Reviewerly/exHarmony huggingface dataset:
JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech

In neural text-to-speech (TTS), two-stage system or a cascade of separately learned models have shown synthesis quality close to human speech. For example, FastSpeech2 transforms an input text to a mel-spectrogram and then HiFi-GAN generates a raw waveform from a mel-spectogram where they are called an acoustic feature generator and a neural vocoder respectively. However, their training pipeline is somewhat cumbersome in that it requires a fine-tuning and an accurate speech-text alignment for optimal performance. In this work, we present end-to-end text-to-speech (E2E-TTS) model which has a simplified training pipeline and outperforms a cascade of separately learned models. Specifically, our proposed model is jointly trained FastSpeech2 and HiFi-GAN with an alignment module. Since there is no acoustic feature mismatch between training and inference, it does not requires fine-tuning. Furthermore, we remove dependency on an external speech-text alignment tool by adopting an alignment learning objective in our joint training framework. Experiments on LJSpeech corpus shows that the proposed model outperforms publicly available, state-of-the-art implementations of ESPNet2-TTS on subjective evaluation (MOS) and some objective evaluations.
Intelligent Task Offloading in Fog Computing Based Vehicular Networks

Connected vehicles in vehicular networks will lead to a smart and autonomous transportation system. These vehicles have a large number of applications that require wireless connectivity by using cellular vehicle-to-everything (C-V2X). The infrastructure of C-V2X comprises multiple roadside units (RSUs) that provide direct connectivity with the on-road vehicles. Vehicular traffic applications are mainly categorized into three major groups such as emergency response traffic, traffic management and infotainment traffic. Vehicles have limited processing capabilities and are unable to process all tasks simultaneously. To process these offloaded tasks in a short time, fog servers are placed near the RSUs. However, it is sometimes not possible for the fog computing server to process all offloaded tasks. In this work, a utility function for the RSU to process these offloaded tasks is designed. In addition, a knapsack-based task scheduling algorithm is proposed to optimally process the offloaded tasks. The results show that the proposed scheme helps fog nodes to optimally scrutinize the high-priority offloaded tasks for task execution resulting in more than 98% of emergency tasks beingprocessed by fog computing nodes.
Advanced land imager superiority in lithological classification utilizing machine learning algorithms

Abstract Different types of remote sensing data are commonly used as inputs for lithological classification schemes, yet determining the best data source for each specific application is still unresolved, but critical for the best interpretations. In addition, various classifiers (i.e., artificial neural network (ANN), maximum likelihood classification (MLC), and support vector machine (SVM)) have proven their variable efficiencies in lithological mapping, yet determining which technique is preeminent is still questionable. Consequently, this study aims to test the potency of Earth observing-1 Advanced Land Imager (ALI) data with the frequently utilized Sentinel 2 (S2), ASTER, and Landsat OLI (L8) data in lithological allocation using the widely accepted ANN, MLC, and SVM, for a case study in the Um Salatit area, in the Eastern Desert of Egypt. This area has a recent geological map that is used as a reference for selecting training and testing samples required for machine learning algorithms (MLAs). The results reveal (1) ALI superiority over the most commonly used S2, ASTER, and L8; (2) SVM is much better than MLC and ANN in executing lithologic allocation; (3) S2 is strongly recommended for separating higher numbers of classes compared to ASTER, L8, and ALI. Model overfitting may negatively impact S2 results in classifying small numbers of targets; (4) we can significantly enhance the classification accuracy, to transcend 90% by blending different sensor datasets. Our new approach can help significantly in further lithologic mapping in arid regions and thus be fruitful for mineral exploration programs.
Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset

Breast cancer is a critical public health issue and a leading cause of cancer-related deaths among women worldwide. Its early diagnosis and detection can effectively help in increasing the chances of survival rate. For this reason, the diagnosis and classification of breast cancer using Deep learning algorithms have attracted a lot of attention. Therefore, our study aimed to design a computational approach based on deep convolutional neural networks for an efficient classification of breast cancer histopathological images by using our own created dataset. We collected overall 328 digital slides, from 116 of surgical breast specimens diagnosed with invasive breast carcinoma of non-specific type, and referred to the histopathology department of the National Institute of Oncology in Rabat, Morocco. We used two models of deep neural network architectures in order to accurately classify the images into one of three categories: normal tissue-benign lesions, in situ carcinoma or invasive carcinoma.Both Resnet50 and Xception models achieved comparable results, with a small advantage to Xception extracted features. We reported high degrees of overall correct classification accuracy (88%), and sensitivity (95%) for detection of carcinoma cases, which is important for diagnostic pathology workflow in order to assist pathologists for diagnosing breast cancer with precision. The results of the present study showed that the designed classification model has a good generalization performance in predicting diagnosis of breast cancer, in spite of the limited size of the data. To our knowledge, this approach can be highly compared with other common methods in the automated analysis of breast cancer images reported in literature.
Imbalanced Data Classification Using Hybrid Under-Sampling with Cost-Sensitive Learning Method

The topic of class inequalities and class integration has been a significant problem in supervised learning. To optimize cost function, most supervised learning algorithms presume that the classes being considered are a similar cardinality and that supposition does not matter to imbalanced datasets in the case of sub-optimal classification results. Consequently, various methods have been proposed that correct imbalanced datasets such as under-sampling, over-sampling, ensemble-based methodologies, and cost-sensitive learning. However, the lack of information triggers the under-sampling and over-sampling to endure higher runtime and potential overfitting. Also, there is a disadvantage for a cost-sensitive method to handle insufficiently specified costs. To solve this issue, a new hybrid HUSCSLBoost framework has been proposed to manipulate imbalanced data through three key steps: data cleaning, data balancing, and cost accumulation. HUSCSLBoost uses Tomek-Link algorithms for eliminating noise data in the first stage and then creates several balanced subsets applying the random under-sampling. Finally, the CSLBoost method involves each balance subsets like any boost iteration, such as RUSBoost. The inclusion of a cost concept into the value update calculation for each case is based on its hardness to minimize the lack of information introduced by undertaking tests. HUSCSBoost was applied to 27 imbalanced datasets, and the outcome is considerably better than the current best-performing RUSBoost method.
Human drone interaction in delivery of medical supplies: A scoping review of experimental studies

The COVID-19 pandemic, ageing populations and the increasing shortage of skilled workers pose great challenges for the delivery of supplies for people with and without care needs. The potential of drones, as unmanned air vehicles, in healthcare are huge and are discussed as an effective new way to delivery urgent medicines and medical devices, especially in rural areas. Although the advantages are obvious, perspectives of users are important particularly in the development process. Investigating human drone interaction could potentially increase usefulness and usability. The present study aims to perform a systematic scoping review on experimental studies examining the human drone interaction in deliveries of drugs and defibrillators.Two databases (MEDLINE and CINAHL) and references of identified publications were searched without narrowing the year of publication or language. Studies that investigated the human drone interaction or medical delivery with drones in an experimental manner were included (research articles). All studies that only simulated the delivery process were excluded.The search revealed 83 publications with four studies being included. These studies investigated the user experience of drone delivered defibrillators, but no study was identified that investigated the human drone interaction in the delivery of drugs. Three categories of human drone interaction were identified: landing, handover, and communications. Regarding landing and handover, the most important issue was the direct physical contact with the drone while regarding communications users need clearer instructions about drone´s direction, sound and look like.The identified studies used technology-driven approaches by investigating human drone interaction in already existing technologies. Users must become integral part of the whole development process of medical drone services to reduce concerns, and to improve security, usability and usefulness of the system. Human drone interaction should be developed according to the identified categories of human drone interaction by using demand- and technology-driven approaches.
ERNIE-GeoL: A Geography-and-Language Pre-trained Model and its Applications in Baidu Maps

Pre-trained models (PTMs) have become a fundamental backbone for downstream tasks in natural language processing and computer vision. Despite initial gains that were obtained by applying generic PTMs to geo-related tasks at Baidu Maps, a clear performance plateau over time was observed. One of the main reasons for this plateau is the lack of readily available geographic knowledge in generic PTMs. To address this problem, in this paper, we present ERNIE-GeoL, which is a geography-and-language pre-trained model designed and developed for improving the geo-related tasks at Baidu Maps. ERNIE-GeoL is elaborately designed to learn a universal representation of geography-language by pre-training on large-scale data generated from a heterogeneous graph that contains abundant geographic knowledge. Extensive quantitative and qualitative experiments conducted on large-scale real-world datasets demonstrate the superiority and effectiveness of ERNIE-GeoL. ERNIE-GeoL has already been deployed in production at Baidu Maps since April 2021, which significantly benefits the performance of various downstream tasks. This demonstrates that ERNIE-GeoL can serve as a fundamental backbone for a wide range of geo-related tasks.
Cascading CMOS-Based Chaotic Maps for Improved Performance and Its Application in Efficient RNG Design

We present a general framework for improving the chaotic properties of CMOS-based chaotic maps by cascading multiple maps in series. Along with two novel chaotic map topologies, we present the 45 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$nm$ </tex-math></inline-formula> designs for four CMOS-based discrete-time chaotic map topologies. With the help of the bifurcation plot and three established entropy measures, namely, Lyapunov exponent, Kolmogorov entropy, and correlation coefficient, we present an extensive chaotic performance analysis on eight unique map circuits (two under each topology) to show that under certain constraints, the cascading scheme can significantly elevate the chaotic performance. The improved chaotic entropy benefits many security applications and is demonstrated using a novel random number generator (RNG) design. Unlike conventional mathematical chaotic map-based digital pseudo-random number generators (PRNG), this proposed design is not completely deterministic due to the high susceptibility of the core analog circuit to inevitable noise that renders this design closer to a true random number generator (TRNG). By leveraging the improved chaotic performance of the transistor-level cascaded maps, significantly low area and power overhead are achieved in the RNG design. The cryptographic applicability of the RNG is verified as the generated random sequences pass four standard statistical tests namely, NIST, FIPS, Diehard, and TestU01.
Finite-Time Dynamic Event-Triggered Fuzzy Output Fault-Tolerant Control for Interval Type-2 Fuzzy Systems

The finite-time dynamic event-triggered fuzzy output feedback fault-tolerant control problem is studied in this article for the interval type-2 (IT2) Takagi–Sugeno fuzzy system with parameter uncertainties and actuator faults. A fuzzy state observer is first developed to solve the immeasurable state problem. Second, by using the sampled estimating states and measured output signals, a dynamic event-triggered mechanism is formulated via integrating sensor-to-observer with observer-to-controller. Third, an observer-based finite-time event-triggered fuzzy fault-tolerant controller is synthesized via the nonparallel distribution compensation design principle. Consequently, the finite-time stable conditions of the addressed IT2 fuzzy system are established by constructing an appropriate Lyapunov function. Furthermore, an output feedback control design algorithm of solving control and observer gains is given in terms of the established sufficient finite-time stable conditions. Finally, a practical example of a nonlinear tunnel diode circuit system is provided to verify the effectiveness of the proposed IT2 fuzzy control scheme.
Teletrabalho na atualidade: quais são os impactos no desempenho profissional, bem-estar e contexto de trabalho?

From the pandemic imposed by the new coronavirus, teleworking proved to be a promising and desirable work arrangement alternative in times of social isolation, so that it has received increasingly priority attention in human resources management research agendas in the public sector. Federal Data Processing Service (Serpro) was one of the pioneers in adopting teleworking in Brazil and, therefore, it was the locus of this study. This study explored the positive and negative sides of teleworking in Serpro from the point of view of managers, teleworkers and colleagues and also compared teleworkers and no teleworkers with regard to the professional performance, work context and work well-being. The research method included questionnaires and textual analysis, analysis of variance and Mann-Whitney tests. About the positive sides of teleworking, the most relevant points were productivity increase and quality of life increase. About negative sides, technical difficulties and social interaction limitations can be pointed out. Results also showed that teleworkers have more positive perceptions about work context, professional performance and work well-being. This study provides a reflective look on adopting and implementing teleworking for public managers.
Alignment of Multi-Sensored Data: Adjustment of Sampling Frequencies and Time Shifts

Objectives:For a more comprehensive clinical picture, measuring vital signs with multiple devices is advantageous due to the acquisition of complementary information (ECG, body movement, temperature and respiration) and the possible compensation of signal loss.Our aim is to find a robust way for the correction of sampling frequencies and the alignment of non-synchronized sensors.Methods: We used data from an experiment including five different devices, which simultaneously measured the activity of the heart and other vital parameters (Hexoskin Hx1 Smart Shirt, SOMNOtouch NIBP, Polar RS800 Multi, eMotion Faros 360 • , NeXus-10 MKII).Our alignment procedure is based on pairwise comparisons of 300 consecutive heart beat intervals to the Hexoskin reference interval sequence, using minimization of the overall absolute sum of differences for alignment.Robust linear regression fits were used to adjust for general deviations in the sampling frequencies and for non-linear resampling in a sliding window.Results:Altering sampling frequencies were identified in Faros and Polar devices in the course of experimental measurements in all of 13 subjects.SOMNOtouch and NeXus had the lowest standard deviation across all subjects.In two identical Faros devices, the average sampling frequency was +0.0293% and +0.0175%.
Content Moderation Remedies

This Article addresses a critical but underexplored aspect of content moderation: if a user’s online content or actions violate an Internet service’s rules, what should happen next? The longstanding expectation is that Internet services should remove violative content or accounts from their services as quickly as possible, and many laws mandate that result. However, Internet services have a wide range of other options—what I call “remedies”—they can use to redress content or accounts that violate the applicable rules. This Article describes dozens of remedies that Internet services have actually imposed. It then provides a normative framework to help Internet services and regulators navigate these remedial options to address the many difficult tradeoffs involved in content moderation. By moving past the binary remove-or-not remedy framework that dominates the current discourse about content moderation, this Article helps to improve the efficacy of content moderation, promote free expression, promote competition among Internet services, and improve Internet services’ community-building functions.
Cancer Detection and Prediction Using Genetic Algorithms

Cancer is a wide category of diseases that is caused by the abnormal, uncontrollable growth of cells, and it is the second leading cause of death globally. Screening, early diagnosis, and prediction of recurrence give patients the best possible chance for successful treatment. However, these tests can be expensive and invasive and the results have to be interpreted by experts. Genetic algorithms (GAs) are metaheuristics that belong to the class of evolutionary algorithms. GAs can find the optimal or near-optimal solutions in huge, difficult search spaces and are widely used for search and optimization. This makes them ideal for detecting cancer by creating models to interpret the results of tests, especially noninvasive. In this article, we have comprehensively reviewed the existing literature, analyzed them critically, provided a comparative analysis of the state-of-the-art techniques, and identified the future challenges in the development of such techniques by medical professionals.
Energy Efficient UAV Flight Control Method in an Environment with Obstacles and Gusts of Wind

This article presents an energy-efficient method of controlling unmanned aircraft (fixed-wing UAVs), which consists of three groups of algorithms: aerial vehicle route planning, in-flight control, and algorithms to correct the preplanned flight trajectory. All algorithms shall take into account the existence of obstacles that the UAV must avoid and wind gusts in the UAV’s area of operation. Tests were carried out on the basis of the UAV mathematical model, stabilization and navigation algorithms, and Dryden turbulence model, considering the parameters of the UAV’s propulsion system. The work includes a detailed description of constructing a network of connection that is used to plan a UAV mission. It presents the algorithm for determining the actual distances between the different points in the field of action, which takes into account the existence of obstacles. The algorithm shall be based on methods for determining the flight trajectory on a hexagonal grid. It presents the developed proprietary UAV path planning algorithm based on a model from a group of algorithms of mixed integer linear problem (MILP) optimization. It presents the manner in which the pre-prepared flight path was used by UAV controllers that supervised the flight along the preset path. It details the architecture of contemporary unmanned aerial vehicles, which have embedded capability to realize autonomous missions, which require the integration of UAV systems into the route planning algorithms set out in the article. Particular attention has been paid to the planning and implementation methods of UAV missions under conditions where wind gusts are present, which support the determination of UAV flight routes to minimize the vehicle’s energy consumption. The models developed were tested within a computer architecture based on ARM processors using the hardware-in-the-loop (HIL) technique, which is commonly used to control unmanned vehicles. The presented solution makes use of two computers: FCC (flight control computer) based on a real-time operating system (RTOS) and MC (mission computer) based on Linux and integrated with the Robot Operating System (ROS). A new contribution of this work is the integration of planning and monitoring methods for the implementation of missions aimed at minimizing energy consumption of the vehicle, taking into account wind conditions.
Designing Sound for Social Robots: Candidate Design Principles

Abstract How can we use sound and music to create rich and engaging human-robot interactions? A growing body of HRI research explores the many ways in which sound affects human-robot interactions and although some studies conclude with tentative design recommendations, there are, to our knowledge, no generalised design recommendations for the robot sound design process. We address this gap by first investigating sound design frameworks in the domains of product sound design and film sound to see whether practices and concepts from these areas contain actionable insights for the creation of robot sound. We then present three case studies, detailed examinations of the sound design of commercial social robots Cozmo and Vector, Jibo, and Kuri, facilitated by expert interviews with the robots’ sound designers. Combining insights from the design frameworks and case studies, we propose nine candidate design principles for robot sound which provide (1) a design-oriented perspective on robot sound that may inform future research, and (2) actionable guidelines for designers, engineers and decision-makers aiming to use sound to create richer and more refined human-robot interactions.