Reviewerly/exHarmony huggingface dataset:
Design and Manufacture an Automatic Mushroom Sprinkler based Internet of Things to Increase Oyster Mushroom Productivity

Oyster mushroom is one of the non-cholesterol food ingredients that have high nutrition and high demand by the public. In addition, oyster mushroom has a much cheaper price than chicken and beef. In the cultivation of oyster mushrooms, temperature and humidity are important factors in the success of cultivation, the optimum temperature for the growth of oyster mushrooms is 16-25°C and the ideal humidity required is 80%-90%. To maintain the temperature and humidity is to do watering or misting. Currently, there are still many farmers who use conventional watering systems that are not practical. Therefore, during the dry season, the productivity of oyster mushrooms tends to decrease due to hot weather conditions and intense watering needs to be done. Based on this, it is necessary to develop an automatic and efficient mushroom watering tool. The research method used is Research and Development (RD). The result achieved is the design of an automatic mushroom sprinkler based on temperature and humidity control. This automatic mushroom sprinkler has a blower component, piezoelectric, ATMega 16 microcontroller, DHT11 temperature sensor, and internet of things (IoT) features. The conclusion of this research is the creation of an automatic mushroom sprinkler that is efficient and easy to use. In addition, the results of the implementation of this tool in the cultivation area can increase the productivity of oyster mushrooms by 34.51%.
Pruning-based oversampling technique with smoothed bootstrap resampling for imbalanced clinical dataset of Covid-19

The Coronavirus Disease (COVID-19) was declared a pandemic disease by the World Health Organization (WHO), and it has not ended so far. Since the infection rate of the COVID-19 increases, the computational approach is needed to predict patients infected with COVID-19 in order to speed up the diagnosis time and minimize human error compared to conventional diagnoses. However, the number of negative data that is higher than positive data can result in a data imbalance situation that affects the classification performance, resulting in a bias in the model evaluation results. This study proposes a new oversampling technique, i.e., TRIM-SBR, to generate the minor class data for diagnosing patients infected with COVID-19. It is still challenging to develop the oversampling technique due to the data's generalization issue. The proposed method is based on pruning by looking for specific minority areas while retaining data generalization, resulting in minority data seeds that serve as benchmarks in creating new synthesized data using bootstrap resampling techniques. Accuracy, Specificity, Sensitivity, F-measure, and AUC are used to evaluate classifier performance in data imbalance cases. The results show that the TRIM-SBR method provides the best performance compared to other oversampling techniques.
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding

Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. However, this crucial module is typically used as a black box, trained independently of the downstream task and on a fixed vocabulary of objects and attributes. This makes it challenging for such systems to capture the long tail of visual concepts expressed in free form text. In this paper we propose MDETR, an end-to-end modulated detector that detects objects in an image conditioned on a raw text query, like a caption or a question. We use a transformer-based architecture to reason jointly over text and image by fusing the two modalities at an early stage of the model. We pre-train the network on 1.3M text-image pairs, mined from pre-existing multi-modal datasets having explicit alignment between phrases in text and objects in the image. We then fine-tune on several downstream tasks such as phrase grounding, referring expression comprehension and segmentation, achieving state-of-the-art results on popular benchmarks. We also investigate the utility of our model as an object detector on a given label set when fine-tuned in a few-shot setting. We show that our pre-training approach provides a way to handle the long tail of object categories which have very few labelled instances. Our approach can be easily extended for visual question answering, achieving competitive performance on GQA and CLEVR. The code and models are available at this https URL.
Radiative transfer model for ground surface irradiance estimation: clear sky

In this work, a physical model based on the radiative transfer equation is presented to estimate the global solar irradiance on top of the Earth’s atmosphere surface. A link is established among the radiative transfer through the atmosphere, the geographic coordinates of a city (longitude, latitude), time of day, and date of the year. The discrete spherical harmonics method is applied to solve the radiative transfer equation. The city of Dschang in Cameroon is taken as an example of application of the model. This city is located at latitude 5°44 ′ N and longitude 10°04 ′ E. It is assumed that the atmosphere of the city is inhomogeneous and composed of non-polarizing Haze L aerosol particles in gamma distribution, while the ground surface diffuses radiation isotropically. The predictions are compared under clear sky conditions to some existing models of irradiance such as the Bird model and Davies and Hay model for a homogeneous atmosphere. Results demonstrate that the present investigation matches well with the Bird and the Davies and Hay models and is valuable for ground solar irradiance estimation. The effects of the inhomogeneity of the atmosphere on total insolation are also studied, and our results indicate that the inhomogeneity of the atmosphere reduces local insolation. The present study shows that the radiative transfer model is an efficient technique for estimating global solar radiation at any level of the stratified atmosphere and under different atmospheric conditions.
Learning sparse deep neural networks with a spike-and-slab prior

Deep learning has achieved great successes in many machine learning tasks. However, the deep neural networks (DNNs) are often severely over-parameterized, making them computationally expensive, memory intensive, less interpretable and mis-calibrated. We study sparse DNNs under the Bayesian framework: we establish posterior consistency and structure selection consistency for Bayesian DNNs with a spike-and-slab prior, and illustrate their performance using examples on high-dimensional nonlinear variable selection, large network compression and model calibration. Our numerical results indicate that sparsity is essential for improving the prediction accuracy and calibration of the DNN.
Competence-Oriented Models of Professional Development of Specialists in the Context of Sustainable Education

The article thoroughly describes the competency-oriented models of professional self-improvement and self-realization of teachers in terms of sustainable development. The educational and methodical support of professional development of teachers for lifelong learning through formal, non-formal and informal education according to innovative competence-oriented models of professional development has been developed and experimentally tested. The results of the study can be used in the process of modernization of the national education system, in particular in the activities of educational and methodological centers of vocational education in the development of curricula, educational programs, qualification requirements for professional activities of teachers, innovative teaching and methodological support.
Immersion in Audiovisual Experiences

Abstract Understanding the concept of immersion and its influencing factors is critical for enabling engaging audiovisual experiences. However, a lack of definitional consensus and suitable methods for assessing immersion hinder research on the subject. This chapter discusses the idea of immersion based on a non-exhaustive literature review of the topic and presents an adaptable definition of immersion that is not limited to virtual reality applications. Additionally, an exploratory experimental paradigm for measuring immersion in audiovisual experiences is described. The description of immersion and the experimental framework presented in this chapter are a starting point for resolving the difference in opinion and developing novel methods to thoroughly explore the concept of immersion respectively.
A Comparative Study on Augmented Analytics Using Deep Learning Techniques

Image augmentation is the most recognized type of data augmentation and intrinsic development for transforming image diversities in the training dataset that belongs to a similar class as the novel image. In the area of image augmentation handling, a collection of operations is shifting, flipping, zooming, cropping, rotation, and transformation in color space. A wide range of applications frequently used the aspects of deep learning are industry, science, and government domain, namely adaptive testing, image classification, computer vision, object detection, and face recognition and has achieved substantial development and accomplishment of deep learning. This study concentrates on the most important challenges present in the image estimation level that have a significant effect on dimension reduction, pooling, and edge detection. The deep learning methods involved here are convolution neural network (CNN), generative adversarial network (GAN), and deep convolution neural network (DCNN). Finally, a comparative study has performed a massive literature survey on various deep learning models.
Bridging the Digital Divide as a Basic Imperative of a Higher Education Modernization

The article is devoted to a thorough study of digital transformation as a necessary condition for the modernization of higher education, which provides for the creation of a modern digital university, which is undergoing radical digitalization of the management systems, educational activities, research, human capital development, creation of digital infrastructure and digital ecosystem. The digital divide is considered as a component of information inequality, which has a technological nature and characterizes unequal opportunities in terms of access, use and production of information and knowledge, as well as the use of new technologies for their effective use in practice.
Genetic Algorithm in Data Mining of Colorectal Images

There is currently no effective analytical method in colorectal image analysis, which leads to certain errors in colorectal image analysis. In order to improve the accuracy of colorectal imaging detection, this study used a genetic algorithm as the data mining algorithm and combined it with image processing technology to perform image analysis. At the same time, combined with the actual requirements of image detection, the gray theory model is used as the basic theory of image processing, and the image detection prediction model is constructed to predict the data. In addition, in order to study the effectiveness of the algorithm, the experiment is carried out to analyze the validity of the data of the study, and the predicted value is compared with the actual value. The research shows that the proposed algorithm has certain accuracy and can provide theoretical reference for subsequent related research.
Image Protection Against Forgery and Pixel Tampering Based on a Triple Hybrid Security Approach

Due to the widespread of advanced digital imaging devices, forgery of digital images became more serious attack patterns. In this attack scenario, the attacker tries to manipulate the digital image to conceal some meaningful information of the genuine image for malicious purposes. This leads to increase security interest about protecting images against integrity tampers. This paper proposes a novel technique for protecting colored images against forgery and pixel tamper. The proposed approach is designed as a hybrid model from three security techniques, Message Digest hashing algorithm (MD5), Advanced Encryption Standard-128 bits (AES), and Stenography. The proposed approach has been evaluated using set of image quality metrics for testing the impact of embedding the protection code on image quality. The evaluation results proved that protecting image based on Least Significant Bit (LSB) is the best technique that keep image quality compared with other two bit-substitution methods. Moreover, the results proved the superiority of the proposed approach compared with other technique in the literature.
Stabilization of Qubit Relaxation Rates by Frequency Modulation

Temporal, spectral, and sample-to-sample fluctuations in coherence properties of qubits form an outstanding challenge for the development of upscaled fault-tolerant quantum computers. A ubiquitous source for these fluctuations in superconducting qubits is a set of atomic-scale defects with a two-level structure. Here we propose a way to mitigate these fluctuations and stabilize the qubit performance. We show that frequency modulation of a qubit or, alternatively, of the two-level defects, leads to averaging of the qubit relaxation rate over a wide interval of frequencies.
Photonic co-processors in HPC: Using LightOn OPUs for Randomized Numerical Linear Algebra

Exact computation of linear algebra operations is challenging or even impossible at extreme scale By leveraging randomization we can get approximate results at reduced computational cost Lighton OPU: The first commercially available photonic Co-Processor
P-SVM Gene Selection for Automated Microarray Categorization

When it comes to computer visioning, the success rates of Convolutional Neural Networks—which are also referred to as CNNs—is majorly controlled as well as accelerated by the strength of their conductive bias. It is strong to the significance of enabling the said type of neural networks able to proffer effective solutions to visioning-associated assignments that come with indefinite weights. That also means CNNs can do the just said without having to go through training. In semblance to this, Long Short-Term Memory—also known as LSTM—possesses a strength-filled inductive unfairness when it comes to preserving raw data over a stretched period of time. Nevertheless, a good number of real-life networks are under the governance of preservation policies, culminating in the re-supply of specific amounts—in the economical and physical systems, for instance. Our first-ever Mass-Conserving LSTM, which can also be called the MC-LSTM, is in adherence to these laws of conservation. It does so by creating an extension to the inductive unfairness on the part of the LTSM, a medium through which the MC-LSTM approach designs the redistribution of those preserved quantities. A cutting-edge introduction, it is designed for neural arithmetic systems for training operations in the arithmetic dimension. Those operations could include additional assignments, which possesses a substantial preservation policy because the total remains constant regardless of time. Additionally, MC-LSTM is implemented into traffic prediction, creating the design for a damped pendulum and a standard set of hydrological data—wherein a state-of-the-art is set for the forecast of apex-level flows. For hydrological purposes, this paper also demonstrates that the MC-LSTM states are in correlation with the real-life procedures, thus making them subject to interpretation.
Convolutional neural networks

Chapter Contents: 5.1 Introduction 5.2 Convolutional neural networks 5.2.1 Motivation 5.2.2 The convolution operation 5.2.3 Parameter sharing and sparse connectivity 5.3 Networks and layers structure 5.4 Kernels and feature maps 5.4.1 Stride 5.4.2 Multidimensional input 5.4.3 Padding 5.5 ReLU layer 5.6 Pooling 5.7 Batch normalisation 5.7.1 Motivation 5.7.2 How does batch normalisation work? 5.8 Common tasks performed by CNNs 5.9 Conclusions References
Exact and Bounded Collision Probability for Motion Planning Under Gaussian Uncertainty

Computing collision-free trajectories is of prime importance for safe navigation. We present an approach for computing the collision probability under Gaussian distributed motion and sensing uncertainty with the robot and static obstacle shapes approximated as ellipsoids. The collision condition is formulated as the distance between ellipsoids and unlike previous approaches we provide a method for computing the exact collision probability. Furthermore, we provide a tight upper bound that can be computed much faster during online planning. Comparison to other state-of-the-art methods is also provided. The proposed method is evaluated in simulation under varying configuration and number of obstacles.
Super‐resolution with adversarial loss on the feature maps of the generated high‐resolution image

Recent studies on image super-resolution make use of Generative Adversarial Networks to generate the high-resolution image counterpart of the low-resolution input. However, while being able to generate sharp high-resolution images, Generative Adversarial Networks based super-resolution methods often fail to produce good results when tested on images having different degradation as the low-resolution images used in the training. Some recent works have tried to mitigate this failure by introducing a degradation network that can replicate the noise of real-world low-resolution images. However, even these methods can produce poor results if a real-world test image differs much from the real-world images in the training data set. This paper proposes the use of adversarial losses on the feature maps extracted by a pre-trained network with the generated high-resolution image as input. This is in contrast to all other Generative Adversarial Networks-based super-resolution methods that directly apply the adversarial loss to the generated high-resolution image. The rationale behind this idea is illustrated, and experimental results confirm that high-resolution images generated by the proposed method achieve better results in both quantitative and qualitative evaluations than methods that directly apply adversarial losses to generated high-resolution images.
Optimization of First-Person Shooter Game Control Using Heart Rate Sensor

As first-person shooter (FPS) games become highly competitive game genres, various supports are needed to improve the gaming environment. In the past, players have focused on gaming mouses and displays, which are expensive and challenging to select from. To understand the influence of the player’s tension on FPS game control, we developed a system that can automatically adjust the speed of the mouse according to the player’s real-time heartbeat during the game. In this study, we developed and evaluated a system to improve the FPS game environment based on the heart rate.
Classically efficient quantum scalable Fermi-Hubbard benchmark

In order to quantify the relative performance of different testbed quantum computing devices, it is useful to benchmark them using a common protocol. While some benchmarks rely on the performance of random circuits and are generic in nature, here we instead propose and implement a practical, application-based benchmark. In particular, our protocol calculates the energy of the ground state in the single particle subspace of a 1-D Fermi Hubbard model, a problem which is efficient to solve classically. We provide a quantum ansatz for the problem that is provably able to probe the full single particle subspace for a general length 1-D chain and scales efficiently in number of gates and measurements. Finally, we demonstrate and analyze the benchmark performance on superconducting and ion trap testbed hardware from three hardware vendors and with up to 24 qubits.
SenTag: A Web-Based Tool for Semantic Annotation of Textual Documents

In this work, we present SenTag, a lightweight web-based tool focused on semantic annotation of textual documents. The platform allows multiple users to work on a corpus of documents. The tool enables to tag a corpus of documents through an intuitive and easy-to-use user interface that adopts the Extensible Markup Language (XML) as output format. The main goal of the application is two-fold: facilitating the tagging process and reducing or avoiding for errors in the output documents. Moreover, it allows to identify arguments and other entities that are used to build an arguments graph. It is also possible to assess the level of agreement of annotators working on a corpus of text.
Lindblad Dynamics and Disentanglement in Multi-Mode Bosonic Systems

In this paper, we consider the thermal bath Lindblad master equation to describe the quantum nonunitary dynamics of quantum states in a multi-mode bosonic system. For the two-mode bosonic system interacting with an environment, we analyse how both the coupling between the modes and the coupling with the environment characterised by the frequency and the relaxation rate vectors affect dynamics of the entanglement. We discuss how the revivals of entanglement can be induced by the dynamic coupling between the different modes. For the system, initially prepared in a two-mode squeezed state, we find the logarithmic negativity as defined by the magnitude and orientation of the frequency and the relaxation rate vectors. We show that, in the regime of finite-time disentanglement, reorientation of the relaxation rate vector may significantly increase the time of disentanglement.
Recommender system of scholarly papers using public datasets.

The exponential growth of public datasets in the era of Big Data demands new solutions for making these resources findable and reusable. Therefore, a scholarly recommender system for public datasets is an important tool in the field of information filtering. It will aid scholars in identifying prior and related literature to datasets, saving their time, as well as enhance the datasets reusability. In this work, we developed a scholarly recommendation system that recommends research-papers, from PubMed, relevant to public datasets, from Gene Expression Omnibus (GEO). Different techniques for representing textual data are employed and compared in this work. Our results show that term-frequency based methods (BM25 and TF-IDF) outperformed all others including popular Natural Language Processing embedding models such as doc2vec, ELMo and BERT.
ACM-NDC study 2020--2021

research-article Free Access Share on ACM-NDC study 2020--2021: ninth annual study of non-doctoral-granting departments in computing Authors: Stuart H. Zweben The Ohio State University The Ohio State UniversityView Profile , Jodi L. Tims Northeastern University Northeastern UniversityView Profile , Cindy Tucker Bluegrass Community and Technical College Bluegrass Community and Technical CollegeView Profile , Yan Timanovsky Association for Computing Machinery Association for Computing MachineryView Profile Authors Info & Claims ACM InroadsVolume 12Issue 4December 2021 pp 30–44https://doi.org/10.1145/3485245Published:15 November 2021Publication History 1citation376DownloadsMetricsTotal Citations1Total Downloads376Last 12 Months376Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF