Reviewerly/exHarmony huggingface dataset:
Dataslip: Into the Present and Future(s) of Personal Data

Most people are entangled with an ever-growing trail of data that results from their daily interactions with products and services. Yet, they are hardly aware of the nature and characteristics of the data within this trail. We design dataslip, a provocative artifact that materializes the personal data trail into a receipt and aims to elicit creepiness. We demonstrate dataslip at two events in Delft, The Netherlands. Dataslip is a starting point to foster conversations with local community members about the underlying challenges and potential alternatives to personal data collection and use. We use these as prompts for further speculation through a collaborative futuring exercise with children, where we part from challenges towards hopeful and empowering futures. We contribute with an artifact that invites individuals to interrogate the current personal data practices they are embedded in and a set of five speculative design scenarios that suggest hopeful and empowering alternatives.
Exploring Superpower Design Through Wi-Fi Twinge

Technology-facilitated "superpowers" are a popular topic in human-computer interaction, providing people with the experience of having superhero-like fantastic abilities. However, science fiction literature tells us that superpowers can also be unfortunate. We designed Wi-Fi Twinge, a system that uses electrical muscle stimulation to extend human perception to sense unseen Wi-Fi signals, giving people an allergy-like twinge reaction to Wi-Fi: a superpower that can be seen as unfortunate while also having potential benefits. Through an in-the-wild study, interviews with 12 participants revealed that although the superpower from Wi-Fi Twinge induced negative feelings and impacted certain day-to-day activities, it improved awareness of self and the surrounding environment. We used these results to offer design tactics for creating future superpower systems that consider potential unfortunate side effects. Ultimately, we aim to advance our understanding of technology-facilitated superpowers.
Data Probes: Reflecting on Connected Devices with Technology-Mediated Probes

We introduce Data Probes, technology-mediated probes designed to reveal some of the inner workings of connected devices, including common embedded sensors and the data they collect. By making these common features both accessible and unfamiliar, the probes supported research participants in looking at these technologies from a different perspective and reflecting on capabilities and behaviours that may be obscured by the design of commercial products. During a study where participants lived and travelled with the probes for a month, we were able to gain generative design insights into people’s attitudes towards and relationships with connected devices, suggesting new opportunities for designs that take alternative approaches to currently entrenched visions of the Internet of Things. We present this exploratory study as an illustration of how a technology-mediated probe might prompt reflection on their technologies and open up new design spaces.
Body Transformation: An Experiential Quality of Sensory Feedback Wearables for Altering Body Perception

Body perception has a significant impact on people's motor, emotional, and social functioning. We evaluated the potential of four different existing wearable prototypes, which provide sound or haptic bodily feedback to alter body perception. In a Research through Design workshop, we invited professional dancers as expert study participants to explore and assess our prototypes. Based on the workshop's insights, we articulate the experiential quality of Body Transformation, which characterizes how dancers perceive and experience their bodies while interacting with such prototypes. The quality encompasses a perceptual and holistic transformation, impacting the feelings about body, movement and emotions, and where the sensory feedback's evocative power is crucial. Additionally, it elicits different transformation valuations. We contribute a deeper understanding of the impact of sensory feedback on body perception, its potential for transforming people's overall body experience, and methodological insights on the potential of working with dancers to evaluate wearable sensory technology.
Mouja: Experiencing AI through Magnetic Interactions

Mouja, or the Magnetic Ouja, is a performance combining real-time neural synthesis with two specifically designed instrument-scores. Because of the inherent arbitrariness and entanglement in the distribution of the sound features in the latent space, with neural synthesis models such as RAVE gesture, interface and sound appear as deeply intertwined, and the development of dedicated, embedded interfaces becomes critical to their understanding and exploration. Thales and Stacco provide an effective and poetic way of playing with and understanding latent dimensions in RAVE. They consist of two tangible interfaces based on permanent magnets, and through the interactions of their magnetic fields they engage the performer in a tactile dance of agencies, where the entanglements of the algorithm are experienced through a dynamic and embodied process of negotiation with the magnetic agency of the instrument-score.
Embodied Machine Learning

Machine learning becomes more prevalent in specialized domains such as medicine and biology every year, but domain expert trust in machine learning continues to lag behind. Researchers have explored increasing rational trust in AI but little research exists focusing on systems that foster affective and normative trust between domain experts and data scientists who create the models. Tools like Project Jupyter have attempted to bridge this gap between data scientists and domain experts, but failed to see uptake in applied fields or to promote collaboration through co-located synchronous work. To address this we present a proof-of-concept tabletop interactive machine learning system for synchronous, co-located model fine tuning. We tested our system with biology experts and data scientists on a cell biology dataset. Results show that our system promotes interactions between domain experts, data scientists, and the model-in-training and fosters domain expert affective and normative trust in the resulting AI model.
LensLeech: On-Lens Interaction for Arbitrary Camera Devices

Cameras provide a vast amount of information at high rates and are part of many specialized or general-purpose devices. This versatility makes them suitable for many interaction scenarios, yet they are constrained by geometry and require objects to keep a minimum distance for focusing. We present the LensLeech, a soft silicone cylinder that can be placed directly on or above lenses. The clear body itself acts as a lens to focus a marker pattern from its surface into the camera it sits on. This allows us to detect rotation, translation, and deformation-based gestures such as pressing or squeezing the soft silicone. We discuss design requirements, describe fabrication processes, and report on the limitations of such on-lens widgets. To demonstrate the versatility of LensLeeches, we built prototypes to show application examples for wearable cameras, smartphones, and interchangeable-lens cameras, extending existing devices by providing both optical input and output for new functionality.
KaavadBits: Exploring Tangible Interactive Storytelling of Branching Narratives through a Kaavad-inspired Installation

This work explores branching narratives through KaavadBits, a tabletop art installation embodying the kaavadiya-jajmaan or the narrator-patron perspective of kaavad baanchana, the Indian storytelling tradition of reciting the kaavad. For diegetic worldbuilding of tales from Panchatantra, a compilation of ancient Indian animal fables, the narrator takes the physical form of a tree, with which the audience interacts using tokens for a seamless multi-modal storytelling experience. Building on the related explorations, we propose a novel design that immerses the audience through choice, character and question-based interactions. We discuss the insights from a pilot user study and directions for future work. Through this paper, we aim to strike consequential tangible, technological and narrative explorations into various lesser-known traditional forms of storytelling that may inspire new interaction techniques, ultimately preserving the intangible heritages.
Take Me to the Dancing: Experiencing Presence of Remote Friend at Our Own Physical Space while Dancing with Him or Her

The experience of dancing with remote friends can provide the opportunity to deepen intimacy in distance. However, existing remote dancing platforms have proven insufficient in conveying the presence of friends in our physical atmosphere. In this paper, we explore ' how can we design the presence of a remote friend' during the dance. To achieve this objective we employed a visual design approach called "sharing the same ground".Our experimental prototype enables two individuals to engage in dancing within their respective physical space, while simultaneously visualizing their footsteps movement to each other in real-time. Using motion sensors to capture foot position and rotation, we project calculated footsteps visuals on the floor to facilitate dancing. Through qualitative analysis, we concluded that the prototype establishes the strong presence of friends engaged in remote dancing.
Port-based entanglement teleportation via noisy resource states

Abstract Port-based teleportation (PBT) represents a variation of the standard quantum teleportation and is currently being employed and explored within the field of quantum information processing owing to its various applications. In this study, we focus on PBT protocol when the resource state is disrupted by local Pauli noises. Here, we fully characterise the channel of the noisy PBT protocol using Krauss representation. Especially, by exploiting the application of PBT for entanglement distribution necessary in realizing quantum networks, we investigate entanglement transmission through this protocol for each qubit considering noisy resource states, denoted as port-based entanglement teleportation (PBET). Finally, we derive upper and lower bounds for the teleported entanglement as a function of the initial entanglement and the noises. Our study demonstrates that quantum entanglement can be efficiently distributed by protocols utilizing large-sized resource states in the presence of noise and is expected to serve as a reliable guide for developing optimized PBET protocols. To obtain these results, we address that the order of entanglement of two qubit states is preserved through the local Pauli channel, and identify the boundaries of entanglement loss through this teleportation channel.
Robust sensitive-information de-identification framework based on relative-position estimation of objects in closed-circuit television videos

Closed-circuit television (CCTV) videos provide important data for intelligent control systems; however, sensitive information such as human faces and vehicle license plates must be de-identified by a de-identification framework before such videos are used. Previous research has mainly focused on enhancing the performance of de-identification methods, and only a few are devoted to detecting sensitive information. To address this issue, this study proposes an artificial intelligence-based sensitive information de-identification framework. The framework detects objects and utilizes their structural information to estimate the relative position of sensitive data, followed by the de-identification process. To enhance the recall of the detection module, this study analyzes difficult-to-detect faces and license plates in CCTV videos and proposes relative position estimation methods. These methods minimize the possibility of sensitive information leakage while reducing the number of locations of sensitive information that need to be annotated. When evaluated using actual urban CCTV videos, the recall of the framework was 84.63%, which is 33.01% higher than that of the conventional algorithm.
Local quantum Fisher information and Jensen-Shannon coherence dynamics of two-spin-qubits XYZ-Heisenberg state

In this study, we investigate the impact of the XYZ-DM-Heisenberg model on quantum resources, including local quantum Fisher information, concurrence, and Jensen-Shannon coherence, in the presence of intrinsic decoherence. By exploring various spin-exchange interactions, with a focus on the role of the y-DM interaction, we reveal how these interactions affect quantum correlations and coherence dynamics. We find that the system transitions from separability to the generation of quantum properties when spin interactions are introduced. The specific parameter choices significantly influence the dynamic map of quantum functions. Notably, strong y-DM interaction, in combination with equal spin interaction, leads to pronounced fluctuations, while weak DM interaction results in sudden death and rebirth in the x direction. Additionally, ferro- and anti-ferromagnetic regimes impact the quantum functions differently. In the presence of intrinsic decoherence, fluctuations decrease, and coherence remains robust compared to the local quantum Fisher information and entanglement, reducing loss.
Digitally Empowering Women Through Community Radio and Mobile Storytelling in India

In the post-COVID-19 era, digital empowerment has become significant, especially with reference to media messages and communication technologies being used to financially empower citizens. As past histories of media are leaving behind a legacy of analog and archaic forms of media content creation, new technologies are building the ‘media archaeology' of past traditions, heritages, epistemologies to generate meanings, and dialogue for new means of storytelling, thereby preserving heritage in ‘digital formats'. The study has some broader mission and objectives with more specific research questions: What are the key themes of digital literacy among community radio practitioners? Which theoretical frameworks can define digital literacy? What are the different tools of digital empowerment? Is there an emerging ‘empowering relationship' among women content creators and consumers?
An AUV collision avoidance algorithm in unknown environment with multiple constraints

Ensuring the safety of Autonomous Underwater Vehicles (AUVs) is paramount for successfully executing their marine operations. Grounded in partial observation, motion characteristics, and safety distance constraints of AUVs navigating in uncharted environments, this study proposes a collision avoidance algorithm to guarantee AUV safety. The algorithm integrates the Dubins Improved Hybrid A* (DIHA*) algorithm and the Fuzzy Heading Avoidance (FHA) algorithm. The DIHA*-FHA algorithm dissects collision avoidance paths into target-oriented global paths and collision avoidance-oriented local paths. Subsequently, algorithmic functions are invoked based on different types of obstacle information. The components are delineated as follows: (1) We are employing the DIHA* algorithm to compute the optimal path solution aligning with the actual motion characteristics of the AUV, leveraging known prior obstacle information in the environment to guide the AUV toward the target. (2) Utilizing the FHA algorithm, grounded in a fuzzy controller, to swiftly respond to obstacle information detected by the AUV during its operation in an unknown environment. This involves calculating a local smooth path that maintains a safe distance from obstacles, facilitating effective collision avoidance. Ultimately, this study substantiates the superior performance of the DIHA*-FHA algorithm in real-time, path quality, and mitigation of local minima issues under multiple constraints in unknown environments, as compared to other algorithms. Experimental results underscore that this algorithm enhances the collision avoidance capability of AUVs in unknown environments with multiple constraints, playing a pivotal role in ensuring AUV safety in practical maritime operations.
Predominant fracture gold in the Jiaodong gold province, China: Constraints on gold mineralization processes

The Jiaodong Peninsula, the largest gold province in China, hosts proven gold resources of ∼ 5,500 t. Knowledge of the accurate occurrence of gold and its formation mechanism is key to a better understanding of the mineralization process of these deposits. In this study, we combined field-emission scanning electron microscopy, laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), electron backscattering diffraction (EBSD), and high-resolution X-ray computed tomography (micro-CT) to investigate the predominant gold occurrence type and its formation processes in the Sanshandao and Xincheng giant gold deposits. Microscopic observations confirmed that visible gold in the two-dimensional mode occurred as fracture gold and inclusions in the pyrite. However, high-resolution micro-CT revealed that almost all visible gold in the three-dimensional mode was connected to the fracture, suggesting that the previously so-called gold inclusions were actually fracture gold. The LA-ICP-MS results showed that the pyrite contained low concentrations of invisible gold (<1 ppm). Thus, fracture gold is the predominant type in the Jiaodong gold province. The EBSD analyses showed that the neighboring pyrite fragments shared a similar crystallographic orientation, and these fragments could be assembled into individual grains. The in-situ fracturing texture resulted from structural activities and/or hydraulic fracturing. By combining petrographic, crystallographic, and geochemical analyses with geological information, the mineralization process of the Jiaodong gold deposits can be summarized as follows. When the gold-bearing fluid reacted with the wall rocks, intensive fluid–rock interactions led to the precipitation of massive amounts of pyrite. Subsequent structural activities and/or hydraulic fracturing broke the pyrite and triggered fluid boiling. Boiling induced gold saturation and precipitation in the cracks of the fractured pyrite. Thus, massive ore fluids with low gold concentrations can also form world-class gold deposits via the synergistic mechanisms of intensive fluid–rock interaction and fluid boiling.
Network mechanism for generating genuinely correlative Gaussian states

Abstract Generating a long-distance quantum state with genuine quantum correlation (GQC) is one of the most essential functions of quantum networks to support quantum communication. Here, we provide a deterministic scheme for generating multimode Gaussian states with certain GQC (including genuine entanglement). Efficient algorithms of generating multimode states are also proposed. Our scheme is useful for resolving the bottleneck in generating some multimode Gaussian states and may pave the way towards real world applications of preparing multipartite quantum states in current quantum technologies.
Enhanced model-driven web application development with code generation using deep learning technique

Advancements in modern technology have led to an endless reliance on the Internet. This has created a great demand for the fast and accurate development of web applications. Web development has benefitted from programming approaches such as Model-Driven Web Engineering (MDWE). MDWE allows developers to choose pre-defined models and utilize them for their requirements. This kind of structural blueprinting, called wireframing, enables efficiency in software development. However, these techniques are seldom understood by people without a technological background. Hence, much of the coding central to a project remains the responsibility of a few tech-educated people. This work proposes an approach that offloads some of the typing to a machine-based code generator. This has been achieved by pairing MDWE methods with Deep Learning capabilities. This ensures a less coding-intensive web development methodology that can be utilized even by non-web developers. This work makes several contributions to improving overall MDWE methods.
Privacy preservation using optimized Federated Learning: A critical survey

FL is a futuristic research topic that enables cross-sectoral training in ML systems in various organizations with some privacy restrictions. This review article establishes the extensive review of FL with different privacy-preserving techniques and the obstacles involved in the existing privacy-preserving model. This review is initiated by providing the background of FL and provides an overview of the technical details of the component involved in FL. Then it provides a brief review of the around 75 articles related to privacy-preserving in the FL-enabled techniques. Compared to the other survey articles this presented review article provides a brief analysis of the different privacy terms utilized in FL. The categorization of the privacy preservation models in FL highlights the significance of the model and the obstacles that limit the application of the particular privacy preservation model in real-time application. Further, this review articles ensure the details about the year of publishing, performance metrics analyzed in different articles along with their achievements. The limitation experienced in each category of the privacy-preserving technique is elaborated briefly, which assists future researchers to explore more privacy-preserving models in FL.
International trade privacy data management system combining Internet of Things blockchain

Currently, international trade (Int’l Trade) often faces risks such as privacy data leakage and transaction security during transactions, which seriously affect the protection of privacy data in trade. Therefore, this article used the Internet of Things and blockchain to attempt to address these risk issues and improve the security of privacy data. By analyzing the security threats in Int’l Trade transactions, this article used additive aggregation functions to analyze the protection effect of privacy data and studied the core issues of Int’l Trade privacy data. This article analyzed the privacy protection model using the Internet of Things blockchain and proposed corresponding Int’l Trade privacy data protection strategies. Among the five surveyed enterprises, data audit and data backup were all above the standard value, while data query only exceeded the standard value for enterprises 2, 3, 4, and 5. The average abnormal value of the enterprise in data anomalies decreased by 0.06 compared to the standard value. Through experimental testing, it has been found that Internet of Things blockchain can effectively improve the privacy data protection effect of Int’l Trade and promote the convenience of Int’l Trade transactions.
Optimization of artificial intelligence cloud computing in information management design

In order to promote the construction of enterprise informatization, the author studied the adaptive optimization method of Efficient Management Information System (EMIS). The author proposes to combine the fuzzy C-means algorithm to form a server clustering algorithm, and adds an improved Drosophila optimization algorithm to overcome the problems of slow Rate of convergence of GRNN and easy to fall into the minimum, and the cloud platform collects 23 performance indicators, the output results of the coordinated evolutionary algorithm are analyzed by the neighborhood rough set analysis of algorithms to select features to avoid the curse of dimensionality problem. The experimental results indicate that, compared with existing research results, the algorithm proposed by the author has increased its speed by 1.43, 3.22, and 3.72 times, respectively; In terms of convergence steps, they have also been reduced by 1.61, 5, and 6 times respectively, and when running the algorithm, the computer’s memory and CPU usage are controlled at around 50%, without affecting normal functionality. This proves that the task scheduling of the cloud platform is more balanced, and indirectly proves the accuracy of the algorithm’s clustering effect.