Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Hand Gesture Recognition Based on Signals From Specialized Data Glove and Deep Learning Algorithms

Gesture recognition as a natural, convenient and recognizable way has been received more and more attention on human–machine interaction (HMI) recently. However, visual-based gesture recognition methods are often restricted by environments and classical wearable device-based strategies are suffered from relatively low accuracy or the complicated structures. In this study, we first design a low-cost and efficient data glove with simple hardware structure to capture finger movement and bending simultaneously. Second, a novel dynamic hand gesture recognition algorithm (DGDL-GR) is proposed to recognize human dynamic sign language, in which a fusion model of convolutional neural network (fCNN) and generic temporal convolutional network (TCN) is fully utilized. The fCNN (fusion of 1-D CNN and 2-D CNN) is proposed to extract time-domain features of finger resistance movement and spatial domain features of finger resistance bending simultaneously. Moreover, due to the superiorities of TCN in sequence modeling task, this work proposes a novel hand gesture recognition method based on the TCN, which includes causal convolution, dilation convolution, and a residual network with appropriate layers. Both long- and short-time dependencies of the hand gesture features are deeply mined and classified in the end. Results of extensive experiments have demonstrated that the proposed DGDL-GR algorithm outperforms many state-of-the-art algorithms on the measure of accuracy, F1 score, precision score, and recall score with the real-world dataset. Moreover, the number of residual blocks and some key hyperparameters of the proposed DGDL-GR algorithm has been studied thoroughly in this work.
Simulations of Internal Charging Effects of Artificial Radiation Belt on Dielectric Material

To find out the law of charging effects of high-energy electrons in artificial radiation belt produced by a high-altitude nuclear explosion (HANE) on the dielectric materials, we present a simulation model of high-energy electrons in artificial radiation belt contributing to internal charging of dielectric material, which are commonly used inside the satellites or spacecraft. The deposition electric parameters such as injection current density, charge deposition rate, and radiation dose rate are obtained by using the Monte Carlo simulation method. The equations of charging electric field of Teflon material have been presented and numerically solved. The distributions of electric field and potential in the dielectric material Teflon charged by artificial radiation belt and the change rules with charging time have been obtained. The results show that the peak charging electric field produced by artificial radiation belt in dielectric material is lower than that produced by 1-MeV single-energy electron under the same injection current density. When the artificial radiation belt generated by the HANE with a yield of 5 kt charges the Teflon material with a thickness of 4 mm, the time needed for charging to reach saturation will be more than 24 h, and the saturated charging electric field can reach 106 V/m, so the discharge risk is low. For artificial radiation belt generated by the HANE with a yield 1.4 Mt, when the time to start charging is within 100 days after the explosion and the charging duration is within 1 h, the maximum charging electric field in Teflon material will exceed 107 V/m and the discharge risk will be high.
Integrated Access and Backhaul: A Key Enabler for 5G Millimeter-Wave Deployments

Integrated access and backhaul (IAB) is an important new feature in 5G NR that enables rapid and cost-effective millimeter-wave (mmWave) deployments through self-backhauling in the same spectrum. IAB deployments can achieve excellent cell edge coverage, for example, uplink rates above 100 Mb/s, while significantly reducing the amount of required fiber. This article provides a primer on IAB, contrasting it with the many failed multihop systems that preceded it. We conduct a large-scale study of coverage and rate performance based on a plausible deployment in Chicago's Lincoln Park neighborhood, utilizing ray tracing in the 39 GHz band. The study demonstrates that, as theory predicts, an IAB solution provides a massive coverage advantage for early mmWave rollouts with only a small number of fiber-connected (donor) base stations, for example, less than 10/km2. We show that as the UE and traffic loads increase over time as the 5G eco-system matures, the per user throughput can be maintained by replacing IAB (relay) nodes with donor nodes, that is, slowly extending the fiber network.
Investigating How Software Characteristics Impact the Effectiveness of Automated Software Fault Tolerance

A number of publications have examined automated fault tolerance techniques for software running on commercial off-the-shelf microcontrollers. Recently, we published an automated compiler-based protection tool called COmpiler Assisted Software fault Tolerance (COAST), a tool that automatically inserts dual- or triple-modular redundancy into software programs. In this study, we use COAST to explore how the effectiveness of automated fault protection varies between different benchmarks, tested on an ARM Cortex-A9 platform. Our hypothesis is that certain benchmark characteristics are more likely than others to influence the effectiveness of automated fault protection. Through neutron radiation testing at the Los Alamos Neutron Science Center (LANSCE), we show that cross section improvements vary from <inline-formula> <tex-math notation="LaTeX">$1.6\times $ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$54\times $ </tex-math></inline-formula> across eight benchmark variants. We then explore the characteristics of these benchmarks and investigate how properties of these benchmarks may impact the effectiveness of automated fault protection. Finally, we leverage a novel fault injection platform to isolate two of these benchmark characteristics and validate our hypotheses.
Markov Chain Monte Carlo-Based Bayesian Inference for Learning Finite and Infinite Inverted Beta-Liouville Mixture Models

Recently Inverted Beta-Liouville mixture models have emerged as an efficient paradigm for proportional positive vectors modeling and unsupervised learning. However, little attention has been devoted to investigate these generative models within discriminative classifiers. Our aim here is to reveal the structure of non-Gaussian data by generating new probabilistic SVM kernels from inverted-Beta Liouville mixture models. The inverted Beta-Liouville has a more general covariance structure and a smaller number of parameters than the inverted Dirichlet and generalized inverted Dirichlet, respectively, which makes it more practical and useful. A principled Bayesian learning algorithm is developed to accurately estimate the model’s parameters. To cope with the problem of selecting the optimal number of components, we further propose a nonparametric Bayesian learning algorithm based on an extended infinite mixture model which may have better modelling and clustering capabilities than the finite model for some applications. Finally, the resulting generative model is exploited to build several efficient probabilistic SVM kernels in order to enhance the expected clustering and modeling performance. Through a number of experimental evaluations involving visual scenes classification, text categorization and texture images discrimination, we prove the merits of the proposed work.
Cross-Corner Delay Variation Model for Standard Cell Libraries

For timing closure of logic circuits, circuit designers must perform sign-offs on a variety of process, voltage, and temperature (PVT) conditions. Designs of advanced logic circuits involve a multitude of voltage islands and operating modes, each of which requires delay characterizations at nearby PVT corners. Furthermore, advanced technologies nodes suffer from corner explosion: while the impact of PVT variations is being exacerbated, process variations are also diversifying, increasing the number of operating conditions exponentially. This paper revisits the importance of cross-corner timing estimations and proposes a delay variation model to mitigate such corner explosion. Our objective is to reduce PVT corner characterization effort for timely static timing analysis on an exploding number of operating conditions. Our proposed Decomposed Propagation Vector Variation Model (DPVVM) decomposes propagation delay and timing constraints into the driving by the receiver cell and its driver cell; by scaling them separately, delay characterization effort is reduced to a fraction of time while realizing accurate timing estimations. We also propose Multi-Dimension Recomposition (MDR) scheme, which exploits a multitude of pre-characterized corners to further improve the consistency of cross-corner timing estimations. As a result, with only 8.0% of a corner characterization effort compared to the full-characterization, DPVVM combined with MDR achieves overall cross-corner timing estimation errors of 4.8% and 5.6% for single cells and complex logic circuits—or improvements of 69% and 61% over the conventional derating method, respectively. Our proposed method’s characterization overhead is 11% over the conventional derating method; the overhead is marginal, accounting for only 0.76% of a full-characterization time.
Impact of pharmaceutical promotional strategies on physicians’ prescription behavior in India

This study focuses on the impact of the promotional strategies used by the pharmaceutical industry on physician’s prescription behavior. This research was mainly carried out through a detailed literature review of past research and self-administered questionnaires. These questionnaires were electronically released and included data from a diverse group of medical doctors. Based on past research and theoretical underpinning, twenty different factors were categorized into four different promotional strategies. Responses to this study were marked on a five-point Likert scale and the relative importance index (RII) was used in order to rank the significant factors of promotional strategies. The study analysis showed that the majority of physicians agreed that the promotional strategies used by pharmaceutical companies impacted their prescription behavior patterns. The study also revealed that rapport, quality of a product and invitations to continuous medical education (CME) conferences speaker are the main factors shaping the prescription behavior of doctors.
Reading Comprehension and Metacognition among Freshmen Students

The present paper presents the common problem about the deficient comprehension of reading in the young university population. For this purpose, we have focused on a sample consisting of first-level students belonging to a university located in Lima city. We have analyzed the relation between inference reading comprehension and the usage of metacognitive strategies in those students enrolled in the programs of education, nursing, systems engineering and accounting. Our study took into account variables such as gender and high school education. We measured the relevant variables by using the proposed strategies described in this paper.
The Thermal Effect on Discharge Coefficient of Sonic Nozzle Based on Reconstructed Body Temperature Distribution by Improved SOR Method

For the sonic nozzles, the discharge coefficient (<inline-formula> <tex-math notation="LaTeX">$C_{d}$ </tex-math></inline-formula>) is an essential parameter of flow characteristics, which is obviously influenced by the thermal effect determined by the nozzle body temperature distribution. In this study, two types of thermocouple arrays for temperature acquisition of the nozzle body were designed to investigate the thermal effect. The temperature isothermal maps of the nozzle body were reconstructed by improved successive over-relaxation (SOR) method combining the simple Kriging interpolation method and particle swarm optimization (SKIM-PSO), which can effectively eliminate the phenomenon of bull’s eye and nonsmoothness of the SKIM. And then, the accuracy of the new algorithm was checked by cross validation with test sets of <inline-formula> <tex-math notation="LaTeX">$d = 5.25$ </tex-math></inline-formula>-mm nozzles with different structures. It is found that the maximum error <inline-formula> <tex-math notation="LaTeX">$\Delta e_{\mathrm {max}}$ </tex-math></inline-formula> of the new algorithm is less than 0.2 °C. The thermal effect on discharge coefficient was analyzed with the above-reconstructed temperature distribution, which is classified into two aspects, the thermal boundary layer correction factor <inline-formula> <tex-math notation="LaTeX">$C_{\mathrm{ T}}$ </tex-math></inline-formula> and the constrained thermal deformation correction factor <inline-formula> <tex-math notation="LaTeX">$C_{\mathrm{ a}}$ </tex-math></inline-formula> related to the throat area. The <inline-formula> <tex-math notation="LaTeX">$C_{\mathrm{ T}}$ </tex-math></inline-formula> is <inline-formula> <tex-math notation="LaTeX">$1- 3.845\text {Re}_{\mathrm{ t}}^{-0.5}\,\,\Delta T/T$ </tex-math></inline-formula> from a similar solution to the boundary layer. Unlike the free expansion, the slopes of the <inline-formula> <tex-math notation="LaTeX">$C_{\mathrm{ a}}$ </tex-math></inline-formula> of constrained expansion for three AISI304 nozzles with <inline-formula> <tex-math notation="LaTeX">$d = 2.00$ </tex-math></inline-formula>, 5.25, and 7.45 mm are <inline-formula> <tex-math notation="LaTeX">$- 1.24 \times 10^{-5}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$+ 1.98 \times 10^{-5}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$+ 2.49 \times 10^{-5}$ </tex-math></inline-formula>, respectively. With the correction of <inline-formula> <tex-math notation="LaTeX">$C_{\mathrm{ T}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$C_{\mathrm{ a}}$ </tex-math></inline-formula>, the prediction error of discharge coefficient is reduced from 0.14%–0.15% to 0.08%–0.1% for <inline-formula> <tex-math notation="LaTeX">$d = 2.00$ </tex-math></inline-formula>- and 7.45-mm two nozzles.
Isolated Word Speech Recognition Using Convolutional Neural Network

This research aims to design and develop an accurate speech recognition system for a set of predefined words collected from short audio clips. It uses The Speech Commands Dataset v0.01 provided by Google’s TensorFlow. Isolated word speech recognition can be implemented in voice user interfaces for applications with key-word spotting. The end goal is to classify and recognize ten words, along with classes for “unknown” words besides the “silence” class. The problems that face the current speech recognition technology like the acoustical noise and variations in recording environments are also solved and addressed here. To extract useful information from the signal, two methods of feature extraction were used: MFCCs and Mel-spectrograms. For classification, the convolutional neural network (CNN) was used. Different models were developed for this research, where each model has different architecture (1D-convnet and 2D-convnet). During training, techniques like batch normalization, regularization, and dropout were added to improve the accuracy and maintain the efficiency of the models. As a result of our experiments, The final model (2D-convnet with MFCC-16000) achieved an accuracy of 97.07% for training and 96.19% for testing.
A Deep Convolutional Neural Network for Classification of Aedes Albopictus Mosquitoes

Monitoring the spread of disease-carrying mosquitoes is a first and necessary step to control severe diseases such as dengue, chikungunya, Zika or yellow fever. Previous citizen science projects have been able to obtain large image datasets with linked geo-tracking information. As the number of international collaborators grows, the manual annotation by expert entomologists of the large amount of data gathered by these users becomes too time demanding and unscalable, posing a strong need for automated classification of mosquito species from images. We introduce the application of two Deep Convolutional Neural Networks in a comparative study to automate this classification task. We use the transfer learning principle to train two state-of-the-art architectures on the data provided by the Mosquito Alert project, obtaining testing accuracy of 94%. In addition, we applied explainable models based on the Grad-CAM algorithm to visualise the most discriminant regions of the classified images, which coincide with the white band stripes located at the legs, abdomen, and thorax of mosquitoes of the Aedes albopictus species. The model allows us to further analyse the classification errors. Visual Grad-CAM models show that they are linked to poor acquisition conditions and strong image occlusions.
Geodetic Distance and Dynamic Outlier Exclusion in EM Optimization of Self Exciting Point Process for Homicide Prediction in Chicago

In this paper, we propose a novel algorithm to improve the state-of-the-art results of homicide prediction in Chicago crime dataset. A marked self-exciting point process (M-SEPP) based epidemic type aftershock sequence (ETAS) model was applied to the Chicago crime dataset by Mohler [1] for improving the prediction rate of homicides over and above the traditional chronic hot spot approach. Expectation-maximization (EM)-type optimization has long been employed for the computation of the maximum likelihood estimates of parameters in the ETAS model. However, improvement in crime prediction has been slow due to the technological challenges in modeling the spatio-temporal distribution of criminal behavior, which results from a complex interaction of biological, social, psychological, and other influencing factors. We propose the GeoDOME algorithm, which incorporates geodetic distance and dynamic outlier exclusion in the EM-type algorithm of the ETAS model. It guarantees significant improvement in the prediction rate of homicides compared to the best result documented to date. A theoretical basis for the principles used in the modified algorithm is also provided. The increase in the prediction accuracy of homicides has been experimentally validated using the same Chicago crime dataset.
Multimodal Chain: Cross-Modal Collaboration Through Listening, Speaking, and Visualizing

Language is an integral part of human interpersonal communication, which is conveyed through multiple sensory channels. This multisensory communication skill has motivated an extensive number of studies on multimodal information processing, which are trying to develop a system that mimics this natural behaviour. For example, automatic speech recognition (ASR) represents listening activity, text-to-speech (TTS) represents speaking, and various image processing models to represent visual perception. Most are trained and tuned independently using parallel examples from the source to the target modality. However, this is not the case in real-life situations, where a lot of paired data are unavailable. Inspired by this self-supervision of the human auditory and visual perception system, we proposed a multimodal chain mechanism with a weakly-supervised chain training strategy that is trained and tuned jointly. In our proposed framework, when the amount of paired training data are insufficient, collaboration among ASR and TTS, image captioning (IC), and image production models can improve their performance through single or dual-loop chain mechanisms. Our experiment result showed that by using such a closed-loop chain mechanism, we can improve a model with both unpaired and unrelated data from different modalities in a semi-supervised manner. Through the collaboration of speech and visual chains, we improve an ASR model performance with an image-only dataset while maintaining the performance of other models.
Development of Intelligent Robotic Process Automation: A Utility Case Study in Brazil

Robotic Process Automation (RPA) refers to process automation applications of traditional Information Technologies based on robot software with the ability to capture and interpret the specific processes of organizations. Studies show that RPAs are able to reduce resources and optimize processes effectively in relation to customers. Some of these call center business processes deal with customers most likely to complain; therefore, a “Proactive Notification” robot was developed to classify these types of customers to be prioritized. This robot defines the creation of an RPA architecture for proactive notifications applied to an electric company in Brazil. The methodology used for the development of this project consisted of data management, predictive models, and peripheral components for sending SMS and making calls. It was tested against all customers in 40 cities (two states) and the model considers the historical basis of 3 years of occurrences to predict customers with a high probability of filing a complaint due to power failure. The results show that customers who were called for this type of problem did not call the call center again to complain, suggesting positive acceptance of the robot. In conclusion, the robot presented herein is capable of making proactive notifications with high precision to customers with the highest probability of complaints, predicting possible problems.
Experimental investigation of corn cob pyrolysis

This paper presents experimental investigation of pyrolysis process of corn cob from Vojvodina (Republic of Serbia). The influence of temperature, reaction time, particle size, and heating rate on pyrolysis products yields was investigated. Studying of biomass pyrolysis kinetics led to the conclusion that thermogravimetry is an appropriate method for explaining decomposition of different biomass types. Experimental research showed that temperature increase leads to decrease in char yield and volatile yield. Mass yield of pyrolysis oil increased with higher heating rate. Methane volume fraction in pyrolysis gas increased with temperature increase, while the volume fraction of carbon dioxide decreased. Particle size affected the methane yield. It was shown that with the increase in particle size, the volume fraction of methane in pyrolysis gas decreased. Based on the char composition obtained after corn cob pyrolysis process, it was determined that char is rich in carbon (74.80%). Lower heating value of cha.
Possibility of Production of 67Cu at a Small-Sized Cyclotron via the (p,α)-Reaction on Enriched 70Zn

The possibility of production of the therapy related /? '-emitting radioisotope Cu (7*,;2 = 61.9 h, Eß-(m^ = 0.6 MeV) at a smallsized cyclotron via the Zn(p,o)Cu reaction was investigated. The excitation function of the reaction was measured for the first time from threshold to 35 MeV using the stacked-foil technique. Thin samples were prepared by electrolytic deposition of 85% enriched Zn on Au-backing. The radiocopper produced in each irradiated sample was separated chemically by extraction methods, and the radioactivity was assayed by high-resolution y-ray spectrometry. The integral yield of Cu was calculated from the measured cross section data. The energy range Ep = 18—>8 appears to be optimum. With 85% enriched Zn the thick target yield of Cu amounts to about 2.0 MBq/μΑΙι (182 MBq/ μΑ at saturation) with low radionuclidic impurity (< 10% "Cu). A higher target enrichment would further reduce the impurity level. Using modern small-sized cyclotrons, it is possible to produce Cu in quantities sufficient for therapeutic applications.