Reviewerly/exHarmony huggingface dataset:
Flowless: Extracting Densest Subgraphs Without Flow Computations

We propose a simple and computationally efficient method for dense subgraph discovery, which is a classic problem both in theory and in practice. It is well known that dense subgraphs can have strong correlation with structures of interest in real-world networks across various domains ranging from social networks to biological systems [Gionis and Tsourakakis `15]. For the densest subgraph problem, Charikar's greedy algorithm [Asashiro `00, Charikar `00] is very simple, and can typically find result of quality much better than the provable factor 2-approximation, which makes it very popular in practice. However, it is also known to give suboptimal output in many real-world examples. On the other hand, finding the exact optimal solution requires the computation of maximum flow. Despite the existence of highly optimized maximum flow solvers, such computation still incurs prohibitive computational costs for the massive graphs arising in modern data science applications. We devise an iterative algorithm which naturally generalizes the greedy algorithm of Charikar. Our algorithm draws insights from the iterative approaches from convex optimization, and also exploits the dual interpretation of the densest subgraph problem. We have empirical evidence that our algorithm is much more robust against the structural heterogeneities in real-world datasets, and converges to the optimal subgraph density even when the simple greedy algorithm fails. On the other hand, in instances where Charikar's algorithm performs well, our algorithm is able to quickly verify its optimality. Furthermore, we demonstrate that our method is significantly faster than the maximum flow based exact optimal algorithm. We conduct experiments on datasets from broad domains, and our algorithm achieves $\sim$145$\times$ speedup on average to find subgraphs whose density is at least 90\% of the optimal value.
A Multiple-Chaotic Approach for Steganography

In a recent work, chaos has been utilized to modify addresses of message bits while hidden in a cover image.In this study, we extend the above technique to include multiple chaotic maps for increased security.Three systems have been modified using chaotic-address mapping for image steganography in the spatial domain.The first system, the well-known LSB technique, is based on the selection of pixels and then hides secret message in the Least Significant Bits LSBs of the given pixel.The second system is based on searching for the identical bits between the secret message and the cover image.The third system is based on the concept of LSB substitution.It employs mapping of secret data bits onto the cover pixel bits.To increase the security performance of the above chaos-based steganographic techniques, multiple-chaotic maps are introduced in this study by using multiple formulas to generate chaotic sequences used to track the addresses of shuffled bits.The generated chaotic sequences were evaluated to determine the randomness (using correlation tests) and the chaotic characteristics of a nonlinear system (using Lyapunov exponent, Poincaré section and 0-1 test).The performance and security levels of the proposed techniques were evaluated by using Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), histogram analysis and correlative analysis.The results show that the proposed method performs existing systems.
MixModule: Mixed CNN Kernel Module for Medical Image Segmentation

Convolutional neural networks (CNNs) have been successfully applied to medical image classification, segmentation, and related tasks. Among the many CNNs architectures, U-Net and its improved versions based are widely used and achieve state-of-the-art performance these years. These improved architectures focus on structural improvements and the size of the convolution kernel is generally fixed. In this paper, we propose a module that combines the benefits of multiple kernel sizes and we apply the proposed module to U-Net and its variants. We test our module on three segmentation benchmark datasets and experimental results show significant improvement.
SOME NEW CONSTRUCTIONS OF LINEAR CODES INCLUDING A WIDE FAMILY OF MDS CODES

Let ‎$‎mathbb{Z}_p‎$‎ be the finite field of integers modulo ‎$‎p‎$‎, where ‎$‎p>3‎$‎ is a prime integer. This paper presents new constructions of linear codes over ‎$‎mathbb{Z}_p‎$‎‎. Based on our construction, linear codes of length ‎$‎p-1‎$‎, including a wide family of MDS codes, and codes of length ‎$‎(p-1)^2‎$ ‎are ‎constructed.‎ we shall discuss the parameters of the codes defined while describing a generator matrix for the first family.
Private Information Retrieval from Heterogeneous Uncoded Storage Constrained Databases with Reduced Sub-Messages.

We propose capacity-achieving schemes for private information retrieval (PIR) from uncoded databases (DBs) with both homogeneous and heterogeneous storage constraints. In the PIR setting, a user queries a set of DBs to privately download a message, where privacy implies that no one DB can infer which message the user desires. In general, a PIR scheme is comprised of storage placement and delivery designs. Previous works have derived the capacity, or infimum download cost, of PIR with uncoded storage placement and also sufficient conditions of a storage placement design to meet capacity. However, the currently proposed storage placement designs require splitting each message into an exponential number of sub-messages with respect to the number of DBs. In this work, when DBs have the same storage constraint, we propose two simple storage placement designs that satisfy the capacity conditions. Then, for more general heterogeneous storage constraints, we translate the storage placement design process into a problem. We design an iterative algorithm to solve the filling problem where, in each iteration, messages are partitioned into sub-messages and stored at subsets of DBs. All of our proposed storage placement designs require a number of sub-messages per message at most equal to the number of DBs.
Advocacy for Research Evidence in Academic Public Policy Development.

The World Ranking of Universities The impetus for this review came from reading the July 2019 Webometrics global survey of universities. The Webometrics survey is embraced widely in Africa, but it is less respected around the world when compared to more influential reviews like the Academic Ranking of World Universities, UK Times Higher Education World University Rankings and QS World University Rankings. The low prestige is because Webometrics consider primarily institution web-presence and activities instead of the quality of instruction, student learning, and research productivity; the central core functions of the universities. In the most recent Webometrics survey, the University of Cape Town is the first mentioned university in Africa but ranked number 274 in the world1. The study also revealed the dominance of South African universities, capturing nine of the top ten institutions in Africa. The University of Ibadan first listed Nigerian university, ranked number 17 in Africa and number 1,233 in the world; outperformed by the University of Ghana, which ranked 16 in Africa and 1,209 globally. After perusing through the report, I immediately called a colleague to share the bad news of the overall poor performance of African universities. We both agreed that the result of the survey is symbolic of the quality of education decline in Nigeria; a country with an educational system that was once the envy of most African nations. Given the dismal ranking of Nigerian universities, our conversation quickly shifted to another equally important academic topic - the recurring and apathetic lack of evidence when private and government establishments in Africa put forth public policies. As I begin to write this review, the motion picture by Jerry Maguire titled Show me the Money immediately came to mind. The film is a Hollywood romantic comedy-drama sports movie that grossed more than $273 million and ranked ninth highest in revenue in 1996. Cuba Gooding Jr. won the Academy Award for best-supporting actor role while Tom Cruise won the Golden Globes for best actor in a motion picture musical or comedy. He also bagged three other Guild Awards for his performance in the movie. But this review is not about Tinseltown, the land of make-believe. It is about the need to use empirical data when formulating public policies. For two decades now, evidence-based practice is globally accepted across different academic disciplines. Despite these developments, many academic policy decisions are still made in a vacuum without bibliometric research evidence by many science academies and government establishments in particular.
Динамический хаос в нелинейной системе с 1/f-спектром

In the system of two nonlinear differential equations, proposed to explain the physical nature of the 1/f spectra, chaotization of the trajectories is revealed under periodic external action on one of the equations. External noise effects lead to stochastic resonance and low-frequency 1/f behavior of power spectra. Stochastic resonance and 1/f behavior of power spectra corresponds to the maximum information entropy, which indicates the stability of a random process.
Segmentation Criteria in the Problem of Porosity Determination based on CT Scans

Porous materials are widely used in different applications, in particular they are used to create various filters. Their quality depends on parameters that characterize the internal structure such as porosity, permeability and so on. Computed tomography (CT) allows one to see the internal structure of a porous object without destroying it. The result of tomography is a gray image. To evaluate the desired parameters, the image should be segmented. Traditional intensity threshold approaches did not reliably produce correct results due to limitations with CT images quality. Errors in the evaluation of characteristics of porous materials based on segmented images can lead to the incorrect estimation of their quality and consequently to the impossibility of exploitation, financial losses and even to accidents. It is difficult to perform correctly segmentation due to the strong difference in voxel intensities of the reconstructed object and the presence of noise. Image filtering as a preprocessing procedure is used to improve the quality of segmentation. Nevertheless, there is a problem of choosing an optimal filter. In this work, a method for selecting an optimal filter based on attributive indicator of porous objects (should be free from 'levitating stones' inside of pores) is proposed. In this paper, we use real data where beam hardening artifacts are removed, which allows us to focus on the noise reduction process
O profissional da informação e o seu compromisso ético com a procedência da informação: uma análise do fenômeno das fake news à luz do IFLA Code of Ethics for Librarians and other information workers

Information Science (CI) interacts with different fields of knowledge through a wide spectrum of processes ranging from the production of information from a socially generated knowledge, through its organization in different ways, to reach its use and appropriation by society so that a new knowledge can be generated, in a helical process (Guimaraes 2009). However, it requires that its processes are permeated by reliability, thus avoiding damages to the user and society as a whole. These damages, among others, may come from the so-called Fake News, news that occur and circulate in various media and that, for the most part, intentionally manufacture or manipulate content and thus lead to a dissemination - and appropriation - of false information. The World Wide Web and its current configuration made it easy to exchange and share information, thus enabling the creation, processing and dissemination of content at any time, but also, as Froehlich (2017) emphasizes, has made possible the dissemination of prejudices, prejudices and stupidity, so that, as the author points out, concomitant with the direct information was created the right to ignorance. Today, Fake is an effective challenge for the information professionals, demanding an increasingly critical and cautious look at the facts that are constantly exploited by such false news, thus creating a scenario that highlights an issue (Froehlich, 1994, 1997, 2017) interferes with ethical decisions, such as social responsibility and respect for other individuals / institutions, which lead to a commitment to information and to the user ( Guimaraes 2000). As a result, the International Federation of Library Associations (IFLA) brought to light, in 2012, the Code of ethics for librarians and other information workers (IFLA 2012), and more recently the infographic How to Spot Fake News (IFLA 2017) as options to be followed in order to avoid misconduct and spread of false news by information professionals. In order to achieve the objective of the research that aims to analyze the Fake domain from the perspective of the code of ethics proposed by IFLA and, thus, to identify the ethical dimensions of this code more directly susceptible to the Fake phenomenon, the analysis of content (Bardin 1977) was applied and it was contacted that the Fake reach all dimensions provided for in the IFLA code, with particular emphasis on the commitment of the professional with the information itself and its access, as well as on their commitment to the well-being of the user, revealing, in the identification and combat of the Fake News, a new facet of the ethical concerns of these professionals.
[Flyer of:] Workshop on Scholarly Digital Editions, Graph Data-Models and Semantic Web Technologies

Flyer for the international GraphSDE2019, putting together scholars from all over the world for discussing the advancement in the use of graph data-models and semantic web technologies for scholaryl editing.
Direct calculation of out-of-sample predictions inmulti-class kernel FDA

After a two-class kernel Fisher Discriminant Analysis (KFDA)has been trained on the full dataset, matrix inverse updates allow for thedirect calculation of out-of-sample predictions for different test sets. Here,this approach is extended to the multi-class case by casting KFDA in anOptimal Scoring framework. In simulations using 10-fold cross-validationand permutation tests the approach is shown to be more than 1000x fasterthan retraining the classifier in each fold. Direct out-of-sample predictionscan be useful on large datasets and in studies with many training-testingiterations.
Towards Further Understanding of Sparse Filtering via Information Bottleneck

In this paper we examine a formalization of feature distribution learning (FDL) in information-theoretic terms relying on the analytical approach and on the tools already used in the study of the information bottleneck (IB). It has been conjectured that the behavior of FDL algorithms could be expressed as an optimization problem over two information-theoretic quantities: the mutual information of the data with the learned representations and the entropy of the learned distribution. In particular, such a formulation was offered in order to explain the success of the most prominent FDL algorithm, sparse filtering (SF). This conjecture was, however, left unproven. In this work, we aim at providing preliminary empirical support to this conjecture by performing experiments reminiscent of the work done on deep neural networks in the context of the IB research. Specifically, we borrow the idea of using information planes to analyze the behavior of the SF algorithm and gain insights on its dynamics. A confirmation of the conjecture about the dynamics of FDL may provide solid ground to develop information-theoretic tools to assess the quality of the learning process in FDL, and it may be extended to other unsupervised learning algorithms.
Deck.gl: Large-scale Web-based Visual Analytics Made Easy

In this paper, we demonstrate how deck.gl, an open-source project born out of data-heavy visual analytics applications, has grown into the robust visualization framework it is today. We begin by explaining why we built another data visualization framework in the first place. Then, we summarize our design goals (distilled from our interactions with users) and discuss how they guided the development of the framework's main features. We use two real-world applications of deck.gl to showcase how it can be applied to simplify the creation of data-heavy visualizations. We also discuss our lessons learned as we continue to improve the framework for the larger visualization community.
고등학교 과학탐구실험 교과서에 제시된 탐구 활동 분석

The science inquiry-experiment is new subject developed for high school students according to the 2015 revised science national curriculum. The subject was focused on improving scientific inquiry ability of high school students on the basis of the inquiry and experiment. We examined characteristics of inquiry activities presented in science inquiry-experiment textbooks. In order to do this, types of inquiry activities presented in science inquiry-experiment textbooks were comparatively analyzed according to units and publishers. Types of inquiry activities were categorized into experiment & observation, data interpretation, investigation-discourse & presentation, discussion, expression, science writing, and simulation. Experiment and observation, data interpretation, investigation-discourse & presentation, and expression were more among them, the other hand, discussion, science writing and simulation were somewhat presented. As a result, there were some differences in activity types and frequency according to the publishers. Science inquiry-experiment teachers need to proceed with the teaching and learning process by appropriately modifying inquiry contents and types of inquiry. It is necessary to continuously monitor whether the objectives of the 2015 revised science national curriculum are being applied and reflected through on-site science classes, and supplement them through subsequent studies.
Activity Recognition with Wristband Based on Histogram and Bayesian Classifiers

With the increasing popularity of wristbands and their applications on the health monitoring, the recognition of various activities has become a significant feature which helps the users to assess and review their daily practices [1]. As a result, it can improve their health via the recorded information. The core task of the activity recognition is to automatically classify a large amount of motion data recorded by the accelerometers to one of the predefined activity classes such as eating, walking, etc. In contrast to the conventional recognition approaches [2], the algorithms are required to be simple, efficient and accurate as the battery powers and the allowable computational powers of the wristbands are very limited. Some approaches directly transfer the motion data to the mobile phones by the bluetooth so that more sophisticated computations can be performed. Nevertheless, the transmission of the raw digital motion data can consume a lot of power and occupy a considerable bandwidth. This paper proposes an algorithm that highly reduces the communications between the wristbands and the mobile phones by firstly preprocessing and labeling the raw motion data based on their characteristics. Only the labels and some encoding values are transferred to the mobile phones for performing the classification. In order to achieve the high accuracy, the classification is performed by combining different approaches such as the histogram approach and the Bayesian classifier approach. The obtained results show that the proposed approach achieves a good accuracy, which just uses some labels and the encoding value information.
한국어 교재에 대한 텍스트기호학적 연구 시론

The purpose of this study is to examine Korean language textbooks from the viewpoint of text-semiotics and to explore the applicability of text-semiotics as a methodology of Korean language textbooks research. For this, the definitions of texts were reviewed. And it was investigated how Begaugrande and Dressler’s seven standards of textuality are revealed at the point of ‘communication participants’, ‘acceptance patterns of Korean language textbooks’, and ‘structure of Korean language textbooks’. (Chonnam National University)
Design method of Temporary Horizontal Visibility Graph for information sources impact network

The absence of the efficient methods for the design of the information sources impact network does not allow defining the influence of the information sources on one another accurately and detecting the primary sources of the information spreading. The work represents a new approach - Temporary Horizontal Visibility Graph (THVG) method, which is based on the HVG algorithm modification. This method allows building the information sources impact networks and making the assumptions about the primary sources of information. Obtained method uses sources rating in data flow analysis. It is more effective than the usual Horizontal Visibility Graph design method in the following criteria: F-measure - by 7-9%, completeness - by 7-10% and accuracy - by 6-8% more effective.
Learning Classifiers on Positive and Unlabeled Data with Policy Gradient

Existing algorithms aiming to learn a binary classifier from positive (P) and unlabeled (U) data generally require estimating the class prior or label noises ahead of building a classification model. However, the estimation and classifier learning are normally conducted in a pipeline instead of being jointly optimized. In this paper, we propose to alternatively train the two steps using reinforcement learning. Our proposal adopts a policy network to adaptively make assumptions on the labels of unlabeled data, while a classifier is built upon the output of the policy network and provides rewards to learn a better strategy. The dynamic and interactive training between the policy maker and the classifier can exploit the unlabeled data in a more effective manner and yield a significant improvement on the classification performance. Furthermore, we present two different approaches to represent the actions sampled from the policy. The first approach considers continuous actions as soft labels, while the other uses discrete actions as hard assignment of labels for unlabeled examples.We validate the effectiveness of the proposed method on two benchmark datasets as well as one e-commerce dataset. The result shows the proposed method is able to consistently outperform state-of-the-art methods in various settings.
Política Customizada de Balanceamento de Réplicas para o HDFS Balancer do Apache Hadoop

Data replication is a fundamental mechanism of the Hadoop Distributed File System (HDFS). However, the way data is spread across the cluster directly affects the replication balancing. The HDFS Balancer is a Hadoop integrated tool which can balance the storage load on each machine by moving data between nodes, although its operation does not address the specific needs of applications while performing block rearrangement. This paper proposes a customized balancing policy for HDFS Balancer based on a system of priorities, which can be adapted and configured according to usage demands. The priorities define whether HDFS parameters, or whether cluster topology should be considered during the operation, thus making the balancing more flexible.