Reviewerly/exHarmony huggingface dataset:
Attention-Guided Memory Model for Video Object Segmentation

Semi-supervised video object segmentation (S-VOS) is defined as pixel-wise separating the object of interest specified to initial mask during inference period. For small object, the exploitable information contained in single frame is limited, making S-VOS task more challenging. Existing methods cannot reach a balance between accuracy and speed on small object sequences. To resolve the problem, we develop an Attention-Guided Memory model (AGM) for video object segmentation by introducing two novel modules, namely Joint Attention Guider (JAG) and spatial-temporal feature fusion (STFF). For accuracy, JAG employs multi-dimension attention mechanism to generate salient feature map, which highlights the object area through visual guide, spatial guide and channel guide. Further, STFF integrates more complete spatial-temporal information by fusing previous memory feature, current high-level salient feature and low-level features, which provides an effective representation of small object. For speed, the STFF employs several light-weight RNNs whose embedded computation architecture is more efficient than the explicit query approach used in the state-of-the-art models. We conduct extensive experiments on DAVIS and YouTube-VOS datasets. For small object on DAVIS 2017, AGM obtains 63.5 $$\%$$ $$ \mathrm{{\mathcal{J}}} \& \mathrm{{\mathcal{F}}}$$ mean with 28.0 fps for 480p, which achieves similar accuracy with about 5x faster speed compared with the state-of-the-art method.
Signature Verification Using Neural Network

Abstract: In this paper we are about to present a latest improved off-line signature verification system using global and texture features of the signatures. This version is based on the technique that applies pre-processing on the signature to get a binary image and then calculate the global and features points from it and than maintain a updated vector. All calculations are done on the basis of these feature points. The feature vector obtained from the global and texture features is used to compare with the feature vector of incoming testing signature. Based on the values obtained, the network will decide the appropriateness of the signature. The suggested scheme discriminates between original and the forged signatures using artificial neural network (ANN) for training and verification of signatures. The method takes care of simple and the random forgeries and the skilled forgeries are also eliminated in greater extent. The objective of the work is to reduce two vital parameters, False Acceptance Rate (FAR) and the False Rejection Rate (FRR). So the results are expressed in terms of FAR and FRR and subsequently comparative analysis has been made with standard existing techniques. Results obtained by our proposed algorithm are more efficient than most of the existing techniques.
South Africa's Healthcare Systems, Technology and Nursing

Nursing Informatics in Africa does not exist as most readers of this text understand it. This chapter gives an insight into the role of nursing and why there is a paucity of nursing informatics. We also describe the unique landscape of healthcare delivery and technology use in Africa, particularly in South Africa. We specifically look at the private and public hospitals and clinics in South Africa that are very similar to the majority of the previously colonialised countries in Africa. The level of poverty and associated pandemic and endemic diseases highlighted over the last four decades include HIV/AIDS associated with the resurgence of TB and the endemics of cholera, Ebola, malaria and rabies. The lack of universal healthcare across Africa’s and South Africa’s impoverished populations is seen in the scarcity of medical and nursing staff and facilities. We detail the technologies being introduced and show how, even in their simplest form, these systems can enhance and help change the provision of knowledge and support to the caring professions. In particular, South Africa has undergone a massive change in government approach and policies towards universal health.
Correction to: Improvement of similarity–diversity trade-off in recommender systems based on a facility location model

Abstract There is a growing interest in the offering of novel alternative choices to users of recommender systems.
SIMPENDI-PHB: Sistem Informasi Manajemen Pengelolaan Penelitian dan Pengabdian Masyarakat Berbasis Website

Document management for submission of proposals, assessments, and reports on research and service activities at the Research and Community Service Center of the Harapan Bersama Polytechnic is still manually carried out and not computerized. To facilitate the process of implementing, managing research and service management as well as supervising research, service, publication, and evaluation activities, an information system was built using the waterfall method. This system has been created under the name SIMPENDI PHB by involving 9 actors. The result is that the system runs according to the functions of the actors involved. This system was tested with black box and usability. The results of system testing (black box) is that 9 actors involved in this system have been running according to the function of each actor. As for the usability results by involving several actor menus, namely as many as 30 users consisting of 6 managers in related units, 4 users from the assessment team, and the proposer users (lecturers) is a UI/UX score of 86% in the "Very good" category.
Opinions of Students on Adult Education in Croatia

Adult education is an important part of the life of every individual and community because the acquisition of knowledge, skills, and competencies ensures the progress of society. Every state that strives for the development and creation of a knowledge society is aware of the importance of providing educational opportunities for people at all periods of human life. Croatia has long recognized the importance of adult education in the concept of lifelong learning. Following that, qualitative research was conducted to examine the opinions of 154 students of the teacher's module on adult education. Among other things, answers were given to questions related to student notions of adult education, the importance, and functions of adult education, the role of educators in adult education, the position and prospects of adult education in Croatia. Although it was expected that students-future professors will express positive opinions, perceptions, and expectations related to adult education, some of the students' answers are quite indicative and suggest that teacher education programs should certainly include content that deals with adult education to academic citizens became fully aware of its importance and acquired certain competencies for working with adults.
Instructional Design of Korean Grammar Education Centered on FonF and TBLT

This study meta-analyzed previous studies related to FonF (Focus on Form) of Korean grammar education. In addition, it investigated the perceptions and the grammar learning forms of the students. Finally, it presented a model that can be applied to actual classes. Studies related to FonF in the field of Korean education mainly dealt with grammatical markers and endings, focusing on output rather than input, as well as focusing on dictogloss and consciousness-raising tasks. Also, studies related to TBLT (Task-based Language Teaching) focused on functions or forms rather than grammar items. However, it is necessary for us to understand the tasks in FonF and TBLT methods that go by the same name by dividing them into ‘technique’ and ‘activity’. In addition, 91% of learners said that they did not have experience learning grammar with the TTT model, but that it was necessary to learn grammar by doing task activities while interacting with one another. Based on these findings, this study presented a class design that can improve fluency and accuracy through the use of practical tasks, while still focusing on form and meaning, by using SNS (which learners often encounter in real life). Through this class design, learners experience the communication process while self-recognizing and checking ‘-아/어지다’ and ‘-게 되다’, which have the same meaning as ‘change’.
On the families of polynomials forming a part of the Askey–Wilson scheme and their probabilistic applications

We review the properties of six families of orthogonal polynomials that form the main bulk of the collection called the Askey--Wilson scheme of polynomials. We give connection coefficients between them as well as the so-called linearization formulae and other useful important finite and infinite expansions and identities. An important part of the paper is the presentation of probabilistic models where most of these families of polynomials appear. These results were scattered within the literature in recent 15 years. We put them together to enable an overall outlook on these families and understand their crucial r\^{o}le in the attempts to generalize Gaussian distributions and find their bounded support generalizations. The paper is based on 65 items in the predominantly recent literature.
Implementation of a virtual classroom in Moodle for learning mathematics within the framework of relational education

The departmental school Pio XII, located in Colombia, is one of the educational centers that implemented the relational education system. Thanks to this process, shortcomings were evidenced in the personalized attention offered by teachers. In this sense, the present research aims to establish the incidence of implementing a virtual classroom designed in Moodle for the learning of mathematics under the considerations of the relational system with the seventh-grade students of this institution. It was carried out with a quantitative approach, based on a correlational cross-sectional design, where seven previously designed instruments were applied. The information of the three scenarios was collected: before, during, and after the implementation of the virtual classroom, which led to making comparisons of 46 different processes. The results reveal that the virtual classroom makes the analyst's attention time more efficient since it allows the student to develop metacognitive skills that strengthen her autonomy process. In addition, as a consequence, it was shown that the virtual classroom generates a progressive increase in the students' academic results.
The MEKANISASI BAGAN TANCAP MENGGUNAKAN ROLLER PENGANGKAT JARING DI PANGANDARAN, JAWA BARAT

Pangandaran is located in the southern coast of the Java Island. Some of Pangandaran fishers operate lift net (bagan) to catch fish and shrimp. During hauling process, the net is lifted manually using a roller that potentially harm the fishers. The purpose of this study was to design a mechanical roller that applicable for lift net. The analysis of design requirement, such as calculation of torque, horse power, and reduction power with gear mechanism for operating the roller were applied in this research. The results showed that the net lifting operation required 4.8 HP or 3.6 KW of diesel engine to power a hydraulic pump. For the maximum load, when the net is fully loaded with fish, the operation required 67.91 HP or 50.64 KW. It means power from the diesel motor was not enough to support at maximum load. Therefore, a gear mechanism with a ratio of 1:14 was applied. However, this method reduces the angular speed of the gear which leads to reduction on net pulling speed.&#x0D; Keywords: Lift net, hydraulic, horse power, power, and lifnet roller
The impact of transparency stage and overseas refund investment inflows on the human improvement index

This paper examines the impact of the transparency stage and overseas refund investment inflows at the human improvement index. This paper aims to explore the mediating effect of the economic boom at the impact of transparency ranges and foreign direct funding inflows at the human improvement index. This research was carried out in 9 countries in Southeast Asia with a research length from 2012 to 2019. The facts analysis system changed into done the usage of course evaluation. The outcomes indicate that the transparency stage and overseas direct investment have a tremendous influence on economic growth and the human improvement index. Economic growth turned into additionally located to impact the human development index significantly. Therefore, monetary expansion is verified to mediate the transparency stage effect on the human development index. But the economic increase isn't always proven to have a mediating effect on the impact of foreign direct funding at the human improvement index. Therefore, based totally on the outcomes of this examination, the authorities of each use in Southeast Asia need to create a transparent government to increase economic increase and human improvement index. Further, outside investment needs to be recommended to make an excellent monetary boom and human development index.
Blockchain Towards Prioritization-Based Distributed Storage of Big Data for Internet of Vehicles

This paper proposes a prioritization-based distributed storage of big data processing application in Internet of Vehicle (IoV) system. Designing a scalable, high-performance big data distributed storage system for IoV, an advanced data-processing system for car services. The novel contribution focused on developing vehicular multi-channel control protocol that control the prioritization of services, according to bit rate, transmit power, speed, inter-vehicle distance. The proposed scheme can achieve higher performance in IoV storage system. Index Terms- IoV, sensor fusion, distributed storage, prioritization, edge computing, cloud computing.
A novel fog-cloud architecture to process serverless functions with adaptive timeout

This paper presents a novel architecture to handle serverless functions with adaptive timeout, leveraging prediction to foresee how long the incoming request will take to finish based on historical data. This decision-making process aims to ensure that no request will be discarded, while maximizing execution throughput and offloading requests from the fog to the cloud when needed.
KnowMe: A Module to Improve the Efficiency of Resource Allocation in Data Center Networks

In recent years, a new paradigm called network-application integration (NAI) has been introduced which enables applications to express their requirements to the network. By applying NAI in data center networks (DCNs), the DCN controller can better serve applications. We refer to this as data center network-application integration (DC-NAI). However, a critical issue in DC-NAI is how applications use the assigned resources. Selfish applications may under-utilize or over-utilize the assigned resources which can negatively impact the existing applications. To overcome this challenge, in this paper, we propose a DCN controller add-on module called KnowMe that takes the applications&#x2019; behavior into consideration and improves the efficiency of the resource allocation. The KnowMe module is composed of two components: the resource utilization prediction and the resource assignment learning. The first component predicts the future applications&#x2019; resource usage by looking at their previous resource usage behavior. The second component makes decisions about the applications&#x2019; resource requests considering the current network status. Simulation results confirm the efficiency of our proposed module and its ability to mitigate the selfish behavior of applications. The results show that under network resource limitation, the selfish applications in KnowMe-enabled DCNs experience 40&#x0025; higher resource request rejection rate comparing to the well-behaved applications.
Agile Software Development during the COVID-19 Pandemic: A Technology Company Survey

A common baseline is that Agile-based software development is conducted by co-located teams working in well-equipped office workspaces.The COVID-19 pandemic and subsequent lockdowns have cast new light on those assumptions.Suddenly, developers were no longer co-located with their teams and their well-equipped workspaces were vacant.How did the lockdowns, and the speed in which they were implemented, affect developers and development efforts?Did the lockdowns lead to diminished product quality?How was employee productivity impacted?A survey questionnaire was created to answer these questions.
Creating English Environment at Elementary School in Barito Kuala, south Kalimantan

Language is a habit. As a habit, language is used everyday . We can use language as spoken language, written language and even sign language. As one of international language, English has to master well. One of the effort to make it happen is having English environment. This community service wants to describe about English environment in elementary school. This community service was done at elementary in Batola, South Kalimantan. The participants were the teachers from two (2) elementary schools in Batola, South Kalimantan. The result of this community service, the teachers have known how to create English environment at their school and practice it well in their school’s community&#x0D;
Post hoc visual interpretation of convolutional neural network model for earthquake detection using feature maps, optimal solutions, and relevance values&amp;#160;

&amp;lt;p&amp;gt;In recent years, it became clear that the seismological community is adopting deep learning (DL) models for many diverse tasks such as problems of discrimination and classification of seismic events, earthquake detection and phase picking, generalised phase detection, earthquake early warning etc. Many models that have been developed and tested reach quite high accuracy values. However, it has been showed that their performances depend on the DL architecture, on the training hyperparameters and on the datasets that are used for training. To help the community to understand how final results and a model&amp;amp;#8217;s performance depend on each of these different aspects, we propose implementing some techniques that target the black-box nature of DL models. In this study we applied three visualisation technique to a convolutional neural network (CNN) classification model for the earthquake detection. The implemented techniques are: feature map visualisation, backward optimisation and layer-wise relevance propagation methods. These can help us answer questions such as: How is an earthquake represented within a CNN model? What is the optimal earthquake signal according to a CNN? Which parts of the earthquake signal are more relevant for the model to correctly classify an earthquake sample? These findings can help us understand why the model might fail, how to build better model architectures, but also whether there is a physical meaning embedded in a model from training samples. The CNN used in this study had been trained for single-station detection, where an input sample is a 25 seconds long three-component waveform. The model outputs a binary target: an earthquake (positive) and a noise (negative) class. Following our two output classes, our training database contains a balanced number of samples from both classes. The positive samples span a wide range of earthquakes, from local to teleseismic, with a focus on the local and regional ones. Our analysis showed that the CNN model correctly identifies earthquakes within the sample window, while the position of the earthquake in the window is not explicitly given (based on the high relevance values). The model handles well earthquakes of different distance and magnitude values, without having any physical information about them during the training process. Thus, the model constructs highly abstract latent space where different earthquakes can eventually fit (can be shown by visualising feature maps). We also notice that having non-filtered training samples with low signal to noise ratio does not disrupt the model to generate distinct feature maps, which is crucial for the successful earthquake detection process. Finally, interpretation techniques proved to be useful for having an insight of how the CNN model treats input samples, which is beneficial for understanding whether the architecture is well designed for this task.&amp;lt;span&amp;gt;&amp;amp;#160;&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;
Evaluation of an Android-Based Application for Monitoring the Spread of Covid-19 in Real-Time

The increase in the number of patients infected with the Covid-19 virus from day to day shows a fluctuating number. Meanwhile, the spread of the COVID-19 virus is spreading rapidly in various ways and is difficult to predict. Therefore, mapping the spread of the COVID-19 virus is needed. Currently, the existing deployment maps are offline maps that are updated one or more times a day. It does not show real-time deployment and so rapid prevention is not possible. This study aims to develop a mapping system for the spread of the COVID-19 virus in real-time. For this reason, in this study, an Android-based COVID-19 spread monitoring application was developed in real-time using the Prototype Development Model method. The application is developed with an easy user interface to receive it well. The results of the created application show that users who use this application can connect in real-time with the developed map. Furthermore, the location of users who are exposed to or suspected of being exposed to the COVID-19 virus can be displayed in real-time on the developed map. With the map of the COVID-19 spread, it is hoped that early prevention of the spread of the virus and the introduction of patterns of virus spread can be carried out.
When Neural Networks Using Different Sensors Create Similar Features

Multimodal problems are omnipresent in the real world: autonomous driving, robotic grasping, scene understanding, etc... Instead of proposing to improve an existing method or algorithm: we will use existing statistical methods to understand the features in already-existing neural networks. More precisely, we demonstrate that a fusion method relying on Canonical Correlation Analysis on features extracted from Deep Neural Networks using different sensors is equivalent to looking at the output of the networks themselves.
Domain object hierarchies inducing multi-level models

Abstract Conceptual modeling of domain object hierarchies, such as product hierarchies or organization hierarchies, is difficult due to the intricate nature of nonphysical domain objects organized in such hierarchies. Modeling domain object hierarchies as part-whole hierarchies covers their hierarchical structure, yet to capture their meaning, part-whole hierarchies have to be combined with specialization and multi-level instantiation. To this end we introduce the deep domain object (DDO) multi-level modeling pattern and approach. With the DDO approach, subclasses and metaclasses are induced by and integrated with the part-whole hierarchy. The approach is aligned with the multi-level theory (MLT) and formalized by a metamodel and a set of deductive rules implemented in F-Logic. The proof-of-concept prototype is used for automated application of the pattern and for querying induced multi-level models.