Reviewerly/exHarmony huggingface dataset:
Social appreciation of the interaction with an anthropomorphic robot

Abstract Artificial intelligence is one of the main topics characterizing the 21 st century. The quick development of these new intelligent systems brings challenges into the daily life of consumers and companies. Even though artificial intelligence contributes to an increased quality of life, by simplifying the life of its users and the processes of companies, in the same time it can affect the cognitive abilities of humans. By taking over different tasks and having access to information, the AI will influence human decision-making and the human will be somehow dependent on the actions and decisions of the AI. In this paper we focus on the impact of the social appreciation on the consumers’ perception regarding anthropomorphic AI. We have investigated the extent to which anthropomorphic AI devices impact human capabilities and the social appreciation of owning an AI. The results show that AI with human-like physical appearance are perceived with higher anthropomorphic characteristics, but the physical appearance of the AI does not affect the social appreciation and the human capabilities of the owner. The average values of the items, measuring the direct perception show that consumers rather believe that AI does not affect their human abilities and owning a robot is not strongly influenced by the social reference group. This result has to be further investigated in a third person perspective and in a different social context.
Basic Yoga Movement Guide at Dewata Studio Based on Augmented Reality

Yoga is a meditation activity where someone decides whole mind to control five senses and body as a whole. Yoga members at Dewata Studio do yoga that is done with breathing exercise, body exercise, and meditation. All these exercises have benefits increase body fitness and overcoming depression. But many yoga members are having trouble to understand basic yoga movement. There are so many reasons why yoga members having trouble to understand basic movement, on of the reason is because COVID-19 pandemy, they can’t go to the Dewata Studio, so they can’t understand and explore basic yoga movement. As we know, basic yoga movement is the most important basic if we want to learn about yoga. There are 15 basic yoga movements at Dewata Studio that all yoga members must mastered before moving on to the next stage. This research help yoga members to understand and explore basic yoga movement by using an android application based on augmented reality. The application using by each member of Dewata Studio yoga member and give an explanation each movement. Using this application must under supervision of yoga trainers at Dewata Studio. With this application it is hoped that all yoga members at Dewata Studio can understand and explore basic yoga movement.
Research on Design of News Video Retrieval System Based on Semantics

Relying on news shot boundary detection algorithm, key frame extraction method and other technologies, a hierarchical news video semantic index structure is proposed, and a retrieval and analysis system model based on news video semantics is constructed, and news video collection modules are set up. Index module, retrieval module, database module, text information, motion frame information, time information, etc. of various news shots, make information collection, boundary detection, key frame marking and extraction, semantic index, determine the relationship between each frame and dynamic key frame, and then complete the key information recognition and extraction of news video images, voice, and subtitles.
Application Design and Implementation for the Automotive Industry with Virtual Reality

The concept of "Virtual Reality" has entered our lives by adding virtual reality glasses to the computers, smartphones, or tablets that we frequently use with the developing technology day by day. Virtual reality includes the virtual world and three-dimensional virtual objects created in the computer environment. Virtual Reality technology aims to enable people to interact with objects by immersing them in the virtual world through equipment (VR glasses, hand controls, treadmills, etc.). This technology can be used in education, health, shopping, etc. used, in many fields. In this paper, virtual reality technol-ogy was used in an automobile store application. The study aims to enable people to read the necessary information about the selected automobile by choosing a car and color. Users will be able to visit and interact with this auto shop with virtual reality equipment in their environment. The 3D models required for the study were made with the Blender program, the software was made with the Unity3D game engine, and the application was developed. The application was tested with the participation of 26 people. As a result, the feedback received from users has been positive. It is thought that the study will be developed and contribute to the literature in different ways.
Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model

Around the world, the video surveillance system has gained wide acceptance and astonishing growth due to its broad applications. The surveillance system has become a paramount tool and benchmark for analyzing the harmony and safety of society. Anomaly detection and its associated applications play a key role in the integrity of the system. The aim of anomaly detection is to find rare and sparse occurrences of events from videos. Developing an accurate and time-efficient system i s s till remains c hallenging due to t he dynamic nature of a nomalies. The deep learning-based end-to-end system with full use of both spatial and temporal features from the input videos is proposed. The model combines the use of 2DCNN and Stacked LSTM to extract frame-level features through an anisotropic Gunnar Farneback Optical Flow algorithm. The system is evaluated on the benchmarked datasets namely UCSD Ped1 and UCSD Ped2, and it achieves an AUC of 95% and 94% respectively. The experimental results indicate that the proposed method is superior to state-of-the-art algorithms.
A Multinomial Naïve Bayes Classifier for identifying Actors and Use Cases from Software Requirement Specification documents

A software Requirements Specification (SRS) document is an NL (Natural Language) written textual specification that documents the functional and non-functional requirements of the system and various expectations of clients in a software development project. To understand the different requirements of the system, developers make use of this SRS document. In this paper, we apply Naive Bayes classifiers - Multinomial and Gaussian over different SRS documents and classify the software requirement entities (Actors and Use Cases) using Machine Learning based methods. SRS documents of 28 different systems are considered for our purpose and we define labels for the entities Actor and Use Case. Multinomial Naive Bayes is a popular classifier because of its computational efficiency and relatively good predictive performance. Out of the classifiers tried out, the Multinomial Naive Bayes recognizes Actors and Use Cases with an accuracy of 91%. Actors and Use Cases can be extracted with high accuracy from the SRS documents using Multinomial Naive Bayes, which then can be used for plotting the Use Case diagram of the system. Automated UML (Unified Modeling Language) model generation approaches have a very prominent role in an agile development environment where requirements change frequently. In this work, we attempt to automate the Requirement Engineering (RE) phase that can improve and accelerate the entire Software Development Life Cycle (SDLC).
Collaborative Networking in Education: Learning Across International Contexts

There is growing consensus on the need to prepare future generations in environments of collaboration and cooperation. This way of understanding education cross borders in the quest for networking. A wide range of theoretical bases supports networking, which underlines its potential in the educational arena. There are few comparative studies that explore the forms that networking takes in education at the international level. This article discusses a series of examples from various countries in order to add to the current knowledge about networking in education in different parts of the world. The examples selected were chosen after a review of the international specialist literature (prior to Covid-19). This was followed by a screening process of the documentary sources based on temporal and thematic criteria, notably including research published in the past decade with content directly related to collaborative networking in diverse educational contexts. Many initiatives have been carried out in a wide range of school settings over the last few years through a combination of top-down interventions, accountability measures and changes in governance arrangements. This article focuses on proposals from countries whose collaboration networks can boast long experiences, together with a synthesis of the main advances reported in the area.
Bright Sunshine Duration Index-Based Prediction of Solar PV Power Using ANN Approach

The grid integration of solar photovoltaic (PV) systems has recently grabbed considerable research attention. Simultaneously, the grid has been subjected to disturbances due to PV generations' variability, uncertainty, and intermittency; therefore, accurately estimating the weather-dependent PV power is imperative. The daily global solar radiation, temperature, and sunshine duration of a location can reflect its weather condition; hence, they are used to estimate PV power output using artificial neural network (ANN). A sunshine duration index, “k,” has been introduced to classify a location's weather condition. Accordingly, two weather conditions are considered based on “k,” and solar PV power estimation models are developed for both cases (Condition-I: 0<k<0.8 and condition-II: 0.8k1). The performance of the proposed ANN-based models is evaluated using error metrics, namely, mean absolute percentage error (MAPE) and relative root mean square error (RRMSE). The temperature of the considered location has resulted in a minimum error in estimating PV power output. The ANN model for 0.8k1 has resulted in a MAPE of 1.888 % with temperature as input. The ANN model for 0<k<0.8 has resulted in a MAPE of 10.599 % with temperature as input. Excellent performance is noticed with the developed forecasting models in estimating PV power. These models are helpful for feasibility studies of PV power plant installations and economic scheduling.
Efficient Asynchronous Multi-Participant Vertical Federated Learning

Vertical Federated Learning (VFL) is a private-preserving distributed machine learning paradigm that collaboratively trains machine learning models with participants whose local data overlap largely in the sample space, but not so in the feature space. Existing VFL methods are mainly based on synchronous computation and homomorphic encryption (HE). Due to the differences in the communication and computation resources of the participants, straggling participants can cause delays during synchronous VFL model training, resulting in low computational efficiency. In addition, HE incurs high computation and communication costs. Moreover, it is difficult to establish a VFL coordinator (a.k.a. server) that all participants can trust. To address these problems, we propose an efficient Asynchronous Multi-participant Vertical Federated Learning method (AMVFL). AMVFL leverages asynchronous training which reduces waiting time. At the same time, secret sharing is used instead of HE for privacy protection, which further reduces the computational cost. In addition, AMVFL does not require a trusted entity to serve as the VFL coordinator. Experimental results based on real-world and synthetic datasets demonstrate that AMVFL can significantly reduce computational cost and improve the accuracy of the model compared to five state-of-the-art VFL methods.
Phraseological units functions implementation in the discursive space of the Russian-speaking Internet

The purpose of the research is to consider the aspects of some functions of phrasemes in the Russian-language Internet discourse, primarily in connection with their role in forming significant elements of network communication. The relevance of the study is determined by the fact, that the problem of implementation of certain functions of set expressions in Internet discourse has not yet been fully explored and requires close attention to the interdependence of phraseology and other Internet phenomena. The material of the article are contexts with phrasemes, collected by the method of continuous sampling only from the proper network genres: Internet forums, network blogs, Internet comments, social networks, etc. The author analyzes the features of phraseology and its pragmatic potential. The method of continuous sampling, classification-typological and interpretive methods, as well as the method of discourse-contextual analysis of linguistic units were used. The methodological basis was formed by articles on the functional aspects of phraseology and Internet linguistics. The study proved that, on the one hand, the Internet is an actual environment for studying the linguistic units functioning, and phraseology itself in the new speech and discursive conditions remains in demand. It is shown that phraseology and its functions in online communication create proper discursive features of the Internet: expressiveness, colloquialism, neogenity, etc., and also take part in emerging different phenomena of network origin: Internet memes, “demotivators,” threads of Internet comments.
Private Equity and Innovations in Blockchain

Advocates of blockchain claim widespread impact will be forthcoming shortly. However, the path of innovation in blockchain is uncharted. Investors will assess where blockchain innovation will profit first and most. The timing and impact of each blockchain innovation is a key consideration for business managers. Link, Ruhm, and Siegel (2014) suggest private equity investments forecast which innovations are likely to succeed. Previous research on private equity gives insight on why that ownership structure is especially successful. Private equity investments are collected for two periods and categorized according to basic blockchain components, basic cryptocurrency payment components, and decentralized applications, particularly applications in financial services. The results suggest investors have acted prudently, investing strongly in building blocks, payment systems and fundamental applications, showing a primary emphasis on refining blockchain and realizing standard blockchain protocols before investing in highly complex applications. Further, financial services applications dominate, suggesting investors see financial services as far and away the most likely industry for imminent deconstruction.
A novel compound topological invariant for isomorphism detection of planar kinematic chains

Abstract. The isomorphism identification of the kinematic chain (KC) based on graph theory definition has no advantage in efficiency, especially when the number of links in the KCs is large. The topological characteristic constants for isomorphism identification, such as the power value sequence (PVS), least distance matrix sequence (LDMS), and loop number (LN), are proposed. The fourth PVS, the LDMS, and the LN are compared and arranged in descending order, to form a strong complementary chain, which can identify KCs of at least 15 links with 4 degrees of freedom (DOF). The method is applied to the complete atlas of the following: 8-link 1 DOF, 9-link 2 DOF, 10-link 1 DOF, 12-link 1 DOF, 13-link 2 DOF, and 15-link 4 DOF planar single-joint KCs, 6-link 1 DOF and 7-link 1 DOF planetary gear trains, 8-link 1 DOF planar multiple joint KCs, and contracted graphs with up to six independent loops. All results are in agreement with the reported ones in the literature. Thus, the proposed method possesses good versatility and has been verified as being reliable and efficient.
The Role of Artificial Intelligence in the Evolution of Brand Voice in Multimedia

Digital automation and artificial intelligence (AI) have transformed over decades as more organizations communicate with audiences utilizing multimedia platforms globally. With digitalization, brand voice has become necessary in brand communication with users, and conversational AI interprets inputs. The aim is to explore how AI has evolved brand voice in multimedia and its interdependencies. Qualitative research design is applied based on content analysis of various multimedia applications. Initially, the role of AI in the evolution of brand voice, AI in multimedia, and the role of brand voice in multimedia were reviewed, highlighting the research gap. By drawing implications from shared study areas, the interdependence of these three notions was determined. This paper finds that AI plays a crucial role in evolving, developing, predicting, and analyzing brand voice in multimedia, resulting in the current life cycle of the brand voice. The interdependence diagram and brand voice life cycle reveal that AI defines brand voice's effectiveness and helps evolve it by offering suggestions. AI-powered engines are crucial to the success of multimedia platforms, and thus the paper introduces AI-powered two-way integration architecture.
Exploring Face Detection and Recognition in Steganography

Security of the online transmission of personal information is of utmost importance these days. Integrating face detection and recognition with steganography techniques improves the security of the online transmission system. However, the integration of these three techniques creates many challenges for the researchers in the field. Detection and recognition of face in the image/video has challenges such as facial expression, varying poses, scares, angle, aging, illumination, variation in size of the image, similar looks, motion, and many more. Steganography, in combination with face detection and recognition, also has innumerable challenges like high complexity, different file formats, integration cost increases, low visual quality of output file, etc. In this chapter, a comprehensive review of face detection and recognition in steganography has been done. The challenges associated with these techniques are described and then the solutions for them are also suggested.
"This Has Nothing to Do With Clouds": A Decolonial Approach to Data Centers in the Node Pole

The environmental impacts of Big Tech fall disproportionately on economically poor people and countries, meanwhile material wealth concentrates to the few at the top. This essay examines how cloud computing uses the logic of coloniality and extractivism to conclude that âpost-extractive modernityâ is a conceptual fallacy. Using Facebookâs sprawling data center in the âNode Poleâ region of Sweden as an example, weâll find how the data centerâs waste heat effectively serves as a foil to Big Techâs promise of a post-extractive vision. Furthermore, this will challenge the countryâs veneer of colonial neutrality by underscoring how the data centers subsist on the ore fields of Norrbotten, effectively staging their progression from an industry that widely contributed to colonization. In sum, this discussion highlights how cloud computing is a form of dissociation from the materiality of data, that creates distance from the impact of cloud consumption in deliberate, albeit dangerous, ways.
Role of presidential issued identity cards (pids) in alleviating the street vendor’s business challenges in Dar es Salaam City

The focus of the study was to assess the role of Presidential Issued Identity Cards (PIDs) in alleviating the street vendor’s business challenges in Dar es Salaam. Specifically, the study aimed to examine the influence of Presidential Identity Cards in alleviating the street vending business challenges, explore factors inhibiting the utilization of Vendors IDs cards in alleviating the street vendors’ challenges and identify the nature (financial, social relation, special) of problem that was solved by the establishment of PIDs. The study was conducted in Dar es Salaam city, where a sample of 130 respondents was selected. The study collected primary data by using questionnaires. Statistical Package for Social Science (SPSS) was used for descriptive and multiple regression analysis. The study found that Presidential Identity Cards have no significant influence on the reduction of street vendor business challenges. Furthermore, the study found that the establishment of PIDs fails to solve numerous problems facing street vendors, these are; financial, business problems, social problems and lack of permanent areas. The study concludes that despite the provision of Presidential Identity Cards to street vendors, most of the street vendors are lacking business support from the appropriate authorities. The study recommends that the provision of PIDs to street vendors should target to reduce challenges facing street vendors.
Explores the role of fast fashion in sustainability: stylish killer for the environment

The fashion industry has never been bigger. It turns out more wasteful. With increasing incomes, fashion has also come on its own, evolving from being just clothes in large parts of the world to being a part of ones’ identity. Fashion todays are the second largest biggest polluter in the world after the oil and gas industry. Impacts from the fashion industry include over 92 million tonnes of waste produced per year and 1.5 trillion litres of water consumed. On the basis of these environmental impacts, we outline the need for fundamental changes in the fashion business model, including a deceleration of manufacturing and the introduction of sustainable practices throughout the supply chain, as well a shift in consumer behaviour namely, decreasing clothing purchases and increasing garment lifetimes. These changes stress the need for an urgent transition back to ‘slow’ fashion, minimizing and mitigating the detrimental environmental impacts, so as to improve the long-term sustainability of the fashion supply chain. This article will be help to understanding of the factors affecting the Role of Fast Fashion in Sustainability and Stylish Killer for the Environment.
Human Gender and Age Detection Based on Attributes of Face

The main target of the work in this paper is to detect the gender and oldness of a person with an accurate decision and efficient time based on the number of facial outward attributes extracted using Linear-Discriminate Analysis to classify a person within a certain category according to his(her) gender and age. This work was deal with color facial images via the Iterative Dichotomiser3 algorithm as a classifier to detect the oldness of a person after gender detected. This paper used the Face-Gesture-Recognition-Research-Network aging dataset. All facial images in the dataset were categorizing into binary categories using k-means. This is followed by the process of dividing all samples according to age classes that belonging to each specific sex category. Thus, this division process enabled us to reach a quick and accurate decision. The results showed that the accuracy of the proposal was 90.93%, and F-measure was 89.4.