Reviewerly/exHarmony huggingface dataset:
Procedimento para análise gráfica em materiais digitais de redes sociais sobre DIU de cobre

The document presents the procedure used for the analysis of static graphic materials on copper IUD in Brazilian social networks, especially materials that are produced by non-designers, for the monography "Access to information on copper IUDs: an analysis of online graphic material".The protocol of analysis developed sought to recognize and understand these spontaneous manifestations of graphic material for dissemination of information about the intrauterine device, in all its layers of understanding, from the syntactic to the relationship with users.In this article the authors and stages of the analysis of the object will be exposed in order to present the protocol determined by the authors to fulfill the objectives of the monograph.
Parallel Data Streaming Analytics in the Context of Internet of Things

We are living in an increasingly connected world, where the ubiquitously sensing technologies enable inter-connection of physical objects, as part of Internet of Things (IoT), and provide continuous massive amount of data. As this growth soars, benefits and challenges come together, which requires development of right tools in order to extract valuable information from data. For that, new techniques (e.g. data stream processing) have emerged to perform continuous single pass analysis and enhance parallelism. However, employing such techniques is not a trivial task due to its challenges such as partial knowledge of the data and the trade-off between parallelism and consistency. Moreover, depending on the source, data volumes may fluctuate over time which requires the degree of parallelism to be adapted in runtime. In this work, we contribute to the design of computational infrastructures and development of tools to address these challenges. In this regard, we focus on two problem domains. First, we target continuous data analysis and particularly focus on data clustering, as a significant representative problem, to extract information from massive data, generated by high-rate sensors. We propose Lisco, a single-pass continuous Euclidean distance-based clustering which exploits the inherent ordering of the spatial and temporal data, and its parallel counterpart, P-Lisco, to enhance pipeline- and data-parallelism. These algorithms provide high throughput of results with low latency, through pushing the processing closer to the data sources. Moreover we provide a framework, DRIVEN, that performs a continuous bounded error approximation to compress the volumes of data, and then transmits the compressed data to next layers of the IoT architecture to perform clustering on it, in a continuous fashion, using generalized form of Lisco. The compression in data retrieval speeds up the transmission of the data while preserving very similar clustering quality as raw data transmission. In the second domain, we target the elasticity in data streaming to utilize computational resources in runtime regarding the data rate fluctuations. For that, we provide a stream processing framework, STRETCH, and introduce the concept of virtual shared-nothing parallelization that is able to adapt the resources, maximize the throughput and latency, and preserve determinism. Thorough experimental evaluations on architectures representative of high-end servers and of resource-constrained embedded devices indicate the scalability benefits of all proposed algorithms.
MODELING THE PERFORMANCE OF THE COMPUTER NETWORK APPLIED TO TELECOMMUNICATION

This paper is the presentation of a mathematical modeling of the use of computer networks at the end of the main use of telecommunication, as we can see below telecommunication is at the origin the transmission of the voice and currently it is in the computer networks that this transport is done by the technology of the voice over IP the latter having a lot of parameters for a better service starting with the temporal constraint and the different parameters of the internet. These services do not offer guarantees without having to study and model their behavior
Research for the Hotel Industry Economic Trends by Machine Learning Methodology

For more accurate prediction of the future condition, various methods and approaches have been continuously studied throughout the most of areas. Especially in the business field which has characteristics of monetary value and rapid change related, the future prediction becomes the regular issues for solving.BR Hotel industry is one of the main areas to estimate and predict the economic trend due to the complexity of changes relation with each country’s economic condition and relationships among the countries. But most of relevant researches for predictive modeling are depending on the some assumptions which are sometimes unstable and improper.BR This study suggests the prediction model using the updated methodology so-called machine learning which is free from the assumptions. Among the machine learning methodologies, well-known and proper technique for the trends forecasting, support vector machine is applied with several parameters and also data based solution approach is suggested with prediction error comparison
Ανάπτυξη εικονικού εργαστηρίου και χρήση του για δοκιμές διείσδυσης (Penetration Testing)

The aim of the thesis is to create a Virtual Lab which is used to examinevarious Ethical techniques.The term Hacking is examined for its definition, history and meaning, andthe official Penetration Testing Model is presented and analyzed. The varioustypes of are presented, as well as the software tools available for theirexecution.Using some of these tools, the Virtual Lab is being created, in whichpossible scenarios of attacks are simulated. The scenarios examined are from thepoint of view of the Ethical Hacker and focus on compliance with the officialPenetration Testing Standard.
Clustering Over Multiple Evolving Data Streams of the Traffic Cyber-Physical Systems

Extracting and retaining useful information from multiple data streams of the traffic Cyber-Physical Systems (CPS) generated by practical applications have attracted an increasing amount of attentions from related researchers. In this paper, the Incremental Clustering framework is proposed for multiple sensor data streams by low rank approximation Matrix Factorization (IC-MF) monitors the distribution of clusters over multiple sensor data streams based on their correlation. In the IC-MF, both the low-rank matrix approximation and matrix factorization-based clustering are applied, and IC-MF incorporates the historical results and the relationship between the nodes of the current step and previous step. To improve the accuracy of the increments, a low-rank approximation of the adjacency matrix is obtained at each time step, and makes IC-MF work directly in the low-rank subspace. The main idea of IC-MF is to make use of the similarity between two consecutive time steps to quickly update the approximating subspace. The performance and efficiency of the algorithm are demonstrated by traffic CPS experiments on the real and synthetics data sets. The results show that the proposed algorithm surpasses the existing methods for clustering multiple evolving data streams over time.
A secure client side deduplication scheme in cloud storage environments

Freshly identified and endorsed the trend of holding cloud-based services for large scale content storage, processing, and distribution. Security is the top concerns for open cloud environments. Towards these security challenges, we propose and implement, on Open Stack Swift, a new client-side deduplication scheme for securely storing and sharing outsourced data via the public cloud. The originality of our proposal is twofold. First, it ensures better confidentiality towards unauthorized users. That is, every client computes a per data key to encrypt the data that he intends to store in the cloud. hence, data access is with the data owner. Second, by accessing rights in the metadata file, an authorized user can decipher an encrypted file only with his private key. It elaborates a computationally low-cost method for making all log entries generated prior to the logging machine's compromise impossible for the attacker to make an attempt and also impossible to undetectably modify or destroy.
A Unified Lattice Model and Framework for Purity Analyses.

Analyzing methods in object-oriented programs whether they are side-effect free and also deterministic, i.e., mathematically pure, has been the target of extensive research. Identifying such methods helps to find code smells and security related issues, and also helps analyses detecting concurrency bugs. Pure methods are also used by formal verification approaches as the foundations for specifications and proving the pureness is necessary to ensure correct specifications. However, so far no common terminology exists which describes the purity of methods. Furthermore, some terms (e.g., pure or side-effect free) are also used inconsistently. Further, all current approaches only report selected purity information making them only suitable for a smaller subset of the potential use cases. In this paper, we present a fine-grained unified lattice model which puts the purity levels found in the literature into relation and which adds a new level that generalizes existing definitions. We have also implemented a scalable, modularized purity analysis which produces significantly more precise results for real-world programs than the best-performing related work. The analysis shows that all defined levels are found in real-world projects.
UHRP: Uncertainty-Based Pruning Method for Anonymized Data Linear Regression

Anonymization method, as a kind of privacy protection technology for data publishing, has been heavily researched during the past twenty years. However, fewer researches have been conducted on making better use of the anonymized data for data mining. In this paper, we focus on training regression model using anonymized data and predicting on original samples using the trained model. Anonymized training instances are generally considered as hyper-rectangles, which is different from most machine learning tasks. We propose several hyper-rectangle vectorization methods that are compatible with both anonymized data and original data for model training. Anonymization brings additional uncertainty. To address this issue, we propose an Uncertainty-based Hyper-Rectangle Pruning method (UHRP) to reduce the disturbance introduced by anonymized data. In this method, we prune hyper-rectangle by its global uncertainty which is calculated from all uncertain attributes. Experiments show that a linear regressor trained on anonymized data could be expected to do as well as the model trained with original data under specific conditions. Experimental results also prove that our pruning method could further improve the model’s performance.
Inner-Imaging Convolutional Networks.

Despite the tremendous success in computer vision, deep convolutional networks suffer from serious computation costs and redundancies. Although previous works address this issue by enhancing diversities of filters, they have not considered the complementarity and the completeness of the internal structure of the convolutional network. To deal with these problems, a novel Inner-Imaging architecture is proposed in this paper, which allows relationships between channels to meet the above requirement. Specifically, we organize the channel signal points in groups using convolutional kernels to model both the intra-group and inter-group relationships simultaneously. The convolutional filter is a powerful tool for modeling spatial relations and organizing grouped signals, so the proposed methods map the channel signals onto a pseudo-image, like putting a lens into convolution internal structure. Consequently, not only the diversity of channels is increased, but also the complementarity and completeness can be explicitly enhanced. The proposed architecture is lightweight and easy to be implemented. It provides an efficient self-organization strategy for convolutional networks so as to improve their efficiency and performance. Extensive experiments are conducted on multiple benchmark image recognition data sets including CIFAR, SVHN and ImageNet. Experimental results verify the effectiveness of the Inner-Imaging mechanism with the most popular convolutional networks as the backbones.
A HYBRID DATA CLUSTERING ALGORITHM USING MODIFIED KRILL HERD ALGORITHM AND K-MEANS

Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper, a new hybrid data clustering approach which combines the modified krill herd and K-means algorithms, named as K-MKH, is proposed. K-MKH algorithm utilizes the power of quick convergence behaviour of K-means and efficient global exploration of Krill Herd and random phenomenon of Levy flight method. The Krill-herd algorithm is modified by incorporating Levy flight in to it to improve the global exploration. The proposed algorithm is tested on artificial and real life datasets. The simulation results are compared with other methods such as K-means, Particle Swarm Optimization (PSO), Original Krill Herd (KH), hybrid K-means and KH. Also the proposed algorithm is compared with other evolutionary algorithms such as hybrid modified cohort intelligence and K-means (K-MCI), Simulated Annealing (SA), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means++. The comparison shows that the proposed algorithm improves the clustering results and has high convergence speed.
Applying a Pneumatic Interface to Intervene with Rapid Eating Behaviour.

Higher eating rates are positively correlate with obesity. In this paper, we propose the design of a new eating utensil that can reduce eating rate by interfering with eater's ability to eat quickly. This utensil can change its rigidity and shape by deflating itself to interfere with eating. In this study, a low fidelity proof-of-concept prototype device has been designed to provide physical resistance in order to help people reduce their eating rate. The proposed prototype could be used to demonstrate the feasibility of applying a pneumatically actuated shape-changing interface to embed physical resistance into an eating utensil.
Multi-feature local sparse representation for infrared pedestrian tracking

Robust tracking of infrared (IR) pedestrian targets with various backgrounds, e.g.appearance changes, illumination variations, and background disturbances, is a great challenge in the infrared image processing field.In the paper, we address a new tracking method for IR pedestrian targets via multi-feature local sparse representation (SR), which consists of three important modules.In the first module, a multi-feature local SR model is constructed.Considering the characterization of infrared pedestrian targets, the gray and edge features are first extracted from all target templates, and then fused into the model learning process.In the second module, an effective tracker is proposed via the learned model.To improve the computational efficiency, a sliding window mechanism with multiple scales is first used to scan the current frame to sample the target candidates.Then, the candidates are recognized via sparse reconstruction residual analysis.In the third module, an adaptive dictionary update approach is designed to further improve the tracking performance.The results demonstrate that our method outperforms several classical methods for infrared pedestrian tracking.
Speech of the RAS academician N.A. Kolchanov

Speech of the RAS academician N.A. Kolchanov
I was never a user, but it ...

I was never a user, but it sounds like a great service. A real shame it's shutting down.I'm wondering why they aren't open-sourcing it, that
Identifying the Intensity of Variability Changes in Software Product Line Evolution.

The evolution of a Software Product Line (SPL) typically affects a variety of artifact types. The intensity (the frequency and the amount) in which developers change variability information in these different types of artifacts is currently unknown. In this paper, we present a fine-grained approach for the variability-centric extraction and analysis of changes to code, build, and variability model artifacts introduced by commits. This approach complements existing work that is typically based on a feature-perspective and, thus, abstracts from this level of detail. Further, it provides a detailed understanding of the intensity of changes affecting variability information in these types of artifacts. We apply our approach to the Linux kernel revealing that changes to variability information occur infrequently and only affect small parts of the analyzed artifacts. Further, we outline how these results may improve certain analysis and verification tasks during SPL evolution.
Autoencoder based Visibility Enhancement Technique for Hazy Scenes

This paper proposes a single image dehazing model built with the autoencoder network. The Outdoor images captured during the inclement weather conditions such as haze, fog and sandstorms generally exhibit visibility degradation. Due to the unclear visibility, transportation system may suffer a lot. So, an automatic method has been proposed to enhance the visibility of images even in the bad weather condition too. There are so many traditional haze removal methods available, but there is a scope to improve the visibility of the image. To improve the visibility of the images still better, deep learning techniques are preferred in this work. The proposed method enhances the visual quality of the input hazy image. Experimentation is carried out with the FRIDA database images and natural hazy images. The result of this work is superior than the state of art in terms of the performance measures such as PSNR, E metric and metric.
ANALISIS KEBUTUHAN AIR BERSIH PADA INSTALASI PENGOLAHAN AIR BERSIH KECAMATAN SANGA SANGA KABUPATEN KUTAI KARTANEGARA

Clean water is a very important part of all the basic needs of living things, especially for humans. Humans use water for various purposes in their daily lives. This research was conducted at the Clean Water Treatment Plant of PDAM Tirta Mahakam, Sanga-Sanga Branch, Kutai Kartanegara Regency, East Kalimantan Province, aimed at analyzing the need for clean water for population growth in 2018 to 2028. From the data obtained from PDAM Tirta Mahakam the discharge capacity of clean water produced is 40 liters / second, with the longest distant distribution network at 13,000 meters leading to the village, the type of pipe used is HDPE pipe with a diameter of 203 , 2 mm, with a roughness level of 130. The results of the calculation and analysis of clean water needs in Sanga-Sanga Subdistrict, noramal water needs in 2028 (10-year projection) of 38,703 liters / second and the peak water demand in 2028 (peak discharge) of 67,731 liters / second. From the results of the hydraulic analysis the main distribution pipeline network towards the neighborhood is very much needed, the flow velocity in the pipe is 1.052 meters / second with a pressure loss of 76.2 meters, so that the distribution of clean water needs of the community can be maximized.
Computação Pervasiva: Ética, privacidade e segurança na Era da Internet das Coisas

This article explores communications advances emphasisizing transformations the Internet of Things announces.Also, ethical issues in a polarized world and interest in information in the mesh of networks.It shows antitrust sanctions to the largest communication companies; cites divergences between public and private sectors; privacy breach; information collected and applied for commercial or strategic purposes.Although companies act within legal frameworks and intelligence agencies claim licit reasons for the monitoring, Snowden and the CEOs of technology giants wage a debate that deserves attention.This study shows an already present reality, depicting situations in which we are inserted, without 13º Congresso Brasileiro de Pesquisa e Desenvolvimento em Design, Univille, Joinville (SC) 05 a 08 de novembro de 2018 realizing the consequences of a world widely interconnected.It points consequences of this connectivity articulated by companies based in North America in the verge of global proportions conflict.Finally, it proposes measures to protect personal information, and guidelines for an information ecology for the announced Internet of Everything.
Non-contact detection of hidden objects under clothes

The security portal is a set of technical devices united by a single software in an information environment with a common database. We design the portal for highly effective detection of the maximum number of threats: metal objects and electronic products hidden on the body and in clothes.
RDAP Mirroring Protocol (RMP)

The Registration Data Access Protocol (RDAP) is used by RegionalInternet Registries (RIRs) and Domain Name Registries (DNRs) toprovide access to their resource registration information. While mostclients can retrieve the information they need on an ad hoc basis fromthe public services maintained by each registry, there are instanceswhere local copies of those remote data sources need to be maintained,for various reasons (e.g. performance requirements). This documentdefines a protocol for transferring bulk RDAP response data and forkeeping a local copy of that data up to date.
Strategic Logics Behind Blockchain Projects: Capturing Value in Decentralized Ecosystems

Ecosystems have an increasing role for competitive advantage. Such ecosystems are usually built around digital platforms. However, the centralized structure of those ecosystems does not always prov...
PERBAIKAN CITRA MALAM (TIDAK INFRARED) DENGAN METODE HISTOGRAM EQUALIZATION DAN CONTRAST STRETCHING

Images taken with a digital camera with ambient lighting that are lacking will produce a dark image and it is difficult to recognize objects inside. In such conditions, image enhancement (image enhancement) is needed in order to get an image display with a better form of visualization. Many techniques or methods are used in image enhancement. This research has produced an application program to improve the quality of night images with the histogram smoothing method (Histogram Equalization) and contrast stretching (Contrast Stretching) as well as the smoothing method and the median filter method to eliminate noise. The parameters measured in improving the image quality of the night based on the methods used are the peak signal to-noise ratio (PSNR). The results of this study are that night image quality (not infrared) can be improved by several methods, namely: grayscale, smoothing, histogram equalization and contrast stretching as preprocessing and immultiply as the final process. The level of accuracy of improving the quality of night images (not infrared) is 85%.
Compressed Sensing-Based Multi-Abnormality Self-Detecting and Faults Location Method for UAV Swarms

The security of Unmanned Aerial Vehicle (UAV) swarms is threatened by the deployment of anti-UAV systems under complicated environments such as battlefield. Specifically, the faults caused by anti-UAV systems exhibit sparse and compressible characteristics. In this paper, in order to improve the survivability of UAV swarms under complicated environments, we propose a novel multi-abnormality self-detecting and faults location method, which is based on compressed sensing (CS) and takes account of the communication characteristics of UAV swarms. The method can locate the faults when UAV swarms are suffering physical damages or signal attacks. Simulations confirm that the proposed method performs well in terms of abnormalities detecting and faults location when the faults quantity is less than 17% of the quantity of UAVs.