Reviewerly/exHarmony huggingface dataset:
The effect of augmented reality versus traditional advertising: a comparison between neurophysiological and self-reported measures

Abstract Despite the growing application of augmented reality in advertising, there is limited understanding about how customers respond to their interaction with the augmented reality advertising and how it differs from a standard paper-based advertising. Augmented reality ads are immersive, interactive, and lifelike, which means they may help companies create an emotional connection with their customers. The authors test if customers would respond in terms of emotional and affective intensity differently to augmented reality versus standard paper-based advertising. The results of two laboratory studies that consider physiological measures of arousal (galvanic skin response), self-reported measure of affect intensity and willingness to pay show that the higher willingness to pay for customers exposed to augmented reality as opposed to standard paper-based ads is driven by the physiological arousal, but not by the self-reported affect intensity and that processing fluency possibly underlies consumer’s enhanced emotional responses toward AR. These results suggest that replacing traditional advertisements with augmented reality advertisements enhances customer physiological responses and willingness to pay, with possible implications on customer segmentation and marketing communication.
Human-computer interaction in foreign language learning applications: Applied linguistics viewpoint of mobile learning

The current development in mobile learning has seen an unprecedented rise in the last decade and, moreover, the present pandemic situation has speeded up the process of eLearning implementation even in areas that were still rather reluctant in this respect. The paper attempts to provide the results and analysis of the subjective satisfaction of the users of these foreign language learning apps. Qualitative research was conducted at the Faculty of Informatics and Management of the University of Hradec Kralove, the Czech Republic, as a qualitative study through a set of guided interviews with students/users. The respondents (n=18) were the students of information science and data science of the university; therefore, their opinion can prove to be very useful as they are well informed and they also represented the youngest generation, i.e. Millennials, who may have rather different needs from the previous generations, and these needs are, naturally, reflected in their use of technology. The tested apps belong to the most popular and most downloaded free apps for foreign language learning currently available in the iOS and Android markets. The findings of the research clearly show that the main concern of the users of the apps was the lack of AI utilization and old-fashioned interface of the apps despite the fact that they are relatively new. Another major concern was the old-fashioned design, and regarding human-computer interaction in these apps, the lack of machine learning and deep learning strategies.
RubySlippers: Supporting Content-based Voice Navigation for How-to Videos

Directly manipulating the timeline, such as scrubbing for thumbnails, is the standard way of controlling how-to videos. However, when how-to videos involve physical activities, people inconveniently alternate between controlling the video and performing the tasks. Adopting a voice user interface allows people to control the video with voice while performing the tasks with hands. However, naively translating timeline manipulation into voice user interfaces (VUI) results in temporal referencing (e.g. "rewind 20 seconds"), which requires a different mental model for navigation and thereby limiting users' ability to peek into the content. We present RubySlippers, a system that supports efficient content-based voice navigation through keyword-based queries. Our computational pipeline automatically detects referenceable elements in the video, and finds the video segmentation that minimizes the number of needed navigational commands. Our evaluation (N=12) shows that participants could perform three representative navigation tasks with fewer commands and less frustration using RubySlippers than the conventional voice-enabled video interface.
Tandem Deep Learning Side-Channel Attack Against FPGA Implementation of AES

The majority of recently demonstrated deep-learning side-channel attacks use a single neural network classifier to recover the key. The potential benefits of combining multiple classifiers with ensemble learning method have not been fully explored in the side-channel attack's context. In this paper, we show that, by combining several CNN classifiers which use different attack points, it is possible to considerably reduce (more than 40% on average) the number of traces required to recover the key from an FPGA implementation of AES by power analysis. We also show that not all combinations of classifiers improve the attack efficiency.
Rethinking Domain Generalization Baselines

Despite being very powerful in standard learning settings, deep learning models can be extremely brittle when deployed in scenarios different from those on which they were trained. Domain generalization methods investigate this problem and data augmentation strategies have shown to be helpful tools to increase data variability, supporting model robustness across domains. In our work we focus on style transfer data augmentation and we present how it can be implemented with a simple and inexpensive strategy to improve generalization. Moreover, we analyze the behavior of current state of the art domain generalization methods when integrated with this augmentation solution: our thorough experimental evaluation shows that their original effect almost always disappears with respect to the augmented baseline. This issue open new scenarios for domain generalization research, highlighting the need of novel methods properly able to take advantage of the introduced data variability.
MorpheesPlug: A Toolkit for Prototyping Shape-Changing Interfaces

Toolkits for shape-changing interfaces (SCIs) enable designers and researchers to easily explore the broad design space of SCIs. However, despite their utility, existing approaches are often limited in the number of shape-change features they can express. This paper introduces MorpheesPlug , a toolkit for creating SCIs that covers seven of the eleven shape-change features identified in the literature. MorpheesPlug is comprised of (1) a set of six standardized widgets that express the shape-change features with user-definable parameters; (2) software for 3D-modeling the widgets to create 3D-printable pneumatic SCIs; and (3) a hardware platform to control the widgets. To evaluate MorpheesPlug we carried out ten open-ended interviews with novice and expert designers who were asked to design a SCI using our software. Participants highlighted the ease of use and expressivity of the MorpheesPlug.
Why Don’t Developers Detect Improper Input Validation? '; DROP TABLE Papers; --

Improper Input Validation (IIV) is a software vulnerability that occurs when a system does not safely handle input data.Even though IIV is easy to detect and fix, it still commonly happens in practice.In this paper, we study to what extent developers can detect IIV and investigate underlying reasons.This knowledge is essential to better understand how to support developers in creating secure software systems.We conduct an online experiment with 146 participants, of which 105 report at least three years of professional software development experience.Our results show that the existence of a visible attack scenario facilitates the detection of IIV vulnerabilities and that a significant portion of developers who did not find the vulnerability initially could identify it when warned about its existence.Yet, a total of 60 participants could not detect the vulnerability even after the warning.Other factors, such as the frequency with which the participants perform code reviews, influence the detection of IIV.
Exploiting Object-of-Interest Information to Understand Attention in VR Classrooms

Recent developments in computer graphics and hardware technology enable easy access to virtual reality headsets along with integrated eye trackers, leading to mass usage of such devices. The immersive experience provided by virtual reality and the possibility to control environmental factors in virtual setups may soon help to create realistic digital alternatives to conventional classrooms. The importance of such settings has become especially evident during the COVID-19 pandemic, forcing many schools and universities to provide the digital teaching. Researchers foresee that such transformations will continue in the future with virtual worlds becoming an integral part of education. Until now, however, students' behaviors in immersive virtual environments have not been investigated in depth. In this work, we study students' attention by exploiting object-of-interests using eye tracking in different classroom manipulations. More specifically, we varied sitting positions of students, visualization styles of virtual avatars, and hand-raising percentages of peer-learners. Our empirical evidence shows that such manipulations play an important role in students' attention towards virtual peer-learners, instructors, and lecture material. This research may contribute to understanding of how visual attention relates to social dynamics in the virtual classroom, including significant considerations for the design of virtual learning spaces.
ESPC_NASUnet: An End-to-End Super-Resolution Semantic Segmentation Network for Mapping Buildings From Remote Sensing Images

Higher resolution building mapping from lower resolution remote sensing images is in great demand due to the lack of higher resolution data access, especially in the context of disaster assessment.High resolution building layout map is crucial for emergency rescue after the disaster.The emergency response time would be reduced if detailed building footprints were delineated from more easily available low-resolution data.To achieve this goal, we propose a super-resolution semantic segmentation network called ESPC_NASUnet, which consists of a feature super-resolution module and a semantic segmentation module.To the best of authors' knowledge, this is the first work to systematically explore a deep learning-based approach to generate semantic maps with higher spatial resolution from lower spatial resolution remote sensing images in an end-to-end fashion.The experimental results for two datasets suggest that the proposed network is the best among four different end-to-end architectures in terms of both pixel-level metrics and object-level metrics.In terms of pixel-level F 1-score, the improvements are greater than 0.068 and 0.055.Regarding the object-level F 1-score, the disparities between ESPC_NASUnet and other end-to-end methods are more than 0.083 and 0.161 in the two datasets, respectively.Compared with stage-wise methods, our end-to-end network is less impacted by low-resolution input images.Finally, the proposed network produces building semantic maps comparable to those generated by semantic segmentation networks trained with high-resolution images and the ground truth utilizing the two datasets. Index Terms-Building extraction
Automatic cervical lymphadenopathy segmentation from CT data using deep learning

The purpose of this study was to develop a fast and automatic algorithm to detect and segment lymphadenopathy from head and neck computed tomography (CT) examination. An ensemble of three convolutional neural networks (CNNs) based on a U-Net architecture were trained to segment the lymphadenopathies in a fully supervised framework. The resulting predictions were assessed using the Dice similarity coefficient (DSC) on examinations presenting one or more adenopathies. On examinations without adenopathies, the score was given by the formula M/(M + A) where M was the mean adenopathy volume per patient and A the volume segmented by the algorithm. The networks were trained on 117 annotated CT acquisitions. The test set included 150 additional CT acquisitions unseen during the training. The performance on the test set yielded a mean score of 0.63. Despite limited available data and partial annotations, our CNN based approach achieved promising results in the task of cervical lymphadenopathy segmentation. It has the potential to bring precise quantification to the clinical workflow and to assist the clinician in the detection task.
Bits Under the Mattress: Understanding Different Risk Perceptions and Security Behaviors of Crypto-Asset Users

Crypto-assets are unique in tying financial wealth to the secrecy of private keys. Prior empirical work has attempted to study end-user security from both technical and organizational perspectives. However, the link between individuals’ risk perceptions and security behavior was often obscured by the heterogeneity of the subjects in small samples. This paper contributes quantitative results from a survey of 395 crypto-asset users recruited by a novel combination of deep and broad sampling. The analysis accounts for heterogeneity with a new typology that partitions the sample in three robust clusters – cypherpunks, hodlers, and rookies – using five psychometric constructs. The constructs originate from established behavioral theories with items purposefully adapted to the domain. We demonstrate the utility of this typology in better understanding users’ characteristics and security behaviors. These insights inform the design of crypto-asset solutions, guide risk communication, and suggest directions for future digital currencies.
What Happens After Death? Using a Design Workbook to Understand User Expectations for Preparing their Data

Digital data has become a key part of everyday life: people manage increasingly large and disparate collections of photos, documents, media, etc. But what happens after death? How can users select and prepare what data to leave behind before their eventual death? To explore how to support users, we first ran an ideation workshop to generate design ideas; then, we created a design workbook with 12 speculative concepts that explore diverging approaches and perspectives. We elicited reactions to the concepts from 20 participants (18-81, varied occupations). We found that participants anticipated different types of motivation at different life stages, wished for tools to feel personal and intimate, and preferred individual control on their post-death self-representation. They also found comprehensive data replicas creepy and saw smart assistants as potential aides for suggesting meaningful data. Based on the results, we discuss key directions for designing more personalized and respectful death-preparation tools.
An Interface for Enhanced Teacher Awareness of Student Actions and Attention in a VR Classroom

Networked VR is gaining recognition as a way to provide remote presentations or classes when in-person meetings are difficult or risky to conduct. However, the tools do not provide as many cues about audience actions and attention as in-person meetings, for example, subtle face and body motion cues are missing. Furthermore, the field of view and visual detail are reduced, and there are added problems such as motion sickness, network disconnections, and relatively unrestricted avatar positioning. To help teachers understand and manage students in such an environment, we designed an interface to support teacher awareness of students and their actions, attention, and temperament in a social VR environment. This paper focuses on how different visual cues are integrated into an immersive VR interface that keeps relevant information about students within the teacher's visual field of attention. Cues include floating indicators, centrally-arranged face icons with gaze information, tethers and other indicators of avatar location, and options to reduce the amount of presented information. We include a pilot study of user preferences for different cue types and their parameters (such as indicator style and placement with respect to the teacher).
Research on Application of Deep Learning Algorithm in Image Classification

Image classification is an important research direction in the field of computer vision. Image classification algorithm can distinguish different kinds of pictures by classifying the features extracted from the original pictures. In essence, deep learning is actually the technology of simulating and analyzing the human brain through the construction of deep neural network, or learning and interpreting related data by simulating the human brain. As an important information carrier, image has become an important form for people to acquire and transmit information. For massive images, the number of images that each user really needs is very small, so it is of great significance to find the images that users need within the effective time. Based on this, this paper analyzes the research and application of deep learning in image recognition, hoping to further enhance the application effect of deep learning and make it play a greater role in the field of image recognition.
A submarine pipeline segmentation method for noisy forward-looking sonar images using global information and coarse segmentation

The submarine pipeline is an important way to transport marine resources such as oil and gas. For maintaining submarine pipelines, autonomous underwater vehicles (AUVs) equipped with sonars are leveraged. Whether the complete submarine pipelines can be segmented from the complex and noisy background is the key to path planning and navigation, which determines the efficiency of AUVs. Existing methods for segmenting sonar images such as Markov random field (MRF) are insufficient to achieve this goal. Inspired by the similarities between submarine pipelines in sonar images and salient objects in natural images, we propose a novel method to extract pipelines which can be summarized as three steps. First, an effective denoising method via curvelet thresholding is proposed to suppress speckle noise. Second, we attempt to compute the probability whether a pixel belongs to submarine pipelines by global information containing four similarities. Two of them are shape similarity and angle similarity, which are proposed according to the characteristics of pipelines in sonar images. Third, we propose a coarse segmentation method based on bootstrapped support vector machines (SVMs) and a fusion strategy to generate binary segmentation results precisely and completely. We captured 15 sonar sequences via the AUV equipped with a forward-looking sonar at Qingdao National Deep-Sea Base and selected 262 sonar images from the sequences for evaluating the segmentation performance. The proposed method is compared with other advanced methods. The experimental results show that our method achieves a better performance than other methods, and demonstrate the feasibility of segmenting submarine pipelines inspired by saliency segmentation.
Factors That Affect E-Learning Platforms after the Spread of COVID-19: Post Acceptance Study

The fear of vaccines has led to population rejection due to various reasons. Students have had their own inquiries towards the effectiveness of the vaccination, which leads to vaccination hesitancy. Vaccination hesitancy can affect students’ perception, hence, acceptance of e-learning platforms. Therefore, this research attempts to explore the post-acceptance of e-learning platforms based on a conceptual model that has various variables. Each variable contributes differently to the post-acceptance of the e-learning platform. The research investigates the moderating role of vaccination fear on the post-acceptance of e-learning platforms among students. Thus, the study aims at exploring students’ perceptions about their post-acceptance of e-learning platforms where vaccination fear functions as a moderator. The current study depends on an online questionnaire that is composed of 29 items. The total number of respondents is 630. The collected data was implemented to test the study model and the proposed constructs and hypotheses depending on the Smart PLS Software. Fear of vaccination has a significant impact on the acceptance of e-learning platforms, and it is a strong mediator in the conceptual model. The findings indicate a positive effect of the fear of vaccination as a mediator in the variables: perceived ease of use and usefulness, perceived daily routine, perceived critical mass and perceived self-efficiency. The implication gives a deep insight to take effective steps in reducing the level of fear of vaccination, supporting the vaccination confidence among educators, teachers and students who will, in turn, affect the society as a whole.
Robustness of on-Device Models: Adversarial Attack to Deep Learning Models on Android Apps

Deep learning has shown its power in many applications, including object detection in images, natural-language understanding, and speech recognition. To make it more accessible to end users, many deep learning models are now embedded in mobile apps. Compared to offloading deep learning from smartphones to the cloud, performing machine learning on-device can help improve latency, connectivity, and power consumption. However, most deep learning models within Android apps can easily be obtained via mature reverse engineering, while the models' exposure may invite adversarial attacks. In this study, we propose a simple but effective approach to hacking deep learning models using adversarial attacks by identifying highly similar pre-trained models from TensorFlow Hub. All 10 real-world Android apps in the experiment are successfully attacked by our approach. Apart from the feasibility of the model attack, we also carry out an empirical study that investigates the characteristics of deep learning models used by hundreds of Android apps on Google Play. The results show that many of them are similar to each other and widely use fine-tuning techniques to pre-trained models on the Internet.
Living with Uncertainty and Stigma: Self-Experimentation and Support-Seeking around Polycystic Ovary Syndrome

Polycystic Ovary Syndrome (PCOS) is a condition that causes hormonal imbalance and infertility in women and people with female reproductive organs. PCOS causes different symptoms for different people, with no singular or universal cure. Being a stigmatized and enigmatic condition, it is challenging to discover, diagnose, and manage PCOS. This work aims to inform the design of inclusive health technologies through an understanding of people's lived experiences and challenges with PCOS. We conducted semi-structured interviews with 10 women diagnosed with PCOS and analyzed a PCOS-specific subreddit forum. We report people's support-seeking, sense-making, and self-experimentation practices, and find uncertainty and stigma to be key in shaping their unique experiences of the condition. We further identify potential avenues for designing technology to support their diverse needs, such as personalized and contextual tracking, accelerated self-discovery, and co-management, contributing to a growing body of HCI literature on stigmatized topics in women's health and well-being.