Reviewerly/exHarmony huggingface dataset:
Joint Space Control of Aerial Manipulator in Flight using Optimized PD Controller

This research paper presents a model for the dynamics of an aerial manipulator consisting of a planar 2R manipulator attached to a quadcopter with an H-configuration. This research aims to develop a control strategy to ensure stable manipulation while the aerial manipulator is in flight. The paper presents an optimized PD controller for controlling joint space and a near-optimal parameter tuning procedure using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Numerical experimental results show that the quadcopter can reach the desired position and the manipulator can manipulate in joint space, thus proving the effectiveness of the proposed control strategy. This research contributes to developing control strategies for aerial manipulators and provides a basis for future research.
Effectiveness of Haptics Gloves for Learning Motor Skills in Virtual Reality Scaffolding Simulator

The present study aimed to compare the effectiveness of hand controllers and haptic gloves as interaction devices in virtual reality scaffolding simulator training. A task-specific virtual reality exercise was designed to assess participants’ performance and a between-subject design was employed, with participants randomly assigned to either a hand controller or haptic glove group. 14 participants took part in the study, 7 in each group. Both groups participated in training and evaluation sessions using the simulator. Feedback on usability was collected via a questionnaire measuring aspects such as ease of use, realism, comfort, and virtual hand manipulation. The results of the study indicated the equal significant in usability aspects between the hand controller and haptic glove groups.
Real-Time Serviceable Path Planning using UAVs for Waterborne Vehicle Navigation during Floods

Autonomous navigation and formation control of multi-UAV systems pose a significant challenge for the robotic systems that operate in partially observable, dynamic and continuous environments. This paper addresses the problem of multi-UAV cooperative sensing and coverage of a flood-struck region to identify serviceable paths to critical locations for waterborne vehicles (WBV) in real time. A serviceable path is defined as a location that is obstacle free and has adequate water level for possible movement of WBVs. We develop a deep reinforcement learning model to learn a cooperative multi-UAV policy for real-time coverage of a flooded region. The coverage information gathered by the UAVs captures the presence of obstacles present in the path connecting the start and target/critical locations given by the shortest Manhattan distance. This coverage information is utilized by the path planning algorithm, i.e., MEA*, to minimize the number of expansion nodes and identify a serviceable path quickly. To conserve energy, UAVs initially follow a guided path to explore the optimal route. If obstacles are encountered, the UAVs search nearby areas for an alternate path to reach the critical location(s). The proposed approach, MEA* MADDPG, is compared with other prevalent techniques from the literature over real-world inspired simulated flood environments. The results highlight the significance of the proposed model as it outperforms other techniques when compared over various performance metrics.
Scientific Python (SciPy) based Simulation and Control of Underactuated Robotic System

This tutorial describes scientific python (SciPy) for solving ordinary differential equations (ODEs) appearing in diverse physical systems and a line-by-line explanation of the working codes for engineering and science students and practitioners. For better understanding, an engineering case study is considered in detail to explain the SciPy implementation. The case study belongs to a highly challenging, unstable and nonlinear underactuated system: rotary inverted pendulum (RIP). The current tutorial presents both SciPy simulations along with an experimental investigation of the RIP system. The analytical and experimental results show a good match with a maximum per cent error of the order of 10-4 units. Elapsed computational times are also compared between contemporary scientific computing platforms, such as MATLAB and SciPy. In the computational experimental trials of RIP system, it is found that the SciPy code has 84% faster computational time as compared to the same code being executed in the MATLAB environment. The executable codes are available for educational and research purposes at https://github.com/hmavi/SciPy_Tutorial/blob/main/Rotary_Inverted_Pendulum.py.
A Scientometric Analysis and Critical Review of Research on English for Specific Purposes Using Citespace

A few researchers conducted systematic reviews of literature on this field to understand the historical development of English for Specific Purposes. However, a holistic review combining scientometric analysis and critical review in the last ten years is still lacking. Based on 1528 bibliometric records from 2011 to 2022 retrieved from the Web of Science core collection, the study aims to provide a systematic and objective overview of ESP research via Citespace. The results provide references for understanding this field's research status quo, themes, and current trends.
Listening to the Future: Using Participatory Sound Fiction to Engage Young People in Urban Design

In spatial planning of urban areas, there is a need for new methods for meaningful inclusion of less represented voices as those of young people. This study focuses on how participatory design, design fiction and sound design can be combined to engage youth in urban planning processes. This is investigated by developing and testing a method called participatory sound fiction. The method was tested with a group of young residents in a suburban area in Sweden. Sound fictions of the suburb in 2170 were created and discussed among the youth participants. The results show that through discussions and speculations about the future, important insights were revealed about the youths’ reality, which can be of value in spatial planning processes as well as in understandings of how the youth experiences their living area. Furthermore, the study found that sound can broaden youths’ perspective on their immediate environment, that participatory sound fiction has the potential to engage youths, and that youths prefer to discuss the present to feel that their voices are being heard.
The Museum is Dreaming: Re-Imagining the Museum through Feminist Values and Data Practices in Design Fiction

Museums and galleries are in a constant process to generate and maintain meaningful, long-lasting relationships with visitors. With the exponential increase of accessible and novel technologies, they are now understood as key mediators of this practice—particularly as the collection and utilisation of personal data re-conceptualises what visitor engagement might look like. However, this shift comes with its own challenges: Creating data is not a neutral undertaking: It is always at the risk of entrenching already existing marginalisation within cultural spaces, as well as creating new forms of oppression. This paper presents a design fiction that is 1) based on previous research conducted with museum staff and visitors, and 2) generated and evaluated through feminist values as a guiding framework. Through the combination of these approaches, data streams and methods, we outline the potential of technology to aid or inhibit the concepts of accessibility, power, and personal data in a museum. Finally, we outline Feminist Data Design Fiction as a potential approach and several design implications to make the museum a more pluralistic place—for data collection and beyond.
Towards Panopticons of Convenience: Power in the Nordic Smart Home Assemblage

The introduction of smart home technologies shifts the social relationships in the home by replacing parts of the home assemblage with digital components. This article makes two contributions, first it analyses the influence of panopticons of convenience, which it conceptualises as ‘The acceptance of additional surveillance upon one's life for the purpose of acquiring actual or presumed convenience’ in smart homes. Second, through an empirical expert interview study it identifies several examples of how intentions and implementations of smart home technologies facilitate behaviours that cause or reinforce inequalities in the home. These examples are then used to understand how the panopticons of convenience contribute to the abjection of the less tech-savvy residents of these homes and to reflect on how these technologies shape energy consumption in the home.
Artificial Design Fiction: Using AI as a Material for Pastiche Scenarios

This paper considers the use of AI to generate material for design fiction. It takes climate change an example topic and briefly sketches related literature in climate fiction, sustainable HCI and design fiction. ChatGPT and text to image generators are then used to create critical and speculative pastiche scenarios drawing on existing styles and characters. The paper contrasts the AI pastiches with design fictions by academics and novelists. Although unedited AI output is likely to be formulaic it is argued that this material can be shaped into more interesting design fiction through critical prompting.
Multi-Speed Global Contextual Subspace Matching for Few-Shot Action Recognition

Few-shot action recognition (FSAR) aims to classify unseen query actions into categories represented by a few labeled support videos. Most current FSAR methods adopt the frame-level matching mechanism that requires continuous actions to be represented by a fixed number of frame features. However, this could compromise the completeness of the contextual video information and make it difficult to handle video features of varying frame sampling speeds. In this paper, we propose a multi-speed global contextual subspace matching (MGCSM) method that generates global contextual action subspace representations from videos containing different numbers of frames to preserve contextual semantic information. Specifically, we propose to obtain the scale-agnostic information of embedding video features using a global contextual aggregation (GCA) module and then generate the discriminative action subspace representation with an action subspace generation (ASG) module. Furthermore, we introduce a multi-speed subspace matching (MSM) mechanism that generates a multi-speed classification score by integrating the similarities between query videos and support subspaces of varying sampling speeds. The proposed method is embedding-agnostic and can be combined with most mainstream embedding networks without model re-designs. Comprehensive and reproducible experiments on standard datasets demonstrate our method's superior performance compared to existing state-of-the-art methods.
Find Your Match - Collaboratively Carrying A Pair of Horizontal Screens Towards Convivial Interactions

Moving in synchrony, full-bodied involvement, eye contact, a common goal, and a shared sense of achievement are all widely argued as important qualities for fostering social connection and team building. Yet it is surprising how few interactive products, devices or environments combine all of these possible shortcuts or catalysts for fostering interpersonal bonds. To help address this, we present a novel installation offering multiple participants the shared control of a walk-up-and-use interface, simultaneously engaging both with digital media content and being co-present with other users. Central to this was a pair of fabric projection screens that offered or required multiple people to lift them in order for projected animations to be in focus. Half of each projection was controlled by an accelerometer rolling on the surface of the screen, whereas the other half of the display was controlled by a second accelerometer rolling on the surface of another screen. Thus providing a puzzle-like activity for participants who physically manipulated their screens and discovered a composite image comprising two matching halves. Based upon observations conducted at an informal exhibition this approach shows promise for fostering conviviality but seems to offer too complex an interaction for casual users.
FUTURE MACHINE: Making Myths &amp; Designing Technology for a Responsible Future: Making Myths and Entanglement: Community engagement at the edge of participatory design and user experience

This paper explores the unique methods and strategies employed by a team of artists, in collaboration with engineers, programmers, a climate scientist, researchers and members of the public, who have come together to create the Future Machine, with the aim to continue this longitudinal project for 30 years. We explore the process of designing Future Machine – the provenance and sustainability of the materials, the participatory design process, how Future Machine's interactivity is evolving over time and how the technology might be sustained for 30 years. We investigate the role that Future Machine plays in engaging the human populations across these diverse places, exploring how the artists used strategies of entanglement and myth-making to support the interactions between the ecologies, technologies, non-human inhabitants, narratives and neighbourly relations that are emerging as the project evolves over time. In conclusion we propose that the artist strategies of entanglement and encouraging myth-making reveal new approaches to experiential design. We propose that these strategies provide opportunities to open up a set of disruptive/radical/novel design challenges for HCI, that encourage sustainable and positive forms of interactions for a world increasingly impacted by anthropogenic climate and environmental change
On Futuring Body Perception Transformation Technologies: Roles, Goals and Values

Body perception transformation technologies augment or alter our own body perception outside of our usual bodily experience. As emerging technologies, research on these technologies is limited to proofs-of-concept and lab studies. Consequently, their potential impact on the way we perceive and experience our bodies in everyday contexts is not yet well understood. Through a speculative design inquiry, our multidisciplinary team envisioned utopian and dystopian technology visions. We surfaced potential roles, goals and values that current and future body perception transformation technologies could incorporate, including non-utilitarian purposes. We contribute insights on such roles, goals and values to inspire current and future work. We also present three provocations to stimulate discussions. Finally, we contribute methodologically with insights into the value of speculative design as a fruitful approach for articulating and bridging diverse perspectives in multidisciplinary teams.
Energy-Dissipation Support Technology for Large Deformation Tunnels Based on the Post-Peak Behavior of Steel Plate Buckling: A Case Study

Large deformations can easily occur when tunneling through weak surrounding rock with high underground stresses. Under high-stress environments, the surrounding rock stores a large amount of strain energy, and the strain energy stored in the soft and weak surrounding rock enclosure is dissipated through the large traveling plastic deformation. Therefore, the effective use of the surrounding rock’s bearing capacity and energy dissipation is a feasible solution to the large deformation problem. Based on the energy principle, this study designs a high compressibility, large stroke energy-dissipation element, which transforms the traditional support into energy-dissipation support and solves the problem of large deformation by reasonably utilizing the “displacement” of the surrounding rock. The experimental results showed that the steel plate assembled structure had an initial peak value of about 15 MPa, a constant resistance of about 2 MPa with a large stroke, and a compression rate of more than 75%, with good post-compression flexural peak characteristics. Elastic–plastic buckling analyses of defective steel plate composite structures were carried out using finite element analysis software, and the deformation and force characteristics of a variety of steel plate composite structures after buckling under actual field conditions were investigated. Based on the 3D laser scanning system, the thickness of the upper and lower steel plates of the steel plate composite structure was set to 1 cm, the height of the vertical plate was set to 28 cm, and the thickness was set to 8 mm. The original rigid supporting method was converted into one of compression support by circumferentially embedding the steel plate composite structure into the initial support structure. The resulting support configuration offered a high safety factor because the pressure of the surrounding rock was released by the deformation of the steel plate composite structure.
[WORKSHOP] Fabulating Futures: Exploring the Possibilities for Value Based Design

As digital technologies become further enmeshed within every facet of our societies, an increasing and sustained critique of the structural harms that these technologies produce and reproduce have emanated from a diverse set of interconnected communities and researchers including feminist STS studies, design justice communities, black futurity, and praxes of decoloniality among others. While these critiques offer meaningful insights into our ontological relationship with technologies, the question becomes how to move beyond critique of existing artifacts and towards building new technosocial imaginaries from the values contained within these alternative methods and ways-of-being in the world. This workshop invites participants into a game utilizing methods of critical and speculative design as a pedagogical approach to think past the taken for granted ideas of ‘what-is’ in our world, unpacking ‘how what is’ and to move towards ‘what could or ought to be.’
InspirNET: An Unsupervised Generative Adversarial Network with Controllable Fine-grained Texture Disentanglement for Fashion Generation

Texture constitutes the color and fabric of fashion items. Its choice in fashion items can directly express the personality and emotional state of a wearer. Despite the rapid development of intelligence-driven fashion design, it remains challenging to achieve independent control over texture without affecting other attributes, due to the highly intertwined nature of texture space. To accomplish fine-grained texture disentanglement, we propose InspirNET, an unsupervised disentangled generative adversarial framework, that manipulates textures in a fine-grained latent space so as to produce new textures effectively, aiming to broaden the range of fashion options available to common users with distinct textures as well as boosting designers' potential for fashion innovation and inspiration. Specifically, we first introduce an auto-fashion attribute encoder to map the input fashion item into texture and structure spaces. To achieve unsupervised fine-grained texture disentanglement, our model proposes a K-textures disentanglement module that decomposes the texture space into several orthogonal vectors, each of which is empowered to control an independent texture element. In particular, by employing an orthogonal eigenvector to interpolate with another, a multitude of new textures can be generated easily. Qualitative and quantitative experiments demonstrate that our InspirNET can effectively utilize decomposed orthogonal vectors to generate a wide range of fashion items with diverse textures. Our model exhibits superior performance over state-of-the-art methods in terms of maintaining the authenticity of texture transfer.
"\"Call me Kiran\" – ChatGPT as a Tutoring Chatbot in a Computer Science Course"

Natural language processing has taken enormous steps during the last few years. The development of large language models and generative AI has elevated natural language processing to the level that it can output coherent and contextually relevant text for a given natural language prompt. ChatGPT is one incarnation of these steps, and its use in education is a rather new phenomenon. In this paper, we study students’ perception on ChatGPT during a computer science course. On the course, we integrated ChatGPT into Teams private discussion groups. In addition, all the students had freedom to employ ChatGPT and related technologies to help them in their coursework. The results show that the majority of students had at least tested AI-powered chatbots, and that students are using AI-powered chatbots for multiple tasks, e.g., debugging code, tutoring, and enhancing comprehension. The amount of positive implications of using ChatGPT takes over the negative implications, when the implications were considered from an understanding, learning and creativity perspective. Relatively many students reported reliability issues with the outputs and that the iterations with prompts might be necessary for satisfactory outputs. It is important to try to steer the usage of ChatGPT so that it complements students’ learning processes, but does not replace it.
Where do Databases and Digital Forensics meet? A Comprehensive Survey and Taxonomy

We present a systematic literature review and propose a taxonomy for research at the intersection of Digital Forensics and Databases. The merge between these two areas has become more prolific due to the growing volume of data and mobile apps on the Web, and the consequent rise in cyber attacks. Our review has identified 91 relevant papers. The taxonomy categorizes such papers into: Cyber-Attacks (subclasses SQLi, Attack Detection, Data Recovery) and Criminal Intelligence (subclasses Forensic Investigation, Research Products, Crime Resolution). Overall, we contribute to better understanding the intersection between digital forensics and databases, and open opportunities for future research and development with potential for significant social, economic, and technical-scientific contributions.
Diff4Rec: Sequential Recommendation with Curriculum-scheduled Diffusion Augmentation

Sequential recommender systems often suffer from performance drops due to the data-sparsity issue in real-world scenarios. To address this issue, we bravely take advantage of the strength in diffusion model to conduct data augmentation for sequential recommendation in this paper. However, there remain two critical challenges for this scarcely-explored topic: (i) previous diffusion models are mostly designed for image generation aiming to capture pixel patterns, which can hardly be applied in data augmentation for sequential recommendation aiming to capture the user-item relations; (ii) given a specific diffusion model capable of user-item interaction augmentation, it is non-trivial to guarantee that the diffusion-generated data can always bring benefits towards the sequential recommendation model. To tackle these challenges, we propose Diff4Rec, a curriculum-scheduled diffusion augmentation framework for sequential recommendation. Specifically, a diffusion model is pre-trained on recommendation data via corrupting and reconstructing the user-item interactions in the latent space, and the generated predictions are leveraged to produce diversified augmentations for the sparse user-item interactions. Subsequently, a curriculum scheduling strategy is designed to progressively feed the diffusion-generated samples into the sequential recommenders, with respect to two levels, i.e., interaction augmentation and objective augmentation, to jointly optimize the data and model. Extensive experiments demonstrate that our proposed Diff4Rec framework is able to effectively achieve superior performance over several strong baselines, capable of making high-quality and robust sequential recommendations. We believe the proposed Diff4Rec has the promising potential to bring paradigm shift in multimedia recommendation.