Reviewerly/exHarmony huggingface dataset:
Adapting Multidevice Deployments During a Pandemic: Lessons Learned From Two Studies

The emergence of the COVID-19 pandemic brought into evidence some of the challenges of deploying and managing user studies out-of-the-lab. Satisfying new health guidelines required adapting to remote and contactless procedures, which in turn impacted recruitment, participant involvement, and technology delivery and configuration. Such challenges are endemic to many user studies. However, the emergence of the pandemic forced us to confront them head on in two distinct multidevice deployment studies. Changing research design, deployment strategies, and study management allowed us to reflect on some of the core challenges for all field related technological interventions and provided insight into how researchers might conduct more responsive, flexible, and robust studies outside the lab more broadly. Our reflections suggest simple but important ways that researchers can design flexibility, responsiveness, and empathy into all future user studies in- the-wild.
A Prior Information Heuristic based Robot Exploration Method in Indoor Environment

The Rapidly-exploring Random Tree (RRT) based method has been widely used in robotic exploration, which achieves better performance than other exploration methods in most scenes. However, its core idea is a greedy strategy, that is, the robot chooses the frontier with the largest revenue value as the target point regardless of the explored environment structure. It is inevitable that before a certain area is fully explored, the robot will turn to other areas to explore, resulting in the backtracking phenomenon with a relatively lower exploration efficiency. In this paper, inspired by the perception law of bionic human, a new exploration strategy is proposed on the basis of the prior information heuristic. Firstly, a lightweight network model is proposed for the recognition of the heuristic objects. Secondly, the prediction region is formed based on the position of the heuristic object, and the frontiers in this region are extracted by the method of image processing. Finally, a heuristic information gain model is designed to guide the robot to explore, which allocates priority to the frontiers within the heuristic object area, so that the robot can make effective use of the prior knowledge of the room in the scene. Priority is given to the exploration of one room completely and then to the next, which can greatly improve the efficiency of exploration. In the experimental studies, we compare our method with RRT based exploration method in different environments, and the experimental results prove the effectiveness of our method.
Robustness with respect to class imbalance in artificial intelligence classification algorithms

Artificial intelligence (AI) algorithms, such as deep learning and XGboost, are used in numerous applications including autonomous driving, manufacturing process optimization and medical diagnostics. The robustness of AI algorithms is of great interest as inaccurate prediction could result in safety concerns and limit the adoption of AI systems. In this paper, we propose a framework based on design of experiments to systematically investigate the robustness of AI classification algorithms. A robust classification algorithm is expected to have high accuracy and low variability under different application scenarios. The robustness can be affected by a wide range of factors such as the imbalance of class labels in the training dataset, the chosen prediction algorithm, the chosen dataset of the application, and a change of distribution in the training and test datasets. To investigate the robustness of AI classification algorithms, we conduct a comprehensive set of mixture experiments to collect prediction performance results. Then statistical analyses are conducted to understand how various factors affect the robustness of AI classification algorithms. We summarize our findings and provide suggestions to practitioners in AI applications.
Federated learning through model gossiping in wireless sensor networks

Federated learning (FL) has been recently proposed to achieve distributed learning in communication in an efficient and privacy-preserving way. Unfortunately FL cannot be applied as it is in wireless sensor networks, because it requires a large number of transmissions of the model parameters and because it involves huge energy and communication resource consumption at nodes that are physically closer to the point where models calculated by the federated learners are aggregated. In this paper, such claim will be demonstrated and we will investigate how gossiping, combined with FL, can be used to achieve higher energy efficiency and bandwidth saving. Early results show that the gossiping can help in improving significantly the performance in terms of resource efficiency.
Selection of breast features for young women in northwestern China based on the random forest algorithm

In the research of breast morphology, numerous breast features are measured, whereas only a few parameters are adopted for classification. Therefore, how to extract the key variables from the multi-dimensional features in a rational way is an issue that is focused upon. This study aimed to reduce the complexity of the dimensionality reduction for further improving the objectivity and interpretability of the selected breast features. Since the random forest (RF) algorithm can quantify the feature importance during training, the method was adopted to determine the optimal breast features for classification and recognition in this paper. Firstly, the anthropometric data of 360 females from northwestern China aged from 19 to 27 years were measured by non-contact three-dimensional body scanning technology and the contact manual measurement method. Then, the k-means clustering was applied to categorize breast shapes, and the RF algorithm was utilized to quantify and rank the importance of 25 breast features. Finally, to verify the availability of the RF algorithm on breast feature selection, the t-distributed stochastic neighbor embedding method was adopted to visualize the distribution of breast shape clusters into two dimensions. Meanwhile, four neural networks were determined to recognize the breast morphology. The results demonstrate that fewer breast features can effectively increase the accuracy of breast shape classification and recognition. The best performance of breast shape classification and recognition is obtained when the number of breast features is 13. In this case, the average Hamming loss of four neural networks is the smallest (0.1136). Interestingly, the bust circumference and the horizontal curve of breasts across the bust points are found to be the most important of the 25 breast features in this paper. The importance of the breast curve features is higher than that of the breast cross-sectional features, while the breast positioning features have the lowest importance. Meanwhile, the RF algorithm is verified to be more effective than traditional dimensionality reduction methods, such as principal component analysis, hierarchical clustering, and recursive feature elimination. The approach developed in this paper can be generalized to the dimensionality reduction of other body morphology.
RADE: resource-efficient supervised anomaly detection using decision tree-based ensemble methods

The capability to perform anomaly detection in a resource-constrained setting, such as an edge device or a loaded server, is of increasing need due to emerging on-premises computation constraints as well as security, privacy and profitability reasons. Yet, the increasing size of datasets often results in current anomaly detection methods being too resource consuming, and in particular decision-tree based ensemble classifiers. To address this need, we present RADE—a new resource-efficient anomaly detection framework that augments standard decision-tree based ensemble classifiers to perform well in a resource constrained setting. The key idea behind RADE is first to train a small model that is sufficient to correctly classify the majority of the queries. Then, using only subsets of the training data, train expert models for these fewer harder cases where the small model is at high risk of making a classification mistake. We implement RADE as a scikit-learn classifier. Our evaluation indicates that RADE offers competitive anomaly detection capabilities as compared to standard methods while significantly improving memory footprint by up to $$12\times $$ , training-time by up to $$20\times $$ , and classification time by up to $$16\times $$ .
Comparing Boosting and Bagging for Decision Trees of Rankings

Abstract Decision tree learning is among the most popular and most traditional families of machine learning algorithms. While these techniques excel in being quite intuitive and interpretable, they also suffer from instability: small perturbations in the training data may result in big changes in the predictions. The so-called ensemble methods combine the output of multiple trees, which makes the decision more reliable and stable. They have been primarily applied to numeric prediction problems and to classification tasks. In the last years, some attempts to extend the ensemble methods to ordinal data can be found in the literature, but no concrete methodology has been provided for preference data. In this paper, we extend decision trees, and in the following also ensemble methods to ranking data. In particular, we propose a theoretical and computational definition of bagging and boosting, two of the best known ensemble methods. In an experimental study using simulated data and real-world datasets, our results confirm that known results from classification, such as that boosting outperforms bagging, could be successfully carried over to the ranking case.
Weak dissipation for high-fidelity qubit-state preparation and measurement

Highly state-selective, weakly dissipative population transfer is used to irreversibly move the population of one ground state qubit level of an atomic ion to an effectively stable excited manifold with high fidelity. Subsequent laser interrogation accurately distinguishes these electronic manifolds, and we demonstrate a total qubit state preparation and measurement (SPAM) inaccuracy $\epsilon_\mathrm{SPAM} < 1.7 \times 10^{-4}$ ($-38 \mbox{ dB}$), limited by imperfect population transfer between qubit eigenstates. We show experimentally that full transfer would yield an inaccuracy less than $8.0 \times 10^{-5}$ ($-41 \mbox{ dB}$). The high precision of this method revealed a rare ($\approx 10^{-4}$) magnetic dipole decay induced error that we demonstrate can be corrected by driving an additional transition. Since this technique allows fluorescence collection for effectively unlimited periods, high fidelity qubit SPAM is achievable even with limited optical access and low quantum efficiency.
TLRM: Task-level Relation Module for GNN-based Few-Shot Learning

Recently, graph neural networks (GNNs) have shown powerful ability to handle few-shot classification problem, which aims at classifying unseen samples when trained with limited labeled samples per class. GNN-based few-shot learning architectures mostly replace traditional metric with a learnable GNN. In the GNN, the nodes are set as the samples' embedding, and the relationship between two connected nodes can be obtained by a network, the input of which is the difference of their embedding features. We consider this method of measuring relation of samples only models the sample-to-sample relation, while neglects the specificity of different tasks. That is, this method of measuring relation does not take the task-level information into account. To this end, we propose a new relation measure method, namely the task-level relation module (TLRM), to explicitly model the task-level relation of one sample to all the others. The proposed module captures the relation representations between nodes by considering the sample-to-task instead of sample-to-sample embedding features. We conducted extensive experiments on four benchmark datasets: mini-ImageNet, tiered-ImageNet, CUB-200-2011, and CIFAR-FS. Experimental results demonstrate that the proposed module is effective for GNN-based few-shot learning.
Extreme learning machine versus classical feedforward network

Abstract Our research is devoted to answering whether randomisation-based learning can be fully competitive with the classical feedforward neural networks trained using backpropagation algorithm for classification and regression tasks. We chose extreme learning as an example of randomisation-based networks. The models were evaluated in reference to training time and achieved efficiency. We conducted an extensive comparison of these two methods for various tasks in two scenarios: $$\bullet$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>∙</mml:mo></mml:math> using comparable network capacity and $$\bullet$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>∙</mml:mo></mml:math> using network architectures tuned for each model. The comparison was conducted on multiple datasets from public repositories and some artificial datasets created for this research. Overall, the experiments covered more than 50 datasets. Suitable statistical tests supported the results. They confirm that for relatively small datasets, extreme learning machines (ELM) are better than networks trained by the backpropagation algorithm. But for demanding image datasets, like ImageNet, ELM is not competitive to modern networks trained by backpropagation; therefore, in order to properly address current practical needs in pattern recognition entirely, ELM needs further development. Based on our experience, we postulate to develop smart algorithms for the inverse matrix calculation, so that determining weights for challenging datasets becomes feasible and memory efficient. There is a need to create specific mechanisms to avoid keeping the whole dataset in memory to compute weights. These are the most problematic elements in ELM processing, establishing the main obstacle in the widespread ELM application.
Distributed Implementation of the Centralized Generalized Labeled Multi-Bernoulli Filter

Distributed scenarios pose a big challenge to tracking and fusion systems. They require the prevention of repeatedly incorporating the same information, which originates from ring closures in the communication path and would affect optimality. Additionally, the multi-sensor multi-object Generalized Labeled Multi-Bernoulli filter update is NP-hard in principle. The method proposed in this paper tackles these problems, as it constitutes a divide and conquer strategy for distributed, synchronized multi-sensor systems with central fusion. Based on a common prediction, local sensor updates are calculated separately, sent back and fused centrally in order to start a new cycle. Thus, the intractable multi-sensor update is split into less complex local single-sensor updates and a novel, low-complexity fusion strategy. The proposed method enables a full parallelization of the optimal multi-sensor Generalized Labeled Multi-Bernoulli and -Generalized Labeled Multi-Bernoulli update. Our approach bases on the Bayes Parallel Combination Rule and can be seen as multi-sensor multi-object Information Matrix Fusion for synchronous sensors, which constitutes a perfect choice in centralized systems with distributed sensors. Finally, we compare the proposed method to the Iterator Corrector approach from literature in detailed simulations.
Modelling global solar irradiance for any location on earth through regression analysis using high-resolution data

The focus of this study is to develop a highly accurate formulation to estimate the day number (DN), solar declination angle (SOLDEC), solar altitude angle (SOLALT) and also to predict the diffuse horizontal irradiance (DHI), direct normal irradiance (DNI) and global horizontal irradiance (GHI) for any location around the world at any time of the day for both short term and long term periods. Regression analysis is done using continuous 12 years of satellite measured historical solar irradiance, weather and solar angle data in the temporal resolution of 10 min for 12 cities around the world such as Kuala Lumpur, Auckland, Tokyo, Riyadh, London, Accra, Antananarivo, Brasilia, Lima, Quito, Ottawa and Honolulu. The models generated through the regression analysis perform better than existing models in predicting the solar irradiance, hence, these models are efficient and reliable for universal global application.
Gaussian Model for 3D Mesh Steganography

Currently,mainstream 3D steganographic algorithms embed data through geometric modifications. To enhance the anti-steganalysis ability of such algorithms,we propose an additive Gaussian noise model for 3D mesh steganography. Our work starts with a theoretical analysis for correlations of vertex coordinate components. Based on this analysis,the whole embedding operation is decomposed into three independent tasks that target three vertex coordinate components,respectively. To obtain vertex-changing probabilities,we construct a payload-limited sender (PLS) problem aimed at minimizing the Kullback-Leibler divergence between the cover and stego mesh distributions for a given payload. Next,vertex coordinates are quantified so that the PLS problem can be solved in practice. Finally,a ternary embedding scheme is taken as a typical steganographic case. Although the proposed Gaussian model is simple and idealized,the experimental results demonstrate that our algorithm possesses the adaptivity and can achieve better anti-steganalysis performance at low payloads than most modern high-capacity 3D mesh steganography based on the geometric modification.
Emergent curriculum in basic education for the new normality in Peru: orientations proposed from mathematics education

The Peruvian basic education and its curriculum have been impacted by the COVID-19 pandemic. As a result, certain trends and phenomena have emerged, which shape the new normality in society and have generated many effects on the mathematics curriculum. For this reason, it is necessary to pose the problem of sketching the orientations of mathematics curriculum for the new situation. In this position paper, I developed an outline based on a description of the problem situation, the demands that the world of work had already been making on the curriculum at the international level, and the research carried out by mathematics education. From these factors, I assert the significant role of critical mathematics education, particularly the motto of reading and writing the world with mathematics. I propose that orientations for the mathematics curriculum in basic education in the new normality should include ethical and political issues, statistical and financial literacy, digital skills as a part of a STEM approach, ethnomathematics from native people, and, finally, a problem-posing and problem-solving approach. In this list, ethical and political issues have primacy.
Nursing students' experience of online peer tutoring based on the grow model: A qualitative study

With the drastic change in the nursing education environment due to the coronavirus pandemic, several attempts have been made in Korea to help nursing students better adapt to the new learning environment.This study aimed to explore nursing students' experience of online peer tutoring based on the Goal-Reality-Options-Will (GROW) model.A qualitative study using content analysis.This study was conducted in the department of nursing at two universities in South Korea.The participants were 14 nursing students who participated as tutors and tutees in the online peer tutoring.Three focus group interviews were conducted with the 14 students. Data were transcribed and analyzed using content analysis.Three categories and nine subcategories were extracted. Online peer tutoring allowed participants to learn using a new approach, promoted their efficiency of studying in multiple aspects, and encouraged them to persevere and advance in academics, thus proving its usefulness as an auxiliary strategy to enhance the efficiency of online learning.Structured online peer tutoring can be a useful tool for enhancing the effectiveness of non-face-to-face education for nursing students. This study's results can serve as meaningful basic data for planning and composing learning activities optimized for the future online nursing education environment.
An Automatic Denoising Method for NMR Spectroscopy Based on Low-Rank Hankel Model

Nuclear magnetic resonance (NMR) spectroscopy, whose time domain data is modeled as the sum of damped exponential signals, has become an indispensable tool in various scenarios, such as biomedicine, biology, and chemistry. NMR spectroscopy signals, however, are usually corrupted by Gaussian noise in practice, raising difficulties in sequential analysis and quantification. The low-rank Hankel property of exponential signals plays an important role in the denoising issue, but selecting an appropriate parameter still remains a problem. In this work, we explore the effect of the regularization parameter of a convex optimization denoising method based on low-rank Hankel matrices for exponential signals corrupted by Gaussian noise. An accurate estimate on the spectral norm of weighted Hankel matrices is provided as a guidance to set the regularization parameter. The bound can be efficiently calculated since it only depends on the standard deviation of the noise and a constant. Aided by the bound, one can easily obtain an auto-setting regularization parameter to produce promising denoised results. Our results on synthetic and realistic NMR spectroscopy data demonstrate a superior denoising performance of the proposed approach over typical Cadzow and the state-of-the-art QR decomposition methods, especially in the low signal-to-noise ratio regime.
Investigating Usability and User Experience of Individually Verifiable Internet Voting Schemes

Internet voting can afford more inclusive and inexpensive elections. The flip side is that the integrity of the election can be compromised by adversarial attacks and malfunctioning voting infrastructure. Individual verifiability aims to protect against such risks by letting voters verify that their votes are correctly registered in the electronic ballot box. Therefore, voters need to carry out additional tasks making human factors crucial for security. In this article, we establish a categorization of individually verifiable Internet voting schemes based on voter interactions. For each category in our proposed categorization, we evaluate a voting scheme in a user study with a total of 100 participants. In our study, we assessed usability, user experience, trust, and further qualitative data to gain deeper insights into voting schemes. Based on our results, we conclude with recommendations for developers and policymakers to inform the choices and design of individually verifiable Internet voting schemes.
Effective and Privacy-preserving Federated Online Learning to Rank

Online Learning to Rank (OLTR) has been primarily studied in the centralised setting, where a central server is responsible to index the searchable data, collect the users' queries and search interactions, and optimize ranking models. A drawback of such a centralised OLTR paradigm is that it cannot guarantee user's privacy as all data (both the searchable one and the one related to user interactions) is collected by the server.