Reviewerly/exHarmony huggingface dataset:
Proposing an Image Enhancement Algorithm Using CNN for Applications of Face Recognition System

Many researches have been going on since last two decades for object recognition, shape matching, and pattern recognition in the field of computer vision. Face recognition is one of the important issues in object recognition and computer vision. Many face image datasets, related competitions, and evaluation programs have encouraged innovation, producing more powerful facial recognition technology with promising results. In recent years, we have witnessed tremendous improvements in face recognition performance from complex deep neural network architectures trained on millions of face images. Face recognition is the most important biometric and stills many challenges such as pose variation, illumination variation, etc. In order to achieve the desired performance when deploying in reality, the methods depend on many factors. One of the main factors is quality of input image. Therefore, facial recognition systems is installed outdoors which are always affected by extreme weather events such as haze, fog. The existence of haze dramatically degrades the visibility of outdoor images captured in inclement weather and affects many high-level computer vision tasks such as detection and recognition system. In this paper, we propose a preprocessing method to remove haze from input images that enhances their quality to improve effectiveness and recognition rate for face identification based on Convolutional Neural Network (CNN) based on the available datasets and our self-built data. To perform the proposed method for outdoor face recognition system, we have improved the system accuracy from 90.53% to 98.14%. The results show that the proposed method improves the quality of the image with other traditional methods.
Visual Locating of Reactor in an Industrial Environment Using the Composite Method

To achieve an automatic unloading of a reactor during the sherardizing process, it is necessary to calculate the pose and position of the reactors in an industrial environment with various amounts of luminance and floating dust. In this study, the defects of classic image processing methods and deep learning methods used for locating the reactors are first analyzed. Next, an improved You Only Look Once(YOLO) model is employed to find the region of interest of the handling hole and a handling hole corner detection method based on the image morphology and a Hough transform is presented. Finally, the position and pose of the reactors will be obtained by establishing a 3D handling hole model according to the principle of a binocular stereo system. To test the performance of the proposed method, a set of experimental systems was set up and experiments were conducted. The results indicate that the proposed location method is effective and the precision of the position recognition can be controlled to within 4.64 mm and 1.68 ° when the cameras are approximately 5 m away from the reactor, meeting the requirements.
Global Business: Virtual Workplaces and Collaborations

The state of the art in technologies and their impact on individuals, organizations, society and governments are on the rise.With constant and rapid innovation in the IT domain, many global organizations are moving to virtual team structures in order to build high performance cultures, improve productivity, and maintain synchronicity within their global structure.Many companies have had success in forming global virtual teams, though many have failed.The purposes of this paper are: (1) explore the use of virtual collaborations in global organizations, (2) explain virtual teams vs. physical teams, (3) address the pros and cons of virtual teams, (4) review the impact of the factors that affect the effectiveness of virtual teams, and ( 5) understand the new kind of worker and the role of the management.
Mean-Removed Product Quantization for Approximate Nearest Neighbor Search

Product quantization (PQ) and its variations are popular and attractive in approximate nearest neighbor search (ANN) due to their lower memory usage and faster retrieval speed. PQ decomposes the high-dimensional vector space into several low-dimensional subspaces, and quantizes each sub-vector in their subspaces, separately. Thus, PQ can generate a codebook containing an exponential number of codewords or indices by a Cartesian product of the sub-codebooks from different subspaces. However, when there is large variance in the average amplitude of the components of the data points, directly utilizing the PQ on the data points would result in poor performance. In this paper, we propose a new approach, namely, mean-removed product quantization (MRPQ) to address this issue. In fact, the average amplitude of a data point or the mean of a date point can be regarded as statistically independent of the variation of the vector, that is, of the way the components vary about this average. Then we can learn a separate scalar quantizer of the means of the data points and apply the PQ to their residual vectors. As shown in our comprehensive experiments on four large-scale public datasets, our approach can achieve substantial improvements in terms of Recall and MAP over some known methods. Moreover, our approach is general which can be combined with PQ and its variations.
A poetisa e sua mestra

In the early 2000s, Adília Lopes claimed that her writing models were
Chibumons: A Positive Effect On Game to Undergraduate Students

In the university daily life, social skill is very important. Social skill helps students to participated and interact with people. In this paper, we present the research and development of a mobile game to develop students’ social skill, 'Chibumons'. The sample group consists of 120 students. The tools used in this research were 'Chibumons', and satisfaction with the game questionnaire. Statistical data analysis used in this research were average, percentage, and standard deviation. The research reveals that games can enhance the social skill of students. This consequently accretes their social skill, boosting up enjoyment, engagement, and make friendships amongst playing. Moreover, this study demonstrates that ‘Chibumons’ is able to promote developing social skill and motivates students in higher education.
Side-Channel Information Leakage of Traffic Data in Instant Messaging

Instant Messaging has been widely applied for both corporate use and personal use in recent years. Major Instant Messaging service providers adopt the Push Technology to ensure the immediacy of message forwarding, which efficiently provides a great convenience for user. However, the immediacy feature causes side-channel information leakage even if some protection measures has been implemented, such as information encryption strategy. In particular, we observe that senders' traffic flows have a strong temporal correlation with those of corresponding recipients, since the messages are forwarded to recipients as soon as they are received by servers. Based on the observation, attackers can infer real-time communications between pairwise users and even the social connections of users. In this paper, we present a methodology framework to validate this side-channel information leakage, which identifies users of real-time communications by matching the pairwise time sequences of traffic flows. We evaluate the method on the collected real-world data. The experimental results show that users' communications can be identified with a high accuracy, and 6 groups of users are inferred to have strong connections based on the data collected from a local area networks.
Python as Multi Paradigm Programming Language

This paper will revolve around Python as a Multi-Paradigm Programming Language."and about how python has evolved to become the best suited and best opted language for developing almost all kinds of Applications.This Paper will also include various characteristics and features of python proving why it is the widely used programming language in recent times.Further, this paper explains about the growth of python in past five years, as an upfront emerging programming language with world's Largest developer community.Advancing further with the paper, this paper consists of the role of python in various disciplines and trending areas of Information Technology.Like IoT
Evaluation Study on the Barriers to Success of Technology Business Incubation Programme in Nigeria – The Moderating Role of Government Policy

The business incubation model has been embraced by numerous countries of the world since the concept was first conceived and developed in the USA.It has now received much attention among policymakers and academics particularly within the field of entrepreneurship and small business research.Even though the popularity of the programme, so many tenant firms are still closing shops before and after their exit from the incubator.The aim of this study was to explore how government policy moderates the relationship between the individual critical success factors (CSF) and incubator performance.The study adopted the evaluative qualitative case study design.The population of the study consisted of ten surveying stakeholders who were purposively chosen between the stakeholders.The study found the evidence of the moderating role of government policy on the relationship between barriers to success and incubator performance.The study recommended that government should develop a user friendly framework that will ease the difficulties incubators usually encounter in the course of providing resources to the incubatees.
Introduction to the Minitrack on Cybersecurity Investigations and Digital Forensics

The continuous amalgamation of technology into everyday life is creating an environment that is conducive to encouraging cybercrimes.As a result, it is becoming increasingly important that organizations and law enforcement agencies have the capability to conduct in-depth and detailed investigations.Hence, corporate and legal responses that address the resulting concerns presented in this mini-track include 'DNA Feature Selection for Discriminating WirelessHART IIoT Devices' and 'Container and VM Visualization for Rapid Forensic Analysis'.These contributions highlight the growing need to investigate and address cybersecurity vulnerabilities in the broad context of today's information-driven society.
Agile Approach for IT Forensics Management

The forensic investigation of cyber attacks and IT incidents is becoming increasingly difficult due to increasing complexity and intensify networking.Especially with Advanced Attacks (AT) like the increasing Advanced Persistent Threats an agile approach is indispensable.Several systems are involved in an attack (multi-host attacks).Current forensic models and procedures show considerable deficits in the process of analyzing such attacks.For this purpose, this paper presents the novel flower model, which uses agile methods and forms a new forensic management approach.In this way, the growing challenges of ATs are met.In the forensic investigation of such attacks, big data problems have to be solved due to the amount of data that needs to be analyzed.The proposed model meets this requirement by precisely defining the questions that need to be answered in an early state and collecting only the evidence usable in court proceedings that is needed to answer these questions.Additionally, the novel flower model for AT is presented that meets the different phases of an investigation process.
THE EFFECTIVENESS OF TEACHING WRITING BY USING PADLET AS THE MEDIA

Using Padlet as media is one of the ways to teach writing. Using Padlet as media help students make the students more excited in writing. The main purpose of the study is to examine the effectiveness of using Padlet as media on students’ writing score of the tenth grade IIS-1. Quantitative was selected as a research approach, whereas the research design used was Quasi Experimental by using posttest-only design. This research used two classes which became experimental and control class. The experimental class was taught writing by using Padlet as media, while the control class was taught without any media. To collect the data, post writing tests in experimental and control group were administered. Data were analyzed by using t-test. The findings indicated that score of the experimental class higher than the control class. Thus, it can be concluded that using Padlet as media in teaching writing is effective.
Discrete Gravitational Search Algorithm (DGSA) applied for the Close-Enough Travelling Salesman Problem (TSP / CETSP)

TSP (travelling salesman problem) is a NP-hard problem, and several exact and heuristics solutions exist. Exact solutions consume too many resources (computation and time) and heuristic solutions do not provide global optimum path. We propose another heuristic variation of the TSP and CETSP solver (DGSA-TSP and DGSA-CETSP), based on discrete gravitational search algorithm and two novel rubber-band algorithm (RBA) implementations; we benchmark it against the GSOA (growing self-organizing array) variant and obtain similar-accuracy results.
From NPR to VR: tracking ocular behavior in immersive virtual reality

&lt;p align="JUSTIFY"&gt;&lt;span style="color: #000000;"&gt;&lt;span&gt;&lt;span style="font-family: Arial, serif;"&gt;&lt;span style="font-size: medium;"&gt;&lt;span&gt;&lt;span&gt;In this work, we present a research about ocular behavior during immersion in virtual reality. To enhance our comprehension of human behavior in immersive conditions, we will focus on vision and &lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style="font-family: Arial, serif;"&gt;&lt;span style="font-size: medium;"&gt;its&lt;/span&gt;&lt;/span&gt;&lt;span style="color: #000000;"&gt;&lt;span&gt;&lt;span style="font-family: Arial, serif;"&gt;&lt;span style="font-size: medium;"&gt;&lt;span&gt;&lt;span&gt; reactivity to non-photorealistic rendering and cinematic procedures. Using physiological data collected in real time with an eye tracker, we compare attentional and pupillary response in order to describe the visual immersion experienced by the user. This proposal could be useful to identify physical sickness produced by perceptual mismatches and to evaluate the cognitive efficiency of virtual reality experiences in different fields of applications, giving feedback to content producers about user´s ocular behavior. &lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;
ANALISIS SENTIMEN TOPIK VIRAL DESA PENARI PADA MEDIA SOSIAL TWITTER DENGAN METODE LEXICON BASED

Abstract : The horror story of Dancer Village in Indonesia is a viral topic that has become a talk of citizens on Twitter social media. Various responses and public opinions emerged related to the truth of the story of supernatural experiences of students during a Real Work Lecture in an East Java region of Indonesia. This study conducted a sentiment analysis of community comments on Twitter social media on the viral topic using the Lexicon Based method. Sentiment classification is divided into 3 classes namely positive, negative and neutral. The research phase consists of data collection, pre-processing, processing (sentiment analysis) and visualization. Data collection uses Twitter Search API with 1000 Penari Desa keywords in Indonesian. The lexicon assessment results from 1000 tweets data obtained 33 positive, 767 neutral and 200 negative. The percentage of tweets containing positive comments by 3.3%, neutral 76.7% and negative by 20%
Klasifikasi Gambar Gigitan Ular Menggunakan Regionprops dan Algoritma Decision Tree

Indonesia has a high biodiversity of snakes. Snake species that exist throughout Indonesia, consisting of venomous and non-venomous snakes. One of the dangers that can be posed by snakes is the bite of several types of deadly snakes. Snake bite cases recorded in Indonesia are quite high with not a few fatalities. Most of the deaths caused by snakebite occur due to errors in the handling procedure for the bite wound. This problem can be overcome one of them if we know how to classify snake bite wounds, whether venomous or non-venomous. In this study, a classification system for snake bite wound image was built using Regionprops feature extraction and Decision Tree algorithm. Snake bite images are classified as either venomous or non-venomous without knowing the kind of the snake. In Regionprops several features are used to help the process of feature extraction, including the number of centroids, area, distance, and eccentricity. Evaluation of the model that was built was found that the parameters of the number of centroids and the distance between centroids had the most significant influence in helping the classification of images of snakebite wounds with an accuracy of 97.14%, precision 92.85%, recall 91.42%, and F1 score 92.06%.
Feature-enhanced one-stage face detector for multiscale faces

The performance of face detection has achieved great progress with the rapid development of the convolutional neural network. However, detecting faces with a wide range of scales is still a challenging and crucial problem, especially for small faces. We focus on designing a strong network architecture, aiming at generating high-quality feature representation to handle the multiscale problem. Specifically, we conduct an in-depth study of three aspects, including feature fusion, attention mechanism, and receptive field, then propose an effective one-stage face detector. First, we design a feature fusion module, which adopts a more effective manner to sufficiently fuse multiscale features for building discriminative feature pyramid representation. Then, we propose an attention enhancement module for introducing the attention mechanism, which makes the network keep more valid information to emphasize the features from facial regions. Finally, we construct a prediction module, which ensures that each detection layer contains rich receptive fields to bring more useful context information. A large number of experiments on the widely used face detection benchmarks (i.e., WIDER FACE, FDDB, PASCAL FACE, and AFW) indicate that the results of our method outperform overwhelming majority of the existing methods and are comparable to the most recent state-of-the-art face detector, which demonstrate that our method is able to detect faces with a wide range of scales effectively.
Evaluation of texts written by patients with endogenous mental disorders

Background: the importance of identifying characteristics of texts written by patients with mental diseases of the endogenous circle is due to the fact that psychological and behavioral disorders in mental diseases manifest themselves in speech activity, including writing, and are little studied in the Russian-language sample. The aim: is to identify the parameters of the texts characteristic of disorders of schizophrenic and affective spectrum, the analysis of the relationship of the selected parameters with clinical symptoms and personality. Materials and methods: the study involved 29 patients with endogenous mental disorders (14 patients diagnosed with F2 according to ICD-10, 15 patients with F3) and 41 healthy people. Clinico-psychological, psychometric methods and statistical analysis were used. Everyone wrote an essay on the topic «Me, others, the world», answered Big Five personality traits, patients filled out the questionnaire SCL-90. Results: it is shown that patients are more focused on themselves, their texts often show an ambivalent or negative attitude toward themselves, a sense of isolation from the world, a sense of instability of the «fragility» of the surrounding world. «Torn» and «pretentious» texts were much more common in patients and «broken» texts were specific only for patients. It is shown that the presence of «pathological characteristics» in the text of patients is associated with the severity of clinical symptoms. Correlations of text «pathological» characteristics with clinical symptoms according to SCL-90 (scales of depression, anxiety, psychotism, general severity of the state, severity of distress) and personal characteristics (introversion, separation, naturalness, emotionality) are shown in patients. Conclusion: thus, the proposed parameters for assessing the texts revealed the peculiarities of perception of oneself and other, a violation of the social orientation of the individual, a decrease in the ability to integrate contradictory and ambivalent phenomena into a holistic view of oneself and the world in the text of patients. «Pathological characteristics» of the text reflect the presence of clinical symptoms and are associated with personality traits.
Improved Discrete Optical Flow Estimation With Triple Image Matching Cost

Approaches that use more than two consecutive video frames in the optical flow estimation have a long research history. However, almost all such methods utilize extra information for a pre-processing flow prediction or for a post-processing flow correction and filtering. In contrast, this paper differs from previously developed techniques. We propose a new algorithm for the likelihood function calculation (alternatively the matching cost volume) that is used in the maximum a posteriori estimation. We exploit the fact that in general, optical flow is locally constant in the sense of time and the likelihood function depends on both the previous and the future frame. Implementation of our idea increases the robustness of optical flow estimation. As a result, our method outperforms 9% over the DCFlow technique, which we use as prototype for our CNN based computation architecture, on the most challenging MPI-Sintel dataset for the non-occluded mask metric. Furthermore, our approach considerably increases the accuracy of the flow estimation for the matching cost processing, consequently outperforming the original DCFlow algorithm results up to 50% in occluded regions and up to 9% in non-occluded regions on the MPI-Sintel dataset. The experimental section shows that the proposed method achieves state-of-the-arts results especially on the MPI-Sintel dataset.
A Dual-SLS for Efficient 3D Scanning of Dynamic Objects

Structured light-based 3D sensing techniques has been an important means for 3D digitization of real objects. However, to obtain a complete 3D model of target, multiple scans are demanded and that makes the operation time-consuming. In this paper, an efficient 3D scanning system is introduced, which is composed of two structured light sensors. The two sensors are placed in opposite directions, and the target is placed between them. With a single scan operation, two point clouds can be obtained and aligned by the proposed system calibration parameters. For some objects with relative simple geometry like human hands, a complete 3D model can be obtained via single scan. But regarding to complex targets, by changing the target orientation several times and using the proposed point cloud registration method, complete 3D model can be obtained more efficiently. Experiments with different objects are implemented to verify feasibility and accuracy of the proposed method.
A novel internal cluster validity index

It is critical to determine the optimal number of clusters (NC) in cluster analysis. Many cluster validity indices have been proposed, such as the Silhouette index and In-group proportion index. However, these validity indices have more time complexity. From the viewpoint of sample geometry, a new internal cluster validity index for determining the optimal NC is proposed. The new index can evaluate the clustering quality of a certain clustering algorithm and determine the optimal NC for many kinds of data sets, including synthetic data sets, benchmark data sets, and real data sets. Compared with many well-known validity indices, the proposed index is more effective and efficient. Theoretical analysis and experimental results show the effectiveness and high efficiency of the new index.