Reviewerly/exHarmony huggingface dataset:
Competitive and Asynchronous Decentralized Federated Learning with Blockchain Smart Contracts

In recent years, machine learning models have evolved, and the training of these models requires large amounts of data. However, the training data often contains sensitive information, raising privacy concerns. Federated Learning has been proposed as a solution to mitigate privacy risks. Despite its advantages, Federated Learning still faces challenges such as the aggregator being a single point of failure, the existence of malicious participants, and the lack of incentives. Combining Federated Learning with blockchain technology could potentially address these challenges. In this study, we propose a new method for asynchronous Federated Learning using blockchain smart contracts. Our proposed method operates autonomously and in a decentralized manner without the need to trust any central organization, making it trustless. We propose an algorithm that motivates workers to submit high-quality models as quickly as possible. Workers’ behaviors are driven by incentive mechanisms. We deployed a smart contract on a local Ethereum blockchain and executed multiple workers. Our evaluation results demonstrate that learning converges and achieves accuracy comparable to conventional Federated Learning, indicating the effectiveness of our proposed method.
Culturally-Informed Co-design of Interactive Digital Tools with Citizen Science Communities

The purpose of this research concerns the integration between Human-Computer Interaction (HCI) and interactive digital tools to increase users’ awareness about sustainability in the context of Citizen Science communities. In particular, this research project aims to design and develop meaningful and effective interactive data storytelling and interactive digital narratives based on HCI methodologies for Citizen Science. Specifically, we will investigate how cultural elements can be implemented in the design process to improve significance and sense of ownership of technological tools for scientific communication. Such tools can be critical for scientific communication in the context of the local communities of citizen scientist, as the knowledge that they produce tend to remain in the niche of their group. Interactive digital tools can convey simple, yet meaningful narrations to the public and enhance both awareness and decision-making processes about sustainability.
A Joint Evaluation Methodology for Service Quality and User Privacy in Location Based Systems

Pervasive and ubiquitous applications provide novel and exciting services leveraging on a multitude of data obtained from people’s devices, adapting the computation to the context in which the user currently is. This improves the service quality of these applications, which can provide a more tailored configuration of the application itself depending on the user context and needs. In these scenarios privacy is of paramount importance, since users must be also be protected against the misuse of their personal data. Analyzing ubiquitous systems in terms of service quality and privacy issues is however a challenging task, due to the heterogeneity of the possible attacks, which makes it difficult to compare two applications. In this paper we propose a novel methodology to jointly evaluate the service quality and the privacy issues in ubiquitous applications in an extensible and comparable way, building on the data available in each part of the system to be analyzed, and defining service qualities and privacy issues so that they can be easily re-used in other analyses. Our evaluation on a candidate application highlights the benefits of our proposal, showing the dependency between privacy levels and service quality, and paving the way for a novel methodology for the definition of these scenarios.
Cybersecurity Analysis in the UAV Domain: the Practical Approach of the Labyrinth Project

In the last decades, Unmanned Aerial Vehicles (UAVs) are finding more and more fields of application. Their flexibility and cost-efficiency make them useful to support complex operations in agriculture, remote sensing or construction, just to name a few. In the Labyrinth project we aim at investigating the applicability of UAV usage to critical scenarios like air, water and road traffic control or emergency, with a strict focus on safety, security and efficiency. This involves also the cybersecurity aspect, which is the main focus of this work. UAVs used in critical applications are in fact potentially exposed to a wide set of cyber threats. The NIST cybersecurity framework [17] defines five different security functions which are: identify, protect, detect, respond and recover. In this paper we address the identify and detect functions with an approach involving threat analysis and anomaly detection. Firstly, we identify which threats pose a significant risk to the Labyrinth use case, for instance leading to the collision of UAVs in case an attacker is successful. Secondly, we present a machine learning-based pipeline aimed at detecting deviations in the position reportings of the drone, to support the detect function during flight operations. The pipeline is tailored to the Labyrinth system reporting needs and is based on unsupervised machine learning to overcome the lack of labeled data. Anomalous points, i.e., points deviating from a coherent path, potentially because of a cyber-attack or a failure, are visually separated from the coherent ones and marked as noise. To prove its robustness, we test the pipeline introducing artificial perturbations in the data.
Usability evaluation of augmented reality visualizations on an optical see-through head-mounted display for assisting machine operations

This study explores the effect of using different visual information overlays and guiding arrows on a machine operation task with an optical see-through head-mounted display (OST-HMD). Thirty-four participants were recruited in the experiment. The independent variables included visual information mode (text, animation, and mixed text and animation) and the use of guiding arrows (with and without arrows). In addition, gender difference was also an objective of this study. The task performance indicators were determined based on task completion time and error counts as well as subjective measures (system usability scale, NASA task load index, and immersion scale). This study used the mixed analysis of variance design to evaluate the main and interaction effects. The results showed that males performed better when using the mixed text and animation mode. Females performed better when using the text mode. In addition, using the mixed text and animation mode demonstrated the best outcome in system usability scale and NASA task load index. For the use of guiding arrows, the task completion time was reduced and the system usability scale, NASA task load index, and immersion scale showed positive effects.
Real time adaptive PID controller based on quantum neural network for nonlinear systems

In this paper, an adaptive proportional-integral-derivative (PID) controller based on a quantum neural network (APIDC-QNN) is introduced. In the proposed neural network structure, three neural layers are used namely: input layer, hidden layer, and output layer. The input and hidden layers use three and six quantum-processing neurons respectively. In contrast, in the output layer, the multiplication processing is used as an activation function free of the output weights which aims to reduce the number of tunable parameters. The output layer with three neurons represents the adaptive PID controller parameters. In this work, the tunable parameters are updated using the Lyapunov stability criterion to guarantee optimization and learning stability. In addition, the plant identification based on the Wiener-type model and quantum neural network is introduced to estimate the sensitivity of the plant output to the control signal. The proposed neural network structure yields only 30 tunable parameters for the proposed controller and 13 tunable parameters for the proposed identifier while the conventional neural network structure in other works requires 90 tunable parameters just for the controller. To evaluate the performance of the proposed APIDC-QNN, it is practically operated on a non-holonomic two-wheel mobile robot (NHWMR). Moreover, to investigate the robustness, superiority, and convergence speed of the proposed APIDC-QNN, some experimental tasks are discussed. The simulation and practical results show the powerful processing and the superiority of the proposed APIDC-QNN over that designed based on conventional neural networks by recording the minimum values of the performance indices.
Understanding the Social Context of Eating with Multimodal Smartphone Sensing: The Role of Country Diversity

Understanding the social context of eating is crucial for promoting healthy eating behaviors. Multimodal smartphone sensor data could provide valuable insights into eating behavior, particularly in mobile food diaries and mobile health apps. However, research on the social context of eating with smartphone sensor data is limited, despite extensive studies in nutrition and behavioral science. Moreover, the impact of country differences on the social context of eating, as measured by multimodal phone sensor data and self-reports, remains under-explored. To address this research gap, our study focuses on a dataset of approximately 24K self-reports on eating events provided by 678 college students in eight countries to investigate the country diversity that emerges from smartphone sensors during eating events for different social contexts (alone or with others). Our analysis revealed that while some smartphone usage features during eating events were similar across countries, others exhibited unique trends in each country. We further studied how user and country-specific factors impact social context inference by developing machine learning models with population-level (non-personalized) and hybrid (partially personalized) experimental setups. We showed that models based on the hybrid approach achieve AUC scores up to 0.75 with XGBoost models. These findings emphasize the importance of considering country differences in building and deploying machine learning models to minimize biases and improve generalization across different populations.
The Promises and Pitfalls of Virtual Reality

Increasingly, Virtual Reality technologies are finding a place in psychology and behavioral neuroscience labs. Immersing participants in virtual worlds enables researchers to investigate empirical questions in realistic or imaginary environments while measuring a wide range of behavioral responses, without sacrificing experimental control. In this chapter, we aim to provide a balanced appraisal of VR research methods. We describe how VR can help advance psychological science by opening pathways for addressing many pernicious challenges currently facing science (e.g., direct replication, prioritizing ecological validity). We also outline a range of unique and perhaps unanticipated obstacles and provide practical recommendations to overcome them.
PCDGAN: generation algorithm of adversarial examples based on color perceptual

Deep neural networks (DNNs) had been proved to be very vulnerable to adversarial examples which generated by adding small perturbation to the input, adversarial examples will mislead DNNs to produce wrong classification results. The quality of adversarial examples should be evaluated from two aspects: attack success rate and visual imperceptibility. The traditional assumption of visual imperceptibility is that perturbation should be tight L2 norm bounds in RGB color space. In the paper, we consider the color perception factors that are more suitable for human eyes on the basis of traditional assumptions. More specifically, we expand the GANs and add color perception factor as a loss function to the training generation model, which made the perturbation more imperceptible. After that, we propose an optimization algorithm of adversarial examples based on Grad-CAM, which is used to optimize the range of perturbation while ensuring the aggressiveness of adversarial examples. Our approach shows that on CIFAR-10 and NLPS 2017 Adversarial Learning Challenge datasets, the adversarial examples created are superior to traditional attack approach in terms of L2 norm and color difference while ensuring attack success rate. The resulting adversarial examples are more natural and usually indistinguishable from the original images.
Generalization of finetuned transformer language models to new clinical contexts

We have previously developed a natural language processing pipeline using clinical notes written by epilepsy specialists to extract seizure freedom, seizure frequency text, and date of last seizure text for patients with epilepsy. It is important to understand how our methods generalize to new care contexts.We evaluated our pipeline on unseen notes from nonepilepsy-specialist neurologists and non-neurologists without any additional algorithm training. We tested the pipeline out-of-institution using epilepsy specialist notes from an outside medical center with only minor preprocessing adaptations. We examined reasons for discrepancies in performance in new contexts by measuring physical and semantic similarities between documents.Our ability to classify patient seizure freedom decreased by at least 0.12 agreement when moving from epilepsy specialists to nonspecialists or other institutions. On notes from our institution, textual overlap between the extracted outcomes and the gold standard annotations attained from manual chart review decreased by at least 0.11 F1 when an answer existed but did not change when no answer existed; here our models generalized on notes from the outside institution, losing at most 0.02 agreement. We analyzed textual differences and found that syntactic and semantic differences in both clinically relevant sentences and surrounding contexts significantly influenced model performance.Model generalization performance decreased on notes from nonspecialists; out-of-institution generalization on epilepsy specialist notes required small changes to preprocessing but was especially good for seizure frequency text and date of last seizure text, opening opportunities for multicenter collaborations using these outcomes.
Neural network architecture with intermediate distribution-driven layer for classification of multidimensional data with low class separability

Abstract Simple neural network classification tasks are based on performing extraction as transformations of the set simultaneously with optimization of weights on individual layers. In this paper, the Representation 7 architecture is proposed, the primary assumption of which is to divide the inductive procedure into separate blocks – transformation and decision – which may lead to a better generalization ability of the presented model. Architecture is based on the processing context of the typical neural network and unifies datasets into a shared, generically sampled space. It can be applicable in the case of difficult problems – defined not as imbalance or streaming data but by low-class separability and a high dimensionality. This article has tested the hypothesis that – in such conditions – the proposed method could achieve better results than reference algorithms by comparing the R7 architecture with state-of-the-art methods, raw mlp and Tabnet architecture. The contributions of this work are the proposition of the new architecture and complete experiments on synthetic and real datasets with the evaluation of the quality and loss achieved by R7 and by reference methods.
Commutation simulator for open quantum dynamics

Abstract Recent progress in quantum simulation and algorithms has demonstrated a rapid expansion in capabilities. The search continues for new techniques and applications to exploit quantum advantage. Here we propose an innovative method to investigate directly the properties of a time-dependent density operator <?CDATA $\hat{\rho} (t)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mover> <mml:mi>ρ</mml:mi> <mml:mo stretchy="false">ˆ</mml:mo> </mml:mover> <mml:mo stretchy="false">(</mml:mo> <mml:mi>t</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:math> . Using quantum commutation simulators, we can directly compute the expectation value of the commutation relation and thus of the rate of change of <?CDATA $\hat{\rho} (t)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mover> <mml:mi>ρ</mml:mi> <mml:mo stretchy="false">ˆ</mml:mo> </mml:mover> <mml:mo stretchy="false">(</mml:mo> <mml:mi>t</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:math> . The approach can be utilized as a quantum eigen-vector solver for the von Neumann equation and a decoherence investigator for the Lindblad equation, by using just the statistics of single-qubit measurements. A simple but important example is demonstrated in the single-qubit case and we discuss extension of the method for practical quantum simulation with many qubits, towards investigation of more realistic quantum systems.
Exploring Frame Difference to Enhance Robustness for Video Steganography on Social Networks

The proliferation of video sharing on social networks has created a novel avenue for covert communication. Since most social networking channels are lossy, researchers have devoted efforts to robust video steganography to establish covert communication on social networks. Although there are various methods available, they often overlook the differences between frames in a video and are designed for a single frame. In this paper, we explore the general process of video recompression and present the frame quantization step (FQS) and interframe mutual information (IFMI) to measure the frame difference in the process of video recompression. Based on the two proposed metrics, we design a heuristic frame selection strategy and then propose a new robust video steganographic method in the DWT-SVD domain. Extensive experiments demonstrate that heuristic frame selection can effectively improve the robustness and reduce the computational complexity of video steganography. Our proposed method provides better robustness and higher efficiency than existing methods for building reliable covert communication on social networks, such as YouTube and Vimeo.
CURRICULAR SYNCOPATIONS: Education to Battle Misinformation

research-article Open Access Share on CURRICULAR SYNCOPATIONS: Education to Battle Misinformation Author: Henry M. Walker Grinnell College Grinnell CollegeView Profile Authors Info & Claims ACM InroadsVolume 14Issue 3September 2023pp 11–16https://doi.org/10.1145/3613454Published:16 August 2023Publication History 0citation42DownloadsMetricsTotal Citations0Total Downloads42Last 12 Months42Last 6 weeks30 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Changing the Ways of Communication and the Use of Sign Systems in Teaching in a Digital Society

The article highlights an important social problem: it traces the change in the ways of communicative activity in the field of education from antiquity to the emergence of a digital society. The study of this phenomenon by specialists of various profiles proves that it has reduced motor activity – humanity has become addicted to sitting in electronic networks, the Internet, as a result of which communication has become passive. The article discusses various iconic forms of virtual communication that lead to a number of negative consequences: psycho-emotional changes, information overload, the appearance of a person’s internal closeness, unwillingness not only to perform useful purposeful actions, but also to move.
Communing with Creative AI

Drawing on historical and contemporary examples, this paper discusses relationships between humans and machines in creative co-production with generative AI. It distinguishes between generative systems as augmenting tools and autonomous collaborators, and adopts the term communion to describe the close degree of exchange that is possible when working with these systems. It draws on historical examples of surrealist and AI artworks, and more recent examples made with Large Language Models and GAN-based generation. It concludes with a discussion of the primacy of text as an entry point to the possibilities of creative AI.
The Kestrel Drone

Artificial intelligence (AI) is redefining roads and skyways with advanced mobility technologies such as autonomous driving and Urban Air Mobility (UAM), suggesting a new paradigm for human mobility. This project sheds new light on drone mobility technology in terms of environmental aesthetics, focusing on global ecological issues. The Kestrel Drone is proposed based on a speculative scenario where "birds" become front-line users of drone technology and share their air routes with drones. At the exhibition hall, five Kestrel Drones were equipped with bird-mimicking wings and an AI tracker. The drones simulated a guiding bird for resident birds in a city. This project aims to evolve AI into a technology that has various parallel values that can coexist with non-human values, thus expanding the possibilities of technology.
Kiss/Crash

This paper describes the art installation of Kiss/Crash: a multi-screen work exploring the subject of AI-imagery and representation as well as the autobiographical themes of loneliness, desire, and intimacy in the digital age. The installation consists of three individual works in a shared space, Kiss/Crash, Me Kissing Me, and Crash Me, Gently, which all play with augmenting, inverting, and negating the iconic image of the kiss using diffusion-based image translation. In the production of this work, several potentially novel video translation techniques were developed and refined to create high-quality results central to these pieces. Throughout our research, we reflect on the nature of images and place diffusion models within a history of image-production technologies that artists have been contending with and responding to for the past one hundred years. This paper aims to extend that artistic tradition with a provocative, original aesthetic and technique that reveals the logic of AI imagery and hints at how our relationship to reality will continue to be stretched and shaped by artificial representations at an accelerating pace.
Sonification of a juggling performance using spatial audio

We developed a new musical instrument by equipping juggling balls with accelerometers, gyroscopes, and WiFi sensors. The system measures acceleration and rotation, allowing us to distinguish between ball movement and static states. We can determine the direction of each throw based on the acceleration data. A movement-driven instrument animates the virtual audio source position based on the throw direction. A different synthesizer is assigned to each ball, resulting in a synesthetic experience. The system integrates the juggling patterns simulator with OSC and MIDI API, allowing real-time rhythm generation and composition.