the student conference is increasing every year. In this regard, there is a need to automate organizational functions that moderators of the conference sections have to solve. The article discusses the process of developing a web-platform for holding a student scientific conference SSUGT, its functionality is given.