Reviewerly/exHarmony huggingface dataset:
Face Completion Using Generative Adversarial Network with Pretrained Face Landmark Generator

This paper, present a novel database of coloured and grey, plausible face images and proposes an improvement method for facial completion.The database contains 389 images of 79 Arab celebrities with automatically generated landmarks acquired from the web in wild-life which is a set of 68 landmark points was defined to provide information about the human face.Face detection applied using Caffe-Model with open cv to extract faces from images then store it in 256 × 256 pixels images.A good inpainting algorithm should produce a realistic face image.The current image face completion methods, recover the damaged areas of face images with low texture, there are problems such as low accuracy of face image recognition after inpainting.Therefore, this paper proposes an improvement method in facial inpainting using Generative Adversarial Network (GAN) with predicted landmarks to provide the structural information about damaged face to help the inpaintor in generating plausible face image.Finally, evaluation for proposed model done over the available datasets CelebA, CelebA-HQ and our Novel Landmarked Face Database for Arab Celebrities.From the quantitative results, our proposed method achieves the maximum score of 34.97, 0.989 and 1.82 on PSNR (Peak Signal to Noise Ratio), SSIM (Structure Similarity Index Measure) and FID (Fréchet Inception Distance) metrics, respectively.
Boyer Moore string-match framework for a hybrid short message service spam filtering technique

Advances in technology and the proliferation of mobile device have continued to advance the ubiquitous nature of computing alongside their many prowess and improved features it brings as a disruptive technology to aid information sharing amongst many online users. This popularity, usage and adoption ease, mobility, and portability of the mobile smartphone devices have allowed for its acceptability and popularity. Mobile smartphones continue to adopt the use of short messages services accompanied with a scenario for spamming to thrive. Spams are unsolicited message or inappropriate contents. An effective spam filter studies are limited as short-text message service (SMS) are 140bytes, 160-characters, and rippled with abbreviation and slangs that further inhibits the effective training of models. The study proposes a string match algorithm used as deep learning ensemble on a hybrid spam filtering technique to normalize noisy features, expand text and use semantic dictionaries of disambiguation to train underlying learning heuristics and effectively classify SMS into legitimate and spam classes. Study uses a profile hidden Markov network to select and train the network structure and employs the deep neural network as a classifier network structure. Model achieves an accuracy of 97% with an error rate of 1.2%.
Tacit Knowledge in Internet Communication: Interface as a Machine for Tacit Knowledge Production

The article aims to analyze the use of tacit knowledge in Internet communication. The authors note that the use of new information and communication technologies and communication platforms plays a special role in modern Internet communication. To identify the specifics of the formation of Internet communication, the authors consider communication platforms as “hybrids”, which include actions of many actors existing in different registers: actions of programmers and various participants in the communication platform; lines of program code; many servers; communication devices of individual users; wires and electromagnetic waves, making it possible to transmit a signal; content that exists at the platform. On this basis, the authors infer that platform users participating in communication are always limited by the mechanisms of its functioning. The analysis of the functioning of interfaces, carried out on the basis of the concept of instrumentality by M. Heidegger, made it possible to conclude that interfaces act not only as tools. They become structures that allow the formation of the users’ “lifeworld”, as well as their behavioral habits. This conclusion was confirmed by a study of the functioning of such communication platforms as Facebook, VK, Instagram. A case study showed that the most important condition for the successful creation of ICT “hybrids” is the formation of user habits and skills of interaction with the interface. As a result of the analysis, the authors infer that the use of tacit knowledge plays a special role in this process. In the analysis of this knowledge, the authors rely on M. Polanyi’s concept and its development in G. Collins’s works that specify “relative” implicit knowledge, “somatic implicit knowledge” and “collective tacit knowledge”. The authors reveal the specifics of the use of implicit knowledge in Internet communication. As a result, they infer that “relational” implicit knowledge is always present in ICT “hybrids” in an explicit form. At this stage of technological development, “somatic” tacit knowledge is not represented in hybrids at all because a person does not yet have a specific experience of being physically in such digital spaces. However, based on the analysis of communication platforms, it can be concluded that the interface can be represented as a “machine” for the production of “collective tacit knowledge”, which allows transforming the experience of participants in Internet communication. A convenient interface for the production of “collective tacit knowledge” helps users to create new meanings and values, while a poorly perceived interface forces users to look for other ways of Internet communication. Hence, the authors conclude that the interface in communication platforms forms the mechanisms for transforming user experience and enables them to independently establish rules of behavior and problem solving within the framework of Internet communication, that is, to create specific conditions for the production of “collective tacit knowledge”.
Ambient Assisted Living an Overview of Current Applications, End-Users and Acceptance

Worldwide an ageing of the population can be observed.This imposes several problems on the concerning societies.There, a higher share of elderlies needs to be taken care of by a diminishing number in younger population.To still be able to provide sufficient care the use of technology is regarded as a solution.In this paper, we are examining the special field of Ambient Assisted Living (AAL) as an application of technology in health care.It mainly focusses on enabling independent living at home for people in need of care through technology.In detail, we aim at giving an overview of current applications, their end-users and their acceptance of AAL.We find that common applications of AAL are smart homes, sensors and robotics.The common end-user of these applications are elderlies.They have a positive opinion towards AAL in general but criticize their price and want to be included in the development process.
Blockchain-enabled secure communications in smart cities

Blockchain is a relative recent research and technological trend, with applications in diverse domains including those associated with a nation’s critical infrastructure sectors (e.g., chemical, commercial facilities, communications, critical manufacturing, dams, defense industrial base, emergency services, and energy). The interest in blockchain is also partly evidenced by the number of submissions we received in this special issue. Of the 54 submissions received, 18 papers were accepted after several rounds of reviews by subject matter experts (i.e., acceptance rate of ∼33.3%). This special issue presents the research advances and describes existing and emerging challenges outlined in these 18 accepted papers, authored by researchers from institutions in Australia, Canada, China, Denmark, Ireland, New Zealand, Spain, United Kingdom, and United States. These accepted papers also reinforce the importance of collaboration across institutions and countries.
Neural Scoring of Logical Inferences from Data using Feedback

Insights derived from wearable sensors in smartwatches or sleep trackers can help users in approaching their healthy lifestyle goals.These insights should indicate significant inferences from user behaviour and their generation should adapt automatically to the preferences and goals of the user.In this paper, we propose a neural network model that generates personalised lifestyle insights based on a model of their significance, and feedback from the user.Simulated analysis of our model shows its ability to assign high scores to a) insights with statistically significant behaviour patterns and b) topics related to simple or complex user preferences at any given time.We believe that the proposed neural networks model could be adapted for any application that needs user feedback to score logical inferences from data.
Online Learning of Foreign Language Program in the Intensive Learning Class at the IAIN Padangsidimpuan Language Development Center

Pandemic learning forcibly moved from face-to-face learning into online learning. It makes teachers and learning transformation students IAIN Language Development Center Padangsidimpuan get a new challenge, they must master the teaching materials and teaching strategies as well as the need to master Technology internet-based learning. This study was carried on to find out how language learning Arabic online and challenges in learning the language at the Center for Language Development IAIN Padangsidimpuan. The research method applied a qualitative descriptive study. Participants were comprised of 178 students and lecturers at IAIN Padangsidimpuan Language Development Center academic year 2019/2020. Research instruments used were questionnaires, and documents from online learning screenshots. Analysis of this study employed the technique of presenting data in a narrative that resulted in the conclusion of the study. The results of this research indicated that the applications used in language teaching at the Center for Development Language were google classroom and WhatsApp. Barriers to learning are inadequate internet networks in a student residence. The biggest challenge of online learning is the lecturer and the students did not concentrate. Moreover, smartphones used in learning provided adverse effects on eye health and lack of interest in language learning. Lack of evaluation of learning and also lack time management that has been specified. A decrease in student achievement on speaking skill due to no reciprocal process orally in the learning process of faculty to students. There is a need for a new strategy at the Center for Language Development IAIN Padangsidimpuan so that learning objectives can be achieved. Lastly, the most important thing is that the preparation of digital facilities and infrastructure in implementing online language learning.
A Bonferroni Mean Based Fuzzy K Nearest Centroid Neighbor Classifier

K-nearest neighbor (KNN) is an effective nonparametric classifier that determines the neighbors of a point based only on distance proximity. The classification performance of KNN is disadvantaged by the presence of outliers in small sample size datasets and its performance deteriorates on datasets with class imbalance. We propose a local Bonferroni Mean based Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN) classifier that assigns class label of a query sample dependent on the nearest local centroid mean vector to better represent the underlying statistic of the dataset. The proposed classifier is robust towards outliers because the Nearest Centroid Neighborhood (NCN) concept also considers spatial distribution and symmetrical placement of the neighbors. Also, the proposed classifier can overcome class domination of its neighbors in datasets with class imbalance because it averages all the centroid vectors from each class to adequately interpret the distribution of the classes. The BM-FKNCN classifier is tested on datasets from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository and benchmarked with classification results from the KNN, Fuzzy-KNN (FKNN), BM-FKNN and FKNCN classifiers. The experimental results show that the BM-FKNCN achieves the highest overall average classification accuracy of 89.86% compared to the other four classifiers.
A Complexity Survey on Density based Spatial Clustering of Applications of Noise Clustering Algorithms

Data Clustering is an interesting field of unsupervised learning that has been extensively used and discussed over several research papers and scientific studies.It handles several issues related to data analysis by grouping similar entities into the same set.Up to now, many algorithms were developed for clustering using several techniques including centroids, density and dendrograms approaches.We count nowadays more than 100 diverse algorithms and many enhancements for each algorithm.Therefore, data scientists still struggle to find the best clustering method to use among this diversity of techniques.In this paper we present a survey on DBSCAN algorithm and its enhancements with respect to time requirement.A significant comparison of DBSCAN versions is also illustrated in this paper to help data scientist make decisions about the best version of DBSCAN to use.
Enhancing Quality of the Stego Image by Using Histogram Partition and Prediction Error

The widespread use of the internet to communicate with each other can introduces data security issues.Therefore, a reliable technique to secure information is becoming mandatory.Data hiding is a method to secure data by hiding it in cover media.In this research, we present a secure data hiding method by using images as cover.This research proposed the use of prediction error (PE) of images and utilization of the histogram-based method to conceal data.The histogram-based method can conceal data and still preserve the quality of the image.However, its lower hiding space can be a drawback.To overcome this, instead of using a histogram from the image's pixel, we calculate the error value of images and create a histogram from it.After that, we divide the histogram into partitions, whose peak is as the reference point for the embedding process.We utilize more than one error value in each partition to further increase the embedding space.Based on the experimental result, with the same image dataset, the average of PSNR values improved by 17.94 dB for general images and 14.95 dB for medical images compared to previous methods.These increases are statistically significant for the visual image quality, where the final PSNR values are between 61 and 66 dB, and between 62 and 68 dB for general and medical images, respectively.It is higher than the minimum standard of stego image quality, which is 30 dB.
Methodological To Digital Transformation of Educational, Research and Business Activity of A University

In the context of the development of digital economy and the widespread adoption of digital technologies in various fields of life and activities of modern society, the digital transformation of the education system and research activities of the country's population becomes relevant. Universities, as the main links in this system, are entering a new stage, a model of their development in which the digitalization of all its activity fields plays a dominant role. The digital transformation of universities involves a comprehensive transformation of all the basic activities of the university and the infrastructure corresponding to the implementation of these types of activities. Due to the rapid development and widespread use of digital technology, the universities have gained new perspectives and opportunities to improve the quality and convenience of the educational process. Due to the proliferation and widespread use of massive open online courses, the universities have become able to compete globally. The widespread introduction of digital technology in the main areas of university activity is the main condition for accelerating innovative transformations in the preparation of bachelors and masters. Digital transformation is becoming a key mechanism for creating a university’s competitive advantage in the global educational space. The purpose of this work is to investigate methodological approaches to the digital transformation of the main types of university activities: educational, research and entrepreneurial.
3D Human Pose Estimation With Spatial Structure Information

Estimating 3D human poses from 2D poses is a challenging problem due to joints self-occlusion, weak generalization, and inherent ambiguity of recovering depth. Actually, there exists spatial structure dependence on human body key points which can be used to alleviate the problem of joints self-occlusion. Therefore, we represent human pose as a directed graph and propose a network implemented with graph convolution to predict 3D poses from the given 2D poses. In the digraph, we determine the connection weight of each edge according to the error distribution of joints estimation. This makes our model robust to noise. By optimizing coarse 3D estimation and adversarial learning, our algorithm can successfully improve the accuracy of estimation and relieve the ambiguity of mapping. Through testing on Human 3.6M and MPI-INF-3DHP datasets, we achieve excellent quantitative performance. More importantly, our algorithm also has a superior generalization to outdoor dataset MPII by the pre-training process.
PERANCANGAN AUGMENTED REALITY BERBASIS ANDROID SEBAGAI PROMOSI TAMAN SRIWEDARI SURAKARTA

Taman Sriwedari Surakarta is a cultural heritage treasure that needs efforts to preserve and introduce historical buildings and cultural values to the public, especially young people, through Android-based augmented reality technology. The purpose of this design is that the public can enjoy historical videos, exterior buildings, and the revitalization of the 2018 Surakarta Sriwedari Park video based on android. The design of Augmented Reality becomes more attractive, interesting, and educational as well as a means of preserving Surakarta Sriwedari Park as a cultural heritage. This design uses the diffusion analysis of innovation, the Multimedia Development Live Cycle method, layout design, selection of typography and colors that have aesthetic values. The results of this design are in the form of identity creation, a catalog book containing markers and information, and an application called SriwedARi. An advertising promotion strategy in an effort to introduce this product uses above and below the line media as well as through online stores. Keyword: Taman Sriwedari Surakarta, Augmented Reality, diffusion of innovation, marker.
Readiness Level of Parents as Student Guide in Online Learning

During the Covid-19 Pandemic, all school activities are carried out at home, including teaching and learning processes ranging from the student of Elementary, Junior High, and Senior High. Even universities in Jakarta. The teacher's job is to teach at school, now turning to parents to accompany their children to study at home. Parents' responsibilities increase and work at home, coupled with children's assistance with online or online systems. Before the Covid-19 Pandemic, the problem of teaching children was fully handed over to teachers in schools. However, after the Covid-19 Pandemic, responsible children learn to turn to parents at home who are not necessarily ready to play a role as a substitute for teachers. The problem in this study is how the readiness of parents to accompany children in online learning? The theory used is the theory of CMC (Computer-Mediated Communication). This research's informant comprises elementary, junior high, and high school children who accompany children to learn online selected by random sampling. This research method is descriptive quantitative that describes parents' readiness level as a companion of children to study online. The results showed that 96% of parents are very ready to accompany children to learn online, 92% instruct children to learn online according to the schedule of learning at school, 82% of parents do not understand the children's lessons, and 16% of parents become grumpy accompanying learning and 47% become stressed. Dan Chen, L., &amp; Chen, L. (2016). In an e-learning environment, students are responsible for their own studies and actively participate in managing the learning process. It is recommended when the teacher gives the assignment to the child, accompanied by how to do it on a gadget or notebook to follow.
Disruptive Technologies for Labor Market Information System Implementation Enhancement in the UAE: A Conceptual Perspective

In December 2019, the world learned about the first outbreak of the novel coronavirus (COVID-19) that first broke out in Wuhan, China.This limited outbreak in a small province of China has rapidly evolved into a global pandemic that has led to a health and economic crisis.As millions of individuals have lost their lives, others have lost their jobs due to the recession of 2020.While the skills and educational mismatch have been a prevalent problem in the UAE labor market, it is logical to assume that the global pandemic has likely increased this problem's extent.Therefore, there is an urgent need to adopt an agile, innovative solution to address the upcoming challenges in the labor markets due to the lack of skilled resources and the fear of future work amid the COVID-19 pandemic.Since industry and academia have identified skills and educational mismatch as a complex and multivariate problem, the paper builds a conceptual case from a system engineering perspective to solve this problem efficiently.Based on the literature reviewed related to disruptive technologies and labor market management systems, the paper proposes a new implementation approach for an integrated labor market information system enabled by the most widely used disruptive technologies components in the UAE (Machine Learning, AI, Blockchain, Internet of Things, Big Data Analytics, and Cloud Computing).The proposed approach is considered one of the immediate course of actions required to minimize the UAE economy's negative impact due to the presence of the skills and educational mismatch phenomena.
Penerapan Model Pembelajaran Blended Learning dalam Meningkatkan Hasil Belajar Siswa Sekolah Menengah Kejuruan

The purpose of this study, namely to improve student learning outcomes in the subject of ledger class XA Accounting at SMK Negeri 1 Ende through the application of blended learning. This type of research used in this research is classroom action research (CAR). The subjecs of this research were 32 students of class XA SMK Negeri 1 Ende. At the same time, the data collection techniques used in this study were: (1) testing, (2) observation, (3) documentation. The research data obtained were analyzed descriptively using simple descriptive statistics. Based on the findings of researchers that learning Basic Accounting, Ledger teaching materials have been done well. In the cognitive domain, it was 18.64% from cycle I to cycle II. In the affective domain, it was 58.90% from cycle I to cycle II. Meanwhile, the psychomotor domain was 27.72% from cycle I to cycle II.