Miracle-dz/UniFAR-FTUs huggingface dataset:
DESIGN AND EVALUATION OF A MICROSENSOR FOR A BIONIC HAND WITH METAMATERIALS- A REVIEW

Prosthetics are the electro-mechanical frameworks which are an incredible assistance in helping the physical difficulties experienced by amputees. These are impelled in various ways, for example, wired associations, pneumatic systems, mechanical systems and shape memory alloys. Mechanical and Pneumatic frameworks that are at present utilized in prosthetics needs exactness, scholarly control and are not exceptionally light in weight. Transducers can be used in which the recognizing components have less weight with closed loop feedback system and precision of the prosthetics can be extended when differentiated with mechanical and pneumatic systems. The demonstration of the bionics can be expanded with the utilization of adaptable sensors in the form of strips and wires. With the utilization of transducers, the conceivable number of degrees of opportunity can be acquired precisely. The detecting factor, constancy in get-together data from sensory system are significant qualities for controlling the prosthetics. Various materials can be used to adjust the dynamic characteristics of transducers and with the utilization of metamaterials in transducers, the resolution can be expanded. The affectability can likewise be expanded with the decrease of structure of the transducer from full scale to small scale level. Shape memory compounds are the activation innovation identified with change of the nuclear structure. These composites can be used in the form of muscle actuators with the end goal that it chips away at the premise of constriction and extension of the prosthetics. The constraints of these detecting components are because of its usefulness in various conditions.
Comparing old maps with cartometric methods – A case study with Bayesian data analysis

The planimetric accuracy of old maps can be calculated using cartometric methods like Helmert transformations. As the choice of control points for these transformations is somewhat arbitrary, this may cause stochastic variations in the results. This paper addresses these stochastic variations and proposes to apply Bayesian data analysis methods to quantify the uncertainty of the cartometric calculations. The paper shows that the Bayesian data analysis is consistent with the deterministic calculations, and it provides safe statistical bounds for the results.
Pneumonia and Hantavirus Detection Using CNN

Pneumonia and Hantavirus are the most threatening diseases due to which many people die. There are many reasons behind this but the main reason is that people don’t get to know about it in early stages due to lack of experts and medical diagnostic facilities. This research paper focuses on developing a diagnostic system for prediction of hanta virus and pneumonia by using the concept of image processing in conjunction with machine learning. Proposed system will detect the disease from X-Ray images and use preprocessing technique that will remove the noise and disturbance in image. Feature extraction process is applied to extract the useful features of underlying image, and feature selection technique will further optimize the top ranking features. CNN algorithm is then applied to classify the images for detection of respiratory diseases. After detection of disease, report will be generated and submitted to patient.
A Frame work of Automatic Analysis System of Electrocardiogram Signals

The aim of this paper is twofold. First, we define an ECG feature parameter set (32 features) which could represent ECG signal as adequately as possible for diagnosing requirements. Second, we design an automatic classification framework. After benchmark point detection, feature parameter will be extracted. And then the classifier methods and its comparison based on SVM and QNN are presented. The long-term objective is to design a thorough system to realize the recognition of real-time ECG signal and enhance medical treatment.
Neural network time series classification of changes in nuclear power plant processes

Time series are typical data output from technological processes. Diagnostics of process data such as model change detection, outlier detection, etc. [1] are often of primary interest for quality management. For autocorrelated processes, many models and procedures have been suggested, many of them based on uni- and multivariate EWMA, AR, ARIMA, CUSUM. Two types of models for stationary univariate series were tested: linear partial least squares autoregresison (PLSAR) and nonlinear perceptron-type feed-forward neural network autoregression (ANNAR) with multistep prediction [5, 7, 8]. Here we describe mainly the ANN based models [2]. Lack of knowledge of statistical properties of prediction is solved by multiple overlay estimates, which makes it possible to assess and predict heteroscedastic variance and construct statistical models and control charts of processes with confidence intervals. It is shown that orthogonal PLSAR models are more stable than ANNAR and its parameters could be used to identify and classify different physical modes of processes. However, though neural network models often behave as unstable black boxes, our results suggest that the ANN solutions form nonconvex subspaces of a limited dimensionality that can be used to classify different types of time series using support vector machines (SVM) classifiers. Expansions of the proposed models for multivariate and nonstationary data are also possible and allow to control and classify more complex processes in technology. Throughout this study, the commercial statistical software QCExpert 3.2 was used [13].
External cavity diode lasers with ridge-waveguide type broad contact semiconductor optical amplifiers

External cavity lasers (ECLs) have been around for many years and are recognised as useful tunable narrow line light sources. In this communication we present space resolved spectral characteristics of the ECLs with a standard glass grating and ridge-waveguide broad-contact optical amplifiers (SOAs). The gain of the SOAs was centered in the range of 960 to 980 nm. The spectral characteristics have been measured with an optical spectrum analyzer. The results are compared with the ones obtained after the glass grating was substituted by a grating made from silicon. Application of such silicon gratings can be considered as a first step towards ECLs made fully in a MEMS configuration.
Cross Modal Retrieval with Querybank Normalisation

Profiting from large-scale training datasets, advances in neural architecture design and efficient inference, joint embeddings have become the dominant approach for tackling cross-modal retrieval. In this work we first show that, despite their effectiveness, state-of-the-art joint embeddings suffer significantly from the longstanding “hubness problem” in which a small number of gallery embeddings form the nearest neighbours of many queries. Drawing inspiration from the NLP literature, we formulate a simple but effective framework called Querybank Normalisation (QB-NORM) that re-normalises query similarities to account for hubs in the embedding space. QB-NORM improves retrieval performance without requiring retraining. Differently from prior work, we show that QB-NORM works effectively without concurrent access to any test set queries. Within the QB-NORM framework, we also propose a novel similarity normalisation method, the Dynamic Inverted Softmax, that is significantly more robust than existing approaches. We showcase QB-NORM across a range of cross modal retrieval models and benchmarks where it consistently enhances strong baselines beyond the state of the art. Code is available at https://vladbogo.github. io/QB-Norm/.
Towards Disturbance-Free Visual Mobile Manipulation

Deep reinforcement learning has shown promising results on an abundance of robotic tasks in simulation, including visual navigation and manipulation. Prior work generally aims to build embodied agents that solve their assigned tasks as quickly as possible, while largely ignoring the problems caused by collision with objects during interaction. This lack of prioritization is understandable: there is no inherent cost in breaking virtual objects. As a result, "well-trained" agents frequently collide with objects before achieving their primary goals, a behavior that would be catastrophic in the real world. In this paper, we study the problem of training agents to complete the task of visual mobile manipulation in the ManipulaTHOR environment while avoiding unnecessary collision (disturbance) with objects. We formulate disturbance avoidance as a penalty term in the reward function, but find that directly training with such penalized rewards often results in agents being unable to escape poor local optima. Instead, we propose a two-stage training curriculum where an agent is first allowed to freely explore and build basic competencies without penalization, after which a disturbance penalty is introduced to refine the agent’s behavior. Results on testing scenes show that our curriculum not only avoids these poor local optima, but also leads to 10% absolute gains in success rate without disturbance, compared to our state-of-the-art baselines. Moreover, our curriculum is significantly more performant than a safe RL algorithm that casts collision avoidance as a constraint. Finally, we propose a novel disturbance-prediction auxiliary task that accelerates learning.1
Predição da Idade Cerebral a partir de Imagens de Ressonância Magnética utilizando Redes Neurais Convolucionais

In this work, deep learning techniques for brain age prediction from magnetic resonance images are investigated, aiming to assist in the identification of biomarkers of the natural aging process. The identification of biomarkers is useful for detecting an early-stage neurodegenerative process, as well as for predicting age-related or non-age-related cognitive decline. Two techniques are implemented and compared in this work: a 3D Convolutional Neural Network applied to the volumetric image and a 2D Convolutional Neural Network applied to slices from the axial plane, with subsequent fusion of individual predictions. The best result was obtained by the 2D model, which achieved a mean absolute error of 3.83 years. -- Neste trabalho s\~ao investigadas t\'ecnicas de aprendizado profundo para a predi\c{c}\~ao da idade cerebral a partir de imagens de resson\^ancia magn\'etica, visando auxiliar na identifica\c{c}\~ao de biomarcadores do processo natural de envelhecimento. A identifica\c{c}\~ao de biomarcadores \'e \'util para a detec\c{c}\~ao de um processo neurodegenerativo em est\'agio inicial, al\'em de possibilitar prever um decl\'inio cognitivo relacionado ou n\~ao \`a idade. Duas t\'ecnicas s\~ao implementadas e comparadas neste trabalho: uma Rede Neural Convolucional 3D aplicada na imagem volum\'etrica e uma Rede Neural Convolucional 2D aplicada a fatias do plano axial, com posterior fus\~ao das predi\c{c}\~oes individuais. O melhor resultado foi obtido pelo modelo 2D, que alcan\c{c}ou um erro m\'edio absoluto de 3.83 anos.
Approximating Multiplicatively Weighted Voronoi Diagrams: Efficient Construction with Linear Size

Given a set of $n$ sites from $\mathbb{R}^d$, each having some positive weight factor, the Multiplicatively Weighted Voronoi Diagram is a subdivision of space that associates each cell to the site whose weighted Euclidean distance is minimal for all points in the cell. We give novel approximation algorithms that output a cube-based subdivision such that the weighted distance of a point with respect to the associated site is at most $(1+\varepsilon)$ times the minimum weighted distance, for any fixed parameter $\varepsilon \in (0,1)$. The diagram size is $O_d(n \log(1/\varepsilon)/\varepsilon^{d-1})$ and the construction time is within an $O_D(\log(n)/\varepsilon^{(d+5)/2})$-factor of the size bound. We also prove a matching lower bound for the size, showing that the proposed method is the first to achieve \emph{optimal size}, up to $\Theta(1)^d$-factors. In particular, the obscure $\log(1/\varepsilon)$ factor is unavoidable. As a by-product, we obtain a factor $d^{O(d)}$ improvement in size for the unweighted case and $O(d \log(n) + d^2 \log(1/\varepsilon))$ point-location time in the subdivision, improving the known query bound by one $d$-factor. The key ingredients of our approximation algorithms are the study of convex regions that we call cores, an adaptive refinement algorithm to obtain optimal size, and a novel notion of \emph{bisector coresets}, which may be of independent interest. In particular, we show that coresets with $O_d(1/\varepsilon^{(d+3)/2})$ worst-case size can be computed in near-linear time.
A Survey of Near-Data Processing Architectures for Neural Networks

Data-intensive workloads and applications, such as machine learning (ML), are fundamentally limited by traditional computing systems based on the von-Neumann architecture. As data movement operations and energy consumption become key bottlenecks in the design of computing systems, the interest in unconventional approaches such as Near-Data Processing (NDP), machine learning, and especially neural network (NN)-based accelerators has grown significantly. Emerging memory technologies, such as ReRAM and 3D-stacked, are promising for efficiently architecting NDP-based accelerators for NN due to their capabilities to work as both high-density/low-energy storage and in/near-memory computation/search engine. In this paper, we present a survey of techniques for designing NDP architectures for NN. By classifying the techniques based on the memory technology employed, we underscore their similarities and differences. Finally, we discuss open challenges and future perspectives that need to be explored in order to improve and extend the adoption of NDP architectures for future computing platforms. This paper will be valuable for computer architects, chip designers, and researchers in the area of machine learning.
EXPERIMENTS AND RESULTS ON THE USE OF ONTOLOGIES IN THE ARTIFICIAL INTELLIGENCE DOMAIN

The field of agent-based systems as part of the Artificial Intelligence domain is, now-a-days, quite popular. There are specialized technologies required for building software agents and it should be communicative, capable, autonomous and adaptive. In fact, these are the key characteristics required to help make the Internet activity more successful. The limiting factors in building such systems are being overcome, and new approaches are emerging from information technology research laboratories around the world. The use of ontology has proven to be essential elements in many applications and thus, they have been successfully applied in agent systems technology, knowledge management systems, and e-commerce platforms. The current research aims to present besides some theoretical aspects and examples of using the web agents for two European cities.
DD-NeRF: Double-Diffusion Neural Radiance Field as a Generalizable Implicit Body Representation

We present DD-NeRF , a novel generalizable implicit ﬁeld for representing human body geometry and appearance from arbitrary input views. The core contribution is a double diffusion mechanism, which leverages the sparse convolutional neural network to build two volumes that represent a human body at different levels: a coarse body volume takes advantage of unclothed deformable mesh to provide the large-scale geometric guidance, and a detail feature volume learns the intricate geometry from local image features. We also employ a transformer network to aggregate image features and raw pixels across views, for computing the ﬁnal high-ﬁdelity radiance ﬁeld. Experiments on various datasets show that the proposed approach outperforms previous works in both geometry reconstruction and novel view synthesis quality.
Assessing the Impact of Attention and Self-Attention Mechanisms on the Classification of Skin Lesions

Attention mechanisms have raised significant interest in the research community, since they promise relevant improvements in the performance of neural network architectures. However, in any specific problem, we still lack a principled way to choose specific mechanisms and hyper-parameters that lead to guaranteed improvements. More recently, self-attention has been proposed and widely used in transformer-like architectures, leading to significant breakthroughs in some applications. In this work we focus on two forms of attention mechanisms, attention modules and self-attention. Attention modules are used to reweigh the features of each layer input tensor. Different modules have different ways to perform this reweighting in fully connected or convolutional layers. The attention models studied are completely modular and in this work they will be used with the popular ResNet architecture. Self-attention, originally proposed in the area of natural language processing makes it possible to relate all the items in an input sequence. Self-attention is becoming increasingly popular in computer vision, where it is sometimes combined with convolutional layers, although some recent architectures do away entirely with convolutions. In this work, we study and perform an objective comparison of a number of different attention mechanisms in a specific computer vision task, the classification of samples in the widely used Skin Cancer MNIST dataset. The results show that attention modules do sometimes improve the performance of convolutional neural network architectures, but also that this improvement, although noticeable and statistically significant, is not consistent in different settings. The results obtained with self-attention mechanisms, on the other hand, show consistent and significant improvements, leading to the best results even in architectures with a reduced number of parameters.
Soil amplification based on seismometer array and microtremor observations in Chiba, Japan

Soil amplification characteristics of earthquake ground motion were investigated in terms of peak ground acceleration and transfer function based on the Chiba array observation records. The amplification of peak ground acceleration occurred mostly at the top soft layer and was similar for the three components. The effects of non-linear response of soil deposits on the transfer function were examined. Transfer functions calculated by ensemble average were close for the two horizontal components while those obtained from a smoothing operation were generally different. Both the transfer functions from the ensemble average and the smoothing operation underestimated the gain factor around the natural frequencies. A two-step smoothing procedure was proposed and a rotary spectrum was used to improve the estimation of the transfer function. Microtremors were observed at the locations of the boreholes where seismometers are buried. The power spectrum and spatial coherency of the microtremors were compared with those of the earthquake ground motion. Emphasis was placed on the wavetypes which dominated the peaks in the power spectra.
Metodologias para avaliação e diagnóstico do estado de isolamentos de papel impregnado com óleo mineral

Cellulose impregnated with mineral oil is the most used insulating material in medium, high or extra high voltage substations, due to its efficacy and low cost. This material is employed in power transformers and its bushings, reactors, capacitors and measurement voltage and current transformers. This paper discusses the methodologies traditionally used to assess the insulating state of impregnated paper and to diagnose faults through the dissipation factor and the concentration of gases dissolved in the oil, regarding some factors that may affect those variables. Uncertainties in measured parameters and analysis methods are also considered. Some results obtained with the application of traditional methodologies (standards) for incipient fault detection based on gas analysis are compared with results obtained from the application of a artificial neural network proposed for this task.
Newsvendor Model with Deep Reinforcement Learning

I present a deep reinforcement learning (RL) solution to the mathematical problem known as the Newsvendor model, which seeks to optimize profit given a probabilistic demand distribution. To reflect a more realistic and complex situation, the demand distribution can change for different days of the week, thus changing the optimum behavior. I used a Twin-Delayed Deep Deterministic Policy Gradient agent (written as completely original code) with both an actor and critic network to solve this problem. The agent was able to learn optimal behavior consistent with the analytical solution of the problem, and could identify separate probability distributions for different days of the week and behave accordingly.
Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks

Measures of Activity of Daily Living (ADL) are an important indicator of overall health but difficult to measure in-clinic. Automated and accurate human activity recognition (HAR) using wrist-worn accelerometers enables practical and cost efficient remote monitoring of ADL. Key obstacles in developing high quality HAR is the lack of large labeled datasets and the performance loss when applying models trained on small curated datasets to the continuous stream of heterogeneous data in real-life. In this work we design a self-supervised learning paradigm to create a robust representation of accelerometer data that can generalize across devices and subjects. We demonstrate that this representation can separate activities of daily living and achieve strong HAR accuracy (on multiple benchmark datasets) using very few labels. We also propose a segmentation algorithm which can identify segments of salient activity and boost HAR accuracy on continuous real-life data.
RumbleML: program the lakehouse with JSONiq

Lakehouse systems have reached in the past few years unprecedented size and heterogeneity and have been embraced by many industry players. However, they are often difficult to use as they lack the declarative language and optimization possibilities of relational engines. This paper introduces RumbleML, a high-level, declarative library integrated into the RumbleDB engine and with the JSONiq language. RumbleML allows using a single platform for data cleaning, data preparation, training, and inference, as well as management of models and results. It does it using a purely declarative language (JSONiq) for all these tasks and without any performance loss over existing platforms (e.g. Spark). The key insights of the design of RumbleML are that training sets, evaluation sets, and test sets can be represented as homogeneous sequences of flat objects; that models can be seamlessly embodied in function items mapping input test sets into prediction-augmented result sets; and that estimators can be seamlessly embodied in function items mapping input training sets to models. We argue that this makes JSONiq a viable and seamless programming language for data lakehouses across all their features, whether database-related or machine-learning-related. While lakehouses bring Machine Learning and Data Wrangling on the same platform, RumbleML also brings them to the same language, JSONiq. In the paper, we present the first prototype and compare its performance to Spark showing the benefit of a huge functionality and productivity gain for cleaning up, normalizing, validating data, feeding it into Machine Learning pipelines, and analyzing the output, all within the same system and language and at scale.