Reviewerly/exHarmony huggingface dataset:
Real time hardware implementation of discrete sliding mode fuzzy controlled buck converter using digital signal processor

This paper deals with the real time hardware implementation of discrete sliding mode fuzzy control (DSMFC) for buck converter using digital signal processor (DSP). Applications like electric vehicle suspension control, flight dynamic control, robot position control and engine throttle position control; sliding mode control (SMC) plays a major role. Hardware realization is difficult with SMC strategy due to the continuous gain change results in chattering problem and actuator or contact may break. To resolve this problem the fuzzy logic (FL) approach has combined with the robust technique discrete sliding mode control (DSMC) to develop a new strategy for DSMFC. The mathematical modeling of the controller is done using MATLAB/Simulink software and practical design of the converter is also realized. The robustness of the controller is proved by introducing sudden change in input voltage as well as load with the help of switching circuit in hardware realization. The obtained practical results are verified by comparing with the simulation output and reference value.
Face mask detection using deep learning on NVIDIA Jetson Nano

&lt;span&gt;In December 2019, the coronavirus pandemic started. Coronavirus desease-19 (COVID-19) is transmitted directly from contaminated surfaces via direct touch. To combat the virus, a multitude of equipment is needed. Masks are a vital element of personal protection in crowded places. As a result, determining if a person is wearing a face mask is critical to assimilating to contemporary society. To accomplish the objective, the model presented in this paper used deep learning libraries and OpenCV. This approach was chosen for safety concerns due to its high resource efficiency during deployment. The classifier was built using the MobileNetV2 structure, which was designed to be lightweight and capable of being utilized in embedded devices such as the NVIDIA Jetson Nano to do real-time mask recognition. The stages of model construction were collecting, pre-processing, splitting data, creating the model, training the model, and applying the model. This system utilized image processing techniques and deep learning to process a live video feed. When someone is not wearing a mask, the output eventually produces an alarm sound through a built-in buzzer. Experimental results and testing were used to verify the suggested system's performance. Including both training and testing, the achieved recognition rate was 99%.&lt;/span&gt;
A combined computing framework for load balancing in multi-tenant cloud eco-system

&lt;span&gt;Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to-one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required.&lt;/span&gt;
Field programmable gate array implementation of multiwavelet transform based orthogonal frequency division multiplexing system

This article offers an efficient design and implementation of a discrete multiwavelet critical-sampling transform based orthogonal frequency division multiplexing (DMWCST-OFDM) transceiver using field programmable gate array (FPGA) platform. The design uses 16-point discrete multiwavelet critical-sampling transform (DMWCST) and its inverse as main processing modules. All modules were designed using a part of Vivado&lt;sup&gt;®&lt;/sup&gt; Design Suite version (2015.2), which is Xilinx system generator (XSG), and is compatible with MATLAB Simulink version R2013b. The FPGA implementation is carried out on a Zynq (XC7Z020-1CLG484) evaluation board with joint test action group (JTAG) hardware co-simulation. According to the results obtained from the implementation tools, the implemented system is efficient in terms of resource utilization and could support the real-time operations.
Classification of plasmodium falciparum based on textural and morphological features

Malaria is a disease caused by plasmodium parasites transmitted through the bites of female anopheles-mosquito that infect the human red blood cell (RBC). The standard malaria diagnosis is based on manual examination of a thick and thin blood smear, which heavily depends on the microscopist experience. This study proposed a system that can identify the life stages of plasmodium falciparum in human RBC. The image preprocessing process was done by illumination correction using gray world assumption, contrast enhancement using shadow correction, extraction of saturation component, and noise filtering. The segmentation process was applied using Otsuthresholding and morphological operation. The test results showed that the use of artificial neural network (ANN) using a combination of texture and morphological features gives better results when compared to the use of only texture or morphology features. The results showed that the proposed feature achieved an accuracy of 82.67%, a sensitivity of 82.18%, and a specificity of 94.17%, thus improving decision-making for malaria diagnosis.
Technical Note: Measuring the thickness of histological sections by detecting fluorescence intensity of embedding foam

Fluorescence intensity of embedding foam in paraffin blocks can be used to measure the thickness of histological microsections. We embedded samples of embedding foam and produced several microsections of varying thicknesses using routine processing and staining. Fluorescence intensity in the blue area of the embedding foam detected with a slide scanner was compared to absolute thickness as measured using confocal microscopy. Correlation analysis displayed a clear linear correlation with convincingly low prediction interval. The concept of measuring thickness of histological microsections by detecting fluorescence intensity of embedding foam is suggested as an approach to high-throughput measuring of histological sections applicable for a fully digitized pathology department. No acquisition of dedicated equipment is required and the method can be applied as a fully automated technique requiring no time consumption.
Comments in Tags: Examining Bookmarking Cultures on AO3

This paper examines the bookmarking tags present on Archive of Our Own (AO3) through a study into the practices from their public annotation. The probe examines the presence of cultures of practice, and usable research data available on the platform. Examined fics had minimums of 25.000 words and seven chapters, published over at least four months. In addition, the number of individual bookmarks was limited to works with 300 or more, selected from disparate fandoms. The number of relevant bookmarks averaged at 11% of the total, a significant representation in the data, pointing to general trends within the archive. The probe revealed several layers of communication, in the forms of interactive tags, pure text commentary, and connections to larger collections. Each of the individual bookmarks, based on content, are classed as “Annotation”, “Curation” and “Communication” for purposes of analysis. These categories also pointed to practices in annotations of targeting specific, individual, audiences. It shows several different trends in users’ application of the bookmark function. These trends of practice go beyond individual fandoms, pointing to cultures pervasive on AO3 as a platform. The probe also presents the difficulties this bring to the study of bookmark data.
The Argument against Quantum Computers

We give a computational complexity argument against the feasibility of quantum computers. We identify a very low complexity class of probability distributions described by noisy intermediate-scale quantum computers, and explain why it will allow neither good-quality quantum error-correction nor a demonstration of "quantum supremacy." Some general principles governing the behavior of noisy quantum systems are derived. Our work supports the "physical Church thesis" studied by Pitowsky (1990) and follows his vision of using abstract ideas about computation to study the performance of actual physical computers.
An Orthogonal Matching Pursuit Variable Screening Algorithm for High-Dimensional Linear Regression Models

Variable selection plays an important role in data mining. It is crucial to filter useful variables and extract useful information in a high-dimensional setup when the number of predictor variables <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>d</mi> </math> tends to be much larger than the sample size <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>n</mi> </math> . Statistical inferences can be more precise after irrelevant variables are moved out by the screening method. This article proposes an orthogonal matching pursuit algorithm for variable screening under the high-dimensional setup. The proposed orthogonal matching pursuit method demonstrates good performance in variable screening. In particular, if the dimension of the true model is finite, OMP might discover all relevant predictors within a finite number of steps. Throughout theoretical analysis and simulations, it is confirmed that the orthogonal matching pursuit algorithm can identify relevant predictors to ensure screening consistency in variable selection. Given the sure screening property, the BIC criterion can be used to practically select the best candidate from the models generated by the OMP algorithm. Compared with the traditional orthogonal matching pursuit method, the resulting model can improve prediction accuracy and reduce computational cost by screening out the relevant variables.
Chinese Cross-Language Definition Recognition Method Based on DE-BP Model

As an important linguistic phenomenon in verbal communication, relayed speech exists in a large number of news texts and is also one of the most prominent features of the Chinese language. However, at present, there are few systematic comparative studies on the recognition of the relayed problems in Chinese language crossover, and the existing methods are highly subjective. This paper makes a qualitative comparative analysis of the reported speech in the Chinese language by the DE-BP model, which combines differential evolution (DE) algorithm and BP (backpropagation) neural network to recognize the Chinese cross-language paraphrase. After that, we obtained some meaningful findings as follows. In the Chinese language, the frequency of indirect paraphrase is the highest, followed by direct paraphrase, while other categories, namely, free indirect paraphrase, free direct paraphrase, and narrative paraphrase of speech acts, are relatively rare. Through the identification and manual labeling of reported verbs and then the word frequency statistics, it is found that the number of reported verbs in English newspapers is dominant in general, and there is a significant difference between them.
Study the effect of Environmental Factors on the performance of Photovoltaic Module

The effects of various environmental factors have been studied on the efficiency of the PV module as an essential part of the work and how to enhance its efficiency by reducing the influences environmental factors. The study deals with the efficiency of PV module and how to calculate and improve them. The measurements were made from December 2020 to August 2021 at 9 am to 5 pm for the weather of the city of Nasiriyah. The examination a single PV module is used, which gave an PMAX equal to 10 Watt. The most important has been studied on the PV is the temperature, whenever its grateful that led to decline in the output voltage and slight decrease in the output current. The output current PV module increased when the rise in the solar irradiance. To mitigate the impact of this factor, air cooling and water cooling had been used through cooling techniques by using DC fan, cooling blocks and water douche. It is clear that from our work we have improve efficiency of the module during the hot weather by 18% from using (air cooling and water cooling) techniques together. For another factors, such as dust, shadow, and exhaust smoke, showed factors no less an important factor should be besides temperature. The greater the quantity of these effects on the surface of the PV module, the more reduce in its efficiency. The influence of these factors appears to significantly reduce the output current due to its blocking of solar irradiance from PV module led to drop in the output voltage.
Optimal design of sewage networks for Al-Jaffryah Quarter and Al-Kut Trade Center using genetic algorithms and spectral analysis of the river to know the effect of network waste.

The essential objective of this study is the development of an appropriate model to obtain the low cost optimization design of the sewage network. The complexity and the huge number of discrete and non-linear constraints in problems of sewage system design make their treatment important. For this aim, an adaptive model of the Genetic Algorithm (GA) of efficient and effective optimization design with a consistent layout is proposed. The MATLAB code was used to optimize the sewage network for the Al Jafriya neighborhood and Al Kut commercial center. Spectral analysis of the Tigris River was also carried out for the area of the estuaries of the studied network to find out the extent of pollution due to the network being a joint network (rain and sewage). According to the obtained results, the developed model achieved the optimum solution with the minimum cost and least generations number. It can be also noticed that the estuaries of the studied storm system have a significant influence on the quality of the river water. Thus, this research proposes the implementation and management of an isolated sewage system that is routed to wastewater treatment plants.
Impact of Cloud, Rain, Humidity, and Wind Velocity on PV Panel Performance

Before the invention of solar panels, solar energy had been used for millions of years. To survive and increase many forms of life, people rely on sunlight. For example, use sunlight for warmth and have only recently begun to use the sun's energy to generate renewable and sustainable electricity. The widespread use of photovoltaic PV systems as a source of renewable energy-derived electricity has prompted researchers to investigate the factors that influence their performance. This paper presents an analytical study for assessing the performance of a 120 Wp PV module. This experiment was conducted at Al-Nasiriyah city ( 31.03 N, 46.16E ). At 180˚ south, and 45˚ elevation, the solar panel is omitted. Using solar panels, solar energy is converted into electrical energy that can power an entire building. The power production from the solar panel depends on several identified factors. In February and June, the solar panel tested the effect of cloud cover, rain, relative humidity and wind velocity. On rainy and cloudy days, average power and irradiance are reduced by 93.32 %, 93.77 %, and 79.97 %, 77.34 %, respectively, when compared to power and irradiance on a clear day (8th February) 693 W/m2 and 76.55 W. Humidity has an inverse relationship with voltage, current, and power. It means that as humidity rises, the value of voltage, current, and power decreases. The performance of a solar panel is directly proportional to the wind velocity. The temperature is inversely proportional to wind velocity, i.e. as wind velocity increases, the temperature of the solar cell module decreases. The results of this study have been analyzed and discussed.
Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization

Contextualized embeddings use unsupervised language model pretraining to compute word representations depending on their context. This is intuitively useful for generalization, especially in Named-Entity Recognition where it is crucial to detect mentions never seen during training. However, standard English benchmarks overestimate the importance of lexical over contextual features because of an unrealistic lexical overlap between train and test mentions. In this paper, we perform an empirical analysis of the generalization capabilities of state-of-the-art contextualized embeddings by separating mentions by novelty and with out-of-domain evaluation. We show that they are particularly beneficial for unseen mentions detection, especially out-of-domain. For models trained on CoNLL03, language model contextualization leads to a +1.2% maximal relative micro-F1 score increase in-domain against +13% out-of-domain on the WNUT dataset
Physical extrapolation of quantum observables by generalization with Gaussian Processes

For applications in chemistry and physics, machine learning (ML) is generally used to solve one of three problems: interpolation, classification or clustering. These problems use information about physical systems in a certain range of parameters or variables in order to make predictions at unknown values of these variables within the same range. The present work illustrates the application of ML to prediction of physical properties outside the range of the training parameters. We define `physical extrapolation' to refer to accurate predictions $y(\mathbf{x^\ast})$ of a given physical property at a point $\mathbf{x^\ast} = [ x^\ast_1, ..., x^\ast_{\cal D} ]$ in the $\cal D$-dimensional space, if, at least, one of the variables $x^\ast_i \in \left [ x^\ast_1, ..., x^\ast_{\cal D} \right ]$ is {\it outside} of the range covering the training data. We show that Gaussian processes (GPs) can be used to build ML models capable of physical extrapolation of quantum properties of complex systems across quantum phase transitions. The approach is based on training GP models of variable complexity by the evolution of the physical functions. We show that, as the complexity of the models increases, they become capable of predicting new transitions. We also show that, where the evolution of the physical functions is analytic and relatively simple, GP models with simple kernels already yield accurate generalization results, allowing for accurate predictions of quantum properties in a different quantum phase. For more complex problems, it is necessary to build models with complex kernels. The complexity of the kernels is increased using the Bayesian Information Criterion (BIC). We illustrate the importance of the BIC by comparing the results with random kernels of various complexity and illustrate a method to obtain meaningful extrapolation results without direct validation in the extrapolated region.
Inertial Measurement Unit-sensor-based Short Stick Exercise Tracking to Improve Health of Elderly People

Short stick exercises have been attracting attention from the viewpoint of preventing falls and improving the health of elderly people and are generally performed under the guidance of instructors and nursing staff at nursing homes.However, in situations such as the COVID-19 pandemic, where people should refrain from unnecessary outings, it is advisable that individuals perform short stick exercises at home and record their exercise implementation status.In this paper, we propose an inertial measurement unit (IMU)-sensor-based short stick exercise tracking method that can automatically record the types and amounts of exercises performed using a short stick equipped with an IMU sensor.The proposed method extracts time-domain and frequency-domain features from linear acceleration and quaternion time-series data obtained from the IMU sensor and classifies the type of exercise using an inference model based on machine learning algorithms.To evaluate the proposed method, we collected sensor data from 21 young subjects (in their 20s) and 14 elderly subjects (79-95 years old), where the participants performed three sets (10 times per set) of eight basic types of short stick exercises (five types for elderly people).As a result of evaluating the proposed method using this data set, we confirmed that when LightGBM was used as the learning algorithm, it achieved F values of 90.0 and 86.6% for recognizing the type of exercise for young and elderly people, respectively.
Neuromorphic computing for short-term wind power forecasting

Abstract There is an upwards trend of applying deep learning to model wind power forecasts. The modelling and training of these architectures may take many computational resources, hindering the possibility of implementing such algorithms for shorter term prediction horizons. Emerging computational architectures such as neuromorphic computing have the potential of real-time learning using brain-inspired algorithms characterized by low latency and low energy consumption. In particular, we introduce spiking neural networks for short-term wind power forecasting, taking into consideration the current development and features of neuromorphic devices.
Control of Human-machine cooperative cart by region attractor to achieve both obstacle avoidance and trajectory tracking

In this paper, we propose a control system design method for a human-machine cooperative system in which a human and machine cooperate to propel a cart to a reference trajectory. In the previously proposed method, the human plays the role of recognizing the environment and determining the propulsive force, while the machine determines the steering angle based on the position and posture of the cart, using a vector field with the reference trajectory as the orbit attractor. However, since this method cannot avoid obstacles on the trajectory, we extend it to a region attractor. By setting an index variable that changes depending on human input, the vector field is designed in the extended dimensional space including the index variable, and the cart can avoid obstacles by moving within the region defined by the reference trajectory with its parallel trajectory depending on human input. In this paper, we describe (1) the design method of the reference trajectory and the parallel trajectory considering the workspace of the steering and the distance between the cart and the wall, (2) the design method of the region attractor in the extended dimension including the index variable, and (3) the experimental verification of the proposed method.