Reviewerly/exHarmony huggingface dataset:
Monocular underwater measurement of structured light by scanning with vibrating mirrors

As a kind of visual detection technology, 3D structured light measurement technology has the advantages of high accuracy, rapid measurement, and active control, which is widely used in the research work of underwater surveys. Since light is refracted as it propagates underwater, the information of the measured object cannot be obtained directly from the captured image, which affects measurement accuracy. Therefore, it is necessary to correct and compensate for the refraction. In this paper, the structure of the combination of linear structured light and optical oscillator is adopted. In order to verify the function and accuracy of the system, experiments were conducted on object measurement in the air and underwater, respectively. The measurement experiments were performed on standard workpieces in air and the measurement results were compared with the real sizes of the workpieces. The relative error of the measurement was within 2%, which verifies the correctness of the construction of the system model and the accuracy of the calibration results. The results of the underwater experiments can approximate the measurement accuracy in air, which proves the correctness of the underwater model construction and the method of optical surface refraction compensation.
Wear Your Heart on Your Sleeve: Using Digital Knitting Machines to Craft Wearable Biodata Portraits

Biofeedback sensors that measure body signals, such as heart rate, are often used for bodily awareness and behavioural change. In contrast, for this project, we wanted to use body sensor data as an artistic resource to craft wearable textile portraits as mementos of a moment in time. During the pandemic, we conducted a user study to design knitted biodata portraits. We met up individually with 20 participants to measure their heart rate, and translated that data into digitally-designed aesthetic patterns for machine knitting. Using a hacked knitting machine, we fabricated these patterns to create 20 personalized wearable shrugs to enable individuals to “wear their heart on their sleeve”. Two years later, when it was safe to do so, we conducted 2 studio workshops with participants, followed by 10 individual interviews. Our qualitative study insights reveal how individuals felt about seeing their own biodata, and the biodata of others, as aesthetic machine-knitted wearables with perceived precious value and attachment.
Creating Design Resources to Scaffold the Ideation of AI Concepts

Advances in artificial intelligence have enabled unprecedented technical capabilities, yet making these advances useful in the real world remains challenging. We engaged in a Research through Design process to improve the ideation of AI products and services. We developed a design resource capturing AI capabilities based on 40 AI features commonly used across various domains. To probe its usefulness, we created a set of slides illustrating AI capabilities and asked designers to ideate AI-enabled user experiences. We also incorporated capabilities into our own design process to brainstorm concepts with domain experts and data scientists. Our research revealed that designers should focus on innovations where moderate AI performance creates value. We reflect on our process and discuss research implications for creating and assessing resources to systematically explore AI's problem-solution space.
Carbon Scales: Collective Sense-making of Carbon Emissions from Food Production through Physical Data Representation

The climate impact of our food consumption is a key issue to sustainability. Yet understanding the food system and the impact it has can be difficult given its abstract nature. In this paper, we report on a Research through Design project aimed at designing and evaluating a data physicalization for supporting collective sense-making of the climate impact of food. Throughout the design process, we have explored the materiality of CO2 emissions and ways to design with less resource use. The resulting data physicalization, Carbon Scales, was evaluated in a three-week field study with 27 participants. Our findings show that collective sense-making can be enabled through interactive data physicalizations and that this can lead to carbon literacy. We expand on a) sustainability through design by arguing for the value of artifacts that let people stay in the interaction as this can support collective sense-making and b) sustainability in design by showcasing the value of designing with an interaction-first and materials-second mindset.
Sensory and emotional dimensions of domesticating new technology: an experiment with new e-bike users in Norway

Cities face major challenges when it comes to sustainability and mobility. Transport’s contribution to climate change is well-established, and people need to move in the most sustainable way to reach the 2030 emissions targets set by the Paris Agreement. One possible pathway towards more sustainable mobility practices is electromobility. The electrification of micro-mobility is happening rapidly, and one of the most popular is the e-bike. For years, electric bikes were relegated to niche status, but they are now experiencing explosive growth in sales in many countries. In this article, we draw on an experiment with new users of e-bikes to study the integration of e-bikes into existing mobility practices and to explore their sustainability potential. Through the lens of domestication theory, we zoom in on the relations that formed between users, technology, and environments in the course of the experiment. Our analysis highlights how emotional and sensory experiences play crucial roles in the adaption of new mobility technologies. Based on our findings, we argue that to reach the sustainability potential of e-bikes, a set of support mechanisms must be developed according to a holistic and relational understanding of mobility that also takes emotions and sensory experiences into consideration.
VGG16 Feature Extractor with Extreme Gradient Boost Classifier for Pancreas Cancer Prediction

The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) is greatly improved by an early and accurate diagnosis. Several studies have created automated methods to forecast PDAC development utilising various medical imaging modalities. These papers give a general overview of the classification, segmentation, or grading of many cancer types utilising conventional machine learning techniques and hand-engineered characteristics, including pancreatic cancer. This study uses cutting-edge deep learning techniques to identify PDAC utilising computerised tomography (CT) medical imaging modalities. This work suggests that the hybrid model VGG16-XGBoost (VGG16-backbone feature extractor and Extreme Gradient Boosting-classifier) for PDAC images. According to studies, the proposed hybrid model performs better, obtaining an accuracy of 0.97 and a weighted F1 score of 0.97 for the dataset under study. The experimental validation of the VGG16-XGBoost model uses the Cancer Imaging Archive (TCIA) public access dataset, which has pancreas CT images. The results of this study can be extremely helpful for PDAC diagnosis from computerised tomography (CT) pancreas images, categorising them into five different tumours (T), node (N), and metastases (M) (TNM) staging system class labels, which are T0, T1, T2, T3, and T4.
OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection

Humankind is witnessing a gradual increase in cancer incidence, emphasizing the importance of early diagnosis and treatment, and follow-up clinical protocols. Oral or mouth cancer, categorized under head and neck cancers, requires effective screening for timely detection. This study proposes a framework, OralNet, for oral cancer detection using histopathology images. The research encompasses four stages: (i) Image collection and preprocessing, gathering and preparing histopathology images for analysis; (ii) feature extraction using deep and handcrafted scheme, extracting relevant features from images using deep learning techniques and traditional methods; (iii) feature reduction artificial hummingbird algorithm (AHA) and concatenation: Reducing feature dimensionality using AHA and concatenating them serially and (iv) binary classification and performance validation with three-fold cross-validation: Classifying images as healthy or oral squamous cell carcinoma and evaluating the framework's performance using three-fold cross-validation. The current study examined whole slide biopsy images at 100× and 400× magnifications. To establish OralNet's validity, 3000 cropped and resized images were reviewed, comprising 1500 healthy and 1500 oral squamous cell carcinoma images. Experimental results using OralNet achieved an oral cancer detection accuracy exceeding 99.5%. These findings confirm the clinical significance of the proposed technique in detecting oral cancer presence in histology slides.
A deep multi-agent reinforcement learning framework for autonomous aerial navigation to grasping points on loads

Deep reinforcement learning, by taking advantage of neural networks, has made great strides in the continuous control of robots. However, in scenarios where multiple robots are required to collaborate with each other to accomplish a task, it is still challenging to build an efficient and scalable multi-agent control system due to increasing complexity. In this paper, we regard each unmanned aerial vehicle (UAV) with its manipulator as one agent, and leverage the power of multi-agent deep deterministic policy gradient (MADDPG) for the cooperative navigation and manipulation of a load. We propose solutions for addressing navigation to grasping point problem in targeted and flexible scenarios, and mainly focus on how to develop model-free policies for the UAVs without relying on a trajectory planner. To overcome the challenges of learning in scenarios with an increasing number of grasping points, we incorporate the demonstrations from an Optimal Reciprocal Collision Avoidance (ORCA) algorithm into our framework to guide the policy training and adapt two novel techniques into the architecture of MADDPG. Furthermore, curriculum learning with the attention mechanism is utilized by reusing knowledge from fewer grasping points to facilitate the training of a load with more points. Our experiments were validated by a load with three, four and six grasping points respectively in Coppeliasim simulator and then transferred into the real world with Crazyflie quadrotors. Our results show that the average tracking deviations from the desirable grasping point to the final position of the UAV can be less than 10 cm in some real-world experiments. Compared with state-of-the-art model-free reinforcement learning and swarm optimisation algorithms, results show that our proposed methods outperform other baselines with a reasonable success rate especially in the scenarios with more grasping points. Furthermore, the learned optimal policies enable UAVs to reach and hover over all the grasping points before manipulation without any collision. We conducted a comprehensive analysis of both targeted and flexible navigation, highlighting their respective advantages and disadvantages.
Cognitive Privacy of Personalized Digital Finance Images on Social Media Platforms Using Artificial Neural Networks

The growing popularity of personalized digital financial imagery on social networking platforms raises issues about cognitive privacy, which means to a person's capacity to restrict access to and exploitation of their private details, thoughts, and sentiments. This abstract investigates the possible privacy hazards connected with individualized digital financial imagery on social media platforms and emphasizes the need for enhanced cognitive privacy safeguards using Artificial Neural Networks (ANN). When individuals post photographs of their financial objectives, accomplishments, and achievements on social media sites, personalized digital finance images have become increasingly popular. Financial statements, financial assets, and income levels are frequently shown in these displays. Personalization of photos on social networking sites creates various privacy concerns, including the possibility of financial fraud and theft. The usage of individualized digital finance imagery on social networking platforms raises issues about cognitive privacy, in addition to the possibility of financial crime and identity theft. The capacity to edit digital pictures and change their content has grown in sophistication, enhancing the prospect of targeted and deceptive advertising. This may persuade consumers to make choices that are not in their greatest advantage, such as investing in dangerous financial goods or overspending. To ensure cognitive privacy, stronger privacy safeguards for personalized digital financial imagery on social media networks are required. This might involve employing the use of encryption and other safety measures to safeguard sensitive data and prevent illegal access. It is equally critical to educate users about the hazards of revealing private finance information on social media sites and to offer them with options to manage their privacy settings. Finally, the usage of personalized digital financial imagery on social networking sites raises issues about cognitive privacy as well as the possibility of targeted and deceptive advertising. To preserve cognitive privacy, it is critical to strengthen privacy safeguards and give users with tools to manage their privacy settings.
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient

Federated Learning (FL) has emerged as a new paradigm for training machine learning models distributively without sacrificing data security and privacy. Learning models on edge devices such as mobile phones is one of the most common use cases for FL. However, Non-identical independent distributed (non-IID) data in edge devices easily leads to training failures. Especially, over-parameterized machine learning models can easily be over-fitted on such data, hence, resulting in inefficient federated learning and poor model performance. To overcome the over-fitting issue, we proposed an adaptive dynamic pruning approach for FL, which can dynamically slim the model by dropping out unimportant parameters, hence, preventing over-fittings. Since the machine learning model's parameters react differently for different training samples, adaptive dynamic pruning will evaluate the salience of the model's parameter according to the input training sample, and only retain the salient parameter's gradients when doing back-propagation. We performed comprehensive experiments to evaluate our approach. The results show that our approach by removing the redundant parameters in neural networks can significantly reduce the over-fitting issue and greatly improves the training efficiency. In particular, when training the ResNet-32 on CIFAR-10, our approach reduces the communication cost by 57%. We further demonstrate the inference acceleration capability of the proposed algorithm. Our approach reduces up to 50% FLOPs inference of DNNs on edge devices while maintaining the model's quality.
Semantic Based Carbon Footprint of Food Supply Chain Management

Climate change is one of the biggest issues we face, besides Covid [6], we head into the future. Many strive to live more sustainably so that the choices made in our daily lives don’t adversely impact our planet. Food is one area of sustainability that is focused on, especially the impact of what we eat and how we can make choices that do not negatively affect the environment. The problem is how to effectively model the environmental impact of food production in a way that is useful to consumers, food manufacturers, and other individuals concerned about sustainability. To address this, an ontology and a knowledge graph are developed to capture CO2 emissions emitted by the various stages involved in the manufacture and distribution of food as a barometer for environmental impact, potential alternatives, and how the CO2 emissions measure up. The scope of our work is limited to the environmental impact of food production as measured by CO2 emissions and categories including feeding animals, land use, and transportation.
Semantic Importance-Aware Communications Using Pre-Trained Language Models

This letter proposes a semantic importance-aware communication (SIAC) scheme using pre-trained language models (e.g., ChatGPT, BERT, etc.). Specifically, we propose a cross-layer design with a pre-trained language model embedded in/connected by the cross-layer manager. The pre-trained language model is utilized to quantify the semantic importance of data frames. Based on the quantified semantic importance, we investigate semantic importance-aware power allocation. Unlike existing deep joint source-channel coding (Deep-JSCC)-based semantic communication schemes, SIAC can be directly embedded into current communication systems by only introducing a cross-layer manager. Our experimental results show that the proposed SIAC scheme can achieve lower semantic loss than existing equal-priority communications.
Lightweight Blockchain-Empowered Secure and Efficient Federated Edge Learning

Federated Learning (FL) has emerged as a privacy-preserving distributed Machine Learning paradigm, which collaboratively trains a shared global model across a number of end devices (clients) without exposing their raw data. However, FL typically assumes that all clients are benign and trust the coordinating central server, which is unrealistic for many real-world scenarios. In practice, clients can harm the FL process by sharing poisonous model updates while the server could malfunction or misbehave. Moreover, the deployment of FL for real-world applications is hindered by the high communication overhead between the server and clients that are often at the network edge with limited bandwidth. To address these key challenges, we propose a lightweight Blockchain-Empowered secure and efficient Federated Learning (BEFL) system. BEFL is built by integrating a communication-efficient and mutual-information guarded training scheme, a cost-effective Verifiable Random Function (VRF)-based consensus mechanism, and Inter-Planetary File System (IPFS)-enabled scalable blockchain architecture. Extensive simulation experiments using two benchmark FL datasets demonstrate that BEFL is resistant against byzantine clients launching data poisoning and model poisoning attacks, fault-tolerant against colluded malicious blockchain nodes, scalable to a large number of blockchain nodes, and communication-efficient at the network edge.
Experimental and numerical investigation of the influence of terrain slope on the performance of single-axis trackers

Certain advantages of using mountainous terrain for the installation of solar plants are well known, such as lower temperature, higher solar irradiance present at high altitudes, and economic aspects related to the lower cost of land. This paper highlights another one: the higher annual solar irradiation incident of single-axis N-S trackers installed on sloping terrain, as compared to horizontal ones. After a theoretical study in which the relevant formulas are derived, numerical simulations are presented. Then we showcase the results of a year-long experiment in which a N-S aligned single-axis tracker prototype was used, at the location of our Department (Gijón, Spain). The experimental results confirm the trends in the formulas and simulations. Finally, theoretical values for the energy gain for different slopes, at locations over the northern hemisphere between latitudes of 6° and 60° are provided. These gains can reach values up to 13.5%.
Transversal injection for direct encoding of ancilla states for non-Clifford gates using stabilizer codes

Fault-tolerant, error-corrected quantum computation is commonly acknowledged to be crucial to the realization of large-scale quantum algorithms that could lead to extremely impactful scientific or commercial results. Achieving a universal set of quantum gate operations in a fault-tolerant, error-corrected framework suffers from a conservation of unpleasantness. In general, no matter what error-correction technique is employed, there is always one element of a universal gate set that carries a significant resource overhead---either in physical qubits, computational time, or both. Specifically, this is due to the application of non-Clifford gates. A common method for realizing these gates for stabilizer codes such as the surface code is a combination of three protocols: state injection, distillation, and gate teleportation. These protocols contribute to the resource overhead compared with logical operations such as a CNOT gate and contribute to the qubit resources for any error-corrected quantum algorithm. In this paper, we introduce a very simple protocol to potentially reduce this overhead for non-Clifford gates: transversal injection. Transversal injection modifies the initial physical states of all data qubits in a stabilizer code before standard encoding and results in the direct preparation of a large class of single qubit states, including resource states for non-Clifford logic gates. Preliminary results hint at high-quality fidelities at larger distances and motivate further research on this technique.
A Multi-Strategy Improved Differential Evolution algorithm for UAV 3D trajectory planning in complex mountainous environments

In response to the complexity of power repair in mountainous areas and the limitations of traditional vehicles due to terrain constraints, this study focuses on the three-dimensional trajectory planning problem of UAVs (Unmanned Aerial Vehicles) in mountainous environments. Our goal is to provide effective solutions for the trajectory planning problem of UAVs in mountainous environments. Firstly, a UAV trajectory planning model is established, incorporating optimization objectives such as energy consumption, trajectory cost, obstacle avoidance cost, smoothing cost, and stability cost. The trajectory planning problem is transformed into an objective function optimization task with multiple performance constraints. To overcome the inefficiency and infeasibility of traditional algorithms in solving complex three-dimensional flight environments, we propose improvements to the Differential Evolution (DE) algorithm through three strategies: incorporating mutation crossover factor optimization strategy, an adaptive guidance mechanism, and an elite disturbance mechanism based on population classification. The Multi-Strategy Improved Differential Evolution (MSIDE) algorithm is introduced, and its time and space complexity are analyzed. Finally, the proposed method is compared with various algorithms through benchmark functions tests, Friedman test, Wilcoxon rank-sum test, simulation experiments in three-dimensional environments, and parameter sensitivity analysis experiments. The simulation results show that compared with the current state-of-the-art algorithms, the MSIDE algorithm improves the objective function value by 11.34% on average in regular terrain and 5.04% on average in complex terrain environments. The results demonstrate the convergence, multi-objective search capability, and global search ability of MSIDE, validating its effectiveness in solving the trajectory planning problem of UAVs in complex mountainous environments.