bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Enjoyment in a Serious Game for Binge Drinking Prevention: Pretest-Posttest Quasi-Experimental Study
Authors: Hong Traci , Cabrera Joshua , Beaudoin Christopher E
Published: October 2020

Background

Although binge drinking peaks at age 21 to 25 years, there is limited research on the effects of serious games in this population, as well as on the process by which playing serious games impacts alcohol-related outcomes. Designed with both health behavioral theory and game theory, One Shot is an online serious game that aims to prevent binge drinking.

Objective

This study utilized a conceptual model for serious video game processes. Using One Shot, the model assessed the following process stages: (1) Alcohol Use Disorders Identification Test-Concise (AUDIT-C); (2) in-game factors of game time and risky alcohol decisions; (3) game enjoyment; and (4) postgame outcomes of intention to drink less and drinking refusal self-efficacy.

Methods

In a one-group pretest-posttest quasi-experimental design, a sample (N=550) of young adults (age 21-25 years) who reported recent binge drinking played the One Shot game. Intention to drink less and drinking refusal self-efficacy were measured at pregame and postgame, with their effects lagged in statistical analysis. Participants were presented with various scenarios in the game that pertained to risky alcohol decisions, which, along with game time, were unobtrusively recorded by the server. A structural equation model (SEM) was used to test the conceptual model, with assessments made to determine if enjoyment mediated the effects of game time and risky alcohol decisions on the 2 postgame alcohol-related outcomes.

Results

A well-fitting SEM demonstrated support for the multistep model, with AUDIT-C predicting risky alcohol decisions (β=.30). Risky alcohol decisions (β=−.22) and game time (β=.18) predicted enjoyment, which, in turn, predicted intention to drink less (β=.21) and drinking refusal self-efficacy (β=.16). Enjoyment significantly (P<.001) mediated the effects of game time and risky alcohol decision on intention to drink less and drinking refusal self-efficacy.

Conclusions

The results support a conceptual model in which staggered individual and in-game factors influence alcohol-related outcomes. Enjoyment is important for participants’ intentions to drink less and beliefs that they can refuse alcohol.
Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study
Authors: Sandhu Sahil , Lin Anthony L , Brajer Nathan , Sperling Jessica , Ratliff William , Bedoya Armando D , Balu Suresh , O'Brien Cara , Sendak Mark P
Published: October 2020

Background

Machine learning models have the potential to improve diagnostic accuracy and management of acute conditions. Despite growing efforts to evaluate and validate such models, little is known about how to best translate and implement these products as part of routine clinical care.

Objective

This study aims to explore the factors influencing the integration of a machine learning sepsis early warning system (Sepsis Watch) into clinical workflows.

Methods

We conducted semistructured interviews with 15 frontline emergency department physicians and rapid response team nurses who participated in the Sepsis Watch quality improvement initiative. Interviews were audio recorded and transcribed. We used a modified grounded theory approach to identify key themes and analyze qualitative data.

Results

A total of 3 dominant themes emerged: perceived utility and trust, implementation of Sepsis Watch processes, and workforce considerations. Participants described their unfamiliarity with machine learning models. As a result, clinician trust was influenced by the perceived accuracy and utility of the model from personal program experience. Implementation of Sepsis Watch was facilitated by the easy-to-use tablet application and communication strategies that were developed by nurses to share model outputs with physicians. Barriers included the flow of information among clinicians and gaps in knowledge about the model itself and broader workflow processes.

Conclusions

This study generated insights into how frontline clinicians perceived machine learning models and the barriers to integrating them into clinical workflows. These findings can inform future efforts to implement machine learning interventions in real-world settings and maximize the adoption of these interventions.
A Self-Administered Multicomponent Web-Based Mental Health Intervention for the Mexican Population During the COVID-19 Pandemic: Protocol for a Randomized Controlled Trial
Authors: Dominguez-Rodriguez Alejandro , De La Rosa-Gómez Anabel , Hernández Jiménez M Jesús , Arenas-Landgrave Paulina , Martínez-Luna Sofía Cristina , Alvarez Silva Joabian , García Hernández José Ernesto , Arzola-Sánchez Carlos , Acosta Guzmán Victoria
Published: October 2020

Background

The COVID-19 pandemic has become a public health emergency of international concern; it has not only threatened people's physical health but has also affected their mental health and psychological well-being. It is necessary to develop and offer strategies to reduce the psychological impact of the outbreak and promote adaptive coping.

Objective

This study protocol aims to describe a self-administered web-based intervention (Mental Health COVID-19) based on the principles of positive psychology supported by elements of cognitive behavioral therapy and behavioral activation therapy to reduce the symptoms of anxiety and depression and increase positive emotions and sleep quality during and after the COVID-19 outbreak through a telepsychology system.

Methods

A randomized controlled clinical superiority trial with two independent groups will be performed, with intrasubject measures at four evaluation periods: pretest, posttest, 3-month follow-up, and 6-month follow-up. Participants will be randomly assigned to one of two groups: self-administered intervention with assistance via chat or self-administered intervention without assistance via chat. The total required sample size will be 166 participants (83 per group).

Results

The clinical trial is ongoing. This protocol was approved by the Research Ethics Board of the Free School of Psychology-University of Behavioral Sciences (Escuela libre de Psicología-Universidad de Ciencias del Comportamiento). The aim is to publish the preliminary results in December 2020. A conservative approach will be adopted, and the size effect will be estimated using the Cohen d index with a significance level (α) of .05 (95% reliability) and a conventional 80% power statistic.

Conclusions

The central mechanism of action will be to investigate the effectiveness of an intervention based on positive psychology through a web platform that can be delivered through computers and tablets, with content that has been rigorously contextualized to the Mexican culture to provide functional strategies to help the target users cope with the COVID-19 pandemic.

Trial Registration

ClinicalTrials.gov NCT04468893; https://clinicaltrials.gov/ct2/show/NCT04468893

International Registered Report Identifier (IRRID)

DERR1-10.2196/23117
The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets
Authors: Xue Jia , Chen Junxiang , Chen Chen , Hu Ran , Zhu Tingshao
Published: October 2020

Background

Family violence (including intimate partner violence/domestic violence, child abuse, and elder abuse) is a hidden pandemic happening alongside COVID-19. The rates of family violence are rising fast, and women and children are disproportionately affected and vulnerable during this time.

Objective

This study aims to provide a large-scale analysis of public discourse on family violence and the COVID-19 pandemic on Twitter.

Methods

We analyzed over 1 million tweets related to family violence and COVID-19 from April 12 to July 16, 2020. We used the machine learning approach Latent Dirichlet Allocation and identified salient themes, topics, and representative tweets.

Results

We extracted 9 themes from 1,015,874 tweets on family violence and the COVID-19 pandemic: (1) increased vulnerability: COVID-19 and family violence (eg, rising rates, increases in hotline calls, homicide); (2) types of family violence (eg, child abuse, domestic violence, sexual abuse); (3) forms of family violence (eg, physical aggression, coercive control); (4) risk factors linked to family violence (eg, alcohol abuse, financial constraints, guns, quarantine); (5) victims of family violence (eg, the LGBTQ [lesbian, gay, bisexual, transgender, and queer or questioning] community, women, women of color, children); (6) social services for family violence (eg, hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (eg, 911 calls, police arrest, protective orders, abuse reports); (8) social movements and awareness (eg, support victims, raise awareness); and (9) domestic violence–related news (eg, Tara Reade, Melissa DeRosa).

Conclusions

This study overcomes limitations in the existing scholarship where data on the consequences of COVID-19 on family violence are lacking. We contribute to understanding family violence during the pandemic by providing surveillance via tweets. This is essential for identifying potentially useful policy programs that can offer targeted support for victims and survivors as we prepare for future outbreaks.
Praktik Deskripsi dan Klasifikasi Khazanah Arsip Paku Alam V (1878-1900) di Puro Pakualaman Yogyakarta
Authors: Nurhamidyah Iskarohma Binti , Nabillah Yofa Pradhani , Mulya Lillyana
Published: October 2020

Description and classification are the core of archives managementactivities. These two stages are often studied in order to understandarchives in the context of their creation and to construct ideal retrievaltools. However, the study of arrangement and description of localarchives is still limited. For this reason, this paper intends to explain thearrangement and description of Paku Alam V archives in PuroPakualaman. This research, which using qualitative descriptive methods,analyzed the main data obtained from participatory observation in theprocess of organizing the Paku Alam V archive, especially the classificationand description stages. The conclusion of this study, namely theclassification and description of the Paku Alam V archives, shows that thePakualaman government has two administrative structures which form thebasis of archival grouping. In addition, archival descriptions written inlocal languages indicate that access to this information is still limited tolocal researchers or those who understand Javanese source languages, asa language of the majority of Paku Alam V archives. Moreover, knowledgeof the classification system and description above can be a guide to readthe archives in its history as a formal communication medium for thezelfbestuur government in Yogyakarta.
O reconhecimento pessoal e a psicologia judiciária: falibilidade do testemunho como reforço do etiquetamento e violação ao in dubio pro reo.
Authors: Magalhães Marina Trindade
Published: October 2020

O presente trabalho aborda o tema do reconhecimento pessoal no Brasil e nos Estados Unidos, bem como o estudo da psicologia judiciária relacionada ao testemunho. O objetivo desta pesquisa é entender qual é o tratamento dado à colheita da prova testemunhal no Brasil? E nos Estados Unidos? Quais são os fatores que influenciam na falibilidade do testemunho? Por fim, a inobservância dos requisitos do art. 226 do Código de Processo Penal representa reforço à Teoria do Etiquetamento e à seletividade penal? Como metodologia, adotou-se a revisão bibliográfica sobre o tema e a análise de alguns acórdãos exemplificativos sobre o assunto. O trabalho visou analisar a literatura, nacional e norte-americana, sobre o reconhecimento pessoal; examinar o entendimento jurisprudencial acerca do tema; abordar variados fatores que podem influenciar no ato do reconhecimento pessoal; e, por fim, debater sobre a seletividade penal, que pode ser reforçada pela inobservância dos critérios legais para o reconhecimento pessoal. Conclui-se que, embora careça de atualizações, o art. 226 do Código de Processo Penal deveria ser respeitado em sua integralidade, de modo a evitar a influência de fatores, como as falsas memórias e as problemáticas que envolvem o reconhecimento cross-racial, o weapon effect, dentre outros. Com respeito à legislação, aliado a técnicas adequadas de entrevista testemunhal, evita-se, assim, o fortalecimento da seletividade penal e da Teoria do Etiquetamento.
O acordo de não persecução penal e o paradigma da prevenção no enfrentamento à corrupção e à macrocriminalidade econômica no Brasil: novas alternativas ao modelo punitivista tradicional.
Authors: Mendes Soraia Da Rosa , Souza Augusto César Borges
Published: October 2020

O presente artigo pretende analisar as recentes alterações legislativas ocorridas no Brasil, em especial a instituição de mecanismos consensuais de solução de conflitos e seus impactos no enfrentamento à corrupção e à macrocriminalidade econômica. Utilizando como referencial teórico a ideia de prevenção técnica, propugnada por Winfried Hassemer, pretende-se avaliar se a justiça penal negociada representa efetiva alternativa ao paradigma punitivista dominante, possuindo maior aptidão para reduzir a impunidade e aumentar as cotas de prevenção de ilícitos contra a Administração Pública e a ordem econômica. Inicialmente, serão abordados alguns aspectos criminológicos dos delitos de colarinho branco, bem como fatores de ordem político-criminal que colocam o ideal de prevenção como vetor central do sistema de justiça criminal contemporâneo. Em seguida, será analisada a insuficiência do modelo tradicional de persecução penal brasileiro, fundado na litigiosidade, e sua ineficiência para lidar com os casos relacionados à macrocriminalidade econômica e à corrupção. Finalmente, serão analisados os principais aspectos atrelados à implementação de novos mecanismos consensuais de solução de conflitos na seara penal, os prognósticos de cunho político-criminal no tratamento da corrupção e da criminalidade organizada e seus impactos na atuação do Ministério Público e do Poder Judiciário.
The authority to order search in a comparative perspective: a call for judicial oversight
Authors: Kremens Karolina
Published: October 2020

Buscas e apreensões são exemplos de meios de investigação dirigidos à descoberta de elementos probatórios, e irregularidades na sua condução de modo legal e razoável acarretam impacto direto na admissibilidade da prova durante o juízo. Contudo, buscas e apreensões por sua natureza também refletem significativamente em intervenções nos direitos individuais, caracterizando tais medidas como coercitivas e com risco de violações à privacidade e à dignidade da pessoa. Por esse motivo, a regulamentação das buscas e apreensões deve se dar de um modo compatível com a necessidade de efetividade das investigações e com uma proteção adequada contra atos abusivos. Uma dessas garantias é determinada pela definição da autoridade competente para ordenar as buscas e apreensões e a sua capacidade de proteger os direitos individuais. Enquanto é uma regra em alguns países (ex. EUA e Alemanha) que a expedição de um mandado de busca e apreensão somente pode ser determinada pelo juízo, em outros (ex. Polônia e Itália) isso pode ser feito pelo Ministério Público. Entretanto, como a maioria das buscas e apreensões são realizadas sem mandado, é necessário analisar em paralelo se medidas sem mandado deveriam ser relegadas à discricionariedade da autoridade executante ou se deveriam ser submetidas a rigoroso controle por outra autoridade. Isso será feito com o objetivo de identificar soluções para assegurar a melhor proteção à pessoa contra buscas e apreensões abusivas e desproporcionais.
El naufragio y la aventura intelectual: paralelismos significativos entre El Criticón y Robinson Crusoe
Authors: Encarnación Sandoval Paola
Published: October 2020

Este artículo estudia la relación entre el motivo del naufragio y el proceso de adquisición del lenguaje en El Criticón de Baltasar Gracián y Robinson Crusoe de Daniel Defoe. El argumento es que el naufragio se configura como un núcleo del relato propicio para desarrollar la aventura intelectual. El análisis de paralelismos entre estos textos, centrado en cuatro elementos —la intención edificante de las obras, el tratamiento del naufragio, el encuentro de los personajes y el proceso de adquisición del lenguaje—, revela una concepción peculiar de la aventura, en la cual el componente intelectual es importante para desarrollar el enfrentamiento de los personajes con sus realidades. El objetivo del estudio consiste en plantear una reflexión en torno a las inquietudes de estos autores sobre el conocimiento como parte de la aventura literaria.
Family-Aware Convolutional Neural Network for Image-based Kinship Verification
Authors:   Institut Teknologi Sepuluh Nopember, Rachmadi Reza , Purnama I ,   Institut Teknologi Sepuluh Nopember, Nugroho Supeno ,   Institut Teknologi Sepuluh Nopember, Suprapto Yoyon ,   Institut Teknologi Sepuluh Nopember
Published: October 2020

Faces is a unique region in our body that can be used as a biometric identity. Furthermore, the face between two people that have a kinship relationship may share the same face features which can be used to decide whether two people have a kinship relationship or not. In this paper, we proposed a family-aware convolutional neural network (CNN) for the visual kinship verification problem. Our proposed classifier is constructed by paralleling the state-of-the-art face recognition model and attaching two additional networks, a family-aware network, and a kinship verification network. The family-aware network weights adjusted by learning features specific to the family using deep metric learning loss while the kinship verification network use softmax loss to learn the kinship verification problem. One of the advantages of our proposed classifier is that the output of the classifier is normalized and can be represented as the probability of two images being kin or non-kin. To preserve the face recognition features extraction ability in the state-of-the-art face recognition model, we freeze the weights of the convolutional layers in the classifier for the training process. In the testing process, the family-aware network is detached to construct the final classifier. Experiments on FIW (Families In the Wild) dataset show that our proposed classifier performs better comparing with classifiers that trained without a family-aware network and the ensemble version of the classifier is comparable with several state-of-the-art methods with an average accuracy of 68.84%.
Evaluating the Impact of Malware Analysis Techniques for Securing Web Applications through a Decision-Making Framework under Fuzzy Environment
Authors:   Babasaheb Bhimrao Ambedkar University, Kumar Rajeev , Alenezi Mamdouh ,   Prince Sultan University, Ansari Md ,   Babasaheb Bhimrao Ambedkar University, Gupta Bineet ,   Shri Ramswaroop Memorial University, Agrawal Alka ,   Babasaheb Bhimrao Ambedkar University, Khan Raees ,   Babasaheb Bhimrao Ambedkar University
Published: October 2020

Nowadays, most of the cyber-attacks are initiated by extremely malicious programs known as Malware. Malwares are very vigorous and can penetrate the security of information and communication systems. While there are different techniques available for malware analysis, it becomes challenging to select the most effective approach. In this context, the decision-making process may be an efficient means of empirically assessing the impact of different methods for securing the web applications. In this research study, we have used a methodology that includes the integration of Fuzzy AHP and Fuzzy TOPSIS technique for evaluating the impact of different malware analysis techniques in web application perspective. This study uses different versions of a university’s web application for evaluating the impact of several existing malware analysis techniques. The findings of the study show that the Reverse Engineering approach is the most efficient technique for analyzing complex malware. The outcome of this study would definitely aid the future researchers and developers in selecting the appropriate techniques for scanning the web application code and enhancing the security.
Facial Inpainting Using Generative Adversarial Network with Feature Reconstruction and Landmark Loss to Preserve Spatial Consistency in Unaligned Face Images
Authors:   Institut Teknologi Sepuluh Nopember, Maulana Avin , Fatichah Chastine ,   Institut Teknologi Sepuluh Nopember, Suciati Nanik ,   Institut Teknologi Sepuluh Nopember
Published: October 2020

Facial inpainting is a process to reconstruct some missing or damaged pixels in the facial image. The reconstructed pixels should still be realistic, so the observer could not differentiate between the reconstructed pixels and the original one. However, there are a few problems that may arise when the inpainting algorithm has been done. There was an inconsistency between adjacent pixels when done on an unaligned face image, which caused a failure to reconstruct. We propose an improvement method in facial inpainting using Generative Adversarial Network (GAN) with additional loss using pre-trained network VGG-Net and face landmark. The feature reconstruction loss will help to preserve deep-feature on an image, while the landmark will increase the result’s perceptual quality. The training process has been done using a curriculum learning scenario. Qualitative results show that our inpainting method can reconstruct the missing area on unaligned face images. From the quantitative results, our proposed method achieves the average score of 21.528 and 0.665, while the maximum score of 29.922 and 0.908 on PSNR (Peak Signal to Noise Ratio) and SSIM (Structure Similarity Index Measure) metrics, respectively.
Preprocessed Mask RCNN for Parking Space Detection in Smart Parking Systems
Authors:   Institut Teknologi Sepuluh Nopember, Naufal Ahmad , Fatichah Chastine ,   Institut Teknologi Sepuluh Nopember, Suciati Nanik ,   Institut Teknologi Sepuluh Nopember
Published: October 2020

This research developed a smart parking system through video data analysis using deep learning techniques that automatically determine the availability of vacant parking spaces. This system has two main stages. The first is the stage of marking the parking position on the image of a parking lot captured by the camera. This research proposes a Preprocessed Region-based Convolutional Neural Network (Mask R-CNN) to mark the parking position on the input image of a full parking lot. The preprocess that combining contrast enhancement using the Exposure Fusion framework, aims to overcome the problem of lighting variations in images taken in an open area. In the second stage, each parking position is examined whether the position is vacant or not using mAlexNet. A series of trials on images with varying light conditions indicate that the Preprocessed Mask R-CNN can improve marking the parking positions with an accuracy of Intersection over Union (IoU) reach 85.80%. The result of marking the parking position is then used in the trial of the availability of parking space on video data using mAlexNet, and achieving an accuracy of 73.73%.
A Robust Medical Images Watermarking Using FDCuT-DCT-SVD
Authors:   Telkom University, Novamizanti Ledya , Wahidah Ida ,   Telkom University, Wardana Ni ,   Telkom University
Published: October 2020

One way to prevent image duplication is by applying watermarking techniques. In this work, the watermarking process is applied to medical images using the Fast Discrete Curvelet Transforms (FDCuT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) methods. The medical image of the host is transformed using FDCuT so that three subbands are obtained. High Frequency (HF) subband selected for DCT and SVD applications. Meanwhile, SVD was also applied to the watermark image. The singular value on the host image is exchanged with the singular value on the watermark. Insertion of tears by exchanging singular values does not cause the quality of medical images to decrease significantly. The experimental results prove that the proposed FDCuT-DCT-SVD algorithm produces good imperceptibility. The proposed algorithm is also resistant to various types of attacks, including JPEG compression, noise enhancement attacks, filtering attacks, and other common attacks.
Neural Style Transfer and Geometric Transformations for Data Augmentation on Balinese Carving Recognition using MobileNet
Authors:   Institut Teknologi Sepuluh Nopember, Darma I , Suciati Nanik ,   Institut Teknologi Sepuluh Nopember, Siahaan Daniel ,   Institut Teknologi Sepuluh Nopember
Published: October 2020

The preservation of Balinese carving data is a challenge in recognition of Balinese carving. Balinese carvings are a cultural heritage found in traditional buildings in Bali. The collection of Balinese carving images from public images can be a solution for preserving cultural heritage. However, the lousy quality of taking photographs, e.g., skewed shots, can affect the recognition results. Research on the Balinese carving recognition has existed but only recognizes a predetermined image. We proposed a Neural Style Geometric Transformation (NSGT) as a data augmentation technique for Balinese carvings recognition. NSGT is combining Neural Style Transfers and Geometric Transformations for a small dataset solution. This method provides variations in color, lighting, rotation, rescale, zoom, and the size of the training dataset, to improve recognition performance. We use MobileNet as a feature extractor because it has a small number of parameters, which makes it suitable to be applied on mobile devices. Eight scenarios were tested based on image styles and geometric transformations to get the best results. Based on the results, the proposed method can improve accuracy by up to 16.2%.
Moving Object Segmentation Using Level Set Algorithm with GWO-KFCM Clustering
Authors:   NBKR Institute of Science & Technology, Raghavulu Vivaram , Prasad Ande ,   Vikrama Simhapuri University
Published: October 2020

Video analytics is widely used to automatically analyze the videos to extract the required information and detect the various events object identification and traffic analysis. The segmentation of the image is referring to extracting the required region from an image. The major objective of the segmentation process is to cluster the images without being affected by the noises. The detection of the moving objects is a challenging task in video analysis due to the dynamic background of the video. The major drawback of the existing Kernel Fuzzy C-Means (KFCM) clustering is initialization of random centroids which increases the execution time to identify the segmented portions. In this research, the Grey Wolf Optimization (GWO) algorithm used to initialize the centroids of required clusters in KFCM and Level Set (LS) Algorithm is used to segment the objects in video sequence. The proposed KFCM-GWO-LS is implemented for moving and static object detection in the videos obtained from SBM-RGBD dataset. For object detection, determining central clusters are important which is performed by using KFCM. GWO helps in finding the best centroids clusters by matching with KFCM. The centroids clusters are segmented by using LS algorithm which undergoes over segmentation problem that is overcome by GWO. As all the three techniques are dependent on one another hybrid of all these techniques obtains better results. The proposed KFCM-GWO-LS is evaluated in terms of Recall, Specificity, Precision and F-measure and the experimental results showed that the proposed method improved the system performance from 0.3 % to 14.35 % compared to existing methods Multi-Sensor Scheduler Algorithm and Statistical Inference Theory model.
A Self-adaptive Hybrid Bio-inspired Optimization Algorithm by Using Sigmoidal Function
Authors:   Mahanakorn University of Technology, Boonserm Prasitchai , Sitjongsataporn Suchada ,   Mahanakorn University of Technology
Published: October 2020

The article presents a new hybrid algorithm, which designs based on traditional bio-inspired optimization algorithms. The algorithm leverages the advantage of Particle Swarm Optimization (PSO), Differential Evolution (DE), and Artificial Bee Colony (ABC), replacing other algorithm weaknesses. A new algorithm we proposed is the Fast bio-inspired Optimization Algorithm (FOA). The DE uses multi-parent for trial vector calculation. It increases the diversity of the solution, while the sigmoidal function adds a self-adaptive characteristic to the proposed algorithm. The function replaces a weighting scheme of PSO. In sub-optimal avoidance, the FOA includes a scout bee behavior from ABC. It makes FOA providing the solution faster than traditional versions, while the solution quality is maintained at an acceptable level. According to a new design, an FOA can reduce the algorithm runtime up to 43.57%, 37.14%, 40.78%, and 31.30% compared to PSO, DE, ABC, and DEPSO, respectively. The DEPSO is the hybrid algorithm between DE and PSO. The best solution to FOA is better than the traditional version of the algorithms. The new algorithm design and the optimization speed improvement are the highlight contribution of this article.
CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application
Authors:   Institut Teknologi Sepuluh Nopember, Dikairono Rudy , Setiawardhana Setiawardhana ,   Institut Teknologi Sepuluh Nopember, Purwanto Djoko ,   Institut Teknologi Sepuluh Nopember, Sardjono Tri ,   Institut Teknologi Sepuluh Nopember
Published: October 2020

The Convolutional Neural Network (CNN) is an object classification method that has been widely used in recent research. In this paper, we propose CNN for use in the self-localization of wheeled soccer robots on a soccer field. If the soccer field is divided into equally sized quadrants with imaginary vertical and horizontal lines intersecting in the middle of the field, then the soccer field has an identical shape for each quadrant. Every quadrant is a reflection of the other quadrants. Superficially similar images appearing in different positions may result in positioning mistakes. This paper proposes a solution to this problem by using a visual modelling of the gyrocompass line mark and omni-vision image for the CNN-based self-localization system. A gyrocompass is used to obtain the angle of the robot on the soccer field. A 360° omni-vision camera is used to capture images that cover all parts of the soccer field wherever the robot is located. The angle of the robot is added to the omni-vision image using the visual modelling method. The implementation of self-localization without visual modelling gives accuracy rates of 0.3262, and this result is increased to 0.6827 with the proposed methods. The experiment was carried out in the robotics laboratory of the Institut Teknologi Sepuluh Nopember (ITS) with the ITS Robot with Intelligent System (IRIS) robot.
On the Performance Balancing for Uplink NOMA Systems
Authors:   Cheongju University, Kim Nam-Soo
Published: October 2020

Outage probability and capacity are the representative performance measures for the quality of service (QoS) in mobile cellular systems. Recently, power back-off scheme is proposed in uplink non-orthogonal multiple access (NOMA) systems. The power back-off scheme improves the performance of a near user, however, decreases that of a far user. In comparison, the scheme indicates the error floors with an outage probability of 2.4×〖10〗^(-1) and 9.1×〖10〗^(-2) with power back-off 5 dB and 10 dB, respectively under the specified condition. To address these drawbacks, we propose an equal average signal-to–interference plus noise ratio (SINR) scheme that derives the same average SINR from active users at the base station (BS) in uplink non-orthogonal multiple access (NOMA) systems. Numerical results show that required signal-to-noise ratio (SNR) for the outage probability of 1×〖10〗^(-3) of the near and far users are close enough within 1 dB, which means an outage balance between two users. And it is noticed that the outage probabilities in the proposed scheme decrease as the increase of the received SNR without error floors. Also, different from the power back-off scheme, we noticed that the capacities of the two users in the proposed scheme are coincident and increase with SNR. The outage probabilities and ergodic capacity of the near and far users are derived in closed-form expressions. The analytical results are conformed by Monte Carlo simulation.
Smart Variable Rotor of Vertical Axis Wind Turbine with Faster Cut-in Speed and Wide Range Extract Power Using Artificial Intelligent
Authors:   Institut Teknologi Sepuluh Nopember, Fadil Jazuli , Soedibyo Soedibyo ,   Institut Teknologi Sepuluh Nopember, Ashari Mochamad ,   Institut Teknologi Sepuluh Nopember
Published: October 2020

This paper presents the standalone type vertical axis wind turbine (VAWT) smart rotor using variable diameter rotor (VDR) in order to tap constant power and maintain cut-in wind speed. VDR is a smart variable rotor capable of operating at a low wind speed, in which the width of diameter rotor is adjustable using actuators. The VDR rotor is connected to the permanent magnet synchronous generator (PMSG) and a DC-DC boost converter. The controller of VDR rotors uses fuzzy logic controller (FLC). The FLC variable inputs are wind speed data and power output, broken down into cluster groups to determine the diameter rotor position. The wind speed data as fuzzy input are produced by wind speed estimator using artificial neural network (ANN) to maintain cut-in speed to be faster. The velocity movement of VDR is limited from 75 cm to 150 cm. The VDR extension increases by 25% when the wind speed decreases from 8 to 6 m/s to obtain consistent power at 150 Watt. The experimental of VDR system is tested at low wind speeds ranging from 1 to 8 m/s as a verification of the control system. The result showed that the VDR produced five times increase in efficiency with faster cut-in wind speed at 2.0 m/s.
Multi Deep Learning to Diagnose COVID-19 in Lung X-Ray Images with Majority Vote Technique
Authors:   Universitas Jember, Fibriani Ike , Widjonarko Widjonarko ,   Universitas Jember, Prasetyo Aris ,   Universitas Jember, Raharjo Angga ,   Universitas Jember, Irawan Dasapta ,   Institut Teknologi Bandung
Published: October 2020

The COVID-19 pandemic has become the focus of world problems that need to be resolved. This is because the rate of spread is speedy and able to take down the world's health system. Therefore, many researchers are focusing their research on solving this problem by doing an initial screening on the X-Ray image of the subject's lungs. One of them is by using Deep Learning. Several articles that talk about implemented Deep Learning for classifying X-Ray images have been published. But most of them are comparing different architecture CNN (Convolutional Neural Network). In this study, the authors try to create a multi-classifier Deep Learning system that consists of nine different CNN architectures and combined with three different Majority Vote techniques. The target of this research is to maximize the performance of classification and to minimize errors because the final decision is a compilation of decisions contained in each CNN architecture. Several models of CNN are tested in this study, both the model which used Majority Vote and Conventional CNN. The results show that the proposed model achieves an accuracy value average F1-Score 0.992 and Accuracy 0.993, according to 5 K-Fold test. The best model is CNN, which used Soft Majority Vote.
Actividad física y tiempos de comida en teletrabajadores y funcionarios presenciales de una universidad estatal de Costa Rica
Authors: Arce Espinoza Lourdes , Rojas Sáurez Karla
Published: October 2020

INTRODUCCIÓN: el teletrabajo es una modalidad laboral relativamemente nueva en Costa Rica, su incursión en el campo laboral ha sido paulatino pero constante, ya que posee muchas ventajas para el empleador y el funcionario; sin embargo, algunos estudios han determinado que esta modalidad laboral, también presenta algunas desventajas en el ámbito de salud física. El sedentarismo y el incumplimiento en tiempos de comidas, son dos de las desventajas que señalan algunos autores como las principales variables que propician el desarrollo de obesidad y por ende un mayor riesgo de desarrollo de enfermedades cardiovasculares. OBJETIVO: comprobar las hipótesis alternativas de  que a) los teletrabajadores realizan más actividad física que los trabajadores presenciales b) los teletrabajadores realizan más tiempos de comida con respecto a los trabajadores presenciales. MÉTODOS: se aplicó una encuesta al grupo de teletrabajadores obteniendo una respuesta de 241 participantes, se aplicó la misma encuesta a un grupo control de trabajadores presenciales obteniéndose una respuesta de 223 participantes. RESULTADOS: se rechazaron las hipótesis alternativas, puesto que los trabajadores presenciales son los que realizan más actividad física y cumplimiento de tiempos de comida. CONCLUSIÓN: El teletrabajo sigue presentando mayores ventajas para el empleador y el funcionario, sin embargo, las desventajas asociadas a esta modalidad como el sedentarismo y el incumplimiento en tiempos de comida  podrían aumentar el riesgo de obesidad y enfermedades cardiovasculares.
Breathing into the Ecological Trauma: The Case of Gruinard Island
Authors: Christoph Solstreif-Pirker
Published: October 2020

With the performative investigation of Gruinard Island, Scotland, an exemplary non-site of anthropocenic extinction, this research exposition aims for alternative ways of encountering space in the midst of the present ecological crisis. The research exposition suggests an inclusive way of breathing, thinking, and living that merges “with experience, art, ethics, technology, mysticism, science, etc.” (François Laruelle: Principles of Non-Philosophy).

Facing our own imminent extinction, ecological thought can no longer fall back on ideologies toward-death but has to investigate how the immediacy of our planetary all can be encountered in the fullness of its ambivalence. With this affirmative approach, a feminine, birthing approach unfolds, that makes the otherness of the environment an ally for forging practices of vibrant becoming, political responsibility, and mutual trust.
Walking the Newsroom: Towards a Sensory Experience of Journalism
Authors: Sander Hölsgens  , Saskia de Wildt  , Tamara Witschge
Published: October 2020

We invite you to join us on a walk through the newsroom of a regional newspaper, Dagblad van het Noorden. We trace how the journalists perceive, articulate, engage, embrace, challenge, are receptive to, and give form to the ‘atmospheres’ of their workspace. The concept of atmospheres is central in how we have looked at the newsroom. On this walk, we explore the spatial, socio-cultural, rhythmic, tonal, and somatic characteristics of the recently redesigned newsroom, using video, sound, text, and drawing. Employing artistic methods, we want to let you experience this newsroom together with us – giving you insight into the journalists’ lived experience of their profession as fundamentally interwoven with the idiosyncrasies of their workspace.

Our host on the walk is online news editor Alfred Meester. Alfred walked us, Saskia and Sander, through the newsroom, which we visited as part of the project Exploring Journalism’s Limits (funded by the Dutch Research Council, NWO, project number: 314-99-205). Also joining us on this day is Ricky Booms, a visual artist invited to reflect on the space alongside us. Along the way, we encounter visual editors, interns, freelancers, editorial staff writers, and learn about the kinds of spaces that resonate with them.

The walk takes approximately 45 minutes.
The Debate Over Subsequent Accounting for Goodwill
Authors: Linsmeier Thomas J. , Wheeler Erika
Published: October 2020

SYNOPSIS

This paper evaluates whether alternative methods of subsequent accounting for goodwill result in accounting numbers that are significantly different from previous methods prescribed by standard setters. Financial statement users have identified significant flaws in both the amortization-and-impairment and impairment-only methods of subsequent accounting for goodwill. In this paper, we (1) summarize the current debate over subsequent accounting for goodwill, (2) empirically examine the decline in goodwill value under the amortization-and-impairment and impairment-only regimes, and (3) empirically investigate whether there are alternative methods that result in significantly different rates and patterns of decline in the value of goodwill (and thus have the potential to address the identified weaknesses of each method). We conclude that the proposed alternative methods provide markedly different patterns and periods over which goodwill is written off and, therefore, have the potential to provide a more faithful representation of the economics of goodwill.

Data Availability: Data are available from the public sources cited in the text.
Projeto SOS Planeta
Authors:   Agrupamento de Escolas Afonso III, Portugal, Martins Saraiva Marta
Published: October 2020

O projeto SOS Planeta - desafio: recicla, reduz, reaproveita, foi iniciado há quatro anos, tendo tido como participantes turmas do 1º Ciclo do Ensino Básico (CEB), pertencentes a escolas da região do Algarve, Portugal. Os alunos desenvolveram ações relacionadas com a educação ambiental sustentável e efetuaram partilhas dessas mesmas ações recorrendo às novas tecnologias da informação e comunicação, através da realização de vídeos e da participação em diversos concursos educacionais. Criaram, também, uma letra original para a música: SOS Planeta que interpretam nos seus vídeos e nas suas apresentações de trabalhos.
Escola não é lugar para o medo: o uso de fotografias de smartphones em sala de aula e na internet
Authors:   Universidade Federal do Tocantins, Brasil, d'Abadia de Sousa Fábio , Silva Sousa Carolina ,   Centro de Investigação em Educação e Psicologia, Portugal
Published: October 2020

The Our discussion in this paper is based in some suggestions related to some results of the use of new technologies of information in class-rooms, especially the use of digital photography made by students with smartphones. We start our suggestions based in the hypothesis that an important part of the teaching network, has not realized yet, that it is possible to help children, teenagers and young adults do deal with the new technologies of information, even though some of our students seem, sometimes, more skillful, than us, the teachers, in the use of some of the gadgets that allows to access internet. We defend that the participation of the teachers is something necessary and essential, especially in the orientation of the ethical aspects of the use of photography in the classroom.
Sistem Informasi Akademik Sekolah berbasis Web di SMK Widya Taruna Kabupaten Karanganyar
Authors: Zuliyana Andi , Anggoro Dimas Aryo
Published: October 2020

Perkembangan sistem informasi berbasis web dewasa ini semakin mempermudah dalam proses pengolahan data dan informasi sehingga dapat meningkatkan kinerja di berbagai bidang termasuk dalam bidang pendidikan. Saat ini SMK Widya Taruna belum menerapkan sistem informasi yang dapat memberikan kemudahan dalam mengelola data akademik sekolah. Seluruh proses pengolahan data masih dikerjakan secara manual, yaitu masih menggunakan lembar kertas dan arsip sehingga memerlukan banyak waktu dan menyebabkan data tersebut mungkin saja hilang ataupun rusak. Penelitian ini bertujuan untuk membangun sistem informasi akademik sekolah berbasis web yang dapat melakukan pengolahan data akademik, khususnya pada pembuatan jadwal pelajaran dan penyusunan rapor melalui situs web yang saling terintegrasi sehingga memeberikan kemudahan bagi guru dan wali kelas dalam membuat jadwal dan rapor serta memudahkan siswa dalam melihat jadwal pelajaran dan nilai rapor secara online. Sistem ini dikembangkan dengan metode System Development Life Cycle (SDLC) model waterfall dengan bahasa pemrograman Hypertext Preprocessor (PHP) berbasiskan framework laravel dan basisdata MySQL. Hasil pengujian melalui black box menunjukan bahawa sistem berjalan baik sesuai dengan fungsinya dan pengujian melalui System Usability Scale (SUS) dengan 30 responden menghasilkan skor sebesar 71 dimana dapat ditarik kesimpulan bahwa sistem sudah termasuk kategori baik dan dapat diterima oleh SMK Widya Taruna.
Sistem Informasi Geografis Pemetaan Sekolah Menengah Atas/Sederajat di Kota Surakarta menggunakan Leaflet Javascript Library berbasis Website
Authors: Renaldi Ridwan , Anggoro Dimas Aryo
Published: October 2020

Pendidikan merupakan salah satu sektor terpenting yang perlu dikembangkan untuk meningkatkan kualitas sumber daya manusia. Banyaknya jumlah sekolah di kota Surakarta memberikan banyak alternatif bagi calon peserta didik dan orang tua murid. Akan tetapi sedikitnya informasi mengenai profil sekolah serta lokasi keberadaanya yang tidak banyak orang tau, menjadikan hambatan bagi calon peserta didik dan orang tua dalam menentukan pilihan. Tujuan dari penelitian ini adalah merancang dan membangun Sistem Informasi Geografis berbasis web untuk pesebaran SMA / SMK di kota Surakarta dengan menggunakan Library Leaflet Javascript untuk menampilkan peta pesebaran SMA / SMK. Aplikasi ini memungkinkan untuk mencari lokasi sekolah di sekitar pengguna dan memberikan informasi mengenai profile sekolah. Diharapkan aplikasi ini dapat membantu masyarakat dalam mengakses informasi mengenai lokasi dan profil sekolah di kota Surakarta. Pengembangan sistem menggunakan metode sekuensial linier (Waterfall) dan dibuat dengan  beberapa komponen yaitu HTML, CSS, Javascript, PHP dan mysql. Hasil dari penelitian ini adalah webgis yang dapat menampilkan lokasi pesebaran sekolah dan profil sekolah di kota Surakarta. Berdasarkan hasil pengujian metode Black-box sistem dapat berjalan sesuai dengan fungsionalitasnya. Sedangkan pada pengujian metode SUS diperoleh score SUS yaitu 79,4, menunjukan sistem diterima oleh masyarakat. Kesimpulan yang di dapat pada penelitian ini adalah Library Leaflet mempermudah dalam pembuatan peta, sistem dapat menampilkan lokasi pada peta dan informasi profil sekolah serta sistem dapat berjalan dengan baik.
The Study of Erigeron Canadensis in the treatment of Dengue
Authors:   R.B.T.S. Govt. Homoeo. Medical College & Hospital Muzaffarpur, Bihar, Kumar Ashutosh
Published: October 2020

From centuries there are many species of plants and animals which have been discovered and are used in the treatment of some or the other disease for serving mankind. Similar to this another specie of a plant was also discovered and was used to treat more than one kind of disease named Erigeron Canadensis. It was found that it is affect full in treating a disease named dengue (CDC). It is a plant whose parts grow above the ground usually used for medicines. People use it for many medication purposes but there is no good scientific evidence to support these uses. It was predicted that it may be useful in dengue prevention used in mosquito repellents or for other factors but still there is n’t enough information known about Erigeron canadensis to know how it might work. The use of Erigeron, Canadensis is mostly in urban or local areas worldwide as it is one of the prevention method from dengue.
Slaavilaisen verbiaspektin vastineet suomessa
Authors: Mikhailov Mikhail , Tommola Hannu
Published: October 2020

Arvioitu teos:

Edyta Jurkiewicz-Rohrbacher: Polish verbal aspect and its Finnish statistical correlates in the light of a parallelcorpus. Helsingin yliopisto 2019. 274 s. isbn 978-951-51-5157-5. Saatavilla verkossa osoitteessa http://urn.fi/URN:ISBN:978-951-51-5158-2.