Reviewerly/exHarmony huggingface dataset:
“The sleep data looks way better than I feel.” An autoethnographic account and diffractive reading of sleep-tracking

Sleep-tracking products are promising their users an improvement to their sleep by focusing on behavior change but often neglecting the contextual and individual factors contributing to sleep quality and quantity. Making good sleep for productive scheduling a personal responsibility does not necessarily lead to better sleep and may cause stress and anxiety. In an autoethnographic study, the first author of this paper tracked her sleep for one month using a diary, body maps and an Oura ring and compared her subjectively felt sleep experience with the data produced by the Oura app. A thematic analysis of the data resulted in four themes describing the relationship between the user-researcher and her wearable sleep-tracker: (1) good sleep scores are motivating, (2) experience that matches the data leads to sense-making, (3) contradictory information from the app leads to frustration, and (4) the sleep-tracker competes with other social agents. A diffractive reading of the data and research process, following Karen Barad's methodology, resulted in a discussion of how data passes through the analog and digital apparatus and what contextual factors are left out but still significantly impact sleep quality and quantity. We add to a canon of sleep research recommending a move away from representing sleep in terms of comparison and competition, uncoupling it from neoliberal capitalistic productivity and self-improvement narratives which are often key contributing factors to bad sleep in the first place.
A novel well log data imputation methods with CGAN and swarm intelligence optimization

Well log data plays a vital role in decision-making, resource assessment, production optimization, and environmental management of oil and gas development. However, when exploring and developing deep and ultra-deep oil and gas, the performance of logging equipment is greatly challenged by high temperature, high pressure, and high corrosion wellbore conditions. Log data often needs to be completed or corrected. Data imputation technology has become a powerful tool to fill the missing in well log data. A well log data imputation method based on deep learning is proposed. The proposed method combines conditional generative adversarial networks (CGAN) with swarm intelligence optimization algorithms. The seismic layer velocity is used as a constraint to guide CGAN to generate well log data that matches geological features. Establish an objective function based on multiple conditions to evaluate the quality of generated samples. The swarm intelligence optimization algorithm is used to minimize the objective function. The case study shows that the proposed method obtains pseudo samples that meet specific scenarios and perform better than other algorithms. The proposed method provides a new approach for deep learning algorithms in sequence data prediction.
Does <scp>AI</scp>‐assisted creation of polyphonic music increase academic motivation? The <scp>DeepBach</scp> graphical model and its use in music education

Abstract Background In the modern music industry, AI music generators have gained particular importance. The use of AI greatly simplifies the creation of polyphony. In addition, it can increase student motivation and interest. Aims This study focuses on the AI‐assisted creation of polyphonic music. The purpose of this study is to determine how creating polyphony through the Deep Bach model impacts the academic motivation of music university students. Materials &amp; Methods Achieving this goal is possible by conducting an experimental training program based on the use of the above‐mentioned model. Results The results show that students in the experimental group have higher motivation than the control group participants. Therefore, AI‐based music creation has the potential to become a new trend in music education. Implications The findings of this study can be useful for music education experts, providing empirical data on the effectiveness of AI in music education. The use of the data can ultimately improve the learning process. Future research can focus on developing alternative AI models as well as investigating their effectiveness in music education.
Cellular geometric semantic genetic programming

Abstract Among the different variants of Genetic Programming (GP), Geometric Semantic GP (GSGP) has proved to be both efficient and effective in finding good solutions. The fact that the operators of GSGP operate on the semantics of the individuals in a clear way provides guarantees on the way the search is performed. GSGP is not, however, free from limitations like the premature convergence of the population to a small–and possibly sub-optimal–area of the search space. One reason for this issue could be the fact that good individuals can quickly “spread” in the population suppressing the emergence of competition. To mitigate this problem, we impose a cellular automata (CA) inspired communication topology over GSGP. In CAs a collection of agents (as finite state automata) are positioned in a n -dimensional periodic grid and communicates only locally with the automata in their neighbourhoods. Similarly, we assign a location to each individual on an n -dimensional grid and the entire evolution for an individual will happen locally by considering, for each individual, only the individuals in its neighbourhood. Specifically, we present an algorithm in which, for each generation, a subset of the neighbourhood of each individual is sampled and the selection for the given cell in the grid is performed by extracting the two best individuals of this subset, which are employed as parents for the Geometric Semantic Crossover. We compare this cellular GSGP (cGSGP) approach with standard GSGP on eight regression problems, showing that it can provide better solutions than GSGP. Moreover, by analyzing convergence rates, we show that the improvement is observable regardless of the number of executed generations. As a side effect, we additionally show that combining a small-neighbourhood-based cellular spatial structure with GSGP helps in producing smaller solutions. Finally, we measure the spatial autocorrelation of the population by adopting the Moran’s I coefficient to provide an overview of the diversity, showing that our cellular spatial structure helps in providing better diversity during the early stages of the evolution.
Facilitating patient portal engagement: a channel expansion and behavior change wheel perspective

Introduction Given the low patient portal adoption rates, the contradictory findings on the relationship between patient-provider communication and patient portal use, and the unclear mechanism of why doctor-patient communication might facilitate portal use as indicated in some existing studies, patient portal engagement warrants further examination. Methods Guided by the behavior change wheel framework and the channel expansion theory, this study examined the facilitators of patient portal engagement and tested the relationship between the facilitators (e.g., social opportunity and psychological capability) through analyzing the HINTS national survey data ( N = 1251). Results We found that patient portal access (a physical opportunity) and physician advocacy (a social opportunity) were two significant predictors of portal engagement while educational attainment was not. We did not find any direct correlation between patient-centered communication (PCC) and patient portal engagement, but instead, found a significant indirect relationship between the two. Discussion To the best of our knowledge, this is the first study to employ the behavior change wheel and channel expansion theory to explain patient portal engagement. Theoretically, our study extended the behavior change theory by further explaining the relationship between the key components (e.g., capability, opportunity) of behavior change. Practical strategies to increase patient portal engagement were proposed.
Prediction for nonlinear time series by improved deep echo state network based on reservoir states reconstruction

Abstract With the aim to enhance prediction accuracy for nonlinear time series, this paper put forward an improved deep Echo State Network based on reservoir states reconstruction driven by a Self-Normalizing Activation (SNA) function as the replacement for the traditional Hyperbolic tangent activation function to reduce the model’s sensitivity to hyper-parameters. The Strategy was implemented in a two-state reconstruction process by first inputting the time series data to the model separately. Once, the time data passes through the reservoirs and is activated by the SNA activation function, the new state for the reservoirs is created. The state is input to the next layer, and the concatenate states module saves. Pairs of states are selected from the activated multi-layer reservoirs and input into the state reconstruction module. Multiple input states are transformed through the state reconstruction module and finally saved to the concatenate state module. Two evaluation metrics were used to benchmark against three other ESNs with SNA activation functions to achieve better prediction accuracy.
Networking concert halls, musicians, and interactive textiles: Interwoven Sound Spaces

Interwoven Sound Spaces is an interdisciplinary project which brought together telematic music performance, interactive textiles, interaction design, and artistic research. A team of researchers collaborated with two professional contemporary music ensembles based in Berlin, Germany, and Piteå, Sweden, and four composers, with the aim of creating a telematic distributed concert taking place simultaneously in two concert halls and online. Central to the project was the development of interactive textiles capable of sensing the musicians’ movements while playing acoustic instruments, and generating data the composers used in their works. Musicians, instruments, textiles, sounds, halls, and data formed a network of entities and agencies that was reconfigured for each piece, showing how networked music practice enables distinctive musicking techniques. We describe each part of the project and report on a research interview conducted with one of the composers for the purpose of analysing the creative approaches she adopted for composing her piece.
Towards co-design of rehabilitation technologies: a collaborative approach to prioritize usability issues

Introduction Early stakeholder engagement is critical to the successful development and translation of rehabilitation technologies, a pivotal step of which is usability testing with intended end-users. To this end, several methods employ end-user feedback to identify usability and implementation issues. However, the process of prioritizing identified issues seldom leverages the knowledge and expertise of the range of stakeholders who will ultimately affect the demand and supply of a device. This paper describes a novel method to prioritize end-user feedback using transdisciplinary stakeholder consultation and address it in subsequent product development. The proposed approach was demonstrated using a case study relating to the development of a novel technology for neural recovery after spinal cord injury. Method Feedback from five individuals with chronic spinal cord injury was collected during two-hour usability evaluation sessions with a fully functional high-fidelity system prototype. A think-aloud and semi-structured interview protocol was used with each participant to identify usability and acceptability issues relating to the system in a 3-phase approach. Phase 1 involved extracting usability issues from think-aloud and semi-structured interview data. Phase 2 involved rating the usability issues based on their significance, technical feasibility, and implementation priority by relevant internal and external stakeholders. Finally, Phase 3 involved aggregating the usability issues according to design and implementation elements to facilitate solution generation, and these solutions were then raised as action tasks for future design iterations. Results Sixty usability issues representing nine facets of usability were rated. Eighty percent of issues were rated to be of moderate to high significance, 83% were rated as being feasible to address, and 75% were rated as addressable using existing project resources. Fifty percent of the issues were rated to be a high priority for implementation. Evaluation of the grouped issues identified 21 tasks which were mapped to the product roadmap for integration into future design iterations. Discussion This paper presents a method for meaningful transdisciplinary stakeholder engagement in rehabilitation technology development that can extended to other projects. Alongside a worked example, we offer practical considerations for others seeking to co-develop rehabilitation technologies.
“Below 58 BPM,” involving real-time monitoring and self-medication practices in music performance through IoT technology

The project presented in this paper illustrates the design process for the development of an IoT system that monitors a specific bio-metric parameter (heart rate) in real time and provides feedback for an opera singer, as well as adding effects that manipulate the sounds emitted by the body during a self-healing practice. This allows the singer to rest and alternate opera singing techniques (which is very demanding) with other less demanding singing techniques and even a self-healing session in case of necessity during a performance. The case study presented in this paper has been developed with and for Eleonora Amianto, an opera singer who suffered from a carotid aneurysm. We performed an idiographic design process, closely collaborating with Eleonora, and developed a wearable IoT that suited her health and artistic needs. In the design of the system, we explore the intersection between self-healthcare and performative arts, focusing on the use of an Internet of Musical Things (IoMusT) system to implement medical prevention and treatment practices in an art performance. The system is developed using open-source tools, allowing for easy replication and improvement, as well as reducing risks of obsolescence and costs of updating. We complement a formal evaluation session with field notes collected during the design phase. We could observe a positive effect of the system on Eleonora's practice and its potential applications within different performative scenarios.
An analysis of register variances between China and the US in international communications

Translation of international communications plays an important role in facilitating the spread of a country’s voice and shaping its national image. This study employs multidimensional analysis to analyze China’s interpretation of international communications and compares it with the speeches from the White House press conferences to uncover the register variances between the two. The findings reveal that China and the U.S. differ in 4 dimensions and 46 individual linguistic features. The former is more informational with explicit written features and can be categorized into the register of learned exposition; the latter is more interactive and persuasive with prominent features of colloquialism, belonging to the register of involved persuasion. Through comparison with the source language, we find that the differences between the two are influenced by multiple factors such as source language, interpreting process, and political considerations. In conclusion, the interpretation of China’s international communications is a “third language code” with distinctive features from the language used by the U.S. in the political scenario, reflecting a meticulous, responsible but less interactive national image. These findings could shed light on the interpreting strategies adopted by government interpreters and further improve the effectiveness of international communications.
Evolutionary Reinforcement Learning: Hybrid Approach for Safety-Informed Fault-Tolerant Flight Control

Recent research in artificial intelligence potentially provides solutions to the challenging problem of fault-tolerant and robust flight control. This paper proposes a novel Safety-Informed Evolutionary Reinforcement Learning algorithm (SERL), which combines Deep Reinforcement Learning (DRL) and neuroevolution to optimize a population of nonlinear control policies. Using SERL, the work has trained agents to provide attitude tracking on a high-fidelity nonlinear fixed-wing aircraft model. Compared to a state-of-the-art DRL solution, SERL achieves better tracking performance in nine out of ten cases, remaining robust against faults and changes in flight conditions, while providing smoother action signals.
Self-Prompting Tracking: A Fast and Efficient Tracking Pipeline for UAV Videos

In the realm of visual tracking, remote sensing videos captured by Unmanned Aerial Vehicles (UAVs) have seen significant advancements with wide applications. However, there remain challenges to conventional Transformer-based trackers in balancing tracking accuracy and inference speed. This problem is further exacerbated when Transformers are extensively implemented at larger model scales. To address this challenge, we present a fast and efficient UAV tracking framework, denoted as SiamPT, aiming to reduce the number of Transformer layers without losing the discriminative ability of the model. To realize it, we transfer the conventional prompting theories in multi-model tracking into UAV tracking, where a novel self-prompting method is proposed by utilizing the target’s inherent characteristics in the search branch to discriminate targets from the background. Specifically, a self-distribution strategy is introduced to capture feature-level relationships, which segment tokens into distinct smaller patches. Subsequently, salient tokens within the full attention map are identified as foreground targets, enabling the fusion of local region information. These fused tokens serve as prompters to enhance the identification of distractors, thereby avoiding the demand for model expansion. SiamPT has demonstrated impressive results on the UAV123 benchmark, achieving success and precision rates of 0.694 and 0.890 respectively, while maintaining an inference speed of 91.0 FPS.
Gaming using different hand gestures using artificial neural network

INTRODUCTION: Gaming has evolved over the years, and one of the exciting developments in the industry is the integration of hand gesture recognition.&#x0D; OBJECTIVES: This paper proposes gaming using different hand gestures using Artificial Neural Networks which allows players to interact with games using natural hand movements, providing a more immersive and intuitive gaming experience.&#x0D; METHODS: Introduces two modules: recognition and analysis of gestures. The gesture recognition module identifies the gestures, and the analysis module assesses them to execute game controls based on the calculated analysis.&#x0D; RESULTS: The main results obtained in this paper are enhanced accessibility, higher accuracy and improved performance.&#x0D; CONCLUSION: To communicate with any of the traditional systems, physical contact is necessary. In the hand gesture recognition system, the same functionality can be interpreted by gestures without requiring physical contact with the interfaced devices.
The development of aesthetic experience through virtual and augmented reality

Abstract Emerging technological innovations offer the potential for experiential engagement through virtual scenarios, yet the viability of this approach for educational purposes remains significantly underexplored. This study aims to assess the feasibility of Augmented Reality (AR) and Virtual Reality (VR) technologies in providing users with aesthetic experiences when visiting digital exhibitions. A total of 190 students participated in this investigation. The control group visited traditional exhibits at the Palace Museum in Beijing. This group underwent a survey to evaluate their acquired aesthetic experience. In contrast, the experimental group, comprising 96 students, engaged with VR/AR scenarios at the Palace Museum in Beijing. Accordingly, students in the experimental group were also surveyed to evaluate both their aesthetic experiences and, additionally, their user experiences. The survey results unveiled significant distinctions in aesthetic experiences between students in the control and experimental groups. Moreover, there were notable correlations between individual variables related to user and aesthetic experiences within the experimental group. Furthermore, the study revealed disparities in both user and aesthetic experiences among male and female students. The findings have implications for aesthetic education teachers and officials in the context of developing sound strategies for providing aesthetic experiences to their students. This information is also of interest to employees of museums, exhibitions, and other cultural facilities, who are interested in holding or hold digital exhibitions.
Complete characterization of a class of permutation trinomials in characteristic five

Abstract In this paper, we address an open problem posed by Bai and Xia in [2]. We study polynomials of the form $$f(x)=x^{4q+1}+\lambda _1x^{5q}+\lambda _2x^{q+4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>f</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:msup> <mml:mi>x</mml:mi> <mml:mrow> <mml:mn>4</mml:mn> <mml:mi>q</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:msub> <mml:mi>λ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:msup> <mml:mi>x</mml:mi> <mml:mrow> <mml:mn>5</mml:mn> <mml:mi>q</mml:mi> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:msub> <mml:mi>λ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:msup> <mml:mi>x</mml:mi> <mml:mrow> <mml:mi>q</mml:mi> <mml:mo>+</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> over the finite field $${\mathbb F}_{5^{k}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>5</mml:mn> <mml:mi>k</mml:mi> </mml:msup> </mml:msub> </mml:math> , which are not quasi-multiplicative equivalent to any of the known permutation polynomials in the literature. We find necessary and sufficient conditions on $$\lambda _1, \lambda _2 \in {\mathbb F}_{5^{k}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>λ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>λ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>∈</mml:mo> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>5</mml:mn> <mml:mi>k</mml:mi> </mml:msup> </mml:msub> </mml:mrow> </mml:math> so that f ( x ) is a permutation monomial, binomial, or trinomial of $${\mathbb F}_{5^{2k}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>5</mml:mn> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>k</mml:mi> </mml:mrow> </mml:msup> </mml:msub> </mml:math> .