Reviewerly/exHarmony huggingface dataset:
Network System’s Vulnerability Quantification Using Multi-layered Fuzzy Logic Based on GQM

Network security is one of the crucial components of an organization’s security system. Much research has been conducted to come up with a clear-cut approach in order to quantify organizations’ network system vulnerabilities. Many security standards such as NIST SP-800 and ISO 27001 with the guidelines and clauses are published with a reasonable outline to pave the ground for a safe track towards secure system design, however, these standards do not clearly show the details of work implementation. In this paper, we apply Fuzzy Logic methodology to quantify each factor and sub-factors derived using Goal Question Metrics in network security. Our procedure follows a bottom-up hierarchy model from the details of a security component to the desired goal in order to address vulnerabilities in a quantified manner in the Department of Transportation. Thus, our approach measures different types of potential vulnerabilities in a network.
Spelling Correction Application with Damerau-Levenshtein Distance to Help Teachers Examine Typographical Error in Exam Test Scripts

This research was intended to create Spelling Correction Application to help teachers examine questions scripts with the capability to found typographical error and give suggestion for non-real word error. This application is built with simple Damerau-Levenshtein Distance method to detect errors and give word suggestions from the typo word. This application can be used by the teacher to examine documents in the form of short answer, essay and multiple choices then save them back in the form of original documents. This application is built using a dictionary lookup consist of 41 312 words in Indonesian. The first test result is the application can detect non-real word errors from 50 sentences that have non-real word error in each sentence and produce an accuracy of 88 %. The second test is try to detect typographical error in exam test script that consist of 15 sample questions, consisting of five essay questions, five short answer, and five multiple choices.
Realidad aumentada como recurso educativo en materias administrativas de alumnos de diseño industrial

Augmented reality is booming in several areas, education is no exception, one of the subjects that is sometimes somewhat complicated for students of industrial design is Market and Consumption, which is why it was proposed as an objective to develop a mobile application with augmented reality that in a first phase includes the theoretical aspects of the subject, then present videos and animations, presenting information reinforcement and integration of exercises of some concepts incorporating various resources. Three stages were identified: 1) general aspects, the participants of the study were established, the materials to be developed, the instruments that would be applied to determine the quality of the application were selected, 2) the Unity software was selected for its development, for both IOS and Android platforms; 3) a rubric on the quality of mobile educational applications was applied to 9 teachers and 64 students randomly chosen from the degree in industrial design, some of the results were 94.7% and 99.6% of acceptance of the application by teachers and students respectively.
Image-Based Visual Servo Control of a Quadrotor under Field of View Constraints Using a Pan-Tilt Camera

In this paper, we propose an image-based visual servo control scheme for precise regulation of the displacement of a quadrotor aerial vehicle with respect to a target object. A pan-tilt camera is used to partially decouple camera orientation and quadrotor maneuver to ensure holonomic dynamics of the overall system. Observable features on the target are exploited to derive an image-based visual servo control law which satisfies the field of view constraints even under coarse depth estimation. Simulation results are provided to illustrate and validate the performance of the proposed control scheme.
A New Fracture Image Segmentation Method Based on MSA-k Clustering Algorithm

For the limitation of traditional clustering algorithm which extracts fracture information. A new method MSA-k Clustering algorithm is proposed for extracting fracture information based on the characteristic parameters of these connected components. In this paper, extraction of fracture information by MSA-k algorithm, which is a combination of Multi-SALP algorithm(MSA) and K-means. The MSA-k algorithm segmented the connected components' parameters, such as the area of the connected component, the minimum outer rectangle aspect ratio of the connected component, and the furthest distance within the connected component, to obtain the complete fracture. Compared with other classical clustering algorithms, MSA-k algorithm is more random. It means that the MSA-k algorithm's global search is stronger and harder to fall into the local optimal solution. The experimental results show that MSA algorithm is better than other swarm optimization clustering algorithms.
Personalised Structure Balance Theory-Based Movie Recommendation System

Recommending appropriate products has become the key to success of E-commerce systems. Collaborative filtering (CF) is the most widely used technology for recommender systems. However, it has several problems such as cold start problem that occurs due to insufficient data, lack of personalisation and scalability. The proposed method is a personalised structure balance theory-based hybrid method (personalised SBT), which attempts to solve the mentioned drawbacks of the traditional CF based system. The problem caused by the lack of data is handled by the application of rules of SBT. The system develops models of user preferences and product features, which are used for personalised recommendation. As it is a model-based system, it is highly scalable to handle Big Data. The process is implemented on Movie Lens-1M dataset, and the rating data of the dataset is used to develop the models that are used for recommendation. Mean Average Error (MAE) is used to evaluate the accuracy of the predicted rating, and Recall is used to evaluate the efficiency of the recommendation.
Deeply supervised group recursive saliency prediction

Fully convolutional neural networks (FCNs) have shown great effects in the saliency detection task. In this paper, we present an FCN based model to aggregate multi-level convolutional features for salient object detection. The main contribution of this work is to make full use of the information extracted by the network while preserving the characteristics of each level. We first propose a novel Intra-stage Feature Aggregating (IFA) module to enhance the detailed information by integrating feature maps from different layers within the same stage of the ResNet-50. We then design an Inter-stage Feature Fusion (IFF) module which exploits contextual information by combining the features from neighboring stages to make each feature map more discriminative. Finally, we propose a Deeply Supervised Group Recursive Prediction (DGR) module to refine the details of the predicted saliency maps and generate the final saliency map. Our approach performs favorably against state-of-the-art methods on five popular datasets and achieves a real-time speed of 30 fps.
Analysis and Summarization of Related Blog Entries Using Semantic Web

To analyze approaches and make use of them effectively and efficiently to generate brand new blogs containing the accurate and appropriate content from different blogs on the same topic by different authors in order to gather and accumulate different point of views at one place in the single blog. Different approaches of clustering, modeling, and summarization are required to be applied to achieve the intended goal. This could help students and other users while finding relevant information in less time at one place in one blog and also for correlation of the topics across documents, pages, blog posts, etc., to offer most relevant content easily to the user. This study aims to review the approaches and techniques used for information integration, search ability, automation, and more demanding tasks like querying and its analysis and also propose an idea for reduction of the manual efforts and time to extract related and relevant information. This is a better idea to put forward that helps filtering irrelevant content and integrating the relevant points from all the blogs and hence creating a brand new blog entry/post.
Two-Stream Convolutional Neural Networks for Emergency Recognition in Images

Emergencies threaten the safety of public lives and properties. If news agencies can timely report emergencies, their subsequent hazards can be significantly reduced. However, in face of massive pictures, the traditional manual screening can no longer meet the needs for news agencies. Therefore, it is necessary to use a more effective method to classify emergencies, which help news agencies choose the right pictures and release them to the public in time. This paper proposes a method to classify emergencies in still images using two-stream convolutional neural networks(CNNs). Firstly, the architecture of our two- stream CNNs is decomposed into object net and scene net, which extract useful information from the perspective of objects and scene context, respectively. Meanwhile, we investigate different methods for two-stream CNNs feature fusion to improve the performance of emergency recognition. Secondly, another binary classifier works after the two-stream CNNs to verify whether the result of the two-stream CNN is the true positive instance of the predicted emergency class. Experimental results confirm the effectiveness of the proposed emergency recognition method.
Variants of Golomb Coding and the <i>n</i>-ary Versions

Golomb coding is a type of entropy encoding scheme for geometric distributions. It consists of two parts, and both parts are coded with variable-length coding, which requires a higher computational effort than fixed-length coding schemes. To solve this issue, the first part of this article presents a variant of Golomb coding that uses fixed-length coding to code the first part. The simulations show that the proposed coding scheme has a higher throughput than Golomb coding, due to the reduction of arithmetic complexity. In the second part, we discuss the n-ary versions of Golomb coding and the proposed coding scheme.
A New Approach of the Rain-Fall Optimization Algorithm Using Parallelization

This chapter introduces a new implementation of the Rain-Fall Optimization Algorithm (RFO) proposed by Kaboli, Sevbaraj, and Rahim in “Rain-Fall Optimization Algorithm. A Population-Based Algorithm for Solving Constrained Optimization Problems” by Kaboli et al. (J Comput Sci 19:31–42, 2017). RFO is a nature-inspired algorithm, which is based on the behavior of the water drops produced by a rainfall going down through a mountain to find the minimum values of specific functions. The algorithm was tested on four multidimensional benchmark functions: Ackley, Griewank, Rosenbrock, and Sphere functions. It was also tested in a four-dimensional function, the Kowalik function. The first step was to match the results of the rewritten algorithm with the results obtained by the original authors. Then the algorithm had to be modified to make some efficiency improvements and to get better results. The main modifications were a new equation to modify the step size for a function called explosion process and a parallel execution of the algorithm with two different restarting techniques: restart to the best and genetic restart to the best.
Clustering Enhanced Encoder–Decoder Approach to Dimensionality Reduction and Encryption

Mukesh, B. R. Madhumitha, Nara Aditya, N. Pai Vivek, Srinivas Anand Kumar M.Dimensionality reduction refers to reducing the number of attributes that are being considered, by producing a set of principal variables. It can be divided into feature selection and feature extraction. Dimensionality reduction serves as one of the preliminary challenges in storage management and is useful for effective transmission over the Internet. In this paper, we propose a deep learning approach using encoder–decoder networks for effective (almost-lossless) compression and encryption. The neural network essentially encrypts data into an encoded format which can only be decrypted using the corresponding decoders. Clustering is essential to reduce the variation in the dataset to ensure overfit. Using clustering resulted in a net gain of 1% over the standard encoder architecture over three MNIST datasets. The compression ratio achieved was 24.6:1. The usage of image datasets is for visualization only and the proposed pipeline could be applied for textual and visual data as well.
Performance Optimization of Big Data Applications Using Parameter Tuning of Data Platform Features Through Feature Selection Techniques

Big data application performance can be optimized by identifying the most impactful set of system parameters of big data platforms. This paper focuses on the identification of optimal system parameter set of Hadoop and Spark data platforms by applying different feature selection techniques. The main objective of the research work is to reduce the job execution time by identifying and tuning only these identified system parameters. The parameters deemed to be less relevant and redundant get eliminated during the feature selection process. The parameters identified using different feature selection algorithms are compared, and empirical analysis is carried. The statistical analysis is used as a cross-validation technique to evaluate the relevance of the identified parameter set and the dependency of platform performance on system parameters.
Implementación de modelo pedagógico para integrar TIC en el tercer nivel de educación parvularia

espanolEl estudio expuesto a continuacion da cuenta de los resultados obtenidos al implementar un modelo para integrar TIC en Educacion Parvularia. Este tiene como finalidad innovar las practicas pedagogicas de las educadoras de parvulos al incorporar las tecnologias en la ensenanza. La intervencion se desarrollo en un colegio privado, donde participaron 16 educadoras y 160 ninos y ninas que asistian a los niveles de transicion. El proyecto se dividio en tres etapas: a) El diagnostico, para conocer el conocimiento teorico de las educadoras en relacion a las TIC, b) Implementacion del modelo propuesto y c) Analisis de los resultados de las etapas uno y dos. Los resultados indican una valoracion positiva de las TIC al incorporar su uso en la planificacion de aula y evaluacion, ademas, la adquisicion de recursos tecnologicos con el apoyo de la comunidad educativa, hicieron que esta propuesta fuera motivante y ludica para los ninos. EnglishThe following study shows the results obtained when implementing a model to integrate ICTs in preschooleducation. The purpose of this new model is to innovate the pedagogical practices of early educators by incorporating technologies into teaching. The intervention took place in a private school, where 16 educators and 160 boys and girls who attended the transition levels participated. The project was divided into three stages: a) The diagnosis: to know the theoretical knowledge of the educators in relation to the ICTs, b) Implementation of the proposed model, and c) Analysis of the results stages one and two. As a result of the implementation of this model, it was possible to establish that teachers assessed positively the use of ICTs, this is shown by the fact that ICTs were incorporated in classroom planning as well as in the evaluation process. With the support of the educational community, new technological resources were acquired which made this proposal more motivating and playful for children
A Normal Sequence Compressed by PPM* But Not by Lempel-Ziv 78

This paper compares the difference in performance of the Prediction by Partial Matching family of compressors (PPM* and the original PPMk) and the Lempel-Ziv 78 (LZ) algorithm. We construct an infinite binary sequence whose worst-case compression ratio for PPM* is 0, while PPMk’s and LZ’s best-case compression ratios are at least 1/2 and 1 respectively. This sequence is normal and is an enumeration of all binary strings in order of length, i.e. all strings of length 1 followed by all strings of length 2 and so on. The sequence is built using repetitions of de Bruijn strings of increasing order.
Detection of A Stable Age in Children for Face Recognition Application

In this work, a novel approach for finding an accurate age of a child to be adapted for an automatic face recognition application is proposed. Our proposed approach initially uses Viola–Jones method to automatically locate a key point on facial components namely nose, eyes and mouth. We extract the intensity value in the region across these key points, find the row mean intensity value in the region of each component and sorted. Then, the corresponding index value of the sorted elements are preserved and represented as a Feature Vector (FV). By using the FV of each component, we find the correlation coefficient between two consecutive ages of a child. Then, the First Order Difference (FOD) of correlation coefficient of two consecutive ages is computed. The Cumulative Histogram (CH) of FOD is estimated and average CH of all components is determined. If the difference between average CH of two consecutive ages is negligibly small, then we considered that age as stable age for face recognition application. The proposed model has been validated on a reasonably sized dataset, which is created during this work. It consists of totally 421 longitudinal face images of 32 children each having 12–15 years face images, single sample per year. Kappa coefficient is used to measure the goodness of the model and results with 50% accuracy.
Paradoxes of Soviet Film Directing (By the Example of R.A. Bykov’s and K.G. Muratova’s Films)

The article aims to analyze the film directing experience of R.A. Bykov and K.G. Muratova, who embody the principles of alternative Soviet cinema. As our research shows, they are united not only by the epoch, but also by archetypal representativeness and personal marginality. This is embodied in the paradoxes of film directing, which are the subject of this research. Both R.A. Bykov and K.G. Muratova were somehow disavowed by the totalitarian regime, and their works were censored. In R.A. Bykov’s case, we also mean his acting work, not just directing. This research is relevant because of the problems of analyzing the narrative content of Russian cinema, and the characteristics of the complex and ambiguous Soviet and post-Soviet period of Russian culture. The article deals with a psychoanalytic discourse of the creators, reveals the common and specific in their lives and works. Its use is justified due to the paradoxes of the creative personality. According to the author, R.A. Bykov’s inferiority complex is associated with his desire to be the best and to realize his potential in all participation areas: not only as a director and actor but also as an organizer and public figure. K.G. Muratova was forced to constantly prove her existence in film directing due to the specifics of this “non- feminine” kind of activity, and therefore she was creating films that did not correspond to gender stereotypes. The article substantiates that the determining factor of creativity is, first, the psychological characteristics of the creator’s personality, and secondly, the social context. The author’s task is to test the psychoanalytic discursive method adapted to the main purpose of cultural analysis. This is explained by the fact that for both the authors, a grotesque theatricalization was a way to comprehend reality and figuratively express their ideas about it, an opportunity to preserve viability in socially and morally intolerant conditions. Archetypal images are the basis for building the drama of a cinematic work. In this article, in the context of R.A. Bykov’s works, the archetypal approach is applied to the film “Aybolit-66” (1967), in the context of K.G. Muratova’s works — to the film “Three Stories” (1997). This allows us to present an in-depth analysis of the main elements of their works, since a single frame or episode is typical for representation of great artists’ works. The author analyzes the male and female archetype, as well as the prototypes of the Child and the Elder implemented in the films. The directors’ appeal to them is dictated by logic, since, according to C. Jung, patterns, being part of the collective unconscious, are universal at the same time when creating and reading a work.
Noise Modeling and Data Augmentation Using Conditional Adversarial Autoencoder

Noising image recognition is an important but challenging task. The images collected by the camera are often accompanied by a variety of noise, which will cause the loss of image information to make recognition difficult. With powerful feature extraction, neural network models can be used to process these complex noising images. However, when the noise model to be learned is complex and the number of learning samples is limited, this may lead to overfitting of the network model. In order to increase the diversity of noising images in limited learning samples and improve the generalization ability of the recognition model, we propose a noise modeling method based on CVAE-GAN. First, the encoder maps a real noising image to hidden layer vectors, which contains noise distribution, image category, and pose information. Then the decoder receives these hidden layer vectors to generate an synthetic image of the specified category and noise level. Finally, during the training process, we combine adversarial loss and smoothl1 loss to make the generated noising image more realistic. Experiments show that compared with other current generative models, our method can more effectively learn the distribution rules of complex noise in real images and generate synthetic images more similar to real noise. These synthetic images generated by our method can can alleviate the overfitting problem caused by insufficient data volume and enhance the generalization ability of image recognition models. By this way, we obtain the state-of-the-art recognition performance on the challenging noising image dataset.
Aplicación de voz, como asistente virtual para el manejo de las emociones en alumnos de la UT Tehuacán

The research has to main objectives: design a voice app (to work with emotional intelligence using a spoken test), as an alternative tool (as a help to a psychologist), that offers the possibility to have a better approach to students from “UT Tehuacán”; using Alexa skill kit to develop the tool. This voice app will ask a series questions to students as part of different psychologist test to detect early emotional patterns. The main goal is to understand how emotions work within students during day, and how to process them in a better way; this way the student can have information to deal to their feelings more efficiently and fulfil their personal goals. This information, will be stored by the app in Amazon Web Service AWS, and it also will be analysed by a backend service; this service will get data to make charts. The research design is transversal, because the data will be obtained in a single moment; it is also applicative due to it seeks to apply acquired knowledge to solve a situation, because it has as objective to find one or more variables in a population; in this case (detect early emotional patterns). In order to support the psycho-pedagogy department and the different educative programs to know possible problems students may have, and find if those problems can be a reason to quit school. For this reason this project can be use as a tool to make decisions.