Reviewerly/exHarmony huggingface dataset:
Virtual Addictions, Teleworking and Artificial Intelligence in the Pandemic

This article points out that during the pandemic of SARS-CoV-2 virus and quarantine, people who were already addicted to the Internet, video games or TV, this was exacerbated in most cases. Other people have had to adapt to the economic and social life generated by the pandemic, launching various online businesses. Some people, however, have developed at least one virtual addiction that has affected their health, family life, self-image, behavior, will, and psychological immunity. The article mentions the types of virtual addictions, the concept of digital dementia (Khoja, 2021) and the profile of digital addicts. Here are some tips to help you keep your balance when it comes to surfing the web. The 20-item test developed by Dr. Kimberly Young, which identifies the level of internet addiction, is mentioned. The article continues with the benefits and risks of telecommuting. The SWOT Analysis is presented as a tool for assessing strengths, weaknesses, opportunities and threats. Artificial intelligence has allocated a special space where arguments are presented that were the basis for the implementation of robot technology and digitization. Starting with the cartoon film, Wall-e, from the brief presentation of the Robot Sophia, we also argued with the help of the myth Pygmalion and Galateeia, the prudent advantages we must have in our relationship with robots. The study, conducted by Oracle and Future Workplace, highlights employees' perceptions of robots and artificial intelligence.
Virtual Warehouse for Merchandise Inventory, Small And Medium Entrepreneurs (SMEs) in The E-Commerce Sector

The advancement of e-commerce marketplace applications has made small and medium entrepreneurs (SMEs) in the e-commerce sector, grow rapidly in the current Era Industrial Revolution 4.0 Era. With limited capital, SMEs businesses must compete by increasing the inventory of various merchandise and large stocks, thus requiring an increase in warehouse capacity to become larger. There is a phenomenon related to the need for large warehouses and inventory for merchandise products of SMEs in the e-commerce sector, to e-commerce marketplace applications that have a virtual warehouse concept.The purpose of this study was to determine the concept of virtual warehouses, inventory management in virtual warehouses and the advantages of virtual warehouses, for SMEs in the E-commerce sector in the Industrial Revolution Era 4.0. This research method is descriptive qualitative. Informants in this study were determined purposively.The Virtual Warehouse is a virtual storage place to accommodate and store inventory data, it’s a product of a digital application system program that functions to accommodate, storage, and record inventory data stock. In the e-commerce marketplace application, the warehouse facility feature is provided virtually by the application system. It’s implementation greatly reduces costs and is very efficient compared to conventional physical warehouses. With mobile device or computer that can operating online and within internet signal range, operating of virtual warehouse can be carried out anytime and anywhere
Deep-stacked autoencoder for medical image classification

Image classification is the most important process in the computer-aided diagnosis system. Feature learning is the major challenge in the classification task where the representation features are refined from the high-dimensional input data. Autoencoder (AE) is a self-supervised neural network that maps the input data with the target output data through the sequence of encoder and decoder layers. It automatically learns the abstract-level representation features with the bottleneck feature of encoder-decoder based neural networks. The encoder layers are trained on various levels in extracting the essential features to represent the image. At last, all levels of encoders are stacked together to form a stacked network. The decoder part is replaced by the final softmax layer for classification. The complete stacked network is fine-tuned for classification in a supervised fashion. In this chapter, a stacked AE model is developed to classify the medical images, particularly skin cancer images, into benign or malignant. From the experimental results, it is noted that the classification accuracy of a stacked AE with fine-tuning is higher than that of a stacked AE without fine-tuning. The experimental results also confirm that the stacked AE model with fine-tuning provides improved results as compared with other classification methods.
A high-performance authentication-encryption scheme

This paper proposes an encryption scheme that enables verifying the source and integrity of the encrypted message. Additionally, the proposed scheme generates a secret key which is shared between the sender/encryptor and the receiver/decryptor for each encrypted message using public key cryptography.
Reversible hiding method using secrets sharing of DNA-XNOR

In this paper, we propose a reversible hiding solution based on secret sharing method using DNA exclusive-NOR (DNA-XNOR) for color images. The DNA-XNOR secret sharing method is used as a secret key matrix to hide and to extract information. We use the mean square error (MSE) to select the optimal embedding value for each pixel. These values will be used to embed the secret information into the image from which we can obtain the highest PSNR value of the embedded images. In our solution, the secret information will be embedded on all three color channels Red (R), Green (G), and Blue (B) of the original image. The proposed algorithm DNA-XNOR is a new method for sharing confidential information in the field of information hiding. Our experimental results show the efficiency of the algorithm and demonstrate its applicability in practice.
Positive psychological content in entrepreneurship education as a prevention of youth destructive behavior

Socialization today is a multidimensional concept and process, which makes it relevant to research the methodology and practice of teaching and educating young people. The introduction presents a socio-psychological perspective on socialization. The research seeks to provide a positive psychological substantiation of the possibility of managing the process of socialization through teaching entrepreneurship to schoolchildren and university students. The materials and methods used in the research constitute the methodological potential of positive psychology in the aspect of its legitimate use in programs for the prevention of destructive behavior of adolescents and youth through entrepreneurship training. The article analyzes Russian and foreign literature regarding the validity of the positive psychological approach as a methodology for developing entrepreneurship training programs providing a constructive alternative to destructive behavior of young people. The psychological and pedagogical axiom of the unity of education and upbringing is considered both in the interpretation of M. Seligman and his followers, and in the interpretation of Russian researchers. Entrepreneurship training offered as an alternative to destructive behavior has positive psychological content which combines the teaching of both well-being skills and achievement skills. The article goes on to examine a specific educational project. In the discussion part of the article, entrepreneurship training is viewed as a factor of constructive socialization of young people. Unless young people are able to use their “strengths of personality” during the transition from high school to university, their socialization might develop in a destructive fashion. Positive education which is based on the strengths of an individual can significantly enrich both the individual’s development and the society as a whole. The main conclusion of our research is that prevention and overcoming of delinquent and destructive behavior of young people can be carried out through teaching entrepreneurship to schoolchildren and university students from the perspective of the applied use of the positive psychological approach.
Privacy Preserving Technologies in US Education.

In the US education sector, data are captured on learners at all stages of the life course with rich, sensitive information on learner demographics, enrollment, achievement, borrowing, and outcomes. Most data are controlled by institutions, who increasingly want to monitor progress from cradle to career, tracking students from early childhood through primary and secondary schools (K-12), on to degrees granted in postsecondary institutions, including continuing and technical education that grant credentials. Collecting, storing, and using such data raises privacy concerns.&#x0D; This presentation describes privacy preserving technologies (PPTs) that have been discussed, tested, implemented – and abandoned – in the education domain. These include both input and output privacy protection methods ranging from absolute lockdowns of learner data to cryptographically-protected data access and analysis systems. We describe most familiar and tested PPTs including secure hashing, secure multiparty computation, trusted execution environments, and differential privacy, highlighting actual applications in schools, departments of education, and educational technology nonprofits and companies.&#x0D; We summarize the factors that limit PPT use and success, including legal, institutional, technical, and cultural barriers and offer recommendations to overcome these barriers. For example, we find gaps in understanding what PPTs are and what problems they solve; a lack of engagement with data controllers, subjects, and regulators; and a lack of human capital to implement and use PPTs. There are also significant research and development needs to continue making PPTs less costly in financial, compute, and complexity terms. Our recommendations suggest topics for continued research into methods that produce disaggregate statistics to analyze equity while protecting privacy, have clear and fair privacy loss budgets, and produce open-source technology, ideally pairing scientists with education experts.
A Novel BlazeFace Based Pre-processing for MobileFaceNet in Face Verification

Face verification is an important security step on mobile devices and many other systems, thus it has to work with high accuracy. Besides the importance of the accuracy in the face verification model, its weight and computational complexity play crucial roles especially in mobile devices. In this study, we aimed to provide novel contribution as pre-processing step for MobileFaceNet without affecting its accuracy. With this contri-bution, overall pipeline has smaller weight and faster inference time by comparison to the available pre-processing models for MobileFaceNet such as multi task cascaded convolutional neural network (MTCNN) and RetinaFace. The face verification test results show the superiority of our proposed model compared to the state-of-the-art models in terms of weight and speed.
Quantum Kernel Based Data Fusion

For quantum classification methods, the quantum kernel has a role in encoding the data set from its original low-dimensional real space into a high-dimensional quantum state space utilizing quantum circuits. Finding quantum kernels that provide an advantage in real-world data classification is a major challenge, especially when dealing with heterogeneous data or a large data set, requiring more qubits than are available on current devices. Here, we propose two new methods: first, we implement a multiple kernel method for data coming from multiple sources, and second, we propose to combine quantum chips to process larger data sets. The latter can be realized by splitting larger quantum circuits into smaller sub-circuits. We experimentally implement a multiple quantum kernel approach for different data sets on IBM quantum computers and benchmark their results.
Research on multi-level point cloud classification method of underground tunnel

Underground tunnel engineering is complex systematic engineering. Classifying the ground objects inside the tunnel automatically and accurately is crucial in tunnel construction surveys. Aiming at this problem, this paper proposes a multi-layer underground tunnel point cloud classification method, which uses the hierarchical clustering structure to deal with the original tunnel point cloud. It extracts four specific ground objects: tunnel surface, track, platform, and pipeline inside the tunnel step by step. Experiments show that this paper&#x2019;s multi-level tunnel point cloud classification method can accurately extract these four types of ground objects. The average accuracy of the projection plane in each experimental area is not less than 95.00&#x0025;, and the average accuracy of point cloud classification is not less than 92.63&#x0025;, which can better meet the needs of the underground tunnel internal construction survey.
Validation of Extrinsic Parameters of a Structured Light Scanner by a Multilateration with Self-Calibration Technique

Structured lighting systems are increasingly used in industrial applications due to their scanning speed and their ability to provide high density of points. Such technologies are also used for the reconstruction of 3D objects while performing reverseÂ engineering and mechanical investigations. Nevertheless, the accuracy of machine vision systems is the main drawback in comparison to coordinates measuring machines. The required internal and external parameters to achieve 3D scanning represents the core contributors to the accuracy achievement, since they are depending on the calibration process. Therefore, this paper is presenting a novel methodology for the optimisation of the calibration process based on optimal pose selection. The experiments demonstrate that the proposed method gives a better estimation of the extrinsic parameters in comparison to classic ones.
Blockchain Technology for IoT Security of Smart City Applications

Blockchain is a technology that initially served as Bitcoin infrastructure technology architecture, and it is characterized by its solid decentralized model that shares the truth fullness of its core. Over the years, the Bitcoin value has grown from a few dollars to more than ten thousand in a volatile scheme and continued to earn acceptance in various well-known organizations. Hence, the scientific community considered its pillar of success, a.k.a. the blockchain, and utilizes it in different applications. Likewise, Internet of things or IoT in short, referring to the billions of sensors interacting and turning the physical world phenomenon into consumable digital data, continues to grow exponentially and conquering all sectors. However, security concerns have become on the rise; thus, it is extremely important to have control over these machines that starts to discreetly inhabit our environment. Hence, blockchain could possibly be an instrument to protect IoT systems from unwanted threats. This chapter provides an overview of IoT, Smart cities, Complex events, and Blockchain technologies and explains how the latter plays an essential role in securing its applications against malicious users. It also discusses different approaches to integrate Blockchain in IoT applications, and the challenges it poses.
CAP’NN: A Class-aware Framework for Personalized Neural Network Inference

We propose a framework for Class-aware Personalized Neural Network Inference (CAP’NN), which prunes an already-trained neural network model based on the preferences of individual users. Specifically, by adapting to the subset of output classes that each user is expected to encounter, CAP’NN is able to prune not only ineffectual neurons but also miseffectual neurons that confuse classification, without the need to retrain the network. CAP’NN also exploits the similarities among pruning requests from different users to minimize the timing overheads of pruning the network. To achieve this, we propose a clustering algorithm that groups similar classes in the network based on the firing rates of neurons for each class and then implement a lightweight cache architecture to store and reuse information from previously pruned networks. In our experiments with VGG-16, AlexNet, and ResNet-152 networks, CAP’NN achieves, on average, up to 47% model size reduction while actually improving the top-1(5) classification accuracy by up to 3.9%(3.4%) when the user only encounters a subset of the trained classes in these networks.
Negotiating Learning Goals with Your Future Learning-Self

This paper discusses the challenges towards designing an educational avatar which visualizes the future learning-self of a student in order to promote their self-regulated learning skills. More specifically, the avatar follows a negotiation-based interaction with the student during the goal-setting process of self-regulated learning. The goal of the avatar is to help the student get insights of their possible future learning-self based on their daily goals. Our approach utilizes a Recurrent Neural Network as the underlying prediction model for expected learning outcomes and goal feasibility. In this paper, we present our ongoing work and design process towards an explainable and personalized educational avatar, focusing both on the avatar design and the human-algorithm interactions.
Platform Pembelajaran Berbasis Digital

This community service activity aims to improve the ability or skills of teachers in using digital-based learning platforms, both online and offline at SD 011 Beringin Jaya Kec. Sentajo Raya Kab. Kuantan Singingi, Riau Province. This community service activity was carried out on teachers as participants, totaling 16 people. The implementation method has two stages, namely: (1) Preparation Stage. The preparation stage carried out includes: interviews with the school, namely the teacher to determine the problems that occur during the teaching and learning process, Preparation of service materials / materials, which include: online and offline learning platforms that are suitable and easy to apply by elementary school children. (2) Service Implementation Stage. The stages in the implementation of the service are carried out online using Google Meet. Each teacher will get a link to enter or join the training. Meanwhile, to carry out these activities, three training methods were used, namely: (1) Demonstration: this was chosen to provide an explanation of the concept of online and offline learning, starting from the definition, benefits, advantages and disadvantages of online and offline learning. (2) Implementation stage: this method is used to provide knowledge and or skills in using online learning platforms and offline learning platforms using applications that have been adapted to the abilities of elementary school children. (3) Discussion: this is done to provide opportunities for teachers who do not understand the material and or the benefits of the application in the teaching and learning process. The expected outcomes in this community service activity are: (1) Improving the ability and skills of teachers in using learning platforms, (2) Publication in the Community Service Journal. The results of the activities for teachers are very helpful in increasing their knowledge about the use of digital-based learning platforms.
Implementation of the Jaccard Similarity Algorithm on Answer Type Description

This study aims to measure the similarity of the answers to the description by using alternative answers as reference answers provided by the lecturer with a view to overcoming the diversity of student answers. This research focuses more on Indonesian language questions and answers by combining the jaccard similarity algorithm and keyword similarity. The results obtained indicate that by adding alternative reference answers, it can increase the correlation value to 0.78% and reduce MAE to 0.55. Likewise, after combining the jaccard similarity algorithm and keyword similarity, the correlation value increased to 0.78% and MAE decreased to 0.49.
Enacting Hot Potatoes to engage and motivate the undergraduate learners in the Business Correspondence course

Technology has become the answer to students’ interests to be engaged in the learning process. In this case, the use of the Hot Potatoes application was employed to facilitate students’ needs in learning the Business Correspondence course. This study was a content analysis that employed the CALL approach as the assistance of students’ learning process (Egbert &amp; Shahrokni, 2018). This study aimed to investigate the implementation of the Hot Potatoes application to assist students to learn the materials of three topics of Business Correspondence, namely Replying to a Job Applicant, Requesting a Service, and Confirming a Service (Lougheed, 2003). The results showed that the use of Hot Potatoes web-based learning brought beneficial impacts to students’ learning process of Business Correspondence course of the fourth-semester students. The researcher discovered that the implementation of this application engaged students’ attention and motivated them to be involved mindful for the learning activity. Moreover, Hot Potatoes exercises helped the students comprehend the elements of a letter and vocabulary and expressions used based on the context of the letter. The researcher suggests that future researchers can fill in the gap on how the Hot Potatoes application can be applied in various courses in English Language Departments.
The impact of game-based student response system on students’ learning outcomes in narrative writing

This paper is based on research aiming to explore and evaluate the effectiveness of a game-based student response system platform, Kahoot, for teaching narrative writing. By using one group pre-test and post-test design, this quantitative research invited the eleventh-grade students of a senior high school in the academic year of 2021/2022, to actively participate in the project. In this case, the researchers administered a pre-test and post-test for measuring the significant difference in students’ scores, before and after the implementation of Kahoot. Based on the paired sample t-test result, the p-value is 0,000. In addition to that, the t-coefficient is greater than the t-table value, which is 18.436 &gt; 1.690. It means that the difference between students’ skills in writing a narrative text before and after the use of Kahoot, is statistically significant. Specifically, Kahoot could enhance students’ writing in terms of organization, ideas development, grammar, mechanics, and writing style. Since the p-value is less than the significant level or 0,000 &lt; 0,05, and the t-coefficient (18.436) is greater than the t-table (1.690), it can be safely concluded that Kahoot could significantly improve students’ writing skills, specifically in writing a narrative text.
THE IMPACT OF SOCIAL MEDIA ON FUTURE GENERATION PERSPECTIVE: BANGLADESH

This Article has been initiated and conducted to highlight the impact of social media on future generation.The present study investigated the effect of social media on our future generation at the perspective of Bangladesh.The generation of present era can involved themselves on social media to show their daily activities, to earn, to have pleasure, to harass others, to create crime and so on.This study will clarify that, how our generation can be biased to that social media.Finally this paper concludes with suggestions for our parents and responsible guardians to resist them from going a dark world.This papers discussed about the importance of social media on our daily life.The findings of that paper can be suggestive for young generation to use the social media for the betterment of their daily life.Social Media is a powerful tools in the prospect of today's world.It should be used well to impact positively and the youth of us will be more prepared for future challenges and contribute meaningfully to the development of Bangladesh.The Teachers and the Parents have an important role to play in achieving this noble objectives.