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Chatbots are becoming increasingly popular as virtual assistants, many businesses are launching If-This-Then-That programs to help them get started. Such systems, on the other hand, often generate chatbots that are stagnant and difficult to handle. The market for chatbots is increasing. Computer systems are often required, and online connections are improved by allowing users to express their needs, desires, or questions naturally and clearly by speaking, tapping, and talking. They're easy to use, perfect for people of all ages, and have the most detailed responses to questions. The majority of today's education is distributed through e-learning. A chatbot is one of the most powerful ways for students to read, as it can answer questions at any time without the need for human interaction. This chatbot is highly capable of overcoming student uncertainty without the need for human interaction. The IF THIS Then THAT paradigm is used by the bulk of chatbots. Natural language processing and deep learning technologies were used to build this chatbot. As a consequence, the chatbot will comprehend questions at a higher level.

Internet of Things (IoT) refers to a technology where computing devices are connected and form a network. IoT faces many security and privacy issues due to less computation power, heterogeneity, and limited resources available with its devices. Data is transferred among these devices with little or no human interaction. Data Confidentiality and Integrity are very critical parameters and can be achieved by securely sharing information in IoT scenarios. Managing and maintaining trust in exchanging information over IoT becomes very significant. Recent researches have focused on the applications of Blockchain technology for assuring trust management in IoT networks. Blockchain provides completely distinct and more secure approaches. This survey paper aims to illustrate the significance of integrating Blockchain technology in the IoT environment to ensure trust among IoT devices. Particularly, first we give an overview and security aspects of IoT and Blockchain technologies. Then we trace out some important challenges and issues of trusted IoT with potential solutions by Blockchain. Following this, we highlight some complications in the integration of Blockchain with IoT. Finally, we present a comparative analysis between traditional and Blockchain-based trust management techniques as proof of work to represent the significance of Blockchain in ensuring trust.

Electronic training activities are still carried out conventionally, namely through a face-to-face system. The trainer has to check the trainees' measurement scores manually, this takes a long time. In addition, during the Covid-19 pandemic, human interaction is limited. Therefore it is necessary to make a tool that can help the training process. The proposed IoT-based electronic training tool uses nodeMCU, with a MySQL database system using IoT (Internet of Things) technology. Data communication media uses Wifi (IEEE 802.11n) with nodeMCU 8266 and INA219 sensors. The data is directly displayed through the internet browser media(PHP). From the results of our research, this tool can monitor the implementation of training for a distance of 8 meters and the sensor response time is less than 1 second. The sensor sensitivity level reaches 99.01% with a measurement accuracy of 0.01. With this tool, checking measurement results can be done quickly and can be done from anywhere.

Facial expressions are most important channels for transmitting affective information in human interaction. In the early 1970s, due to the limitation of weak computing power and powerless algorithms, Automatic facial expression recognition (AFER) entered the freezing period for a long time. However, with the geometrical growth of computing power and the rapid improvement of algorithms, the accuracy of AFER have been significantly enhanced. FER has ushered in the flowering and fruitful application in different fields such as medical treatment, transportation and business. This paper introduces and surveys these recent advances. We first provide a detailed review of the related studies in FER, including emotion representation, well-known datasets, and FER's history. Next, we detailed the background and practical significance of FER technologys application in different fields. We finally summarize some of the scientific and engineering challenges to promote better use of FER in real-world applications.

This work presents a bibliographic analysis of the aspects associated with computer security, from the social and academic point of view. The new times demand a more distant communication, lacking physical contact and with a wide incidence of use of computer media, which facilitate online interaction. However, it is important to highlight the criteria related to computer security, which will be the key necessary to maintain good communications that facilitate learning, human interaction, respect for people and the assessment of appropriate content. In this work, the activities most commonly used in current times where the health situation affects various aspects of daily life are taken into account. As main results, it is observed that computer security must meet certain minimum parameters to guarantee effective communication, and in addition to this, ethics and social values stand out. Keywords: Computer security, effective communication, online interaction. References [1]S. Iñiguez, I. Iñiguez, S. Cruz y A. Iñiguez, «Relationship between problematic internet use and sleep quality during the COVID-19 pandemic,» Universidad, Ciencia y Tecnología, vol. 25, nº 109, pp. 116-123, 2021. [2]F. Mora-Vicarioli y C. Hooper-Simpson, «Collaborative Work in Virtual Learning Environments: Some Reflections and Prospects of Students,» Educare electronic journal, vol. 20, nº 2, pp. 1-26, 2016. [3]E. Navarro, «Historia de la comunicación,» 2005. [Online]. Available: http://documenta.ftp.catedu.es/. [Last Access: 2021]. [4]UTELBLOG, «La radio: imporancia y breve historia,» 22 agosto 2012. [Online]. Available: https://www.utel.edu.mx/blog/10-consejos-para/breve-historia-de-la-radio/. [Last Access: 05 junio 2021]. [5]J. Gomera, «Historia de la eduación a distancia,» 2020. [Online]. Available: https://josegomera.com/academico/conoce-la-historia-de-la-educacion-a-distancia/. [Last Access: 06 06 2021]. [6]Historia Biografía, «La historia de la televisión,» 17 03 2017. [Online]. Available: https://historia-biografia.com/historia-de-la-television/. [Last Access: 06 06 2021]. [7]E. Sánchez, «Seguridad informática en entornos virtuales,» Seguridad, nº 20, pp. 4-16, 2014. [8]O. Mas, P. Jurado, C. Ruiz, E. Ferrández, A. Navío, J. Sanahuja y J. Tejada, «LAS COMUNIDADES VIRTUALES DE APRENDIZAJE,» Current Developments in Technology-Assisted Education, pp. 1462-1468, 2006. [9]H. Pagola, A. Dams y J. Caracoche, «Laboratorio virtual para la enseñanza de seguridad informática,» Universidad de Buenos Aires , Buenos Aires, 2019. [10]M. Salinas, «Entornos virtuales de aprendizaje en la escuela: tipos, modelo didáctico y rol del docente,» Pontificia Universidad Católica de Argentina, Buenos Aires, 2019. [11]L. Romero-Moreno, «La seguridad informática en el trabajo con la plataforma moodle,» Rev. de Humanidades, vol. 17, pp. 169-190, 2010. [12]Revista Tips de Educación, «CIFRAS REVELADORAS DE LA EDUCACIÓN VIRTUAL,» 2020. [13]moodle, 2020. [Online]. Available: https://stats.moodle.org/. [Last Access: 2021].

Objectives Autoverification is the process of evaluating and validating laboratory results using predefined computer-based algorithms without human interaction. By using autoverification, all reports are validated according to the standard evaluation criteria with predefined rules, and the number of reports per laboratory specialist is reduced. However, creating and validating these rules are the most demanding steps for setting up an autoverification system. In this study, we aimed to develop a model for helping users establish autoverification rules and evaluate their validity and performance. Design & methods The proposed model was established by analyzing white papers, previous study results, and national/international guidelines. An autoverification software (myODS) was developed to create rules according to the model and to evaluate the rules and autoverification rates. The simulation results that were produced by the software were used to demonstrate that the determined framework works as expected. Both autoverification rates and step-based evaluations were performed using actual patient results. Two algorithms defined according to delta check usage (Algorithm A and B) and three review limits were used for the evaluation. Results Six hundred seventeen rules were created according to the proposed model. 1,976 simulation results were created for validation. Our results showed that manual review limits are the most critical step in determining the autoverification rate, and delta check evaluation is especially important for evaluating inpatients. Algorithm B, which includes consecutive delta check evaluation, had higher AV rates. Conclusions Systemic rule formation is a critical factor for successful AV. Our proposed model can help laboratories establish and evaluate autoverification systems. Rules created according to this model could be used as a starting point for different test groups.

The technology named Internet of Things (IoT) extends the ability for the man kind and computers to control billions of connectivity entities such as actuators, sensors, and other services. Realizing IoT as a system would permit integration of the cyber-world in an uninterrupted aspect to the distributive environment and will centrally make changes and authorize the human interaction to the outside world. This paper surveys on the major issues of concerns in IoT regarding security and privacy facing few enormous challenges. To accommodate the most promising technology, we briefly review the existing techniques analyzing the features of security architecture helpful in controlling the perquisites for it. Conventional measures of security countermeasures cannot be directly implied to the heterogeneous technology of IoT as there are number of standards and stacks for communication presumed.

Storytelling is a fundamental part of human interaction; it is also deeply social and political in nature. In this article, I explore reproductive storytelling as a phenomenon of sociological consequence. I do so in the context of donor conception, which used to be managed through secrecy but where children are now perceived ‘to have the right’ to know about their genetic origins. I draw on original qualitative data with families of donor conceived children, and bringing my data into conversation with social script theory and the concept of relationality, I investigate the disjuncture between the value now placed on openness and storytelling, and the absence of an existing social script by which to do so. I show the nuanced ways in which this absence plays out on relational playing-fields, within multidimensional, intergenerational relationships. I suggest that in order to understand sociologically the significance and process of reproductive storytelling, it is vital to keep both the role of social scripts, and embedded relationality, firmly in view.

Virtual, augmented and mixed reality (VR, AR and MR) infiltrated not only gaming, industry, engineering, live events, entertainment, real estate, retail, military, etc., but as surveys indicate, also healthcare and education. In all these areas there is a lack of software development experts for VR, AR and MR to meet the needs of practice. Therefore, our intention at the Department of Computer Science, Faculty of Natural Sciences, Matej Bel University in Banská Bystrica, Slovakia, is to focus on the education and enlightenment of these areas. The aim of this article is to show the role of interactivity in different VR applications and its impact on users in three different areas: gaming, healthcare and education. In the case of one application of Arachnophobia, we also present the results of the research using a questionnaire.

Firms are deploying chatbots to automate customer service. However, miscommunication is a frequent occurrence in human-chatbot interaction. This study investigates the relationship between miscommunication and adoption for customer service chatbots. Anthropomorphism is tested as an account for the relationship. Two experiments compare the perceived humanness and adoption scores for (a) an error-free chatbot, (b) a chatbot seeking clarification regarding a consumer input and (c) a chatbot which fails to discern context. The results suggest that unresolved errors are sufficient to reduce anthropomorphism and adoption intent. However, there is no perceptual difference between an error-free chatbot and one which seeks clarification. The ability to resolve miscommunication (clarification) appears as effective as avoiding it (error-free). Furthermore, the higher a consumer’s need for human interaction, the stronger the anthropomorphism - adoption relationship. Thus, anthropomorphic chatbots may satisfy the social desires of consumers high in need for human interaction.

Technologies for enhancing well-being, healthcare vigilance and monitoring are on the rise. However, despite patient interest, such technologies suffer from low adoption. One hypothesis for this limited adoption is loss of human interaction that is central to doctor-patient encounters. In this paper we seek to address this limitation via a conversational agent that adopts one aspect of in-person doctor-patient interactions: A human avatar to facilitate medical grounded question answering. This is akin to the in-person scenario where the doctor may point to the human body or the patient may point to their own body to express their conditions. Additionally, our agent has multiple interaction modes, that may give more options for the patient to use the agent, not just for medical question answering, but also to engage in conversations about general topics and current events. Both the avatar, and the multiple interaction modes could help improve adherence. We present a high level overview of the design of our agent, Marie Bot Wellbeing. We also report implementation details of our early prototype , and present preliminary results., 9 pages

The number of AI applications in education is growing every day. One recent AI application in the educational sector is Chatbot technology, which is used to support teaching and administrative tasks. This document presents the design and implementation of a Chatbot called Tashi-Bot that helps applicants and university students to obtain information from an educational institution about certain academic and administrative processes. Among these are processes related to well-being, tuition, costs, admission, and other services. In order to design the Chatbot, an analysis of the state of the art, methodologies, and suitable tools was carried out, and a survey was conducted to discover the needs of users and their preferences in the use of a Chatbot for this specific purpose. Tashi-Bot was implemented on the SnatchBot platform and later deployed on a Telegram channel. In its evaluation, a final survey was carried out to check on the satisfaction of the users. The results suggest that Tashi-Bot could help applicants and university students to find information on academic and administrative processes with great certainty and without the need for human interaction. Tashi-Bot can be found at: https://web.telegram.org/#/im?p=@TashiE_Bot..

As robots and intelligent agents are given more complex cognitive capabilities, it is only appropriate to assume that they will be able to commit acts of deceit much more readily. And yet, not much attention has been given to investigating the effects that robot deception has on human interaction and their trust in the agent once the deception has been recognized. This paper examines how embodiment influences a person's trust of an intelligent agent that exhibits either deceptive or honest behavior that is either helpful or harmful in a financial scenario. Our results suggest that deceptive behavior decreases human trust no matter if the embodiment is physical or virtual and deceptive behavior decreases trust regardless of if the deception benefits the human. Moreover, it was found that trust levels slightly influence a person's punishment or reward strategies in addition to their desire to reuse the intelligent agent in the future. Although exposure to deception causes negative effects, the majority of participants still found deception permissible when it benefited them. Additionally, physically embodied robots were shown to mitigate the negative aftereffects of deception more than those that were virtually embodied. These results suggest that embodiment choice can have meaningful effects on the permissibility of deception conducted by intelligent agents.

The smartphone is an incredible computing platform. Loaded with powerful processing, vast data storage, near-global connectivity, built-in batteries, and a rich array of sensors, these devices reliably service the needs of billions of users every day. However, when tasked to run just a single application continuously without any human interaction, the smartphone platform becomes surprisingly unreliable. Over the course of a four-month deployment of Android-phone-based cellular gateways in Zanzibar, Tanzania, all 16 deployed phones failed despite significant engineering efforts, and six phones became physically damaged. This article examines what went wrong and how mobile computing platforms could adapt to support more traditional embedded computing roles and workloads.

This paper describes the development and implementation of a blockchain to improve security, knowledge and intel ligence during the communication and col laboration processes between agents under restricted Internet Infrastructure domains. It is a work that proposes the application of a blockchain, independent of platform, in a particular model of agents, but that can be used in similar proposals, since the results in the specific model were satisfactory. Additional ly, the model al lows interaction and, also, col laboration between humans and agents.

Trained human pilots or operators stand out through their efficient, robust, and versatile spatial guidance and control skills. The hypothesis is that trained operators learn sensory-motor primitives previously described as interaction patterns, and proposed as units of behavior for organization and planning of behavior. The interaction patterns emerge as a result of repeated interactions with a task environment. This paper extends a previously presented modeling and analysis framework based on interaction patterns to evaluate human learning of unknown environments. The paper presents a hierarchical clustering method to extract interaction patterns from trajectory data over successive runs, and uses these patterns for the analysis of the perceptual and control characteristics while accounting for different skill levels.

In the next wave of insurgence, humans may endeavour self-reflection which can lead to an effortless talk and to find out if an event will fructify. Training the system on how to make accurate prognostication with the help of machine learning and statistical models can lead to an intelligent personalized conversational system. The Chatbot industry is ever-growing and after the COVID-19 pandemic and rigorous lockdowns all around the world, people have realized the importance of human interaction in their lives. We are developing this model to create a more intimate relationship between the system and humans. For this purpose, many open-source platforms are available. Artificial Intelligence Markup Language (AIML) is derived from Extensible Markup Language (XML) which is used to build up a conversational agent artificially. The success of this project will help the model in observing and understanding human emotions which will ultimately help it to form a more personalized relationship to delineate the future course of events.

Over the last few years, together with the advent of deep learning and the accessibility of depth sensors, home care supervision has risen in demand. This is becoming even more evident with the ongoing COVID-19 epidemic, which has put vulnerable people like the elderly and handicapped at even a more significant health risk and created a need for online and distance-based alternatives for human interaction. As such, a lightweight method for real-time Abnormal Human Activity Detection is proposed. It uses a rolling window approach when processing streams of activities and pinpointing exactly when an abnormality occurs. A combination of an One-Dimensional Convolutional neural network (CNN) and a Long Short-Term Memory (LSTM) network is used, both overlapping in their application but having different advantages and disadvantages. The method achieves a precision of 96.6% and an accuracy of 91% on the TST Fall Dataset v2.

The recent improvements of language models have drawn much attention to potential cases of use and abuse of automatically generated text. Great effort is put into the development of methods to detect machine generations among human-written text in order to avoid scenarios in which the large-scale generation of text with minimal cost and effort undermines the trust in human interaction and factual information online. While most of the current approaches rely on the availability of expensive language models, we propose a simple feature-based classifier for the detection problem, using carefully crafted features that attempt to model intrinsic differences between human and machine text. Our research contributes to the field in producing a detection method that achieves performance competitive with far more expensive methods, offering an accessible “first line-of-defense” against the abuse of language models. Furthermore, our experiments show that different sampling methods lead to different types of flaws in generated text.

COVID-19 pandemic has affected the whole world not only physically but also financially. Thousands of people have died due to this COVID-19 (Coronavirus) epidemic, and still the whole world is struggling to prevent this epidemic. Initially, the government had only an option to stop this epidemic and that was to declare complete lockdown. But during this time phase of lockdown, many people became unemployed. To stop this scenario and for the sake of employment and other financial economy, our government has decided to unlock various sectors. This unlock phase has increased the risk of outbreak of COVID-19 so to avoid risk and to stop spreading of this pandemic disease, the government has issued some important guidelines to be followed mandatorily by each individual during this unlock process. The face mask is one of the compulsory guidelines issued by the government. Wearing a face mask may reduce the chances of spreading corona virus. Many people roaming in public places without wearing a face mask. To monitor such kind of people at a time is not easy manually. So in this paper, we are proposing deep learning-based model that can automatically detect person wearing a mask or not. In this method, a model convolutional neural network (CNN) was trained over thousands of images to detect whether a person wearing a mask or not. Proposed model achieved 93.36 validation accuracy and 98.71 training accuracy. This model can be helpful to stop spreading of coronavirus in such organizations where human interaction is necessary for smooth functioning like hospitals, colleges, gyms, supermarkets.

In order to solve the phenomenon that the malicious information retrieval in the traditional method is not comprehensive and the precision is not high, an efficient retrieval method of malicious information in multimedia big data network based on human-computer interaction is proposed and designed. On the basis of analyzing the principle of information retrieval, the human-computer interaction form is used to divide the metrics of malicious information. On this basis, the human-computer interaction retrieval logic is established, and the malicious information clustering method is used to realize the multimedia big data network. Efficient retrieval of malicious information. Through the method of experimental argumentation analysis, the validity of the human-computer interaction retrieval method is determined. The results show that the method has a high recall rate of 28.31% compared with the traditional method, and the retrieval accuracy is extremely high. In this paper, the method of network malicious information retrieval is effective and suitable for popularization.

The Internet of Things (IoT) can be said as an interconnection of devices that are used in computing, mechanically, digitally, or are objects, people and animals that can be uniquely identified and persist the potential to share data or information over a network without the involvement of human interaction. As it is considered as the most buzzing topic to be discussed and researched upon, it comes under the danger zone for vulnerabilities that we observe in its daily use and routine. This study summarizes whole technology with the countermeasures corresponding to its vulnerabilities which are expressed with their degree of damage.

Human interaction is a natural process in business management. In various indigenous cultures, the natives still use a barter system to reach consensus or balances that determine the essence of their economies. The present investigation consists of the presentation of an unsupervised model based on pure barter. The main contribution sought is to visualize the balance that is achieved in an unsupervised environment of two entities that are close to reaching an agreement. Both Game Theory and Walrasian Theory deal with the problem of exchange of goods. However, the current objective is to show the barter model from its simplest bases for the construction of an unsupervised automatic learning scheme where a system of pairs of agents represent a basic model for decision making when guaranteeing an agreement.

Cloud computing is a paradigm that provides multiple services over the internet with high flexibility in a cost-effective way. However, the growth of cloud-based services comes with major security issues. Recently, machine learning techniques are gaining much interest in security applications as they exhibit fast processing capabilities with real-time predictions. One major challenge in the implementation of these techniques is the available training data for each new potential attack category. In this paper, we propose a new model for secure network based on machine learning algorithms. The proposed model ensures better learning of minority classes using Generative Adversarial Network (GAN) architecture. In particular, the new model optimizes the GAN parameter including the number of inner learning steps for the discriminator to balance the training datasets. Then, the optimized GAN generates highly informative"like real" instances to be appended to the original data which improve the detection of the classes with relatively small training data. Our experimental results show that the proposed approach enhances the overall classification performance and detection accuracy even for the rarely detectable classes for both UNSW and NSL-KDD datasets. The simulation results show also that the proposed model could detect better the network attacks compared to the state-of-art techniques. 2021 IEEE ACKNOWLEDGMENT This work was supported by Qatar University Internal Grant IRCC-2020-001. The statements made herein are solely the responsibility of the author[s]. Scopus

Automatic door control technology for home appliances is becoming more common, however, there are few studies on human interaction in the automatic door of a refrigerator. This paper explores the role of the artifact by examining how automatic-door movements affect people’s perception and emotional satisfaction. In this study, participants who purchased a refrigerator viewed five video clips depicting a refrigerator with various door speeds and movement trajectories. At the same time, participants were asked to fill out a survey and open-ended response. Results show that there was a high emotional satisfaction associated with the door with maximum degrees of opening and closing. We also found that the emotional satisfaction strongly related to automatic door recognition. This suggests that automatically controlled doors can have a positive effect on emotional satisfaction and the specific situation in which smart artifacts interact with user.

In the human interaction with CAs, research has shown that elements of persuasive system design, such as praise, are perceived differently when compared to traditional graphical interfaces.

Machine Learning supports numerous fields for better service on its outcomes. Internet of Things plays vital role in the digital era to collect and transfer data over the network without any human interaction to connect the world in a single system. In the aspect of security and privacy patterns, machine learning has the capability to make optimistic interference on mischievous network connection of IoT (Internet of Things) system. In this paper to focus on security challenges for the intrusion detection in the IoT system. To analysis various attacks for intrusion detection system on types, effects and surface attack over IoT network. And main focus on machine learning classification algorithm implemented on network of IoT system to improve the efficiency on identifying the attacks.

With the advancement of technology and the internet, almost every device is connected to internet. Internet of Things (IoT) can be explained as an extension of the internet in which smart devices are interconnected with each other. While each smart device has unique identifiers, which makes every device unique. Now, IoT has helped in developing smart architectures whether its home, healthcare, financial institutions or industries. There are smart devices everywhere. These smart devices can also communicate to each other in a network and they work together thus eliminating the need for human interaction. IoT is relatively new and is still developing rapidly. So, there are numerous privacy concerns in IoT. IoT can be divided into different architectural layers depending where the IoT is being used. The IoT model discussed in this research report is Service Oriented Architecture (SOA) which is divided into three layers, application, Network and perception layer. In 2020, during the COVID pandemic the reliability on IoT has increased as people are working from home and many of the tasks has been automated using IoT. The number of security attacks on IoT has also been increased in 2020 alone, which has affected many IoT devices. The objective of this research report is to discuss a number of security and privacy challenges in IoT based on the three SOA layers, the objective of this report also covers discussion on the above mentioned three layers, different technologies used in each layer for communication and different attacks and methods which target each specific layer also discussing different security attacks on IoT which occur ed in 2020 during the COVID pandemic phase. This topic is chosen because Internet of Things is becoming important and is impacting everything around us. It is expected that the total number of IoT devices will cross 20 Billion in 2020 and will have an impact of more than $11 Trillion by 2025. Thus, the security of IoT is to be discussed.

Recent years have witnessed the rapid progress of perception algorithms on top of LiDAR, a widely adopted sensor for autonomous driving systems. These LiDAR-based solutions are typically data hungry, requiring a large amount of data to be labeled for training and evaluation. However, annotating this kind of data is very challenging due to the sparsity and irregularity of point clouds and more complex interaction involved in this procedure. To tackle this problem, we propose FLAVA, a systematic approach to minimizing human interaction in the annotation process. Specifically, we divide the annotation pipeline into four parts: find, localize, adjust and verify. In addition, we carefully design the UI for different stages of the annotation procedure, thus keeping the annotators to focus on the aspects that are most important to each stage. Furthermore, our system also greatly reduces the amount of interaction by introducing a light-weight yet effective mechanism to propagate the annotation results. Experimental results show that our method can remarkably accelerate the procedure and improve the annotation quality., Full technical report for the UIST 2020 Poster version

Inventions are made to reduce human efforts and also to ensure security. Robotic inventions decrease the manual effort and create machine human interaction. This paper discusses one such area where inventions and enablements lead to reducing efforts and increase in security. A baggage that follows the human is designed using one of the magnificent technologies in electronics. It aids auto trailing, lost baggage recovery through GPS and GSM, void battery devices reviving, obstacle detection, alerting and smart unlocking.

The Co-19 pandemic affects worldwide classical education. It has led to the optimization of E-Learning. Learning Management Systems integrate the learning materials and human resources systems, but cannot provide human interaction in real-time. Teleconferencing platforms allowed communication in real-time, but cannot integrate the learning materials and human resources systems. In order to optimize real-time interaction in the learning management system, a teleconference-oriented learning management system is proposed. The configuration of database and user interface are required to combine the two systems. The Teleconference Platform was also hired as LMS course. In order to determine the experience of the teleconference-oriented LMS, the User Experience Questionnaire (UEQ) of the proposed LMS and the original LMS is tested using T-Test. UEQ is filled out by 85 students. The result show that attractiveness, perspicacity, performance, dependability, and stimulation reached respectively 0,0002; 0,0030; 0,0000; 0,0005; 0,0084; and 0,0297. It can be concluded that there is significant different experience between teleconference in LMS and original LMS.

The article considers the scheme of languages functioning in speech and communicative activity of an individual. The key idea is that a person who speaks different languages uses them to a certain necessary minimum. A systematic analysis of the content of each language in the activity of an individual will allow you to determine the boundaries and scope, taking into account the need for communication. Such modeling of a person's needs in languages will ensure the effectiveness of the development of their multilingualism. The content of each model is implemented by applying the achievements of modern linguistics.

In this work we address the problem of approaching a group of persons based on a data-driven model, which is gathered in real-world human interaction. Our model estimates an adequate pose for approaching groups of different sizes, providing a mobile robot with the ability to decide how to approach a group.The model is capable of automatically detecting and distinguishing individuals from groups of humans, although only approaches to groups will be focused. From group detection, it computes the 2D poses of the group’s elements and, based on the poses, estimates an approaching pose to join it. The model uses an F-Formation oriented representation of the world that allows estimating an approaching pose to groups regardless of the environment and the arrangement of the elements of the group in space. The model was tested in simulations and real-world experiments. Results show that the model was fully capable of estimating, in both tested scenarios, human-like and socially acceptable approaches to groups.

Instilling trust in high-stakes applications of machine learning is becoming essential. Trust may be decomposed into four dimensions: basic accuracy, reliability, human interaction, and aligned purpose. The first two of these also constitute the properties of safe machine learning systems. The second dimension, reliability, is mainly concerned with being robust to epistemic uncertainty and model mismatch. It arises in the machine learning paradigms of distribution shift, data poisoning attacks, and algorithmic fairness. All of these problems can be abstractly modeled using the theory of mismatched hypothesis testing from statistical signal processing. By doing so, we can take advantage of performance characterizations in that literature to better understand the various machine learning issues.

We introduce a new type of 3D text-based CAPTCHA, called DotCHA, which relies on human interaction and overcomes the limitations of existing 2D and 3D CAPTCHAs. DotCHA asks users to rotate a 3D text model to identify the correct letters. The 3D text model is a twisted form of sequential 3D letters around a center pivot axis, and it shows different letters depending on the rotation angle. Because each model consists of many small spheres instead of a solid letter model, DotCHA is classified as a scatter-type CAPTCHA and resists character segmentation attacks. Moreover, DotCHA is resistant to machine learning attacks because each letter is only identified in a particular direction. We demonstrate that DotCHA is resistant to existing types of attacks while maintaining usability.

This chapter explains, within an interpretive framework derived from affect theory, that Beckett’s paradox-based discourse aptly mobilizes action and solidarity-oriented affects in interpreters through forms of linguistic excess leading to a positive mobilization of anger previously unacknowledged in criticism. This is achieved through corrosive forms of repetition with a difference that meticulously decompose the advertised benefits of any opposition and disjunction-based mode of human interaction, from definitions of “love” within patriarchal frameworks to definitions of equitable exchanges within late capitalist socio-economic structures.

Conversational agents open the world to new opportunities for human interaction and ubiquitous engagement. As their conversational abilities and knowledge has improved, these agents have begun to have access to an increasing variety of personally identifiable information and intimate details on their user base. This access raises crucial questions in light of regulations as robust as the General Data Protection Regulation (GDPR). This paper explores some of these questions, with the aim of defining relevant open issues in conversational agent design. We hope that this work can provoke further research into building agents that are effective at user interaction, but also respectful of regulations and user privacy.

Background: Since 2004, zebrafish have become the state-of-the-art, in vivo model for biomedical research due to their genetic and physiological homology with humans, inexpensive high-quantity breeding, and quick development in a highly-controlled environment suitable for longitudinal studies. New Method: To fully utilize the zebrafish model, a novel, automated, high-throughput system was designed. Shoals of five zebrafish were placed in 16 tanks and automatically fed over two days for a total of 16 training sessions. Color LED lights were used as the stimulus for each shoal coinciding with the release of food for a duration of 20 seconds. This system was tested on two age groups: 6- and 11-month-old. Results: After three training sessions, the median height of the school in the tank during stimulus was significantly higher than that of the naive fish during the first training session. All subsequent training sessions demonstrated similar behaviour. A decline in memory retention, as defined by a reduction in the median height during light stimulus (i.e. no simultaneous food delivery), was observed 8 days post training. Comparison with existing methods: The high-throughput nature of this system allows for simultaneous training of 16 tanks of fish under identical conditions without human interaction and provides a means to rapidly assess their learning and memory behaviours. Conclusion: Results provide a baseline for understanding the normal cognitive processes of learning and memory retention in zebrafish. This work paves the way for future studies on the impacts of therapeutic agents on these cognitive processes.

The paper aims to provide a critical in-depth analysis of Mobile Phone Application's role in enhancing human interaction with E-Retailers, and more specifically, in the Middle East. First, the paper provides an outlook description of the definition and history of Mobile Phone Applications and human interaction and how the two complement each other. Next, the study outlines the various instances that human interaction with Mobile Phone Applications has led to E-retailer success and how it has come about. Then, the discourse provides an analytical look at the E-retailing Business in the Middle East while focusing on the history of E-retailing, business startups in the Middle East, and the revolution of E-retailing in the Middle East. Additionally, the research emphasizes the merits and demerits of using Mobile Phone Applications to promote and enhance E-commerce business in Middle East countries. Using relevant sources and credible references, the analysis will highlight the numerous instances Mobile Phone applications have been used by clients to connect with businesses that maintain an online presence. Using anthological, historical, and descriptive research methods, the arguments portrayed in this paper will heavily reflect on various case studies provided in the article. The case studies feature data representation from research conducted in the Middle East concerning Mobile Phone applications and E-commerce. Through focused approaches, this paper discerns, comprehend, and establishes the framework of the Middle East E-Commerce scene over the years, and outlines how far the industry has made progress through online platforms. Furthermore, emphasis will be laid on the market gaps left to fill in the E-commerce sector in the Middle East and the several approaches to better the industry. Lastly, this paper will conclude by providing my recommendations on the betterment of Mobile Phone Applications and how E-retailing can improve in the Middle East.

In this paper, we address the problem of human interaction recognition. We propose a novel compositional interaction descriptor to represent complex human interactions containing high intra and inter-class variations. The compositional interaction descriptor represents motion relationships on individual, local, and global levels to build a highly discriminative description. We evaluate the proposed method using UT-Interaction and BIT-Interaction public benchmark datasets. Experimental results demonstrate that the performance of the proposed approach is on a par with previous methods.

This article examines the development of Hammarby Lake City in southern Stockholm on a former industrial, waterfront site during the 1990s. The setting may resemble recent global redevelopments of urban waterfronts and docks; however, in it Stockholm needs to be viewed against longer cultural, aesthetic and historical influences. This includes early twentieth-century precedents rooted in civic and residential engagement with the modern and industrial shoreline. In addition, an informal human interaction with the abandoned southern Hammarby harbour evolved during the 1950s through reoccupation by an itinerant community of workers. Such forerunners have often been overlooked in accounts of a late twentieth-century dramatic transformation of industrial waterfronts. The article concludes that there is scope to align the theme of waterfront development more closely to the longer history of the twentieth-century city. This perspective provides a useful counterpoint to the leading view of such spaces as an expression of late capitalism.

The growth of high-quality computing needs triggers the development of infrastructure that requires huge costs and resources. This research applied OpenStack in Metal as a Service (MaaS) environment to provide multitenant infrastructure services in the form of virtual machines (IaaS). The capacity of this IaaS system can be configured based on the needs of the computing resources (CPUs, memory, storage, and network interfaces). Users within the tenant can request infrastructure services as their needs without requiring human interaction with each service provider.Meningkatnya kebutuhan komputasi berkualitas tinggi mendorong pengembangan infrastruktur server yang membutuhkan biaya dan sumber daya besar. Penelitian ini mengaplikasikan OpenStack dalam lingkungan Metal as a Service (MaaS) untuk menyediakan layanan infrastruktur multitenant berupa mesin-mesin virtual sebagai Infrastructure as a Service (IaaS). Kapasitas sistem layanan IaaS ini dapat dikonfigurasi berdasarkan kebutuhan sumber daya komputasi (CPU, memori, ruang penyimpan, dan antarmuka jaringan). Pengguna dalam tenant dapat meminta layanan infrastruktur secara mandiri dengan kapasitas komputasi sesuai yang dibutuhkan.

Purpose This paper aims to explore integrating chatbot applications into libraries to improve reference services. Design/methodology/approach This paper explores the benefits of using chatbots as virtual reference librarians. Emma the Mentor Public Library’s Catbot is used as a case study. Findings Chatbots cannot replicate the complexity of human interaction (both knowledge and emotional), but these can provide a cost-effective way to answer the majority of routine reference questions and direct users to the appropriate service. Originality/value Readers will increase their awareness of how chatbots can streamline the work of the reference department by answering the majority of routine reference questions and freeing library staff to focus on more demanding research and tasks uniquely suited to humans.

We describe a musical cyberworld as a virtual space for curating ethnomusicology, as well as for conducting research: the ethnomusicology of controlled musical cyberspaces. Our cyberworld differs from most online music curation in enabling immersive, social experience. Considering such cyber-exhibition of ethnomusicological research as itself a form of social and musical practice also calls for an ethnomusicology of such exhibits. Research in ethnomusicology has typically been conducted through qualitative fieldwork in uncontrolled settings. By contrast, we design a custom musical cyberworld as a virtual ethnomusicological laboratory, a platform for research geared towards better ways of designing online musical exhibitions for discovery, learning, and aesthetic contemplation, as well as contributing towards our general understanding of the role of music in human interaction and community formation., Nous décrivons ici un cybermonde musical qui est à la fois un espace virtuel de conservation ethnomusicologique et un espace de recherche : l’ethnomusicologie des cyberespaces musicaux contrôlés. Notre cybermonde diffère de la plupart des collections musicales en ligne en ce qu’il permet une expérience sociale d’immersion. Si l’on considère qu’une telle exposition numérique de recherche ethnomusicologique est en elle-même une forme de pratique sociale et musicale, cela exige également de nous que nous fassions l’ethnomusicologie de telles expositions. En général, la recherche en ethnomusicologie s’est effectuée sous la forme de travaux qualitatifs en situations non contrôlées. En contraste, nous concevons ce cybermonde musical d’usage comme un laboratoire ethnomusicologique virtuel, une plateforme de recherche ayant pour finalité de meilleures façons de concevoir les expositions de musique en ligne pour la découverte, l’apprentissage et la contemplation esthétique, ainsi que pour la contribution à notre compréhension d’ensemble du rôle de la musique dans l’interaction humaine et la formation des communautés.

Information spreading analysis is an important problem in the scientific community and is widely studied today using different techniques, from the data analysis to the agent-based modelling. For some extreme situations, like fire or flood, there is little or no reliable information about users’ activity available. That is why an efficient simulation of the urgent scenarios is very important, because analysis of the simulated data can help to provide fast and accurate reaction and save human lives. In this paper, we present a multi-layer agent-based network model for the information diffusion simulation in the urgent scenarios, which allows to investigate agents’ behavior in a variety of situations in the absence of the real data. This model can be used for the urban scenarios simulation in the integration with other agent-based human interaction models. Experimental: results demonstrate good results in comparison with existing works in this area and give a number of insights regarding the further model development.

We address the problem of detecting and recognizing online the occurrence of human interactions as seen by a network of multiple cameras. We represent interactions by forming temporal trajectories, coupling together the body motion of each individual and their proximity relationships with others, and also sound whenever available. Such trajectories are modeled with kernel state-space (KSS) models. Their advantage is being suitable for the online interaction detection, recognition, and also for fusing information from multiple cameras, while enabling a fast implementation based on online recursive updates. For recognition, in order to compare interaction trajectories in the space of KSS models, we design so-called pairwise kernels with a special symmetry. For detection, we exploit the geometry of linear operators in Hilbert space, and extend to KSS models the concept of parity space, originally defined for linear models. For fusion, we combine KSS models with kernel construction and multiview learning techniques. We extensively evaluate the approach on four single view publicly available data sets, and we also introduce, and will make public, a new challenging human interactions data set that we have collected using a network of three cameras. The results show that the approach holds promise to become an effective building block for the analysis of real-time human behavior from multiple cameras.

Goffman’s dramaturgical approach is frequently used to introduce undergraduate students to the sociological understanding of human interaction. While a number of scholars have designed engaging student activities that highlight Goffman’s approach, most of these activities tend to involve atypical embarrassing interactions or norm-breaking behaviors that happen in front of a large public audience. In this way, they are unlike the mundane face-to-face interactions that were the focus of much of Goffman’s work. I have used Goffman’s notion of interactions as an “information game,” discussed at the start of The Presentation of Self in Everyday Life, to create a stimulating in-class exercise that involves students having a routine one-on-one interaction with another student. Responses from 170 students in four Introduction to Sociology classes suggest that students found the activity engaging and believed it gave them a richer understanding of Goffman’s approach to human interaction.

In this paper, we propose an novel interactive outlier detection system called feature-rich interactive outlier detection (FRIOD), which features a deep integration of human interaction to improve detection performance and greatly streamline the detection process. A user-friendly interactive mechanism is developed to allow easy and intuitive user interaction in all the major stages of the underlying outlier detection algorithm which includes dense cell selection, location-aware distance thresholding, and final top outlier validation. By doing so, we can mitigate the major difficulty of the competitive outlier detection methods in specifying the key parameter values, such as the density and distance thresholds. An innovative optimization approach is also proposed to optimize the grid-based space partitioning, which is a critical step of FRIOD. Such optimization fully considers the high-quality outliers it detects with the aid of human interaction. The experimental evaluation demonstrates that FRIOD can improve the quality of the detected outliers and make the detection process more intuitive, effective, and efficient.