Reviewerly/exHarmony huggingface dataset:
User-Centered Evaluation of Different Configurations of a Touchless Gestural Interface for Interactive Displays

Approaches for improving the user experience when interacting with touchless displays have been proposed, such as using activation gestures and representing users as avatars in real-time. However, the novelty of such approaches may hinder users’ natural interaction behavior bringing challenges such as ease of use. In this paper, we investigate how the presence of avatars and their configurations, the usage of activation gestures, and the arrangement of interactive tiles in a touchless visual interface impact users’ experience, usability and task performance. We also compare users’ willingness to promote the interaction setup, perceived task difficulty, and time consumed to perform four different tasks in each configuration. We found that using a squared arrangement of elements, adopting activation gestures to trigger actions, and showing a moving avatar, resulted in the highest perceived usability and user experience, also reducing errors, task completion time, and perceived task difficulty. Our findings support the design of interactive displays to ensure high usability and user experience.
Empowering Users: Leveraging Interface Cues to Enhance Password Security

Passwords are a popular means of authentication for online accounts, but users struggle to compose and remember numerous passwords, resorting to insecure coping strategies. Prior research on graphical authentication schemes showed that modifying the interface can encourage more secure passwords. In this study ( $$N=59$$ ), we explored the use of implicit (website background and advertisements) and explicit (word suggestions) cues to influence password composition. We found that 60.59% of passwords were influenced by the interface cues. Our work discusses how designers can use these findings to improve authentication interfaces for better password security.
Introducing Sharemote: A Tangible Interface for Collaborative TV Control

The TV is among the most favored devices to consume media together with others in shared living spaces. Yet, the interaction with a TV is still limited to one user. This likely causes frustration or leads to long decision-making processes which in turn can reduce belongingness and affiliation with one another and impact the overall TV-watching experience negatively. To overcome this, we investigate opportunities in designing tangible interfaces to support collaborative movie selection among co-located users. Therefore, we present Sharemote - a concept that provides physical genre-based tokens to explore group interests and to narrow down possible movies to select. We evaluated the effect of Sharemote on perceived social interaction in terms of social connectedness, team performance, and co-experience through a controlled mixed-subject lab experiment in groups of three (N = 30). Results show that providing genre-based tokens significantly promotes the overall user experience of jointly selecting a movie and is the users’ preferred choice. Although Sharemote doesn’t lead to a measurable increase in social connectedness and team performance compared to the current standard in homes using a single remote, it supports the investigation of shared interests and thus helps to select a movie everyone enjoys watching. With this work, we contribute toward the design of a novel entertainment experience and discuss the value of tangibles to promote collaborative movie selection.
Eliciting Meaningful Collaboration Metrics: Design Implications for Self-Tracking Technologies at Work

As the workplace collaboration software market is booming, there is an opportunity to design tools to support reflection and self-regulation of collaboration practices. Building on approaches from personal informatics (PI), we aim to understand and promote the use of data to enable employees to explore their work practices, specifically collaboration. Focused on the preparation stage of PI (deciding to track and tools selection), we invited office workers (N=15, knowledge workers in academia) to identify meaningful aspects of their collaboration experience and report them in a logbook for two weeks. We then conducted semi-structured interviews with participants to identify and reflect on metrics related to collaboration experience. We contribute new insights into employees’ motivations and envisioned metrics reflecting their collaboration, including the personal, social, and organizational considerations for collecting and sharing this data. We derive design implications for self-tracking technologies for collaboration.
Comparing Screen-Based Version Control to Augmented Artifact Version Control for Physical Objects

Besides referring to digital twins, the iterative development of physical objects cannot be easily managed in version control systems. However, physical content also could benefit from versioning for structured work and collaborative uses, thereby increasing equality between digital and physical design. Hence, it needs to be investigated what kind of system is most suitable for supporting a physical object version control. Focusing on the visualization of differences between states of a physical artifact, two systems were compared against each other in a lab study: a screen-based solution optimized for 3D models as baseline and an approach that augments a physical artifact with digital information as hypothesis. Our results indicate that the Augmented Artifact system is superior in task completion time but scores a lower usability rating than the baseline. Based on the results, we further provide design considerations for building a physical object version control system.
Nuclear Weapons and the Militarization of AI

Abstract This contribution provides an overview of nuclear risks emerging from the militarization of AI technologies and systems. These include AI enhancements of cyber threats to nuclear command, control and communication infrastructures, proposed uses of AI systems affected by inherent vulnerabilities in nuclear early warning, AI-powered unmanned vessels trailing submarines armed with nuclear ballistic missiles. Taken together, nuclear risks emerging from the militarization of AI add new significant motives for nuclear non-proliferation and disarmament.
Everyday-Inspired Movies: Towards the Design of Movie Recommender Systems based on Everyday Life through Personal Social Media

This paper proposes the idea of movie recommenders that draw from the users’ everyday life happenings as documented through their personal social media posts to produce relevant recommendations. We conducted an experimental study to understand the important dimensions to consider in the design of such a recommendation system. We began with the design hypothesis that matching keywords and categories from users’ social media posts to those from movie plots may increase users’ perceived relevance of movie recommendations. Our analysis revealed that beyond keywords and categories, emotional context and genre of movies are important aspects to consider. Based on these findings, we discuss the implications on the design of movie recommendation systems leveraging users’ everyday life through social media posts.
A Survey of Computer-Supported Remote Collaboration on Physical Objects

The need for remote collaborative work is constantly increasing. Collaboratively adapting digital content, such as documents and images, has come to a stage where it is part of our daily lives. In comparison, remote collaboration on physical objects has matured at a slower pace, even though this is a possible step towards location-independent cooperation and therefore equality in work. In this paper, we present a structured literature review on computer-supported remote collaboration on physical objects from the last 23 years. Our contribution is threefold: First, we provide a comprehensive analysis of the current state of research on the topic of remote collaboration on physical objects. Second, we identify multiple research gaps, such as inclusion of haptic sense, mutual collaboration, and asynchronous collaboration. Third, we analyze code relationships in the selected publications and provide directions for future work in the form of exploratory research questions.
Mapping Virtual Reality Controls to Inform Design of Accessible User Experiences

A lack of accessible controls remains a barrier to disabled users engaging in virtual reality experiences. This paper presents a modified cognitive walkthrough of 120 virtual reality applications to identify 2,284 pairs of operant and resultant actions and creates an inventory of domain objects and their operant and resultant actions in the virtual space. This inventory captures both the form and prevalence of interactions that are expected of users in current virtual reality design. An analysis of this inventory reveals that while many barriers could be addressed by existing solutions, those options currently are not often present in current designs. Further, there are a set of barriers related to embodied controls that represent opportunities and challenges for new and innovative designs in virtual reality.
Blending Conversational Product Advisors and Faceted Filtering in a Graph-Based Approach

Today’s e-commerce websites often provide many different components, such as filters and conversational product advisors, to help users find relevant items. However, filters and advisors are often presented separately and treated as independent entities so that the previous input is discarded when users switch between them. This leads to memory loads and disruptions during the search process. In addition, the reasoning behind the advisors’ results is often not transparent. To overcome these limitations, we propose a novel approach that exploits a graph structure to create an integrated system that allows a seamless coupling between filters and advisors. The integrated system utilizes the graph to suggest appropriate filter values and items based on the user’s answers in the advisor. Moreover, it determines follow-up questions based on the filter values set by the user. The interface visualizes and explains the relationship between a given answer and its relevant features to achieve increased transparency in the guidance process. We report the results of an empirical user study with 120 participants that compares the integrated system to a system in which the filtering and advisory mechanisms operate separately. The findings indicate that displaying recommendations and explanations directly in the filter component can increase acceptance and trust in the system. Similarly, combining the advisor with the filters along with the displayed explanations leads to significantly higher levels of knowledge about the relevant product features.
Design of a Thrust Controller for Propeller Driven Systems Operating at Multiple Wind Velocities and Propeller Angular Velocities

The objective of this study was to achieve thrust control of a propeller propulsion system with two rotor blades rotated by a motor. Previously reported thrust control methods employ constant thrust coefficients or variable thrust coefficients as functions of the rate of progression. However, these methods resulted in thrust response errors with changes in the propeller angular velocity or wind speed. Therefore, this study established a method wherein the thrust coefficient in the controller is a function of the angular velocity of the propeller and wind speed. The effectiveness of the proposed thrust controller was verified experimentally and through simulation. The results indicated that the proposed method provided relatively better thrust control performance under multiple wind conditions.
Through Space and Time: Spatio-Temporal Visualization of MOBA Matches

With data about in-game behavior becoming more easily accessible, data-driven tools and websites that allow players to review their performance have emerged. Among the many different visualizations used as part of these systems, spatio-temporal visualizations which do not rely on animations have received little attention. In this paper, we explore if the established space-time cube (STC) visualization is a suitable means for simultaneously conveying information about space and time to players. Towards this end, we have created a STC visualization for reviewing matches, focusing on Heroes of the Storm as a use case, and conducted a study among 30 Multiplayer Online Battle Arena (MOBA) players to establish how successfully various tasks can be performed and how this kind of 3D representation is received. Our results indicate that such a visualization, despite its complexity, can be usefully applied for match analysis if the design and interaction possibilities are well chosen.
An Evolutionary Strategy Based Training Optimization of Supervised Machine Learning Algorithms (EStoTimeSMLAs)

Evolutionary strategy is increasingly used for optimization in various machine learning problems. It can scale very well, even to high dimensional problems, and its ability to globally self optimize in flexible ways provides new and exciting opportunities when combined with more recent machine learning methods. This paper describes a novel approach for the optimization of models with a data driven evolutionary strategy. The optimization can directly be applied as a preprocessing step and is therefore independent of the machine learning algorithm used. The experimental analysis of six different use cases show that, on average, better results are attained than without evolutionary strategy. Furthermore it is shown, that the best individual models are also achieved with the help of evolutionary strategy. The six different use cases were of different complexity which reinforces the idea that the approach is universal and not depending on specific use cases.
Advanced Machine Learning Framework for Efficient Plant Disease Prediction

Recently, Machine Learning (ML) methods are built-in as an important component in many smart agriculture platforms. In this paper, we explore the new combination of advanced ML methods for creating a smart agriculture platform where farmers could reach out for assistance from the public, or a closed circle of experts. Specifically, we focus on an easy way to assist the farmers in understanding plant diseases where the farmers can get help to solve the issues from the members of the community. The proposed system utilizes deep learning techniques for identifying the disease of the plant from the affected image, which acts as an initial identifier. Further, Natural Language Processing techniques are employed for ranking the solutions posted by the user community. In this paper, a message channel is built on top of Twitter, a popular social media platform to establish proper communication among farmers. Since the effect of the solutions can differ based on various other parameters, we extend the use of the concept drift approach and come up with a good solution and propose it to the farmer. We tested the proposed framework on the benchmark dataset, and it produces accurate and reliable results.
Natural Language Processing in Advertising – A Systematic Literature Review

Computational or programmatic advertising is the new way to advertise products and services online and in real-time. In this emerging type of advertising, Natural language processing (NLP) is a powerful tool for intelligently targeting and placing advertisements at the right time and in the right place for the right audience in a very short period. This study systematically reviewed journal articles, book chapters, and conference proceedings for the last ten years to find out what are the uses, approaches, and challenges that the researchers have been recently facing in making use of natural language processing techniques in the domain of advertising. It is found that in the majority of studies, information extraction and sentiment analysis are still the main focus areas. Only a small number of advanced artificial intelligence (AI) techniques, such as deep learning and speech synthesis, are used. In addition, most of the studies are based on traditional forms of advertising (such as search engines, websites, and job listings), excluding the newer forms of mobile and app-based advertising. The ongoing challenge in the current literature is applying natural language processing to automatically target advertisements.
Radiation Signal Denoising Method of Loaded Coal-Rock Based on ICEEMDAN-PCK-Means-IP

When the coal-rock is loaded, the radiation field changes to produce weak radiation, which is easily perturbed by the environment. In this article, the preprocessing method combining the three-sigma principle and the compound interpolation method was studied, based on the analysis of the composition of the radiation collected directly from the loaded coal-rock. Combined with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm, the K-Means algorithm based on Pearson distance, the DBi index, and improved Pearson coefficient, a radiation denoising method based on ICEEMDAN-PCK-Means-IP was proposed. Then, the effect of each link in the method on the noise reduction of the simulated loaded coal-rock radiation signal was analyzed. Finally, the infrared radiation (IR) signal directly obtained from uniaxial loading experiments on coal-rock was used as an example to compare and verify the denoising effect of the proposed method on the radiation of loaded coal-rock. The results show that the radiation from the directly collected loaded coal-rock will be disturbed by multiple sources, which will drown out the features due to the deformation. The preprocessing method in this article can effectively eliminate the “bad points” in the original data, and its results are highly similar to the data without “bad points.” Moreover, each link of the ICEEMDAN-PCK-Means-IP method affects the denoising effect. Its denoising effect on simulated and real radiation is obviously better than that of other “EMD-like” denoising methods or its combined method, and can effectively extract the deformation-related signals from the original radiation.
On Quantum Hyperparameters Selection in Hybrid Classifiers for Earth Observation Data

Quantum Machine Learning (QML) is an emerging technology that only recently has begun to take root in the research fields of Earth Observation (EO) and Remote Sensing (RS), and whose state of the art is roughly divided into one group oriented to fully quantum solutions, and in another oriented to hybrid solutions. Very few works applied QML to EO tasks, and none of them explored a methodology able to give guidelines on the hyperparameter tuning of the quantum part for Land Cover Classification (LCC). As a first step in the direction of quantum advantage for RS data classification, this letter opens new research lines, allowing us to demonstrate that there are more convenient solutions to simply increasing the number of qubits in the quantum part. To pave the first steps for researchers interested in the above, the structure of a new hybrid quantum neural network for EO data and LCC is proposed with a strategy to choose the number of qubits to find the most efficient combination in terms of both system complexity and results accuracy. We sampled and tried a number of configurations, and using the suggested method we came up with the most efficient solution (in terms of the selected metrics). Better performance is achieved with less model complexity when tested and compared with state-of-the-art (SOTA) and standard techniques for identifying volcanic eruptions chosen as a case study. Additionally, the method makes the model more resilient to dataset imbalance, a significant problem when training classical models. Lastly, the code is freely available so that interested researchers can reproduce and extend the results.
Full Coverage of Confined Irregular Polygon Area for Marine Survey

This paper considers the coverage path planning (CPP) problem for marine surveys, where the survey vehicles’ movements are confined in the irregular polygon search area. So this is a sort of milling problem [1] with the minimum number of turns in order for improvement of the acquired sonar image quality. For this purpose, this paper proposes a novel method called CbSPSA (calculation based shortest path search algorithm). Especially in the irregular corner area, this algorithm can easily calculate a kind of shortest path for its full coverage of this irregular area. For any given polygon, it can always be partitioned into convex polygon(s). In this paper, we only consider the case where these divided convex polygons are all connected one by one. In this case, the shortest path with minimum turns in each convex polygon can be easily searched by CbSPSA. And further by simply linking all of these pieces, the total coverage path for any given polygon area can be constructed. On the other hand, for a given polygon, usually there are several different cases of partition. Among the searched coverage paths for each of these different partitions, the final optimal coverage path is determined through a predefined criteria. Numerical simulation and analyses are carried out to demonstrate the effectiveness of the proposed method.
Training SAR Ship Detectors From Scratch with Customized Convolutional Neural Network

Deep learning-based object detectors have demonstrated remarkable performance in recent years, including their application in Synthetic Aperture Radar (SAR) ship detection. However, these detectors often adopt backbones borrowed from classification models, requiring pre-training as a preliminary step. In addition, these borrowed backbones have several limitations that hinder their effectiveness when applied to SAR images. To overcome these drawbacks and improve accuracy, we designed a lightweight backbone called SAR ship detection backbone and an attention frontend network called SAR ship detection frontend to train ship detectors from scratch. The former is a meticulously designed backbone that leverages techniques such as cross-spatial partial dense blocks to extract features and has improved efficiency in accurately locating small ships. The latter is a carefully designed frontend network equipped with an attention module for prediction. The attention module comprises a spatial attention path, channel attention path and identity mapping path. It facilitates the network's learning of more meaningful spatially and channel-wise features. In addition, we defined the scales and aspect ratios of bounding boxes in datasets. The one2one label assigning strategy was used to match every anchor box with the corresponding ground truth. Complete intersection over union loss was also used to improve the performance of regression. The results obtained from public datasets show a favorable tradeoff between accuracy and time consumption. We also found that the detectors trained from scratch relied on the volume of the training dataset. The findings of this work can be used as a reference for researchers to design SAR ship detectors tailored to their specific requirements and demands.