Reviewerly/exHarmony huggingface dataset:
Identifying Interaction and Awareness Services in Mobile Collaborative Applications

The interaction and awareness services play a key role on the impact that mobile collaborative systems have on their application domains. Identifying these services at early design stages is not only challenging, but also mandatory to determine the feasibility and scope of the system to be developed. Based on a literature review, this paper presents a set of interaction and awareness services that helps designers determine what services to embed into mobile applications, depending on interaction needs that must be addressed.
A Methodological Framework for Facilitating Explainable AI Design

The advancement of artificial intelligence (AI) technologies has enabled many feats that were previously thought to be impossible. However as AI systems become prevalent, there is an increasing need to interpret how they work in order to build trust with the users. In important fields like medical diagnosis and self driving cars, there are high stakes that require the AI system to perform well and provide reasonable explanations for their decisions. While there are many techniques developed to make black box algorithms more transparent, there is a lack of design methodologies to allow design teams to systematically surface and address explainability issues during the AI solution design process. To bridge this gap, we propose the Explainability in Design (EID) methodology to address this gap in the literature. EID enables AI software designers to surface and explore complex explainability issues systematically, which might potentially be overlooked otherwise. With our methodological tool, we aim to decrease the barrier of entry, the time and experience needed to effectively make well informed decisions for integrating explainability into their AI solutions.
Private Set Intersection for Viewing History with Efficient Data Matching

We propose an efficient and secure data sharing method to share the same viewing history of TV program with users. Private set intersection (PSI) protocol enables users to share the same elements in each database without revealing anything. Viewing history has a high affinity to the PSI protocol and a potential to lead to privacy-friendly services. In fact, viewing history has two kinds of data-sets: a program name and its meta-data. In our scheme, we make those data-sets into a hierarchical tree structure and then execute efficient data matching of the PSI protocol by using characteristics of viewing history.
Development of an Algorithm to Artificially Create Virtual Brain Deformations for Brain DICOM

This study proposes an algorithm to artificially create brain shifts in patients’ brain DICOM images. Then, we evaluate the SIFT, AKAZE, ORB, and BRISK feature point extraction algorithms for the automatic detection of local brain shifts from a patient’s pre-and postoperative DICOM images. Accurate and automatic detection of brain shifts could contribute toward the generation of accurate brain deformation models. The evaluation results suggested that the BRISK and AKAZE algorithms are most suitable for the above purpose.
Speech-Based Virtual Assistant for Treatment of Alzheimer Disease Patient Using Virtual Reality Environment (VRE)

One of the major diseases that affect the elderly is the Alzheimer disease (AD) which is characterized by partial or complete loss of memory. Early detection and early treatment could help in managing the Alzheimer disease on potential patients. This paper introduces a virtual reality assistant (VRA) into a virtual environment. The VRA gives commands to the user through a wearable immersive virtual reality Google. The patients are made to complete varying levels of tasks of which a deviation from the normal results of the task was an indicator of a potential sufferer of the AD. The VR used Natural Language in Artificial Intelligence for communicating, the tasks were designed with Unity Game Engine and VR cardboard Google was used. The result obtained showed that patients found the Virtual Reality Assistant in the simulation helpful.
Assisting the Assistant

The Computers Are Social Actors (CASA) Paradigm (Nass et al. 1994) describes the interaction between Humans and Computers as fundamentally social. Computers emitting social cues are treated like other humans (Reeves and Nass 1996). This paper aims to evaluate the social norms of reciprocity and politeness in a CASA context using a voice assistant. A 2 × 2 experimental mixed factorial design with N = 66 participants was used. We examined whether participants who have been helped by a voice assistant in a coaching session are more likely to show reciprocal behavior towards the voice assistant compared to participants who have not been helped. In addition, we investigated a possible interviewer-bias (Finkel et al. 1991) when interacting with voice assistants as well as user-sided influencing factors for reciprocal behavior towards voice assistants. Results show that the CASA paradigm can be applied to modern technology. Participants who have been helped complete significantly more tasks for the computer (M = 7.34, SD = 5.11), than participants who have not been helped (M = 4.94, SD = 3.49; t(56.518) = 2.28, p = 0,027, d = .56). An interviewer bias was not found. Contrary to our expectations a retaliation effect was found. Furthermore, the personality trait openness to experience moderates the effect of helpful condition on tasks completed. Future research should consider looking into user-sided influencing factors as personality seems to be a fruitful approach. Moreover, effects of CASA on human-voice assistant interaction need to be considered in practical design and future research.
STATE ADMINISTRATION OF EDUCATION

The problem of education has been and remains one of the leading problems for modern Russian society. It is spreading with even greater force in our time and is becoming even more serious. Every year, the state allocates huge funds for the development of education. Why is education of great importance for the state? Why does the state pay great attention to its development? There is a logical answer to these two questions. Almost any state strives for development and to occupy the first positions in the world market and, as a result, sets itself various goals, the achievement of which will help to bring the state to a leading position. One of the goals is to increase the literacy and education of the population, because the fate of any state depends on the state of the education system.
Pedestrian Physical Education Training Over Visualization Tool

E-learning approaches are one of the most important learning platforms for the learner through electronic equipment. Such study techniques are useful for other groups of learners such as the crowd, pedestrian, sports, transports, communication, emergency services, management systems and education sectors. E-learning is still a challenging domain for researchers and developers to find new trends and advanced tools and methods. Many of them are currently working on this domain to fulfill the requirements of industry and the environment. In this paper, we proposed a method for pedestrian behavior mining of aerial data, using deep flow feature, graph mining technique, and convocational neural network. For input data, the state-of-the-art crowd activity University of Minnesota (UMN) dataset is adopted, which contains the aerial indoor and outdoor view of the pedestrian, for simplification of extra information and computational cost reduction the pre-processing is applied. Deep flow features are extracted to find more accurate information. Furthermore, to deal with repetition in features data and features mining the graph mining algorithm is applied, while Convolution Neural Network (CNN) is applied for pedestrian behavior mining. The proposed method shows 84.50% of mean accuracy and a 15.50% of error rate. Therefore, the achieved results show more accuracy as compared to state-of-the-art classification algorithms such as decision tree, artificial neural network (ANN).
Emerging Technologies and Innovation to Reach Out to Vulnerable Populations in Nepal

In the dawn of the Fourth Industrial Revolution (Industry 4.0) shaped by Artificial Intelligence (AI) blockchain, Internet of Things, multiverse, and quantum computing, the emphasis on creativity and innovation, entrepreneurship, and technology development also affect the ways the so-called non-profit sectors design and implement development and humanitarian programmes in developing countries like Nepal. Following the 2015 Nepal Earthquake Response, World Vision International Nepal (WVI Nepal) has ideated, prototyped, and scaled up community-focused innovative solutions like SIKKA, KITAB Bazar, and Participatory Disaster Risk Assessment (PDRA) tools integrated into its development and humanitarian initiatives. SIKKA uses blockchain technology to ensure cash and voucher distributions are transparent and trackable, and the system has been used to assist 92,788 people during the COVID-19 response. Similarly, KITAB Bazar, an online marketplace platform, has served 53,412 children in 831 community schools by providing 133,008 supplementary reading books in local languages through the platform. Likewise, the PDRA tool capacitates communities to identify disaster-prone areas and engage stakeholders for response through mobile applications.
Machine Learning for Hate Speech Detection in Arabic Social Media

(WARNING: This paper may contain some offensive words) Over the past few years, abusive language and cyberbullying have known a great increase on social media in general. This phenomenon has encouraged efforts to propose solutions able to detect and prohibit such behavior. Most of these solutions are dedicated to English, but the ones that can handle Arabic are, to the best of our knowledge, rare. Many reasons lie behind this situation including the informality and ambiguity of the Arabic dialects, as well as the use of Arabic/Arabizi combinations. In this paper, we will use a collection of Arabic YouTube comments that are annotated as either “hateful” or “inoffensive” to compare the ability of five machine learning algorithms to perform correct classification on hateful Arabic comments. The algorithms are Logistic Regression, Naïve Bayes, Random Forests, Support Vector Machines, and Long Short-Term Memory. The performance metrics are Accuracy, F1-Score, Precision, and Recall.
First-Person View Drones and the FPV Pilot User Experience

First-person view (FPV) drones provide an immersive flying experience to pilots and are becoming popular for recreational purposes. In this paper, we study FPV pilots’ flight preferences and how they interact with drones. First, we conducted an online survey with 515 pilots. We found that most pilots build their drones, have five drones or more, fly for three years or less, fly one to five hours per week, and prefer acrobatic flight mode. We present pilots’ preferences in equipment, background, involvement with social media, competitions, and sponsorship. We also show the results of a second user study in which we interviewed five experienced pilots. We discuss their flight preferences, the correlation between FPV flying and social media presence, and how to improve the FPV user experience. Our results allow the understanding of FPV pilots’ culture and how they interact with drones, enabling future work in the field.
Eye Tracking to Evaluate the User eXperience (UX): Literature Review

User eXperience (UX) shapes the way how users interact with products, systems, and services therefore it is necessary to be able to accurately evaluate how this interaction behaves. We propose a review of some of the most relevant articles and how their discoveries impact this line of research including a list of metrics that jointly translate the raw variables captured by an eye tracker to results related to aspects of the UX model of a product. It is also discussed how state of the art Passive Eye tracking Technologies (PET) offer a cheap and simple way of implementing these experiments within the budget limit constraints, reviewing their pros and cons for future investigations that wish to apply this technology for evaluating UX and even carry out massive Eye Tracking studies with experiments done remotely with the help of cloud technology.
DeepFake Creation and Detection Using LSTM, ResNext

Technology was created as a means to make our lives easier. There is nothing more fast-paced than the advancements in the field of technology. Decades ago, virtual assistants were only a far-fetched imagination; now, these fantasies have become a reality. Machines have started to recognize speech and predict stock prices. Witnessing self-driving cars in the near future will be an anticipated wonderment. The underlying technology behind all these products is machine learning. Machine learning is ingrained in our lives in ways we cannot fathom. It may have many good sides but it is misused for personal and base motives. For example, various forged videos, images, and other content termed as DeepFakes are getting viral in a matter of seconds. Such videos and images can now be created with the usage of deep learning technology, which is a subset of machine learning. This article discusses the mechanism behind the creation and detection of DeepFakes. DeepFakes is a term generated from deep learning and fake. As the name suggests, it is the creation of fabricated and fake content, distributed in the form of videos and images. Deep learning is one of the burgeoning fields which has helped us to solve many intricate problems. It has been applied to fields like computer vision, natural processing language, and human-level control. However, in recent years, deep learning-based software has accelerated the creation of DeepFake videos and images without leaving any traces of falsification which can engender threats to privacy, democracy, and national security. The motivation behind this research article was to spread awareness among the digitally influenced youth of the twenty-first century about the amount of fabricated content that is circulated on the internet. This research article presents one algorithm used to create DeepFake videos and, more significantly, the detection of DeepFake videos by recapitulating the results of proposed methods. In addition, we also have discussed the positive aspects of DeepFake creation and detection, where they can be used and prove to be beneficial without causing any harm.
Decision Tree Induction Through Meta-learning

Symbolic or explainable learning models stand out within the Machine Learning area because they are self-explanatory, making the decision process easier to be interpreted by humans. However, these models are overly responsive to the training set used. Thus, even tiny variations in training sets can result in much worse precision. In this research we propose a meta-learning approach that transforms a Random Forest into a single Decision Tree. Experiments were performed on classification datasets from different domains. Our approach using precision (positive reliability) performs as good as a Random Forest with no statistically significant differences. Yet, its advantage is the interpretability provided by a single decision tree. Results indicate that it is possible to obtain a resulting model which is easier to interpret than a Random Forest, still with higher precision than a standard Decision Tree.
Augmented Reality Filters and the Faces as Brands: Personal Identities and Marketing Strategies in the Age of Algorithmic Images

This paper analyzes the so-called "augmented reality filters" (ARF), a technology that makes it possible to produce and spread widely on social media a particular type of video selfies that are manipulated live while filming – for example, by modifying the somatic characters of the producer's face. The first part of the paper analyzes ARFs in the light of a socio-semiotics of dispositives. This approach makes it possible to identify three interconnected aspects of ARFs: their technological consistency, which is closer to mixed reality than to augmented reality; their socio-psychological uses, and in particular personal identity construction through body image manipulation; and finally, their economic-political implications, linked to face recognition and social surveillance. The second part of the paper focuses on the marketing uses of ARFs and, in particular, on branded ARFs transforming users' faces. In these cases, the radical involvement of brands in defining the identity of users requires a profound rethinking of the mechanisms of trust that bind them to consumers.
Investigating Topic-Agnostic Features for Authorship Tasks in Spanish Political Speeches

Authorship Identification is the branch of authorship analysis concerned with uncovering the author of a written document. Methods devised for Authorship Identification typically employ stylometry (the analysis of unconscious traits that authors exhibit while writing), and are expected not to make inferences grounded on the topics the authors usually write about (as reflected in their past production). In this paper, we present a series of experiments evaluating the use of feature sets based on rhythmic and psycholinguistic patterns for Authorship Verification and Attribution in Spanish political language, via different approaches of text distortion used to actively mask the underlying topic. We feed these feature sets to a SVM learner, and show that they lead to results that are comparable to those obtained by the BETO transformer when the latter is trained on the original text, i.e., when potentially learning from topical information.
Bio-Centred Interaction Design: A New Paradigm for Human-System Interaction

Biodiversity loss has been an issue mentioned for many years, but now, more than at any other time in history, action is needed. In terms of sustainability, design has long integrated several aspects that concern themselves with biodiversity, such as eco-design, bio-design, and sustainable design. But it is necessary to develop tools that designers can include in the creative processes and thus provide an adequate response to the needs of creating sustainable products, systems, or services. In this context, and beyond the anthropocentrism that places the human being at the centre of the universe, we propose a biocentrism that states that all life forms are important and that the other species do not exist to simply serve humans. On the other hand, in the context of interaction design, Human-Centred Design (HCD) is an ISO (International Organization for Standardization) standardized concept. In Standard Ergonomics of Human-System Interaction – Part 210: Human-Centred Design for Interactive Systems (ISO 9241-210), there is no description about environmental concerns. The concept proposed in this article is Bio-Centred Design, which integrates a third pillar – environmental sustainability – in addition to those already approved by the standard – the economic and social pillars. We propose this to be a fundamental pillar for the future of the planet and development of sustainable products, systems, and services.
Usability in Automated Teller Machines Interfaces: A Systematic Literature Review

Due to technological progress, financial institutions have included ATMs as one of their main channels as a way to decentralize their services. However, there is a gap between user expectations and their perceptions regarding what ATM interfaces offer. As a result, several users feel dissatisfied after using ATMs and many times this dissatisfaction is related to the difficulty of use, design flaws and the fact of committing many errors when interfaces have a low degree of usability. In this sense, in this study we present a Systematic Literature Review (SLR) about usability on ATM interfaces. With this study, we want to understand the current situation of the problems mentioned before, so we seek to know the problems and challenges that have been presented lately for these electronic media, as well as the solutions that have addressed these problems, and the techniques and methods used to carry out these designs or redesigns. For this, the protocol proposed by Kitchenham was followed. Scopus, ACM Digital Library, Alicia and IEEE Digital Library were searched, and finally 51 papers were selected as relevant. With this information it was possible to identify and analyze challenges, usability issues, usability evaluations, and techniques and methods used to carry out designs or redesigns, as well as case studies of designs or redesigns in the ATM domain. We found that this topic is being developed in recent years, that there are common challenges encountered, and that designs, redesigns and usability evaluations have been carried out in this domain under different methods, techniques and frameworks. However, several of these usability issues persist today.
Evaluation of the Difference in the Reality of the Bow Device with and Without Arrows

This research is related to physical e-sports, which emphasize physicality, progress by training the body, and are played using abilities equivalent to those of reality. In physical e-sports, devices that resemble real-life tools such as bows and guns are used as an interface, and the ability to operate them is the same as in reality. In particular, I focused on the bow. The bow device that I am currently using shoots in the air without using arrows for safety reasons. Therefore, to what extent is there a difference in the experience of using a bow with or without an arrow? Verification of the safety of not using arrows. The experiment was conducted to see how the experience would change when a device that uses arrows but does not fire them is installed. In order to obtain objective data, I made a bow-launching device, and measured the values of the acceleration sensor and strain gauge attached to the bow for evaluation. Based on these results, I will investigate the optimal form of bow device for physical e-sports.
Enabling Situation-Aware User Interface Behavior by Exploiting Emotions and Advanced Adaptation Techniques

SitAdapt is an architecture and runtime system for building adaptive interactive applications. The system is integrated into the PaMGIS framework for pattern- and model-based user interface construction and generation. This paper focuses on the situation-rule-based adaptation process and discusses the different categories of adaptations. As the system observes the user during sessions and collects visual, bio-physical, and emotional data about the user that may vary over time, SitAdapt, in contrast to other adaptive system environments, is able to create situation-aware adaptations in real-time that reflect the user’s changing physical, cognitive, and emotional state. With this advanced adaptation process, both, task accomplishment and user experience can be significantly improved. The operation of the system is demonstrated with an example from an adaptive travel-booking application.