Reviewerly/exHarmony huggingface dataset:
Evaluation of drive-off procedures in dynamic driving simulators: Multisensory cues, interaction and sense of presence

The importance of a realistic driving feeling for the validity of driving simulator studies has been investigated by many researchers. These investigations focus on rather abstract test objectives like driving experience or safety aspects and indicate that a high sense of presence is important to achieve realistic behaviour of the driver. However, it is not clear if this must similarly be true for driving dynamic studies with precise test objectives like acceleration or jerk. In this paper, we investigate the influence of multisensory cues and interaction with the virtual vehicle on the experienced sense of presence and the evaluation of drive-off procedures. In a three-part experimental study with different variations of the virtual reality and the driver’s interaction with the virtual vehicle, the sense of presence is measured and compared in a between-groups design. Furthermore, the evaluations of nine drive-off acceleration profiles are compared. The results show that diverse multisensory cues do not significantly impact the experienced sense of presence regarding the general presence, spatial presence or realism. As expected, interacting with the virtual vehicle leads to higher involvement ratings. The results of the acceleration profiles evaluations indicate that multisensory cueing has a negligible effect on the evaluation of the drive-off behaviour under the tested conditions. The interaction of the driver with the virtual vehicle offers the test subject a better experience in virtual reality.
Simulation evaluation of virtual reality in interior design effect display and practice mode innovation

With the continuous improvement and promotion of new technologies in the information age, virtual reality technology has been widely used. Virtual interaction technology has also begun to penetrate into the application market. At present, virtual reality technology has been widely recognized in the field of interior design, which can be used for target display based on virtual exhibition halls, so it is sought after by many users. This form is different from the previous rigid scene reproduction methods such as images and videos. Customers do not have to be in the actual scene, as long as they can run and watch through the network, and get a sense of reality. This paper develops a kind of virtual interior design system based on VR technology and divides it into two categories: One is virtual reality based on software technology and the other is virtual reality based on virtual hardware technology. The technical principle is based on the following four steps: preprocessing, model transformation, image fusion, and registration, which are tested by simulation experiments. The algorithm proposed in this paper can determine whether the Cu is continuously divided in advance, so as to improve coding efficiency, accelerate the effect display of interior design, and improve customer experience. The intended task of the system design is to bring immersive experience to the target users, so that they can get the most real perception at anytime and anywhere. This paper introduces the virtual reality technology into the interior design to carry out the system application analysis and effect display.
End-to-End Secure Messaging with Traceability Only for Illegal Content

As end-to-end encrypted messaging services become widely adopted, law enforcement agencies have increasingly expressed concern that such services interfere with their ability to maintain public safety. Indeed, there is a direct tension between preserving user privacy and enabling content moderation on these platforms. Recent research has begun to address this tension, proposing systems that purport to strike a balance between the privacy of “honest” users and traceability of “malicious” users. Unfortunately, these systems suffer from a lack of protection against malicious or coerced service providers. In this work, we address the privacy vs. content moderation question through the lens of pre-constrained cryptography [Ananth et al., ITCS 2022]. We introduce the notion of set pre-constrained ( $$\textsf{SPC}$$ ) group signatures that guarantees security against malicious key generators. $$\textsf{SPC}$$ group signatures offer the ability to trace users in messaging systems who originate pre-defined illegal content (such as child sexual abuse material), while providing security against malicious service providers. We construct concretely efficient protocols for $$\textsf{SPC}$$ group signatures, and demonstrate the real-world feasibility of our approach via an implementation. The starting point for our solution is the recently introduced Apple PSI system, which we significantly modify to improve security and expand functionality.
End-to-End Encrypted Zoom Meetings: Proving Security and Strengthening Liveness

In May 2020, Zoom Video Communications, Inc. (Zoom) announced a multi-step plan to comprehensively support end-to-end encrypted (E2EE) group video calls and subsequently rolled out basic E2EE support to customers in October 2020. In this work we provide the first formal security analysis of Zoom’s E2EE protocol, and also lay foundation to the general problem of E2EE group video communication. We observe that the vast security literature analyzing asynchronous messaging does not translate well to synchronous video calls. Namely, while strong forms of forward secrecy and post compromise security are less important for (typically short-lived) video calls, various liveness properties become crucial. For example, mandating that participants quickly learn of updates to the meeting roster and key, media streams being displayed are recent, and banned participants promptly lose any access to the meeting. Our main results are as follows:
Non-interactive Blind Signatures for Random Messages

Blind signatures allow a signer to issue signatures on messages chosen by the signature recipient. The main property is that the recipient's message is hidden from the signer. There are many applications, including Chaum's e-cash system and Privacy Pass, where no special distribution of the signed message is required, and the message can be random. Interestingly, existing notions do not consider this practical use case separately. In this paper, we show that constraining the recipient's choice over the message distribution spawns a surprising new primitive that improves the well-established state-of-the-art. We formalize this concept by introducing the notion of non-interactive blind signatures ( $$\textsf{NIBS}$$ ). Informally, the signer can create a presignature with a specific recipient in mind, identifiable via a public key. The recipient can use her secret key to finalize it and receive a blind signature on a random message determined by the finalization process. The key idea is that online interaction between the signer and recipient is unnecessary. We show an efficient instantiation of $$\textsf{NIBS}$$ in the random oracle model from signatures on equivalence classes. The exciting part is that, in this case, for the recipient's public key, we can use preexisting keys for Schnorr, ECDSA signatures, El-Gamal encryption scheme or even the Diffie-Hellman key exchange. Reusing preexisting public keys allows us to distribute anonymous tokens similarly to cryptocurrency airdropping. Additional contributions include tagged non-interactive blind signatures ( $$\textsf{TNIBS}$$ ) and their efficient instantiation. A generic construction in the random oracle or common reference string model based on verifiable random functions, standard signatures, and non-interactive proof systems.
Another Round of Breaking and Making Quantum Money:

This work provides both negative and positive results for publicly verifiable quantum money.
A Lower Bound on the Length of Signatures Based on Group Actions and Generic Isogenies

We give the first black box lower bound for signature protocols that can be described as group actions, which include many based on isogenies. We show that, for a large class of signature schemes making black box use of a (potentially non-abelian) group action, the signature length must be $$\varOmega (\lambda ^2/\log \lambda )$$ . Our class of signatures generalizes all known signatures that derive security exclusively from the group action, and our lower bound matches the state of the art, showing that the signature length cannot be improved without deviating from the group action framework.
Quadcopter Prototype Stability Analysis Using Matlab Simscape Library

Abstract Nowadays, the use of quadcopters in daily life has become important due to its capabilities and ability to carry out many tasks in many fields like civil, military, industrial, and agricultural fields. The modelling of the quadcopter and deeply understanding its movements is very important to ensure that the simulations of its behaviour are as close as possible to reality and also helps us to design a flight controller. In this work, we used a modern technique on MATLAB (Simscape) to simulate a quadcopter in real-time. At first, we build a quadcopter using Simscape multibody then we simulated the PID regulator, the command algorithms, and the motor model with the applied forces on the body to achieve the global model that we can use to study the movement of the quadcopter on the three-axis which ensure a stable functioning. The results obtained show the stability of the four movements of the quadcopter (roll, pitch, yaw, and altitude).
Crossing the Threshold: Pathways into Makerspaces for Women at the Intersectional Margins

In this paper, we focus on makerspace practices that can hinder or enable access for under-resourced women of colour who are novices to making, predominantly migrants and refugee women. We report on a 21 month long ethnographic fieldwork at nine makerspaces across two metropolitan Australian cities. The findings present barriers and opportunities around access of under-resourced women of colour into makerspaces. We present these findings through four themes: first impressions and visual representations; dimensions of the enabling environment; role of community partnerships; and intersectional identities of women. We conclude by discussing three contributions this paper makes to the HCI and CSCW literature, with supporting lessons and recommendations so that makerspaces no longer encode the involvement of only certain types of users (predominantly well-educated white males or white females), to the exclusion of women at the intersectional margins. If makerspaces can facilitate an enabling environment appropriate to be inclusive of women who face intersecting oppressions, the benefits of the maker movement can expand substantially.
Reimagining Open Data during Disaster Response: Applying a Feminist Lens to Three Open Data Projects in Post-Earthquake Nepal

Open Data has become a prominent ideal in humanitarian information work and is increasingly promoted for crisis situations to increase effectiveness, accountability, and empower citizens. However, like all socio-technical systems, open data platforms for disasters make implicit and explicit assumptions about data, data users, disasters, and the context of use. In this paper, we turn to feminist theory to examine three open data projects rolled out in the aftermath of the 2015 earthquake in Nepal. We used the seven principles of Data Feminism introduced by D'Ignazio and Klein to design an evaluative framework for the three projects. We use this framework to highlight and link the socio-political nature of both disasters and open data platforms. In our results, we highlight significant gaps in how these projects made labor (in)visible, engaged with affective aspects of disaster, addressed context, and challenged power. We argue that these gaps are reflective of dominant practices in open data for disasters and serve as opportunities for designers and crisis informatics researchers to reimagine the potential of such projects. We propose four ways of doing so based on feminist principles and values.
Twinned Residual Auto-Encoder (TRAE)—A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images

The detection of the COronaVIrus Disease 2019 (COVID-19) from Computed Tomography (CT) scans has become a very important task in modern medical diagnosis. Unfortunately, typical resolutions of state-of-the-art CT scans are still not adequate for reliable and accurate automatic detection of COVID-19 disease. Motivated by this consideration, in this paper, we propose a novel architecture that jointly affords the Single-Image Super-Resolution (SISR) and the reliable classification problems from Low Resolution (LR) and noisy CT scans. Specifically, the proposed architecture is based on a couple of Twinned Residual Auto-Encoders (TRAE), which exploits the feature vectors and the SR images recovered by a Master AE for performing transfer learning and then improves the training of a "twinned" Follower AE. In addition, we also develop a Task-Aware (TA) version of the basic TRAE architecture, namely the TA-TRAE, which further utilizes the set of feature vectors generated by the Follower AE for the joint training of an additional auxiliary classifier, so to perform automated medical diagnosis on the basis of the available LR input images without human support. Experimental results and comparisons with a number of state-of-the-art CNN/GAN/CycleGAN benchmark SISR architectures, performed by considering ×2 , ×4 , and ×8 super-resolution (i.e., upscaling) factors, support the effectiveness of the proposed TRAE/TA-TRAE architectures. In particular, the detection accuracy attained by the proposed architectures outperforms the corresponding ones of the implemented CNN, GAN and CycleGAN baselines up to 9.0%, 6.5%, and 6.0% at upscaling factors as high as ×8 .
CommunityBots: Creating and Evaluating A Multi-Agent Chatbot Platform for Public Input Elicitation

In recent years, the popularity of AI-enabled conversational agents or chatbots has risen as an alternative to traditional online surveys to elicit information from people. However, there is a gap in using single-agent chatbots to converse and gather multi-faceted information across a wide variety of topics. Prior works suggest that single-agent chatbots struggle to understand user intentions and interpret human language during a multi-faceted conversation. In this work, we investigated how multi-agent chatbot systems can be utilized to conduct a multi-faceted conversation across multiple domains. To that end, we conducted a Wizard of Oz study to investigate the design of a multi-agent chatbot for gathering public input across multiple high-level domains and their associated topics. Next, we designed, developed, and evaluated CommunityBots - a multi-agent chatbot platform where each chatbot handles a different domain individually. To manage conversation across multiple topics and chatbots, we proposed a novel Conversation and Topic Management (CTM) mechanism that handles topic-switching and chatbot-switching based on user responses and intentions. We conducted a between-subject study comparing CommunityBots to a single-agent chatbot baseline with 96 crowd workers. The results from our evaluation demonstrate that CommunityBots participants were significantly more engaged, provided higher quality responses, and experienced fewer conversation interruptions while conversing with multiple different chatbots in the same session. We also found that the visual cues integrated with the interface helped the participants better understand the functionalities of the CTM mechanism, which enabled them to perceive changes in textual conversation, leading to better user satisfaction. Based on the empirical insights from our study, we discuss future research avenues for multi-agent chatbot design and its application for rich information elicitation.
To Err is AI: Imperfect Interventions and Repair in a Conversational Agent Facilitating Group Chat Discussions

Conversational agents (CAs) can analyze online conversations using natural language techniques and effectively facilitate group discussions by sending supervisory messages. However, if a CA makes imperfect interventions, users may stop trusting the CA and discontinue using it. In this study, we demonstrate how inaccurate interventions of a CA and a conversational repair strategy can influence user acceptance of the CA, members' participation in the discussion, perceived discussion experience between the members, and group performance. We built a CA that encourages the participation of members with low contributions in an online chat discussion in which a small group (3-6 members) performs a decision-making task. Two types of errors can occur when detecting under-contributing members: 1) false-positive (FP) errors happen when the CA falsely identifies a member as under-contributing and 2) false-negative (FN) errors occur when the CA misses detecting an under-contributing member. We designed a conversational repair strategy that gives users a chance to contest the detection results and the agent sends a correctional message if an error is detected. Through an online study with 175 participants, we found that participants who received FN error messages reported higher acceptance of the CA and better discussion experience, but participated less compared to those who received FP error messages. The conversational repair strategy moderated the effect of errors such as improving the perceived discussion experience of participants who received FP error messages. Based on our findings, we offer design implications for which model should be selected by practitioners between high precision (i.e., fewer FP errors) and high recall (i.e., fewer FN errors) models depending on the desired effects. When frequent FP errors are expected, we suggest using the conversational repair strategy to improve the perceived discussion experience.
How Mass surveillance Crowds Out Installations of COVID-19 Contact Tracing Applications

During the COVID-19 pandemic, many countries have developed contact tracing technologies to curb the spread of the disease by locating and isolating people who have been in contact with coronavirus carriers. Subsequently, understanding why people install and use contact tracing applications is becoming central to their effectiveness and impact. However, involuntary systems can crowd out the use of voluntary applications when several contact tracing initiatives are employed simultaneously. To investigate this hypothesis, we analyze the concurrent deployment of two contact tracing technologies in Israel: centralized mass surveillance technologies and a voluntary contact tracing mobile app. Based on a representative survey of Israelis (n=519), our findings show that positive attitudes toward mass surveillance were related to a reduced likelihood of installing contact tracing apps and an increased likelihood of uninstalling them. These results also hold when controlling for privacy concerns, attitudes toward the app, trust in authorities, and demographic properties. We conclude the paper by suggesting a broader framework for analyzing crowding out effects in ecosystems that combine involuntary surveillance and voluntary participation.
"When the Beeping Stops, you Completely Freak Out" - How Acute Care Teams Experience and Use Technology

Technology use in acute care has so far been studied with a predominant focus on safety and efficiency rather than the experience of acute care staff. In the present paper, we report a qualitative study including 130 hours of participant observation on acute care teams and retrospective interviews with nine anesthesiologists on their experiences with technology. Our approach is theoretically guided by four modern HCI perspectives, namely psychological need satisfaction, activity theory, embodied interaction, and media equation theory. We analyzed the interview and observation data using a reflexive thematic analysis and identified four themes. Technology as necessary (evil) describes the pervasiveness of technology and how acute care teams depend on technology. While technology as second patient covers the additional load for anesthesiologists on a functional and emotional level, technology as problem solver and safety net highlights relieving aspects of technology use. Technology as artificial limb focuses on so-called transparent tools that extend the body and capabilities of users. We then discuss the identified themes concerning the previously presented theories. Based on our findings and theoretical perspectives, we report general insights for research and design in acute care, such as the benefits of mixed methods or the importance of the gut feelings of experts. Finally, we propose possible directions for future work, such as developing a UX questionnaire adapted to the context of safety-critical domains, such as acute care.
Youths' Usage of an Informal Chatting Tool in an After-school 3D Print Shop

Digital technological advancements have been a driving force in the continued evolution of the modern workplace. One notable change is an increased reliance on informal chatting tools in support of both distributed and co-located work. However, while researchers have recognized the growing prominence of these tools in work, little is known about how to best support youth who have grown up with access to interconnected technologies in acquiring and practicing the relevant skills needed to effectively communicate using these tools. In this study, we studied youth's utilization of Slack, a popular workplace chatting tool, in an after-school 3D print shop. Using in-depth semi-structured interviews, we uncovered the factors that supported or hindered youth employees' Slack use in a technical work context. This work offers implications for how to design youth workplace communication and coordination protocols that support the needs of this population.
What Mid-Career Professionals Think, Know, and Feel About Phishing: Opportunities for University IT Departments to Better Empower Employees in Their Anti-Phishing Decisions

Phishing attacks, in which deceptive messages purporting to be from a legitimate contact are used to trick recipients and acquire sensitive information for the purposes of committing fraud, are a substantial and growing problem for organizations. IT departments and professionals may put in place a variety of institutional responses to thwart such attacks, but an organization's susceptibility to phishing also depends on the decisions and actions of individual employees. These employees may have little phishing expertise but still need to react to such attempts on a daily basis. Based on 24 semi-structured interviews with mid-career office workers (70.8% women, averaging 44 years old, with a bachelor's degree or more) at two universities in the midwestern United States, we find that employees self-describe a wide range of levels of awareness of, and confidence, competency and investment in, the organization's proscribed anti-phishing policies and practices. These employees also describe variation in the ways they would prefer to increase their perceived performance levels in all of these areas. In this paper, we argue that in order to empower employees to be better collaborators in an organization's anti-phishing efforts, organizations should embrace a range of efforts akin to the range of expertise among the users themselves. We make four such empowering recommendations for organizations to consider incorporating into their existing anti-phishing policies and practices, including suggestions to 1) embrace educating non-expert users more fully on organizational processes and consequences, 2) provide employees with a standing one-to-one communication channel between them and an IT phishing point-of-contact, 3) keep employees in the loop once phishing reports are made, and 4) avoid testing employees with "gotcha" assessments.