Reviewerly/exHarmony huggingface dataset:
Fluorescence strobo-stereoscopy for specular reflection-suppressed full field of view imaging

This paper introduces fluorescence strobo-stereoscopy (FSS) to suppress strong specular reflection and enable the full field of view (FFOV) 3D surface imaging while the part is rotating. Specular reflection off the target surface significantly degrades the image quality and becomes critical for highly reflective surface measurements. In FSS, the fluorescent dye-doped fluid applied on the machined surface is excited upon incident ultra-violet light and becomes a new light source by Stokes' Law. Thus, specular reflection off of smooth surface can be suppressed by separating the fluorescent light from the excitation light. The developed FSS comprises a pair of imaging cameras, spatial filters, and an excitation light source. As a result, FSS effectively rejected the specular reflection and improved the FFOV 3D surface image quality of the machined part by enhancing contrast in the rotating target surface. Such enhancements in 3D imaging allowed to identify manufacturing tolerance of the part and to detect the surface features. The axial and lateral accuracy errors of FSS were 2.3% and 1.4% with the target size of 4.07 mm and 0.215 mm, respectively. A whole view reconstruction of the cylindrical target sample was performed, and the corresponding cylindricity and diameter deviation were assessed. The fluid media effect and the target surface quality effect were discussed.
Neural Predictors’ Sensitivity and Robustness

The results of the application of deep neural predictors depend on a multitude of factors which compose the experimental settings. We report all the specific information to ensure the reproducibility of a wide number of numerical experiments. A sensitivity analysis on some critical aspects is provided in order to prove the robustness of our setting. Considering the long-term behavior of the predictors, those trained for the one-step forecasting are able to reproduce the statistical properties of the attractor, i.e., the so-called attractor's climate, whereas the multi-step ones are unsuitable for replicating these statistical properties but provide an accurate forecasting up to several Lyapunov times. Lastly, we provide some remarks on the training procedure of the different predictors and introduce some advanced neural architectures to give an overview of possible advantages/disadvantages with respect to those implemented in this study.
The Segmentation of Knee MR Image Using Nested Deep Network and Attention Mechanism

Purpose: Magnetic resonance imaging is of great significance in clinical diagnosis of knee joint. Constructing knee model based on the segmentation of MR images is important for many applications, especially the local specific absorption rate estimation which needs electromagnetic simulation. Methods and Materials: In this paper, we proposed a method using a nested deep network, U-Net++, and attention mechanism to strengthen the segmentation effect of the tissues. The residual module was used to enhance the convergence ability of the network. In addition, Multi-scale deep supervision was performed to preserve the rich semantic features of the decoder paths. Furthermore, we also used a multiclass classification guided module to reduce false positives in image segmentation and improve the overall accuracy of image segmentation. Results and Discussion: Compared with current mainstream methods, the proposed method achieved better performance of tissue segmentation on the dataset of T1-weighted sagittal images collected by our laboratory, especially for cartilage and meniscus. Conclusion: The method integrating U-Net++ and attention mechanism is hopeful to be used to construct knee model for local specific absorption rate estimation.
Optimizing the arrangement of fixed light modules in new autonomous surgical lighting systems

Several novel autonomous lighting systems for illuminating the surgical site (e.g. SmartOT) consist of a large number of swiveling light modules placed on the ceiling instead of two or three movable OR lights. For such a new type of lighting system for operating rooms, the initial placement of the light modules is of great importance, since the light modules cannot be moved during surgery. For this reason, we present a novel approach for optimizing the arrangement of light modules in a new autonomous lighting system that exploits the special characteristics of an operating room and the surgeries that take place there by taking into account occluding geometry (e.g., surgeons and medical staff) via point cloud recordings. We have implemented a function that provides the brightness at the surgical site for an arbitrary arrangement of light modules at a given time of a point cloud recording. Based on this brightness function, we defined a fitness function which we used for optimization. Using cross-validation, we compared the results of three optimization algorithms (Greedy, SGA, SADE) to naive arrangements of light modules. By performing these optimizations on point cloud recordings of nine real open abdominal surgeries, we obtain arrangements that achieve up to 41% higher minimum brightness during surgery compared to naive arrangements.
Jardim das Cartas a Presence Ecology

research-article Jardim das Cartas a Presence Ecology: a multimodal and dynamic flow in an installation Share on Authors: Jônatas Manzolli Institute of Arts, Interdisciplinary Nucleus for Sound Studies (NICS), University of Campinas (UNICAMP), Brasil Institute of Arts, Interdisciplinary Nucleus for Sound Studies (NICS), University of Campinas (UNICAMP), BrasilSearch about this author , Mariana Baruco Machado Andraus Institute of Arts, Interdisciplinary Nucleus for Sound Studies (NICS), University of Campinas (UNICAMP), Brasil Institute of Arts, Interdisciplinary Nucleus for Sound Studies (NICS), University of Campinas (UNICAMP), BrasilSearch about this author Authors Info & Claims ARTECH 2021: 10th International Conference on Digital and Interactive ArtsOctober 2021 Article No.: 36Pages 1–9https://doi.org/10.1145/3483529.3483680Online:20 February 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
FedSP: Federated Speaker Verification with Personal Privacy Preservation

Automatic speaker verification (ASV) has been widely applied in a variety of industrial scenarios. In ASV, the universal background model (UBM) needs to be trained with a large variety of speaker data so that the UBM can learn the speaker-independent distribution of speech features for all speakers. However, the sensitive information contained in raw speech data is important and private for the speaker. According to the recent European Union privacy regulations, it is forbidden to upload private raw speech data to the cloud server. Thus, a new ASV model needs to be proposed to alleviate data scarcity and protect data privacy simultaneously in the industry. In this work, we propose a novel framework named Federated Speaker Verification with Personal Privacy Preservation, or FedSP, which enables multiple clients to jointly train a high-quality speaker verification model and provide strict privacy preservation for speaker. For data scarcity, FedSP is based on the federated learning (FL) framework, which keeps raw speech data on each device and jointly trains the UBM to learn the speech features well. For privacy preservation, FedSP provides more strict privacy preservation than traditional basic FL framework by selecting and hiding sensitive information from raw speech data before jointly training the UBM. Experimental results on two pair speech datasets demonstrate that FedSP has superior performances in terms of data-utility and privacy preservation.
A novel dynamic gesture understanding algorithm fusing convolutional neural networks with hand-crafted features

Dynamic gestures have attracted much attention in recent years due to their user-friendly interactive characteristics. However, accurate and efficient dynamic gesture understanding remains a challenge due to complex scenarios and motion information. Conventional handcrafted features are computationally cheap but can only extract low-level image features. This leads to performance degradation when dealing with complex scenes. In contrast, deep learning-based methods have a stronger feature expression ability and hence can capture more abstract and high-level image features. However, they critically rely on a large amount of training data. To address the above issues, a novel dynamic gesture understanding algorithm based on feature fusion is proposed for accurate dynamic gesture prediction. It leverages the advantages of handcrafted features and transfer learning. Aimed at small-scale dynamic gesture data, transfer learning is introduced for capturing effective feature expression. To precisely model the critical temporal information associated with dynamic gestures, a novel feature descriptor, namely, AlexNet2, is proposed for effective feature expression of dynamic gestures from the spatial and temporal domain. On this basis, a decision-level feature fusion framework based on support vector machine (SVM) and Dempster–Shafer (DS) evidence theory is constructed to utilize handcrafted features and AlexNet2 to realize high-precision dynamic gesture understanding. To verify the effectiveness and robustness of the proposed recognition algorithm, analysis and comparison experiments are performed on the public Cambridge gesture dataset and Northwestern University hand gesture dataset. The proposed gesture recognition algorithm achieves prediction accuracies of 99.50% and 96.97% on these two datasets. Experimental results show that the proposed recognition framework exhibits a better recognition performance in comparison with related prediction algorithms.
Vital dataveillance: investigating data in exchange for vitality through South Korea’s COVID-19 technogovernance

Abstract Through a case study of South Korea's tracking infrastructure, this study critically examines new surveillance technologies deployed during the COVID-19 pandemic. I consider the ways that these technologies were widely framed as beneficial and benign, contributing to their widespread acceptance, and normalizing their pervasive and intrusive tracking capacities. By employing cluster criticism analysis of the Korean health authorities' official reports, I argue that Korean health authorities discursively construct their ICT-based tracking infrastructure as democratic technologies of control. They also position Korean citizens as data subjects entitled to the right to know about vitality and caring subjects responsible for the vitality of others through their provision of data, an exchange which this paper terms as vital dataveillance. Through the concept of vital dataveillance, this study illuminates the new types of data governance and data rights of the pandemic.
Multilevel Monte-Carlo for Solving POMDPs Online

Planning under partial obervability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMDP). Although solving POMDPs is computationally intractable, substantial advancements have been achieved in developing approximate POMDP solvers in the past two decades. However, computing robust solutions for systems with complex dynamics remains challenging. Most on-line solvers rely on a large number of forward-simulations and standard Monte-Carlo methods to compute the expected outcomes of actions the robot can perform. For systems with complex dynamics, e.g., those with non-linear dynamics that admit no closed form solution, even a single forward simulation can be prohibitively expensive. Of course, this issue exacerbates for problems with long planning horizons. This paper aims to alleviate the above difficulty. To this end, we propose a new on-line POMDP solver, called Multilevel POMDP Planner (MLPP), that combines the commonly known Monte-Carlo-Tree-Search with the concept of Multilevel Monte-Carlo to speed-up our capability in generating approximately optimal solutions for POMDPs with complex dynamics. Experiments on four different problems of POMDP-based torque control, navigation and grasping indicate that MLPP substantially outperforms state-of-the-art POMDP solvers.
Word-meaning access

Abstract This chapter focuses on the process by which stored knowledge about a word’s form (orthographic or phonological) maps onto stored knowledge about its meaning in adult comprehenders. This mapping is made challenging by the ambiguity that is ubiquitous in natural language: most familiar words can refer to multiple different concepts. This one-to-many mapping from form to meaning within the lexicon is a core feature of word-meaning access. Fluent, accurate word-meaning access requires that comprehenders integrate multiple cues in order to determine which of a word’s possible semantic features are relevant in the current context. Specifically, word-meaning access is guided by (i) distributional information about the a priori relative likelihoods of different word meanings and (ii) a wide range of contextual cues that indicate which meanings are most likely in the current context.
Privacy-Preserving and Reliable Federated Learning

In Internet of Things (IoT), it is often impossible to share datasets owned by different participants (usually IoT devices) for machine learning model training due to privacy concerns. Federated learning (FL) is a promising technique to address this challenge. However, existing FL schemes face the problem of how to avoid low-quality/malicious update. To solve this problem, we propose a privacy-preserving and reliable federated learning scheme (PPRFLS) to select reliable participants and evaluate the quality of the participants' updates. Analysis shows that the proposed scheme achieves data privacy and model reliability.
Multivariate cluster-weighted models based on seemingly unrelated linear regression

A class of cluster-weighted models for a vector of continuous random variables is proposed. This class provides an extension to cluster-weighted modelling of multivariate and correlated responses that let the researcher free to use a different vector of covariates for each response. The class also includes parsimonious models obtained by imposing suitable constraints on the component-covariance matrices of either the responses or the covariates. Conditions for model identifiability are illustrated and discussed. Maximum likelihood estimation is carried out by means of an expectation-conditional maximisation algorithm. The effectiveness and usefulness of the proposed models are shown through the analysis of simulated and real datasets.
Contribution of Vocal Tract and Glottal Source Spectral Cues in the Generation of Acted Happy and Aggressive Spanish Vowels

The source-filter model is one of the main techniques applied to speech analysis and synthesis. Recent advances in voice production by means of three-dimensional (3D) source-filter models have overcome several limitations of classic one-dimensional techniques. Despite the development of preliminary attempts to improve the expressiveness of 3D-generated voices, they are still far from achieving realistic results. Towards this goal, this work analyses the contribution of both the the vocal tract (VT) and the glottal source spectral (GSS) cues in the generation of happy and aggressive speech through a GlottDNN-based analysis-by-synthesis methodology. Paired neutral expressive utterances are parameterised to generate different combinations of expressive vowels, applying the target expressive GSS and/or VT cues on the neutral vowels after transplanting the expressive prosody on these utterances. The conducted objective tests focused on Spanish [a], [i] and [u] vowels show that both GSS and VT cues significantly reduce the spectral distance to the expressive target. The results from the perceptual test show that VT cues make a statistically significant contribution in the expression of happy and aggressive emotions for [a] vowels, while the GSS contribution is significant in [i] and [u] vowels.
Exploring the Need for Blockchain-Based National Population Census

National population census provides the basis for governments’ financial, economic, health and education policies for its populace. It plays a vital role in mapping a country’s growth and financial trajectories and it is the single most valuable and shared resource among government departments and apparatuses. The centralized, traditional methodologies currently in use are faced with several challenges including and not limited to high costs, privacy issues, enumerating unsafe areas and reduced cooperation. This research aims to analyze through a systematic literature review the drawbacks and challenges of the current traditional methodologies used in housing and population census to identify if a decentralized system would assist in mitigating them. The drawbacks identified are population coverage, ethnic and racial discrimination, privacy concerns, census data distribution, cost of census, and cooperation and participation. The research, even though at an embryonic stage, shows that blockchain-based solutions may be a candidate for solving several of the above mentioned challenges while laying at the same time the foundations of our research on blockchain-based systems for tackling with other challenges faced within census such as that of the missing people.
The Ragpicking DMI Design: The Case for Green Computer Music

This article introduces the idea of sustainable development of DMIs, discussing how fundamental concepts like e-Waste, Ragpicking and Green Computing can be (re)used to minimize or eliminate where possible the environmental impact of DMIs. The arguments and fundamentals for this initiative are presented, as also some examples to illustrate our ideas. Our intention is to contribute to the reflection by the Computer Music community on the subject.
Bert-based Knowledge Graph Completion Algorithm for Few-Shot

Knowledge graphs have shown increasing value in semantic search, intelligent Q&amp;A, data analysis, natural language processing, visual understanding, IoT devices, etc. It is undeniable that knowledge graphs have become the mandatory path for the artificial intelligence fields development. However, the information contained in existing knowledge graphs is incomplete, which attracts a large number of researchers to enhance the completeness of knowledge graphs utilizing knowledge graph completion methods. Most traditional embedding-based knowledge graph completion models use structural information within data-rich triples from the knowledge graph is limited by the long-tail distribution of the relations in the triples. To address this problem, we propose a Bert-based knowledge graph completion algorithm for few-shot knowledge graphs, with the main goal of implementing the knowledge graph completion task with only a few sample triples of training instances. We improve the baseline model GMatching for handling few-shot knowledge graphs by introducing the Bert pre-trained linguistic representation model to enhance the semantic representation of entities and relations in the triples. Through experiments, we demonstrate that our improved model B-GMatching achieves good results.
Adaptive Evolution Strategies for Stochastic Zeroth-Order Optimization

We consider solving a class of unconstrained optimization problems in which only stochastic estimates of the objective functions are available. Existing stochastic optimization methods are mainly extended from gradient-based methods, faced with the challenges of noisy function evaluations, hardness in choosing step-sizes, and probably ill-conditioned landscapes. This paper presents a stochastic evolution strategy (SES) framework and several adaptation schemes to avoid these challenges. The SES framework combines the ideas of population sampling and minibatch sampling in exploiting the zeroth-order gradient information, efficiently reducing the noise in both data selection and gradient approximation. In addition, it admits approximating the gradients using a non-isotropic Gaussian distribution to better capture the curvature information of the landscapes. Based on this framework, we implement a step-size adaptation rule and two covariance matrix adaptation rules, where the former can automatically tune the step-sizes and the latter are intended to cope with ill-conditioning. For SES with certain fixed step-sizes, we establish a nearly optimal convergence rate over smooth landscapes. We also show that using the adaptive step-sizes allows convergence at a slightly slower rate but without the need to know the smoothness constant. Several numerical experiments on machine learning problems verify the above theoretical results and suggest that the adaptive SES methods show much promise.
A Multi-Scale Convolutional Neural Network for Rotation-Invariant Recognition

The Internet of things (IoT) enables mobile devices to connect and exchange information with others over the Internet with a lot of applications in consumer, commercial, and industrial products. With the rapid development of machine learning, IoT with image recognition capability is a new research area to assist mobile devices with processing image information. In this research, we propose the rotation-invariant multi-scale convolutional neural network (RIMS-CNN) to recognize rotated objects, which are commonly seen in real situations. Based on the dihedral group D4 transformations, the RIMS-CNN equips a CNN with multiple rotated tensors and its processing network. Furthermore, multi-scale features and shared weights are employed in the RIMS-CNN to increase performance. Compared with the data augmentation approach of using rotated images at random angles for training, our proposed method can learn inherent convolution kernels for rotational features. Experiments were conducted on the benchmark datasets: MNIST, FASHION-MNIST, CIFAR-10, and CIFAR-100. Significant improvements over the other models were achieved to show that rotational invariance could be learned.
Three Gods in Eleusis: Discovering Tangible and Intangible Cultural Heritage of Ancient Eleusis

The present study concerns the design and implementation of a serious game for the on-site and remote promotion and dissemination of knowledge regarding the mythology of ancient Eleusis in an interactive, fun and educational way. Specifically, while playing, the user discovers artifacts exhibited in the archaeological museum and related to the archaeological site of Eleusis, unfolding the content of the myth of Persephone. The story takes place in a realistic three-dimensional virtual environment that simulates the Archaeological Museum of Eleusis. The selected archaeological artifacts, in combination with the narration of the myth of Persephone, transfer the knowledge about the historicity of the place in an experientially playful way. The main purpose of the study is to introduce a novel, interactive playful narrative digital museum experience, in which the user is guided through a mechanism to simultaneously acquire knowledge both for the intangible and tangible heritage of a site with significant historical value. The evolution of the scenario (story) is dynamic and based on the choices of the user. The content (text etc.) of the system is presented through a dynamic multilingual approach, which offers the management of multiple different languages and personalized content based on the target group. The system relies on cross-platform technologies to cover a wide range of users.