Reviewerly/exHarmony huggingface dataset:
Packing tree degree sequences

A degree sequence $D=(d_1,\dots, d_n)$ is a series of non-ne\-ga\-tive integers. A degree sequence is graphical if there exists a vertex labeled graph $G$ in which the degree of vertex $v_i$ is exactly $d_i$ for $i=1,\dots,n$. The graph $G$ is called a realization of $D$. The \emph{edge-disjoint realization problem} is the following: given a $c \times n$ degree matrix $D=\{(d_{1,1},\dots, d_{1,n} ),$ $(d_{2,1},\dots,d_{2,n} ),\dots, (d_{c,1},\dots, d_{c,n})\}$, in which each row of the matrix is a graphical degree sequence, decide if there exists pairwise edge-disjoint realizations of these degree sequences on the same vertex set $V=\{v_1,v_2,\dots,v_n\}$. Such set of edge-disjoint graphs is called a realization of the degree matrix. A realization can also be presented as an edge colored simple graph, in which the edges with a given color form a realization of the degree sequence in a given row of the degree matrix. It is known that the edge-disjoint realization problem is $\NP$-complete even if the number of rows is three and the degrees on each vertex sum up to $n-1$, that is, when a decomposition of the complete graph is required into three subgraphs with prescribed degrees. Moreover, the problem is also $\NP$-complete when the number of rows is two and the sum of the degrees on some of the vertices is less than $n-1$. However, special cases that are computationally tractable are also of interest. A classical result of Kundu \cite{kundutree} shows that deciding if two \emph{tree degree sequences} have edge-disjoint realizations is in $\P$. Motivated by the aforementioned result, we consider special cases of the problem for two tree degree sequences. We show that if there is no node which have degree one in both degree sequences (that is, the requested trees do not share a common leaf node) then they always have edge-disjoint caterpillar realizations. By using a probabilistic method, we prove that two tree degree sequences always have edge-disjoint realizations if each vertex is a leaf in at least one of the trees. This theorem can be extended to more trees: we show that the edge-disjoint realization problem is in $\P$ for an arbitrary number of tree sequences with the property that each vertex is a non-leaf in at most one of the trees, i.e., in every column of the degree matrix there is at most one entry which is greater than one. We also consider the following variant of the edge-disjoint realization problem: given two graphical degree sequences $D_1$ and $D_2$ such that $D_2$ is a tree degree sequence, decide if there exists edge-disjoint realizations of $D_1$ and $D_2$ where the realization of $D_2$ does not need to be a tree. We show that this problem is already $\NP$-complete. Counting, or just estimating the number of distinct realizations of degree sequences is challenging in general. We show that efficient approximations for the number of solutions as well as an almost uniform sampler exist for two tree degree sequences if each vertex is a leaf in at least one of the trees.
Efficient Distributed Knowledge Representation Learning for Large Knowledge Graphs

Knowledge Representation Learning (KRL) has been playing an essential role in many AI applications and achieved desirable results for some downstream tasks. However, two main issues of existing KRL embedding techniques have not been well addressed yet. One is that the size of input datasets processed by these embedding models is typically not large enough to accommodate large-scale real-world knowledge graphs; the other issue is that lacking a unified framework to integrate current KRL models to facilitate the realization of embeddings for various applications. We propose DKRL, which is a distributed KRL training framework that can incorporate different KRL models in the translational category using a unified algorithm template. In DKRL, a set of primitive interface functions is defined to be implemented by various knowledge embedding models to form a unified algorithm template for distributed KRL. The effectiveness and efficiency of our framework have been verified by extensive experiments on both benchmark and real-world knowledge graphs, which show that our approach can outperform the existing ones by a large margin.
Contradictions, Impossibility, and Triviality: A Response to Jc Beall

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Stuff Matters in Participation: Infrastructuring a Co-Sewing Café

This paper explores how acts of use and participation can be better understood and articulated in relation to the socio-material and spatial conditions of “infrastructuring”. Infrastructuring is framed here as an object of design research and of design research, comprising the social activities and skills as well as the material tools and “stuff” that are integral to alternative spaces of production such as Fab Labs and makerspaces. We bring together theories from three different areas of research (peer production, Participatory Design and social practice theory), building a conceptual framework that is used to analyze extensive empirical material gathered while initiating, running and researching a ‘co-sewing cafe’ over 18 months with hundreds of diverse participants. Tracing our understanding of use and participation through literature and case analysis, we use illustrative figures and tables to articulate different types and dimensions of use in relation to one another and in relation to the empirical analysis that is detailed and recounted in various ways. The paper concludes by elaborating how types of use in reference to types of stuff provide insight on participant skills, learning and engagement that can result in change of roles over time.
,,,Kansans at Risk: Strengthened Data Breach Notification Laws as a Deterrent to Reckless Data Storage

Data collection provides attractive opportunities for businesses to improve services, enhance marketing efforts, and generate revenue through data sharing practices. 1As a result, businesses today are collecting mass quantities of data, leading to devastating data breaches. 2Juniper Research projects that United States-based breaches will account for the majority of all data breaches in the world by 2023 because of data stockpiling practices. 3Consumers, businesses, and lawmakers are struggling to adapt to an ever-changing technological landscape.Today, data breaches are inevitable. 4 How can businesses address the threat of hackers with the means to change, adapt, and persist?Of course,
Group theory in solid state physics and photonics: problem solving with Mathematica

"Group theory in solid state physics and photonics: problem solving with Mathematica." Contemporary Physics, 60(1), pp. 108–109
Can Entropy Explain Successor Surprisal Effects in Reading

Human reading behavior is sensitive to surprisal: more predictable words tend to be read faster. Unexpectedly, this applies not only to the surprisal of the word that is currently being read, but also to the surprisal of upcoming (successor) words that have not been fixated yet. This finding has been interpreted as evidence that readers can extract lexical information parafoveally. Calling this interpretation into question, Angele et al. (2015) showed that successor effects appear even in contexts in which those successor words are not yet visible. They hypothesized that successor surprisal predicts reading time because it approximates the reader's uncertainty about upcoming words. We test this hypothesis on a reading time corpus using an LSTM language model, and find that successor surprisal and entropy are independent predictors of reading time. This independence suggests that entropy alone is unlikely to be the full explanation for successor surprisal effects.
Two-Weight Linear Codes and Their Applications in Secret Sharing

Linear codes with few weighs have many applications in secret sharing. Determining the access structure of the secret sharing scheme based on a linear code is a very difficult problem. We provides a method to construct a class of two-weight torsion codes over finite non-chain ring. We determine the minimal codewords of these torsion codes over the finite non-chain ring. Based on the two-weight codes, we find the access structures of secret sharing schemes.
RESEARCH OF THE USE OF AUGMENTED REALITY TO DEVELOP THE ABILITY TO ESTIMATE IN STEM EDUCATION

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 5719-5724ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.1390Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
E-mail recovery from websites using Gravatar

Although the Internet has not been created for anonymity, many users posting comments on blogs hope that they will remain anonymous. The article presents an attack on revealing the e-mail addresses of users posting comments on the sample website using Gravatar. This attack aims to make readers aware of the fact that by posting comments on sites using Gravatara services, we are exposed to the disclosure of our e-mail addresses and sometimes even of our real identity. Keywords: Gravatar, deanonimization, e-mail, MD5 hash function
Aplikasi Pencarian Dengan Menggunakan Algoritma Knuth Morris Pratt Pada Berkas Dokumen Shipment

File search of shipment transactions is vital for a company engaged in export import. If the file is lost or tucked or exchanged, the export and import management cannot be completed on time. The system that has been running in the search for files is still manual by searching for files in physical form in file file cabinets or searching for pdf attachments found in e-mail. With the new file search system, it is expected to save search time and overcome existing problems. This system was developed with ASP.net MVC programming language and SQLite database by applying the Knuth Morris Pratt algorithm. From the results of the effectiveness test, it can be concluded that the effectiveness of the new system is far greater than the old system, which is equal to 85.00%. While for the old system only 45.00%. This shows that the new system is better and more effective than the old system.
El conocimiento profesional docente y el discurso pedagógico de la primera mitad del siglo XX. Una contrastación con el perfil, parámetros e indicadores del desempeño de los profesores de educación básica en México

pedagogical thinking from the first half of the 20th century through the distinguished discourse of five of the most remarkable educator voices in the time: G. Kerschensteiner, A.
Power Efficiency of S-Boxes: From a Machine-Learning-Based Tool to a Deterministic Model

Designing cryptographically good and power-efficient $4\times 4$ S-boxes is a challenging problem in the era of lightweight cryptography. Although the optimal cryptographic properties are easy to determine, verifying the power efficiency of an S-box is nontrivial. The conventional approach of determining the power consumption using commercially available CAD tools is highly time-consuming, which becomes formidable while dealing with a large pool of S-boxes. This mandates the development of automation that should quickly characterize the power efficiency from the Boolean function representation of an S-box. In this paper, we present a supervised machine-learning-assisted automated framework to resolve the problem for $4\times 4$ S-boxes, which turns out to be 14 times faster than the traditional approach. The key idea is to extrapolate the knowledge of literal counts, AND–OR–NOT gate counts in the sum-of-products (SOP) form of the underlying Boolean functions to predict the dynamic power efficiency. We demonstrate the effectiveness of our framework by reporting on a set of power-efficient (involutive) optimal S-boxes from a large set of S-boxes. We also develop a deterministic model using results obtained from supervised learning to predict the dynamic power of an S-box that can be used in an evolutionary algorithm to generate cryptographically good and low-power S-boxes.
Approximate Optimal Transport for Continuous Densities with Copulas

Optimal Transport (OT) formulates a powerful framework by comparing probability distributions, and it has increasingly attracted great attention within the machine learning community. However, it suffers from severe computational burden, due to the intractable objective with respect to the distributions of interest. Especially, there still exist very few attempts for continuous OT, i.e., OT for comparing continuous densities. To this end, we develop a novel continuous OT method, namely Copula OT (Cop-OT). The basic idea is to transform the primal objective of continuous OT into a tractable form with respect to the copula parameter, which can be efficiently solved by stochastic optimization with less time and memory requirements. Empirical results on real applications of image retrieval and synthetic data demonstrate that our Cop-OT can gain more accurate approximations to continuous OT values than the state-of-the-art baselines.
Belief Propagation Network for Hard Inductive Semi-Supervised Learning

Given graph-structured data, how can we train a robust classifier in a semi-supervised setting that performs well without neighborhood information? In this work, we propose belief propagation networks (BPN), a novel approach to train a deep neural network in a hard inductive setting, where the test data are given without neighborhood information. BPN uses a differentiable classifier to compute the prior distributions of nodes, and then diffuses the priors through the graphical structure, independently from the prior computation. This separable structure improves the generalization performance of BPN for isolated test instances, compared with previous approaches that jointly use the feature and neighborhood without distinction. As a result, BPN outperforms state-of-the-art methods in four datasets with an average margin of 2.4% points in accuracy.
Unsupervised Machine Intelligence for Automation of Multi-Dimensional Modulation

In this letter, we propose a new unsupervised machine learning technique for a multi-dimensional modulator that can autonomously learn key exploitable features from significant variations of multi-dimensional wireless propagation parameters, followed by a real-time prediction of the best multi-dimensional modulation mode to be used for the next resilient transmission. The proposed method aims to embrace the potential of the unsupervised K-means clustering into the physical layer of non-coherent multi-dimensional transmission. Simulation results show that the proposed scheme can outperform the benchmarks at a cost of simple offline training.
Ontology Approximation in Horn Description Logics

We study the approximation of a description logic (DL) ontology in a less expressive DL, focusing on the case of Horn DLs. It is common to construct such approximations in an ad hoc way in practice and the resulting incompleteness is typically neither analyzed nor understood. In this paper, we show how to construct complete approximations. These are typically infinite or of excessive size and thus cannot be used directly in applications, but our results provide an important theoretical foundation that enables informed decisions when constructing incomplete approximations in practice.
Action Recognition in Compressed Domain Using Residual Information

Practically, action recognition using deep learning approaches are slow because of high temporal redundancy and large size of the raw video data. One of the solutions for boosting accuracy is calculating optical flows. Generally, extracting motion features are too time-consuming. Therefore, traditional action recognition methods are not suitable for real time applications. On the other hand, compressed videos are available in many situations especially when using mobile devices. We proposed a method that extracts residuals directly from compressed videos by partially decoding the video and feed them to a deep neural network. In general, exploiting the compressed domain features as available information provides a slight reduction in accuracy while the low complexity of this method makes it appropriate for real time applications. The experimental results on multiple first and third person datasets exhibit that while the proposed method provides low computational complexity, the results are highly competitive with traditional approaches in accuracy.
Parametric Manifold Learning of Gaussian Mixture Models

The Gaussian Mixture Model (GMM) is among the most widely used parametric probability distributions for representing data. However, it is complicated to analyze the relationship among GMMs since they lie on a high-dimensional manifold. Previous works either perform clustering of GMMs, which learns a limited discrete latent representation, or kernel-based embedding of GMMs, which is not interpretable due to difficulty in computing the inverse mapping. In this paper, we propose Parametric Manifold Learning of GMMs (PML-GMM), which learns a parametric mapping from a low-dimensional latent space to a high-dimensional GMM manifold. Similar to PCA, the proposed mapping is parameterized by the principal axes for the component weights, means, and covariances, which are optimized to minimize the reconstruction loss measured using Kullback-Leibler divergence (KLD). As the KLD between two GMMs is intractable, we approximate the objective function by a variational upper bound, which is optimized by an EM-style algorithm. Moreover, We derive an efficient solver by alternating optimization of subproblems and exploit Monte Carlo sampling to escape from local minima. We demonstrate the effectiveness of PML-GMM through experiments on synthetic, eye-fixation, flow cytometry, and social check-in data.
Overview of the 2019 open-source IR replicability challenge (OSIRRC 2019)

The Open-Source IR Replicability Challenge (OSIRRC 2019), organized as a workshop at SIGIR 2019, aims to improve the replicability of ad hoc retrieval experiments in information retrieval by gathering a community of researchers to jointly develop a common Docker specification and build Docker images that encapsulate a diversity of systems and retrieval models. We articulate the goals of this workshop and describe the jig that encodes the Docker specification. In total, 13 teams from around the world submitted 17 images, most of which were designed to produce retrieval runs for the TREC 2004 Robust Track test collection. This exercise demonstrates the feasibility of orchestrating large, community-based replication experiments with Docker technology. We envision OSIRRC becoming an ongoing community-wide effort to ensure experimental replicability and sustained progress on standard test collections.
Constraint Embedding for Solving Optimization Problems on Quantum Annealers

Quantum annealers such as the commercially available D-Wave machines are designed to natively solve quadratic unconstrained binary optimization (QUBO) problems. While most of the well-known NP-hard optimization problems can easily be formulated as quadratic binary problems, such formulations also contain constraints, which commonly are added to the objective function in the form of penalties to obtain a QUBO version. However, the standard method for defining such penalties leads to QUBOs that are dense and therefore take too much of the resources of the quantum annealer. In this paper, we describe an alternative approach to the constraint embedding problem that uses mixed-integer linear programming (MILP) and is scalable to problems of arbitrary number of variables.
A computational study on aging effect for facial expression recognition

This work uses newly introduced variations of the sparse representation-based classifier (SRC) to challenge the issue of automatic facial expression recognition (FER) with faces belonging to a wide span of ages. Since facial expression is one of the most powerful and immediate ways to disclose individuals? emotions and intentions, the study of emotional traits is an active research topic both in psychology and in engineering fields. To date, automatic FER systems work well with frontal and clean faces, but disturbance factors can dramatically decrease their performance. Aging is a critical disruption element, which is present in any real-world situation and which can finally be considered thanks to the recent introduction of new databases storing expressions over a lifespan. This study addresses the FER with aging challenge using sparse coding (SC) that represents the input signal as the linear combination of the columns of a dictionary. Dictionary learning (DL) is a subfield of SC that aims to learn from the training samples the best space capable of representing the query image. Focusing on one of the main challenges of SC, this work compares the performance of recently introduced DL algorithms. We run both a mixed-age experiment, where all faces are mixed, and a within-age experiment, where faces of young, middle-aged, and old actors are processed independently. We first work with the entire face and then we improve our initial performance using only discriminative patches of the face. Experimental results provide a fair comparison between the two recently developed DL techniques. Finally, the same algorithms are also tested on a database of expressive faces without the aging disturbance element, so as to evaluate DL algorithms' performance strictly on FER
Reinforcement Learning Based Querying in Camera Networks for Efficient Target Tracking

Surveillance camera networks are a useful monitoring infrastructure that can be used for various visual analytics applications, where high-level inferences and predictions could be made based on target tracking across the network. Most multi-camera tracking works focus on re-identification problems and trajectory association problems. However, as camera networks grow in size, the volume of data generated is humongous, and scalable processing of this data is imperative for deploying practical solutions. In this paper, we address the largely overlooked problem of scheduling cameras for processing by selecting one where the target is most likely to appear next. The inter-camera handover can then be performed on the selected cameras via re-identification or another target association technique. We model this scheduling problem using reinforcement learning and learn the camera selection policy using Q-learning. We do not assume the knowledge of the camera network topology but we observe that the resulting policy implicitly learns it. We evaluate our approach using NLPR MCT dataset, which is a real multi-camera multi-target tracking benchmark and show that the proposed policy substantially reduces the number of frames required to be processed at the cost of a small reduction in recall.
RGB Component Encryption of Video Using AES-256 Bit Key

In our lifestyle, the Internet plays an important role. Because sending information through online is more. Especially video files occupy big space on the internet. Here the one thing we all should know, that is security. Encryption is the best job to fulfill the security thirst. The classical ciphers are Substitution, Transposition etc., and the current trendy modern ciphers like a Symmetric key and Asymmetric key have a different variety of algorithms. Symmetric have a block like DES, 3DES, AES and stream cipher like RC4, RC6 etc., Asymmetric have RSA, ECC etc., Here AES-256 algorithm is used for video encryption with RGB Components, The proposed technique can be analyzed with encryption speed, encryption ratio and throughput of the video files.