Reviewerly/exHarmony huggingface dataset:
Obras francesas na produção científica arquivística brasileira

This paper is part of a broad research project on the internationalization of Archival Science and institutionalization of the discipline in Brazil. It results from a smaller research project about archival works in France referenced in theses, dissertations and TCC’s about archives and/or Archival Science, produced in brazilian stricto sensu graduate programs. This project aims to understand the movements of institutionalization of the Archival Science in Brazil from the french contributions, when mapping the circulation of french works in Brazil. It is a qualitative-quantitative, exploratory, descriptive and explicative, bibliographical and documentary research. The results indicate the predominance of books and archival scientific articles published in France in the 1990s, in French, Portuguese and English. The ICA stands out in the publication of norms for the archival description, as well as Duchein, author who contributed to the institutionalization of the discipline in Brazil, through his technical visits and his works, of great international repercussion. These results confirm the traditional relations between the French and Brazilian Archival Science, demanding new studies that may bring more support to the understanding of these relations today.
Pengelompokan kata berdasarkan kemiripan ucapan pada kamus menggunakan algoritma metaphone pada sistem operasi Android

Indonesia is an archipelagic country consisting of various tribes and cultures and different languages. One of them is the Sumbawa language used by the people of the western part of the island of Sumbawa. Sumbawa language has 4 dialects namely Samawa, Taliwang, Jereweh, and Tongo. In Sumbawa there are also homophonic, homonim, and homograph words. Grouping words based on the similarity of speech to the Sumbawa dictionary used Metaphone algorithm on the android operating system is discussed in this study. Metaphone algorithms can be applied to multiple languages with rules that have been modified according to the desired language characteristic. Based on the results of testing the recall and precision on the new rules Metaphone algorithm can be concluded that the grouping of words based on similarity of speech in Sumbawa language can be said to be effective. The average percentage index of the recall test is 98,97% and the precision is 78,52%.
The Role of Extreme Learning Machine in Multi Classification based Emotion Recognition using EEG Signals

Emotion is one of the most significant parts of the daily interaction among people. Electroencephalography (EEG), which provides the transfer of physiological signals of the nervous system to the computer environment, plays an important role in emotion recognition. In this study, the performance of Extreme Learning Machines (ELM) in multiple classification based emotion recognition on DEAP data set is examined. Features required for the classification are obtained from time domain and frequency domain. Emotions are classified as binary and multiple according to their arousal and valence values, and the classification results are also compared with Support Vector Machines (SVM).
Fine-grained analysis method for Android volatile memory

Abstract Android dominates the mobile operating system market. Volatile memory analysis of Android devices has been the focus of research on mobile forensics technology. However, due to the semantic gap between the kernel and the volatile memory allocator, existing Android volatile memory analysis methods are coarse-grained. With the volatile memory capacity of Android devices increasing, these methods cannot satisfy the need of Android volatile memory analysis accuracy. In this paper, we first discuss the address space layout of Android processes and the management mechanism of Jemalloc, the default Android volatile memory allocator. Then, we bridge the semantic gap by utilizing the boundary auto alignment feature of the data structure of Jemalloc. Finally, we propose a Fine-grained Analysis Method for Android volatile Memory, called FAMAM. Experimental results shows that FAMAM has an accurate data analysis capability as well as a good robustness. In addition, we successfully use FAMAM to discover new storage patterns for username and password of Wechat.
Does Anonymity Matter? A Discoursal Study of Gender Recognition on Arabic Facebook Comments

This study considered the recognition of persons' gender through the differences of the discoursal features and stylistic choices of their Arabic Facebook comments. Three hundred comments on different topics were collected from same-/cross-sex interactions of friends, acquaintances and relatives having similar educational and social backgrounds and with an age range 20- 50. Some research questions were raised as to whether males and females differed in terms of the discoursal features of their comments as well as the ability to recognize the person's gender on the basis of these features. Accordingly, some hypotheses were formulated, viz. males' and females' comments involved distinguished gendered discoursal features and styles, females behaved in line with maintenance roles while males behaved in line with task- oriented roles, and the persons' gender could be predicted through applying the gendered features they had. The study came up with various conclusions that, to some extent, confirmed the hypotheses stated.
High Dynamic Range Imaging Based on Bidirectional Structural Similarities and Weighted Low-Rank Matrix Completion

High dynamic range (HDR) imaging, aiming to increase the dynamic range of an image by merging multiexposure images, has attracted much attention. Ghosts are often observed in a resultant image, due to camera motion and object motion in the scene. Low-rank matrix completion (LRMC) provides an effective tool to remove ghosts. However, user specification of the included or excluded regions is required. In this paper, we propose a novel HDR imaging method based on bidirectional structural similarities and weighted low-rank matrix completion. In our method, we first propose the bidirectional structural similarities containing forward-projection structural similarity (FPSS) and backward-projection structural similarity (BPSS) to divide each image into four groups: motion region, saturated region in the source image, saturated region in the reference image, and static and unsaturated regions. Then, the weight maps and the motion maps constructed based on FPSS and BPSS are introduced in the weighted LRMC model to reconstruct the background irradiance maps. Experiments are conducted on several challenging image sets with complex scene, and the results show that the proposed method outperforms three current state-of-the-art methods and Photoshop cs6 and is robust to the reference image.
SCMS: Tool for Assessing a Novel Taxonomy of Complexity Metrics for any Java Project at the Class and Method Levels based on Statement Level Metrics

Software is the primary and indispensable entity in our technologically driven world.Therefore, quality assurance, and in particular software testing, represents a vital component in the software development cycle.Throughout the years, many tools have been developed to collect metrics of software that had been implemented.These tools have some differences but continue to play an important role in improving the quality of software products.This paper introduces a newly developed tool, named Spectra Complexity Metrics System (SCMS), which compiles a novel taxonomy of complexity metrics of any given software written in the Java programming language.Our suggested metrics have been invented to identify and evaluate the characteristics of Java computer programs.They aim at increasing the efficiency of the testing process by significantly reducing the number of test cases without having a significant drop in test effectiveness.We assess our proposed taxonomy of different complexity metrics based on the product levels (statement, method, and class) and their characteristics.For further evaluation, our software metrics coverage is compared to other existing software metric tools.The results show the novelty of our taxonomy of complexity metrics and the capability of our tool to compute these measurements based on all three of the product level categories.We have published our tool at https://github.
Lightweight Encryption Algorithm Containing SPN Structure for IoT

With the development of information technology, the Internet of Things has increasingly entered all aspects of people’s work and life. In the IoT environment, in order to provide better and faster services, while ensuring data integrity, confidentiality and privacy, people put forward higher requirements for the efficiency of encryption algorithms. However, although the traditional encryption algorithm can guarantee the security, it is not suitable for the security of the Internet of Things in terms of encryption efficiency and energy consumption. In this paper, we propose a lightweight packet encryption algorithm based on the Substitution Permutation Network (SPN) structure. The algorithm consists of several P-boxes and S-boxes. It avoids the problems of high energy consumption and low efficiency caused by the large number of rounds of AES algorithm. Based on the SPN-based block cipher, it introduces the S-box and P-box and the multiple-pass authentication encryption scheme. As well, by proving its block cipher variance and linear analysis, the proposed solution is effective and provides sufficient security through authentication and encryption. This algorithm can be used for low-energy and IoT information transmission processes that require only moderate security.
A Fast Relative Density Method Based on Space Partitioning

Label noise play an important role in classification. It can cause overfitting of learning methods and deteriorate their generalizability. The relative density method is effective in label noise detection, but it has high time complexity. On the other hand, the multi-granularity relative density method reduces the time cost, but the classification accuracy is also reduced. In this paper, we propose an improved relative density method, named the relative density method based on space partitioning (SPRD). The proposed method not only accelerates the label noise detection but also maintains a good classification performance. Also, the parameter k, which is used in the conventional relative density methods, is removed, making the proposed method adaptive. The experiment results on the UCI datasets demonstrate that the proposed method has higher efficiency than the conventional methods and better classification accuracy than the multi-granularity relative density method.
DeepStroke: Understanding Glyph Structure with Semantic Segmentation and Tabu Search

Glyphs in many writing systems (e.g., Chinese) are composed of a sequence of strokes written in a specific order. Glyph structure interpreting (i.e., stroke extraction) is one of the most important processing steps in many tasks including aesthetic quality evaluation, handwriting synthesis, character recognition, etc. However, existing methods that rely heavily on accurate shape matching are not only time-consuming but also unsatisfactory in stroke extraction performance. In this paper, we propose a novel method based on semantic segmentation and tabu search to interpret the structure of Chinese glyphs. Specifically, we first employ an improved Fully Convolutional Network (FCN), DeepStroke, to extract strokes, and then use the tabu search to obtain the order how these strokes are drawn. We also build the Chinese Character Stroke Segmentation Dataset (CCSSD) consisting of 67630 character images that can be equally classified into 10 different font styles. This dataset provides a benchmark for both stroke extraction and semantic segmentation tasks. Experimental results demonstrate the effectiveness and efficiency of our method and validate its superiority against the state of the art.
Unmanned Aircraft Systems Conflict Resolution Using Return-to-Course Maneuver

This paper considers the problem of conflict resolution involving two constant speed aircraft moving in horizontal plane. A planar deconflicting maneuver is proposed that resolves the conflict with desired safety threshold and takes the aircraft back to its original course. Considering maximum bank angle and maximum banking rate of the aircraft, a second-order dynamical relationship between aircraft bank angle and aileron deflection is used to design the maneuver. The maneuver consists of straight line segments and coordinated-turns. Maneuver parameters are computed by translating the problem in a frame of reference relative to the intruder aircraft and resolving the conflict therein. An analytical guarantee is presented for existence of solution for all possible conflicts in this scenario. The solution is expressed in terms of aileron deflection commands required to execute the maneuver. Simulations are carried out for various conflict scenarios showing the efficacy of the proposed maneuver.
Attracting Business Practice in Educational Environment: Expectations and Reality

The article features the use of business practices in the sphere of education, as required by the Federal State Standard. The research objective was to systematize the prerequisites for this process: on the basis of normative documents and on the basis of the requirements employers impose on the competencies of university graduates. The authors consider externship training and involvement of employees and managers into the teaching process as perspective tools. For each tool, they identified problems according to a comparative analysis of the expected outcome and the actual results of their application. The research featured two regional universities. One is a basic university included in the Top 100 of Russian universities, while the other has a narrow industrial specialization. The object of the study was the quality of training of students in terms of their adaptation to the requirements of the professional environment, as well as the tools that can provide this quality. The research employed various methods of sociological research and the method of correlation analysis. The paper focuses on advantages and disadvantages of the tools. The training of university staff seems to require a revision in that teaching personnel have to obtain key competencies in the professional disciplines they teach.
Group Discriminative Least Square Regression

Due to mathematical simplicity and computational efficiency, the least square regression (LSR) has become a popular method for multicategory classification. However, the discriminative and generalization capability of existing methods are limited. In this work, we pursue a structure constraint of the predicted label indicators of data from the same class because such structure ensures good discriminative capability and stability of the classification. To guarantee this, we propose a new regularization on the label transformation matrix. Specifically, we regularize the label transformation matrix by minimizing the difference l2,1 ofnorm and l2,2 norm of the predicted label matrix of each class. We call it group discriminative least square regression (GDLSR). In addition, we also adopt the -dragging technique to relax the binary label for better generalization capability. Experiments on several commonly used datasets show that our method outperforms other state-of-the-art LSR-based methods.
An Estimation of Mixing Coefficients in Image Separation Problem Using Multiwavelet Transforms

Image separation problems, where observed images are weighted superpositions of translations and rotations of original images, are considered. The Algorithms for estimating fine relative translation parameters and relative mixing coefficients are proposed. Numerical experiments show that the proposed Algorithms work well.
Academician Andrei Ershov and Graphs in Programming

Academician Andrei Petrovich Ershov (19 April 1931 - 8 December 1988) was one of the Soviet pioneers in the field of theoretical and systems programming, the founder of the Siberian School of Programming. His significant contributions to establishing informatics as a new branch of science and a new phenomenon of the social life are widely recognized both in Russia and abroad. His pioneering works on graph-theory methods in programming are somewhat less well known but equally important and highly regarded by the experts. A.P. Ershov called graphs to be the basic tool for the programmer, and said that graphs possess a vast, inexhaustible power commensurate with the scale of programming tasks. He made a fundamental contribution to graph theory particularly in the area of programming. In this paper, A.P. Ershov's works on graph methods in programming are considered.
Fine-Grain Level Sports Video Search Engine

It becomes an urgent demand how to make people find relevant video content of interest from massive sports videos. We have designed and developed a sports video search engine based on distributed architecture, which aims to provide users with content-based video analysis and retrieval services. In sports video search engine, we focus on event detection, highlights analysis and image retrieval. Our work has several advantages: (I) CNN and RNN are used to extract features and integrate dynamic information and a new sliding window model are used for multi-length event detection. (II) For highlights analysis. An improved method based on self-adapting dual threshold and dominant color percentage are used to detect the shot boundary. Affect arousal method are used for highlights extraction. (III) For image’s indexing and retrieval. Hyper-spherical soft assignment method is proposed to generate image descriptor. Enhanced residual vector quantization is presented to construct multi-inverted index. Two adaptive retrieval methods based on hype-spherical filtration are used to improve the time efficient. (IV) All of previous algorithms are implemented in the distributed platform which we develop for massive video data processing.
Lightweight Hardware Architecture for Eight-Sided Fortress Cipher in FPGA

In the lightweight domain, various ciphers and their different implementations are introduced to deal with the problem of security in resource scare environment. Eight-sided fortress (ESF) is a lightweight Feistel cipher which uses substitution–permutation network based round function with Serpent Substitution-box(S-box). This work presents a study and comparison of the various hardware architectures of ESF to combat issues of security in an extremely constrained resource environment. For the design of hardware, different techniques of S-box implementation are used. Comparison and evaluation of ESF S-box implementation techniques is done on the basis of latency, throughput, area utilization, and power consumption. It is observed that the Random Access Memory (RAM)-based S-box design gave the best results with the requirement of minimum area for its implementation. This makes it the preferred architecture for resource-limited applications.
The Interaction Interface Between the Model and the Observer Agent in the Simulation System

From the point of view of the problem, the most difficult thing is to separate the model from the task for the experiment. The main thing in this approach is the separation of variables into observable and variable. The observing agent checks the achievement of the stated goals and conditions of the experiment and makes decisions about changes in the variables and / or strategy of the model. A software interface is used to link the model and agent. The model contains potentially observable and variable variables in accordance with a specific task. Variable parameters are usually selected those that are responsible for the behavior of the model (characteristics and parameters of agents). In the interaction of the agent and the model, we consider an interface that will be easy to manage and as informative as possible. It was revealed that with frequent updating of the observed parameters, the model execution time significantly increases. For optimal performance, it is necessary to determine the time intervals with which the values in the interface will be updated.
An eye Opening and Closing Size Detecting Method for Driver

The size of the human eye opening and closing plays a certain role in the human-computer interaction platform, such as the PERCLOS parameter commonly used in driver fatigue detection. Considering that the image in the actual application scene is often accompanied by distance, blur, etc., this paper proposes an algorithm based on BiseNet and Vgg methods which can segment the pupil and iris while outputting the realtime opening ratio of the eye.
Mempromosikan lahan basah dalam pembelajaran pendidikan inklusi melalui mata pelajaran Bahasa Inggris

This research aims to identify the inclusive education learning pattern in order to get the perspective and wetlands based - inclusive education learning through English subject. This research was conducted in SMPN 13, SMPN 15, and SMPN 21. The respondents are teachers; the instrument used is questionnaire and interview. The sampling technique used is purposive sampling in which the reseacher has the consideration to tabulate the number of students tending to have the special needs; however, the schools are the institution not administrating special education.The research design used is descriptive and qualitative. The descriptive means every finding in each item in instrument is described as clear as possible; the qualitative means every description of each item is discussed as deep as possible. The research finding states that there are several students who tend to have the special needs and they need the special treatment for teaching and learning. The teaching and learning required should based on their special needs. The wetlands based – teaching and learning through English subject should be conducted by considering their special needs so that the objective of learning is well achieved.
Human-Centered Design Tools for Smart Toys

The smart toy industry faces challenges to achieve Hardware and Software (H&S) integration since numerous products are not generating enduring value propositions to the consumers. It is possible to achieve better H&S integration by following suitable design practices. Here, we propose four Human-Centered Design (HCD) tools for the development of smart toys solutions. The four HCD tools intervene on idea generation, data collection planning, and both low and high-fidelity prototyping of the solutions. The aim is to assist designers, developers, and engineers in producing better H&S integrated solutions by offering tools that meet HCD principles. The primary usage of the HCD tools with 27 graduate students assisted these multidisciplinary teams in creating five prototypes that were positively evaluated by end-users. Technical evaluation checks for the integrity of the prototypes after testing and results show comparative data on battery consumption and list potential privacy and security vulnerabilities. Improvements include adapting ideation tool to incorporate marketing-oriented strategies, authentication and data encryption for the toolkit, and assessing the tools with professional teams of the industry.