Reviewerly/exHarmony huggingface dataset:
Conic tangents based high precision extraction method of concentric circle centers and its application in camera parameters calibration

Abstract A high-precision camera intrinsic parameters calibration method based on concentric circles was proposed. Different from Zhang’s method, its feature points are the centers of concentric circles. First, the collinearity of the projection of the center of concentric circles and the centers of two ellipses which are imaged from the concentric circles was proved. Subsequently, a straight line passing through the center of concentric circles was determined with four tangent lines of concentric circles. Finally, the projection of the center of concentric circles was extracted with the intersection of the straight line and the line determined by the two ellipse centers. Simulation and physical experiments are carried out to analyze the factors affecting the accuracy of circle center coordinate extraction and the results show that the accuracy of the proposed method is higher. On this basis, several key parameters of the calibration target design are determined through simulation experiments and then the calibration target is printed to calibrate a binocular system. The results show that the total reprojection error of the left camera is reduced by 17.66% and that of the right camera is reduced by 21.58% compared with those of Zhang’s method. Therefore, the proposed calibration method has higher accuracy.
Taming the Chaos: Exploring Graphical Input Vector Manipulation User Interfaces for GANs in a Musical Context

Generative Adversarial Networks (GANs) are a widely used tool for generating highly realistic artificial data. As the output of these networks can show high diversity and novelty, GANs have the potential to be used as creative tools. However, using GANs in this context poses major challenges due to their unpredictability and lack of controllability, making it difficult for creative people to realize their artistic vision. To address this problem, we present two graphical user interfaces that visually order the (otherwise chaotic) latent input space of a GAN that was trained to generate drum samples. Further, these GUIs provide convergent search functions that allow users to fine-tune generated sounds. By doing so, we provide the ability to create sounds more purposefully to sound-affine users such as musicians or sound engineers. Additionally, we present the results of a user study that we conducted in order to explore our approach in accuracy-oriented and creative tasks. Our results indicate that usability and pragmatic qualities play a more important role for users than aesthetic-oriented aspects. Although not improving the accuracy within reproductive tasks, we observed that convergent search functions, if available, were used significantly more often than divergent/randomized search functions.
Knowledge-Supervised Learning: Knowledge Consensus Constraints for Person Re-Identification

The consensus of multiple views on the same data will provide extra regularization, thereby improving accuracy. Based on this idea, we proposed a novel Knowledge-Supervised Learning (KSL) method for person re-identification (Re-ID), which can improve the performance without introducing extra inference cost. Firstly, we introduce isomorphic auxiliary training strategy to conduct basic multiple views that simultaneously train multiple classifier heads of the same network on the same training data. The consensus constraints aim to maximize the agreement among multiple views. To introduce this regular constraint, inspired by knowledge distillation that paired branches can be trained collaboratively through mutual imitation learning. Three novel constraints losses are proposed to distill the knowledge that needs to be transferred across different branches: similarity of predicted classification probability for cosine space constraints, distance of embedding features for euclidean space constraints, hard sample mutual mining for hard sample space constraints. From different perspectives, these losses complement each other. Experiments on four mainstream Re-ID datasets show that a standard model with KSL method trained from scratch outperforms its ImageNet pre-training results by a clear margin. With KSL method, a lightweight model without ImageNet pre-training outperforms most large models. We expect that these discoveries can attract some attention from the current de facto paradigm of "pre-training and fine-tuning" in Re-ID task to the knowledge discovery during model training.
Enhanced Communication Support for Aphasia Using Gesture Recognition: The Bedroom Scenario

Citizens with speech and language disorders, such as Aphasia, often experience difficulties in expressing their needs. Assistive technologies for these disorders rely mostly on graphical interfaces activated by touch or gaze, which do not effectively cover all communication contexts throughout the day and may raise privacy concerns. In the scope of the AAL APH-ALARM project, our main aim is to extend communication support for users with speech and language difficulties (mainly aphasics) in the bedroom environment. We propose a system for supporting communication based on gesture recognition using non-invasive compact sensors worn by the user or deployed in the environment (e.g., bed). A first prototype was implemented using wrist-worn sensors and machine learning to recognize a small set of gestures. Initial results suggest that gesture recognition to enhance communication for people with speech and language impairments is viable, even when in bed.
Empirical evaluation of circuit approximations on noisy quantum devices

Noisy Intermediate-Scale Quantum (NISQ) devices fail to produce outputs with sufficient fidelity for deep circuits with many gates today. Such devices suffer from read-out, multi-qubit gate and crosstalk noise combined with short decoherence times limiting circuit depth. This work develops a methodology to generate shorter circuits with fewer multi-qubit gates whose unitary transformations approximate the original reference one. It explores the benefit of such generated approximations under NISQ devices. Experimental results with Grover's algorithm, multiple-control Toffoli gates, and the Transverse Field Ising Model show that such approximate circuits produce higher fidelity results than longer, theoretically precise circuits on NISQ devices, especially when the reference circuits have many CNOT gates to begin with. With this ability to fine-tune circuits, it is demonstrated that quantum computations can be performed for more complex problems on today's devices than was feasible before, sometimes even with a gain in overall precision by up to 60%.
Intelligent cooperative collision avoidance via fuzzy potential fields

Summary This paper proposes an intelligent cooperative collision avoidance approach combining the enhanced potential field (EPF) with a fuzzy inference system (FIS) to resolve local minima and goal non-reachable with obstacles nearby issues and provide a near-optimal collision-free trajectory. A genetic algorithm is utilized to optimize parameters of membership function and rule base of the FISs. This work uses a single scenario containing all issues and interactions among unmanned aerial vehicles (UAVs) for training. For validating the performance, two scenarios containing obstacles with different shapes and several UAVs in small airspace are considered. Multiple simulation results show that the proposed approach outperforms the conventional EPF approach statistically.
Flexible Robotic Assembly Based on Ontological Representation of Tasks, Skills, and Resources

Technology has sufficiently matured to enable, in principle, flexible and autonomous robotic assembly systems. However, in practice, it requires making all the relevant (implicit) knowledge that system engineers and workers have – about products to be assembled, tasks to be performed, as well as robots and their skills – available to the system explicitly. Only then can the planning and execution components of a robotic assembly pipeline communicate with each other in the same language and solve tasks autonomously without human intervention. This is why we have developed the Factory of the Future (FoF) ontology. At its core, this ontology models the tasks that are necessary to assemble a product and the robotic skills that can be employed to complete said tasks. The FoF ontology is based on existing standards. We started with theoretical considerations and iteratively adapted it based on practical experience gained from incorporating more and more components required for automated planning and assembly. Furthermore, we propose tools to extend the ontology for specific scenarios with knowledge about parts, robots, tools, and skills from various sources. The resulting scenario ontology serves us as world model for the robotic systems and other components of the assembly process. A central runtime interface to this world model provides fast and easy access to the knowledge during execution. In this work, we also show the integration of a graphical user front-end, an assembly planner, a workspace reconfigurator, and more components of the assembly pipeline that all communicate with the help of the FoF ontology. Overall, our integration of the FoF ontology with the other components of a robotic assembly pipeline shows that using an ontology is a practical method to establish a common language and understanding between the involved components.
Integrating Static and Time-Series Data in Deep Recurrent Models for Oncology Early Warning Systems

Machine learning techniques have shown promise in predicting clinical deterioration of hospitalized patients based on electronic health record (EHR). However, building accurate early warning systems (EWS) remains challenging in practice. EHRs are heterogeneous, comprising both static and time-series data. Moreover, missing values are prevalent in both static and time-series data, and the missingness of certain data can be correlated to clinical outcomes. This paper proposes a novel approach for integrating static and time-series clinical data in deep recurrent models through multi-modal fusion. Furthermore, we exploit the correlation of static and time-series data through cross-modal imputation in an integrated recurrent model. We apply the proposed approaches to a dataset extracted from the EHR of 20,700 hospitalizations of adult oncology patients in a research hospital. The experiments demonstrate the proposed approaches outperform the state-of-the-art models in terms of predictive accuracy in generating early warnings for clinical deterioration. A case study further establishes the efficacy of the predictive model for early warning systems under realistic clinical settings.
Text Counterfactuals via Latent Optimization and Shapley-Guided Search

We study the problem of generating counterfactual text for a classifier as a means for understanding and debugging classification.Given a textual input and a classification model, we aim to minimally alter the text to change the model's prediction.White-box approaches have been successfully applied to similar problems in vision where one can directly optimize the continuous input.Optimization-based approaches become difficult in the language domain due to the discrete nature of text.We bypass this issue by directly optimizing in the latent space and leveraging a language model to generate candidate modifications from optimized latent representations.We additionally use Shapley values to estimate the combinatoric effect of multiple changes.We then use these estimates to guide a beam search for the final counterfactual text.We achieve favorable performance compared to recent whitebox and black-box baselines using human and automatic evaluations.Ablation studies show that both latent optimization and the use of Shapley values improve success rate and the quality of the generated counterfactuals.
Open Citation Identifier: Definition

This document describes the Open Citation Identifier (OCI), a globally unique persistent identifier (PID) for the identification of open bibliographic citations stored in a specific database or in other kinds of storages.
Stable feature selection based on brain storm optimisation for high‐dimensional data

Feature selection is a widely used data pre-processing method. However, the research on feature selection stability is very rare. Although there are some related studies that mainly focus on filters rather than wrappers, especially wrappers based on evolutionary algorithms. In this paper, a stable feature selection method called stable brain storm optimisation (SBSO) is proposed. It puts forward a new population initialisation strategy, which treats stable feature selection results as guide information for initialisation and utilises such chaos to initialise population. SBSO also sets up an information archive to store the historical optimal individuals dynamically. A large number of experiments show that SBSO gives excellent classification performance when compared with other methods, and superior feature selection stability when compared with other wrappers.
A survival tree based on stabilized score tests for high-dimensional covariates

A survival tree can classify subjects into different survival prognostic groups. However, when data contains high-dimensional covariates, the two popular classification trees exhibit fatal drawbacks. The logrank tree is unstable and tends to have false nodes; the conditional inference tree is difficult to interpret the adjusted P-value for high-dimensional tests. Motivated by these problems, we propose a new survival tree based on the stabilized score tests. We propose a novel matrix-based algorithm in order to tests a number of nodes simultaneously via stabilized score tests. We propose a recursive partitioning algorithm to construct a survival tree and develop our original R package uni.survival.tree (https://cran.r-project.org/package=uni.survival.tree) for implementation. Simulations are performed to demonstrate the superiority of the proposed method over the existing methods. The lung cancer data analysis demonstrates the usefulness of the proposed method.
Using the Visuo-Haptic Illusion to Perceive and Manipulate Different Virtual Objects in Augmented Reality

The prop-based 3D virtual object manipulation method is widely used for interaction in Augmented Reality (AR) due to its convenience and flexibility. However, when the represented virtual object is different from the physical prop, the look and feel of the object are not well aligned. To address this problem, we present a dynamic finger remapping approach to creating a visuo-haptic illusion that dynamically adjusts the presented virtual hand posture to fit different sizes and shapes of virtual objects in AR. The finger movement toward a physical prop is synchronously remapped to the movement of the virtual fingers towards the corresponding virtual object. We developed a system that enables users to perceive consistent visual and tactile feedback while grasping and releasing various virtual objects represented by a physical prop. We conducted a user study to explore the effect of this visuo-haptic illusion on the perceived size of virtual objects, setting the sizes of the rendered virtual object and the physical prop as independent variables. We found that the perceived size of a virtual object varied with its rendered size in an almost linear fashion, while the physical prop size did not significantly affect the perception. We also conducted a second study to compare our system with a current prop-based method on virtual object manipulation. The results indicated that the remapped hands could effectively improve the realism and naturalness of the experience.
Scoring Performance on the Y-Balance Test Using a Deep Learning Approach

The Y Balance Test (YBT) is a dynamic balance assessment typically used in sports medicine. This work proposes a deep learning approach to automatically score this YBT by estimating the normalized reach distance (NRD) using a wearable sensor to register inertial signals during the movement. This paper evaluates several signal processing techniques to extract relevant information to feed the deep neural network. This evaluation was performed using a state-of-the-art human activity recognition system based on recurrent neural networks (RNNs). This deep neural network includes long short-term memory (LSTM) layers to learn features from time series by modeling temporal patterns and an additional fully connected layer to estimate the NRD (normalized by the leg length). All analyses were carried out using a dataset with YBT assessments from 407 subjects, including young and middle-aged volunteers and athletes from different sports. This dataset allowed developing a global and robust solution for scoring the YBT in a wide range of applications. The experimentation setup considered a 10-fold subject-wise cross-validation using training, validation, and testing subsets. The mean absolute percentage error (MAPE) obtained was 7.88 ± 0.20%. Moreover, this work proposes specific regression systems to estimate the NRD for each direction separately, obtaining an average MAPE of 7.33 ± 0.26%. This deep learning approach was compared to a previous work using dynamic time warping and k-NN algorithms, obtaining a relative MAPE reduction of 10%.
A hybrid model for Predicting Bitcoin Price using Machine Learning and Metaheuristic Algorithms

Cryptocurrencies are considered as new financial and economic tools having special and innovative features, among which Bitcoin is the most popular. The contribution of the Bitcoin market continues to grow due to the special nature of Bitcoin. The investors' attention to Bitcoin has increased significantly in recent years due to significant growth in its prices. It is important to create a prediction system which works well for investment management and business strategies due to the high chaos and volatility of Bitcoin prices. In this study, in order to improve predictive accuracy, Bitcoin price dataset is first divided into a time interval through time window, then propose a new model based on long short-term memory neural networks (LSTM) and Metaheuristic Algorithms. Chaotic Dolphin Swarm Optimization Algorithm is used to optimize the LSTM. Performance evaluation indicated that the proposed model can have more effective predictions and improve prediction accuracy. In addition, the performance of the optimized model is better and more reliable than other models.
Using Multi-inception CNN for Face Emotion Recognition

One integral and necessary part of human behavior is emotion, which affects the way people communicate. Although human beings can recognize and interpret facial expressions, the identification of correct facial expressions continues to be a key and challenging task by computer systems. The main issues stem from the face's non-uniform design and variations in conditions such as light, facial structure, and posture. Several Convolutional Neural Network (CNN) approaches have been introduced for Face Emotion Recognition (FER), but these methods cannot completely reflect temporal variations in facial characteristics. In this study, we use the CMU face data collection of four types of emotions to provide a method for the identification of facial emotions. Four classes of distinguished emotions are happy, sad, angry, and neutral. Pixel values are fed into a Neural Network with different architecture, and the accuracy of those methods has been compared. Restricted Boltzmann machine (RBM), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN), and multi-inception ensemble Convolution Neural Networks are different methods that are used in this research. We note the latter has considerably higher accuracy compared to other ones. The results obtained from the proposed methods Multi-inception CNN is slightly more than 87 percent while for the Restricted Boltzmann Machine (RBM) model it is 26.1 percent and for Deep Belief Networks (DBN) results are almost the same and slightly more than 26 percent finally the results for simple CNN model is 55 percent. Keyword: Face Emotion Recognition, FER, Deep Belief, RBM, multi-inception CNN.
Spatial Augmented Reality System with functions focused on the rehabilitation of Parkinson’s patients

In 2009 Pranav Mistry presented the Sixth Sense project, which used Spatial Augmented Reality to display information directly on the user’s environment using a projector. The user interacts with the virtual elements without the need to use typical interfaces such as screens or keyboards. At present, different projects make use of Virtual Reality in serious games as a mechanism for the rehabilitation of patients. This research presents an update of the Sixth Sense project to study the possibilities offered by Augmented Reality as a mechanism for the rehabilitation of patients with neurological diseases by the Linguistics Area of the University of Cadiz.
Context-aware Execution Migration Tool for Data Science Jupyter Notebooks on Hybrid Clouds

Interactive computing notebooks, such as Jupyter notebooks, have become a popular tool for developing and improving data-driven models. Such notebooks tend to be executed either in the user’s own machine or in a cloud environment, having drawbacks and benefits in both approaches. This paper presents a solution developed as a Jupyter extension that automatically selects which cells, as well as in which scenarios, such cells should be migrated to a more suitable platform for execution. We describe how we reduce the execution state of the notebook to decrease migration time and we explore the knowledge of user interactivity patterns with the notebook to determine which blocks of cells should be migrated. Using notebooks from Earth science (remote sensing), image recognition, and hand written digit identification (machine learning), our experiments show notebook state reductions of up to 55× and migration decisions leading to performance gains of up to 3.25× when the user interactivity with the notebook is taken into consideration.
Computational Narrative Blending Based on Planning

Inspired by conceptual blending models and considering plot generation as a plan-generation problem, this paper proposes a robust method that reuses existing stories to generate new narrative variants. This method generates variants that combine episodes extracted and adapted from different stories that share the same narrative structure. By combining a plan validation algorithm with a basic narrative structure, our method guarantees the logical coherence and general plot structure of the generated narratives. We also propose a new tool to assist amateur/professional writers to visualize all narrative variants created from a set of existing stories. Our experiments created novel, coherent and structured narratives by blending and adapting episodes from old chivalry romance pieces of work and some modern adventure videogames.