Reviewerly/exHarmony huggingface dataset:
Adversarial learning techniques for security and privacy preservation: A comprehensive review

Abstract In recent years, the use of smart devices has increased exponentially, resulting in massive amounts of data. To handle this data, effective data storage and management has required. Cloud computing (CC) is a promising solution to deal with this huge amount of data. Electronic devices are collecting real‐time data from sensors and applications through a wireless communication channel in the digital era. In some cases, CC cannot protect against various malicious attacks in the wireless communication channel. To address this issue, we have used machine learning (ML) and deep learning (DL) techniques for attack detection in a wireless channel on an early basis. It trains a model to predict malicious activities of attackers, which aids in the security of CC's sensitive data. We employed adversarial learning techniques (AL) to add fake data into the model to ensure that the trained model was correct. The trained model can distinguish between the fake and real data from the training samples and improve the training samples' performance. AL provides different defense mechanisms to preserve the privacy of ML‐ and DL‐based model but does not ensure the system's robustness. To improve the system's robustness, we have used federated learning with blockchain technology to make a system more robust, reliable, accurate, and transparent. This integration aids in providing high‐graded security against adversarial attacks. This paper presents a comprehensive review to highlight the recent improvements in AL techniques. Moreover, we explored the various AL applications in security and privacy preservation. Finally, open research issues and future directions are discussed to show future research avenues.
Juvenile Graphical Perception: A Comparison between Children and Adults

Data visualization is pervasive in the lives of children as they encounter graphs and charts in early education and online media. In spite of this prevalence, our guidelines and understanding of how children perceive graphs stem primarily from studies conducted with adults. Previous psychology and education research indicates that children’s cognitive abilities are different from adults. Therefore, we conducted a classic graphical perception study on a population of children aged 8–12 enrolled in the Ivy After School Program in Boston, MA and adult computer science students enrolled in Northeastern University to determine how accurately participants judge differences in particular graphical encodings. We record the accuracy of participants’ answers for five encodings most commonly used with quantitative data. The results of our controlled experiment show that children have remarkably similar graphical perception to adults, but are consistently less accurate at interpreting the visual encodings. We found similar effectiveness rankings, relative differences in error between the different encodings, and patterns of bias across encoding types. Based on our findings, we provide design guidelines and recommendations for creating visualizations for children. This paper and all supplemental materials are available at https://osf.io/ygrdv.
Influence of Chemical Composition and Microvesiculation on the Chromatic Features of the Obsidian of Sierra de las Navajas (Hidalgo, Mexico)

The obsidian of Sierra de las Navajas is well known for its green color and gold hue. In order to relate these features with compositional and microtextural characteristics, we have carried out a microanalytical study by Wave Dispersion System associated to Electron Probe Micro-Analyzer, Scanning Electron Microscope observation, and X-rays micro-tomographic analyses of samples showing different colors (dark to light green, sometimes with bands of different color intensity) and hues (changing, uniform, no hue). In accordance with previous studies, the green color of the obsidian seems to be related to a high iron content, probably in its reduced state. However, no significant difference in composition occurs between dark and light green samples. The SEM observation and microtomographic study revealed the absence of microcrystals and the occurrence of vesicles of different size, shape, and orientation. Lighter green colors are shown by highly vesiculated surfaces, whereas non-vesiculated samples are darker. On the surfaces with a high concentration of coarse vesicles, a uniform golden hue is observed. Decreasing vesiculation gives a hue changing with the angle of incident light. However, when the vesicularity approaches zero, no hue is visible. The iso-orientation of vesicles along preferential directions and their distribution in bands determine the variation in color intensity and hue on differently oriented surfaces. Microvesiculation also influences other characteristics that were important features for the use of obsidian in the past, such as fracture, transparency, and roughness.
Habilyzer: Empowering Office Workers to Investigate their Working Habits using an Open-Ended Sensor Kit

Office work presents health and wellbeing challenges, triggered by unhealthy working habits or environmental factors. While technologies for vitality in the office context gain popularity, they are often solution-focused and fall short in acknowledging personal needs. Building on approaches from personal informatics, we see value in opening up the design space of tracking and sensing technologies for office workers. We designed and deployed an open-ended sensor kit and conducted two complementary studies to investigate the value of empowering office workers to investigate their own working habits. Findings show that Habilyzer triggers curiosity about working habits, and wireless sensors contribute to inquire into those habits, possibly supported by additional tools. We contribute new insights into how an open-ended sensor kit can be designed to support self-tracking practices and underlying reflections in the underexplored context of office work. It is an alternative approach to workplace vitality, moving from solution-oriented technologies to inquiry-enabling tools.
Will AI Image Synthesis Technology Help Constructivist Education at The Online Art Museum?

Motivated by the outbreak of COVID-19, museums have increased interests in online museum and online museum education. This study presents a way to use AI image synthesis technology for online art education guided by a constructivist design approach. An experiment was conducted to empirically test the effectiveness of AI-based art education in the online museum context. A total of 83 participants visited one of 3 different web-based art museums (i.e., AI image synthesis not applied vs. AI image synthesis applied with given photos vs. AI image synthesis applied with self-uploaded photos). Those who experienced the online museum with synthesized images using self-uploaded photos reported a higher level of motivation and satisfaction and to experience in a more constructivist way compared to other conditions.
“I Want It That Way”: Exploring Users’ Customization and Personalization Preferences for Home Assistants

Home assistants are becoming a widespread product, but they mostly come as a compact device and offer very few customization and personalization features, which often leads to dissatisfaction. With the technological advancements, these systems are becoming more adaptable to the users' needs and can better imitate a human personality. To achieve that efficiently, understanding how different users envision their desired assistant is crucial. To identify people's customization and personalization preferences and their desired personality for a home assistant, we designed a set of storyboards depicting a variety of possible features in a domestic setting and conducted a user study (), including a series of semi-structured interviews. Our quantitative results suggest that users prefer an agent which is highly agreeable and has higher conscientiousness and emotional stability. Furthermore, we discuss users' customization and personalization preferences for a home assistant, which could be considered when designing the future generation of home assistants.
Mediated Visits: Longitudinal Domestic Dwelling with Mobile Robotic Telepresence

Mobile Robotic Telepresence (MRP) systems are remotely controlled, mobile videoconferencing devices that allow the remote user to move independently and have a physical presence in the environment. This paper presents a longitudinal study of MRP use in the home, where the first author used an MRP to connect with family, her partner, and friends over a six-month period. Taking an ethnomethodological approach, we present video recorded fragments to explore the phenomenon of 'visiting' where MRP users drop into the home for a period of time. We unpack the more 'procedural' elements—arriving and departing—alongside ways of 'dwelling' together during a visit, and the qualities of mobility, autonomous presence and spontaneity that emerge.
Supporting Data-Driven Basketball Journalism through Interactive Visualization

Basketball writers and journalists report on the sport that millions of fans follow and love. However, the recent emergence of pervasive data about the sport and the growth of new forms of sports analytics is changing writers’ jobs. While these writers seek to leverage the data and analytics to create engaging, data-driven stories, they typically lack the technical background to perform analytics or efficiently explore data. We investigated and analyzed the work and context of basketball writers, interviewed nine stakeholders to understand the challenges from a holistic view. Based on what we learned, we designed and constructed two interactive visualization systems that support rapid and in-depth sports data exploration and sense-making to enhance their articles and reporting. We deployed the systems during the recent NBA playoffs to gather initial feedback. This article describes the visualization design study we conducted, the resulting visualization systems, and what we learned to potentially help basketball writers in the future.
FaceSavr™: Designing Technologies with Allistic Adults to Battle Emotion Echolalia

Emotional self-reliance is a key factor in psychological well-being. Individuals with Allism Spectrum Disorder often face challenges in interpersonal communication and social settings. Especially experiences of emotion echolalia, the mirroring of others’ emotions, can cause not only impairment in communication and relationships with others, but significant emotional distress, putting the mental well-being of allistic people at risk. In this paper, we report on the design of FaceSavr™, a digital textile protective system against emotion echolalia, and describe our participatory design process with seven allistic adults.
When Virtuality Surpasses Reality: Possible Futures of Ubiquitous XR

Is our future heading towards enhancing the human experience with computer-mediated reality? Immersive technology is unique, existing between the world and our senses, letting users traverse wholly virtual environments (i.e. distant places or fantasy worlds) or augment the real world with virtual objects, and any mix of virtuality-reality in-between. This paper explores the philosophical and social ramifications of ubiquitous immersive technology, envisioning a relatively near-future where mainstream technology has been replaced and a dystopian far-future wherein individuals may choose to abandon reality in favour of virtual worlds. Creating design fictions as thought experiments, we explore the open challenges of possible futures in XR, researching tomorrow's technologies today.
Validity of the Open Card Sorting Method for Producing Website Information Structures

When it comes to creating website information structures, open card sorting is the main approach used. However, scientific research for the method's validity is lacking. This paper explores the validity of open card sorting for website structural design. To this end, participants first performed an open card sort for the redesign of a website with usability issues in its information structure. Next, a within-subjects user testing study was performed to compare two functional prototypes that differed only in their structure: one replicated the existing website structure, whereas the other implemented the structure produced by card sort data analysis. Results showed that participants using the redesigned structure had significantly better usability metrics (first click success rate, task time, SEQ score and SUS score) compared to when interacting with the existing structure. These findings provide support for the validity of the open card sorting method for the design of website information structures.
Mobiot: Augmenting Everyday Objects into Moving IoT Devices Using 3D Printed Attachments Generated by Demonstration

Recent advancements in personal fabrication have brought novices closer to a reality, where they can automate routine tasks with mobilized everyday objects. However, the overall process remains challenging- from capturing design requirements and motion planning to authoring them to creating 3D models of mechanical parts to programming electronics, as it demands expertise.
Modern Baselines for SPARQL Semantic Parsing

In this work, we focus on the task of generating SPARQL queries from natural language questions, which can then be executed on Knowledge Graphs (KGs). We assume that gold entity and relations have been provided, and the remaining task is to arrange them in the right order along with SPARQL vocabulary, and input tokens to produce the correct SPARQL query. Pre-trained Language Models (PLMs) have not been explored in depth on this task so far, so we experiment with BART, T5 and PGNs (Pointer Generator Networks) with BERT embeddings, looking for new baselines in the PLM era for this task, on DBpedia and Wikidata KGs. We show that T5 requires special input tokenisation, but produces state of the art performance on LC-QuAD 1.0 and LC-QuAD 2.0 datasets, and outperforms task-specific models from previous works. Moreover, the methods enable semantic parsing for questions where a part of the input needs to be copied to the output query, thus enabling a new paradigm in KG semantic parsing.
MobileTutAR: a Lightweight Augmented Reality Tutorial System using Spatially Situated Human Segmentation Videos

Augmented reality (AR) tutorial systems have a strong potential to help workers improve learning efficiency in the ongoing trend of Industry 4.0. The current state-of-the-art point cloud approach usually requires cumbersome preparation and additional computational resources only to suffer from low-resolution visual results. To overcome the above limitations, we propose MobileTutAR, a lightweight AR tutorial system that runs entirely on mobile devices providing high definition spatially situated tutorial videos. Our approach captures the tutorial images that extract a human's body segmentation combined with a user-defined area of interest. The system then projects the tutoring content spatially in situ, to align with the human expert's recorded position. When playing back, the learner is guided by a navigation-centered user interface to observe the segmentation video from the recorder's original position/orientation. In this way, we deliver a high-definition AR experience without needing any cumbersome equipment, exotic computational resources, or in-depth training.
Applying cognitive principles to model-finding output: the positive value of negative information

Model-finders, such as SAT/SMT-solvers and Alloy, are used widely both directly and embedded in domain-specific tools. They support both conventional verification and, unlike other verification tools, property-free exploration. To do this effectively, they must produce output that helps users with these tasks. Unfortunately, the output of model-finders has seen relatively little rigorous human-factors study. Conventionally, these tools tend to show one satisfying instance at a time. Drawing inspiration from the cognitive science literature, we investigate two aspects of model-finder output: how many instances to show at once, and whether all instances must actually satisfy the input constraints. Using both controlled studies and open-ended talk-alouds, we show that there is benefit to showing negative instances in certain settings; the impact of multiple instances is less clear. Our work is a first step in a theoretically grounded approach to understanding how users engage cognitively with model-finder output, and how those tools might better support users in doing so.
Novel Challenges of Safety, Security and Privacy in Extended Reality

Extended Reality (AR/VR/MR) technology is becoming increasingly affordable and capable, becoming ever more interwoven with everyday life. HCI research has focused largely on innovation around XR technology, exploring new use cases and interaction techniques, understanding how this technology is used and appropriated etc. However, equally important is the investigation and consideration of risks posed by such advances, specifically in contributing to new vulnerabilities and attack vectors with regards to security, safety, and privacy that are unique to XR. For example perceptual manipulations in VR, such as redirected walking or haptic retargeting, have been developed to enhance interaction, yet subversive use of such techniques has been demonstrated to unlock new harms, such as redirecting the VR user into a collision. This workshop will convene researchers focused on HCI, XR, Safety, Security, and Privacy, with the intention of exploring safety, privacy, and security challenges of XR technology. With an HCI lens, workshop participants will engage in critical assessment of emerging XR technologies and develop an XR research agenda that integrates research on interaction technologies and techniques with safety, security and privacy research.
Winds of Change: Seeking, Preserving, and Retelling Indigenous Knowledge Through Self-Organized Online Communities

Technology has provided an environment for connecting indigenous community members and provide a means for them to seek and engage with their indigenous knowledge (IK). Emerging research has examined the effects of social media on specific IK, including the possibility of undermining community agency. In this work, we contrast how indigenous community members engage with IK offline, and in their own self-organized communities online. Through interviews with community members and a study of Facebook Pages and Facebook Groups, we seek to better understand these practices and elicit design recommendations. Our findings describe how community roles have shifted in the presence of technology, notably with absence of elders and the inclusion of “born towns”–community members who live in non-traditional settings. We also find that fluency in the indigenous language served both as a gatekeeper: guarding the community knowledge, while also facilitating discussion surrounding different aspects of IK.
To Be Ethical and Responsible Digital Citizens or Not: A Linguistic Analysis of Cyberbullying on Social Media

As a worldwide epidemic in the digital age, cyberbullying is a pertinent but understudied concern-especially from the perspective of language. Elucidating the linguistic features of cyberbullying is critical both to preventing it and to cultivating ethical and responsible digital citizens. In this study, a mixed-method approach integrating lexical feature analysis, sentiment polarity analysis, and semantic network analysis was adopted to develop a deeper understanding of cyberbullying language. Five cyberbullying cases on Chinese social media were analyzed to uncover explicit and implicit linguistic features. Results indicated that cyberbullying comments had significantly different linguistic profiles than non-bullying comments and that explicit and implicit bullying were distinct. The content of cases further suggested that cyberbullying language varied in the use of words, types of cyberbullying, and sentiment polarity. These findings offer useful insight for designing automatic cyberbullying detection tools for Chinese social networking platforms. Implications also offer guidance for regulating cyberbullying and fostering ethical and responsible digital citizens.
Banking Information Resource Cybersecurity System Modeling

The rapid development of the process of informatization of modern society has necessitated cybersecurity in all spheres of human activity, as the implementation of deliberate or unintentional influences on the information sphere by both external and internal sources can damage security and lead to moral, material, financial, reputational and other forms of damage. The purpose of the paper is to create functional cognitive models to assess the level of their protection. The method of building a fuzzy cognitive map of the state of cybersecurity of banks is used. There have been developed cognitive models to determine the level of protection of the computer network, information security system and critical infrastructure (banks). Scenarios have been developed that reflect the response of the system at the complex maximum attenuation of the impact of the most important cyber threats. In conclusion, the practical implementation of the method provides an opportunity to predict the state of cybersecurity of banks, and contributes to the implementation of the necessary mechanisms to prevent, protect and control access at the appropriate levels of network infrastructure.
Analyzing Deaf and Hard-of-Hearing Users’ Behavior, Usage, and Interaction with a Personal Assistant Device that Understands Sign-Language Input

As voice-based personal assistant technologies proliferate, e.g., smart speakers in homes, and more generally as voice-control of technology becomes increasingly ubiquitous, new accessibility barriers are emerging for many Deaf and Hard of Hearing (DHH) users. Progress in sign-language recognition may enable devices to respond to sign-language commands and potentially mitigate these barriers, but research is needed to understand how DHH users would interact with these devices and what commands they would issue. In this work, we directly engage with the DHH community, using a Wizard-of-Oz prototype that appears to understand American Sign Language (ASL) commands. Our analysis of video recordings of DHH participants revealed how they woke-up the device to initiate commands, structured commands in ASL, and responded to device errors, providing guidance to future designers and researchers. We share our dataset of over 1400 commands, which may be of interest to sign-language-recognition researchers.
Exploiting smart contracts in PBFT-based blockchains: A case study in medical prescription system

Smart contracts allow application developers to automate business processes through a decentralized computation architecture. Contemporary blockchain platforms such as Ethereum and Hyperledger Fabric offer support for smart contracts through consensus mechanisms such as Proof-of-Work (PoW) or other types of transaction validation and ordering services. This article exploits smart contracts in the Byzantine Fault Tolerant (BFT) blockchain platforms. In particular, we explore Tendermint and Hyperledger Besu, BFT blockchain platforms, and apply them to a decentralized e-prescription case study to evaluate their effectiveness. We adopt Hyperledger Besu and Tendermint in this research, given that both are BFT-based blockchains. Also, it is noteworthy that smart contracts in BFT blockchain platforms such as Tendermint are not well established and not widely adopted yet. Our article empirically evaluates the performance of smart contracts in Tendermint and Hyperledger Besu using a decentralized medical prescription case study and compares their results with Ethereum, a PoW blockchain. Our results demonstrate that BFT blockchain platforms are efficient for multi-stakeholder applications such as e-prescription and supply chains. To the best of our knowledge, this is the first study investigating the implementation of smart contracts in BFT blockchain platforms, such as Tendermint and Hyperledger Besu.