Reviewerly/exHarmony huggingface dataset:
Trend of the spread of COVID-19 in Indonesia using the machine learning prophet algorithm

Based on information on the &lt;span&gt;BNPB website on 2 September 2020, the positive rate for coronavirus disease (COVID-19) in Indonesia reached 25.25% on 30 August 2020. This is a big challenge for the Indonesian government to reduce the positivity rate to meet the standards safe accepted by World Health Organization (WHO) is 5%. To ensure the accuracy of government policies, accurate data predictions are needed. Therefore, the prophet's machine learning algorithm can be used to see trends in the spread of COVID-19 in the next one year. This algorithm has a fairly high level of accuracy because the data contains time variables which are adjusted to the dataset. In several previous research, the dataset was vast uncertain and small. Meanwhile in this research, data was taken from 2 March 2020 to 12 February 2021 on the KawalCOVID19 website. This data is used to predict from 13 February 2021 to 12 February 2022. There are 3 data used; namely data confirmed, recovered and died. Based on the analysis, the confirmed patient was 22.60-42.11%, died amounted to 21.67%-39.00%, and recovered by 22.53-41.82%. The prediction percentage that the average cases died was 2.43% every day. The accuracy of data confirmed was 43.97%, died was 72.50% and recovered was 84.24%.&lt;/span&gt;
Context-Enriched Machine Learning-Based Approach for Sentiment Analysis

The advent of technology has given rise to a tremendous rise in people sharing information and voicing their opinions over different platforms. Several research studies have been focused on the analysis and prediction of political events through the “sentiment” perspective. Here, we present a comparative analysis of different approaches used by various researchers in this area. Also, an experiment-based analysis is presented with sentiment analysis for the situation of reservations in India with the consideration of public opinion on tweets, news reviews, and word of influential leaders with a huge number of public following to ensure that their opinions had a considerable impact with a bigger audience. We use a novel analysis model by employing SVM with grid search for sentiment analysis. The detailed discussion showcased is an intensive study of different methodologies adopted, which is of use to researchers looking to pursue this domain.
Joint non-parametric estimation of mean and auto-covariances for Gaussian processes

Gaussian processes that can be decomposed into a smooth mean function and a stationary autocorrelated noise process are considered and a fully automatic nonparametric method to simultaneous estimation of mean and auto-covariance functions of such processes is developed. The proposed empirical Bayes approach is data-driven, numerically efficient, and allows for the construction of confidence sets for the mean function. Performance is demonstrated in simulations and real data analysis. The method is implemented in the R package eBsc.1
Dual Complementary Prototype Learning for Few-Shot Segmentation

Few-shot semantic segmentation aims to transfer knowledge from base classes with sufficient data to represent novel classes with limited few-shot samples. Recent methods follow a metric learning framework with prototypes for foreground representation. However, they still face the challenge of segmentation of novel classes due to inadequate representation of foreground and lack of discriminability between foreground and background. To address this problem, we propose the Dual Complementary prototype Network (DCNet). Firstly, we design a training-free Complementary Prototype Generation (CPG) module to extract comprehensive information from the mask region in the support image. Secondly, we design a Background Guided Learning (BGL) as a complementary branch of the foreground segmentation branch, which enlarges difference between the foreground and its corresponding background so that the representation of novel class in the foreground could be more discriminative. Extensive experiments on PASCAL-5i and COCO-20i demonstrate that our DCNet achieves state-of-the-art results.
Support Vector Machine Classification Based on Improved Harris Hawk Optimization Algorithm

Abstract Support vector machine (SVM) has good classification effect on small samples and data sets with errors. We propose to improve the Harris Hawk optimization algorithm (HHO) to optimize the SVM to further improve its classification performance. The parameters of the SVM affect its classification accuracy, but there are certain difficulties in the selection of parameters, so the HHO is used to select the optimal parameters of the SVM. The HHO is easy to fall into local optimum and the convergence accuracy is affected to some extent. Chaotic perturbation convergence and nonlinear time-varying updating strategy are introduced to maintain the randomness and diversity of populations in the iterative process of HHO, enhance the search ability, enable the algorithm to jump out of the local optimum, and improve the convergence performance. Through three comparative algorithms tested on six UCI datasets, it is demonstrated that the parameters selected by the improved HHO proposed can lead to better classification accuracy and stability of the SVM.
Fall Detection Method for Embedded Devices

The challenges of the aging population is becoming more and more prominent worldwide.Among them, in the face of the elderly fall phenomenon, human fall detection technology research and development has practical application value.Because of a large number of network parameters in the field of fall detection and the limited computing power of embedded devices, which makes it difficult to run on the embedded platform, this paper proposes an OT-YOLOV3 (OpenCV+Tiny-YOLOV3) fall detection method.In this method, Gaussian processing and other operations are used to preprocess the fallen image to avoid the influence of the angle change of the image on the recognition result.Then, the feature extraction network in Tiny-YOLOV3 was replaced by the MobileNet network to increase the number of network layers and reduce the number of parameters and calculations in the model.At the same time, the multi-scale prediction method was used to improve detection accuracy.Experimental results show that the accuracy of the proposed model is 10% higher than that of the YOLOV3 (You Only Look Once Version three) model, 4% higher than that of the Tiny-YOLOV3 model, 3% higher than that of the YOLOV3 model, 3% higher than that of Tiny-YOLOV3 model, and the model size is only 45% of that of YOLOV3 model and 65% of Tiny-YOLOV3.Compared with YOLOV3 and Tiny-YOLOV3 processing methods, the drop recognition effect is significantly improved and the model memory is reduced, which meets the requirements of real-time and efficient detection for embedded devices.
Cybercrime in Ghana and Victims Accounts

The main thrust of the study was to examine how cybercrime victims are being lured into virtual space by scammers and later swindled. Qualitative research method was advanced to explore the dynamics of cybercrime activities in Ghana. Secondary data were sourced from the Criminal Investigation Department of the Ghana Police Service. Purposive sampling technique was outlined to engage with the respondents. Structured interview guide was considered as a data collection instrument. In the data analysis, each of the in-depth interviews was transcribed as soon as the information was gathered and developed into codes. Six (6) internet victims took part in the study. The investigation revealed that people become internet victims as a result of unrealistic profit ventures, online romance, raffle schemes, America green card lottery and rent apartment deals. Out of the six cases, only one culprit was imprisoned and even the approach was conventional policing strategy considering the volatile nature of the cybercrime related offences. It was concluded from the discussions that the National Commission for Civic Education (NCCE) should create public awareness about the social and economic consequences of cybercrime activities in the country. The education should further emphasize on the social engineering tactics employed by the conmen to dupe their client on the internet.&#x0D; &#x0D; Received: 8 March 2022 / Accepted: 25 April 2022 / Published: 5 May 2022
Automatic stent recognition using perceptual attention U‐net for quantitative intrafraction motion monitoring in pancreatic cancer radiotherapy

Abstract Purpose Stent has often been used as an internal surrogate to monitor intrafraction tumor motion during pancreatic cancer radiotherapy. Based on the stent contours generated from planning CT images, the current intrafraction motion review (IMR) system on Varian TrueBeam only provides a tool to verify the stent motion visually but lacks quantitative information. The purpose of this study is to develop an automatic stent recognition method for quantitative intrafraction tumor motion monitoring in pancreatic cancer treatment. Methods A total of 535 IMR images from 14 pancreatic cancer patients were retrospectively selected in this study, with the manual contour of the stent on each image serving as the ground truth. We developed a deep learning–based approach that integrates two mechanisms that focus on the features of the segmentation target. The objective attention modeling was integrated into the U‐net framework to deal with the optimization difficulties when training a deep network with 2D IMR images and limited training data. A perceptual loss was combined with the binary cross‐entropy loss and a Dice loss for supervision. The deep neural network was trained to capture more contextual information to predict binary stent masks. A random‐split test was performed, with images of ten patients (71%, 380 images) randomly selected for training, whereas the rest of four patients (29%, 155 images) were used for testing. Sevenfold cross‐validation of the proposed PAUnet on the 14 patients was performed for further evaluation. Results Our stent segmentation results were compared with the manually segmented contours. For the random‐split test, the trained model achieved a mean (±standard deviation) stent Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), the center‐of‐mass distance (CMD), and volume difference were 0.96 (±0.01), 1.01 (±0.55) mm, 0.66 (±0.46) mm, and 3.07% (±2.37%), respectively. The sevenfold cross‐validation of the proposed PAUnet had the mean (±standard deviation) of 0.96 (±0.02), 0.72 (±0.49) mm, 0.85 (±0.96) mm, and 3.47% (±3.27%) for the DSC, HD95, CMD, and . Conclusion We developed a novel deep learning–based approach to automatically segment the stent from IMR images, demonstrated its clinical feasibility, and validated its accuracy compared to manual segmentation. The proposed technique could be a useful tool for quantitative intrafraction motion monitoring in pancreatic cancer radiotherapy.
An Optimized Extreme Learning Machine for Predicting Loan Default in Peer-to-peer Lending Based on an Enhanced Honey Badger Algorithm

Loan status prediction aims to reveal credit risks that are difficult to quantify and analyze in P2P lending. Since the complex data types used by web platforms involve unstructured information such as text, this task can be viewed as a new challenge in P2P. This paper proposes a novel hybrid intelligence model for loan default prediction in P2P lending based on Extreme Learning Machine (ELM) and an enhanced Honey Badger Algorithm (EHBA). The improved nature-inspired meta-heuristic algorithm is used to tune the parameters of ELM to improve predictive performance. An empirical analysis using real-world data from the Lending Club in the USA, which contains 3,000 records and 24 different variables, shows that the proposed model can improve loan default prediction performance compared with KNN, ANN, RF, SVM, KSVM, ELM, GA-ELM, PSO-ELM, GWO-ELM, AOS-ELM, MPA-ELM, and HBA-ELM.
A Deep Learning Approach to Solving Morphological Analogies

Analogical proportions are statements of the form “A is to B as C is to D”. They support analogical inference and provide a logical framework to address learning, transfer, and explainability concerns. This logical framework finds useful applications in AI and natural language processing (NLP). In this paper, we address the problem of solving morphological analogies using a retrieval approach named ANNr. Our deep learning framework encodes structural properties of analogical proportions and relies on a specifically designed embedding model capturing morphological characteristics of words. We demonstrate that ANNr outperforms the state of the art on 11 languages. We analyze ANNr results for Navajo and Georgian, languages on which the model performs worst and best, to explore potential correlations between the mistakes of ANNr and linguistic properties.
Task allocation and coordination process in distributed agile software development: an ontology based approach

Distributed agile software development (DASD) has gained much popularity over the past years. It relates to Agile Software Development (ASD) being executed in a distributed environment due to factors such as low development budget, emerging software application markets and the need for more expertise. DASD faces a number of challenges with respect to coordination and communication issues. Task allocation in such an environment thus becomes a challenging task. Adopting proper task allocation strategy is crucial to overcome challenges and issues in DASD. Various studies highlight the challenges being faced by DASD and have proposed solutions in the form of framework or models. Knowledge models in the form of ontologies can help to solve certain issues and challenges by providing a proper representation of data that is shareable among distributed teams. Several ontologies with respect to task allocation exist. However, ontologies incorporating factors and dependencies influencing task allocation process in DASD are limited. An ontology representing the knowledge related to task allocation and coordination is important for proper decision making in organizations. Based on an in-depth literature review and a survey conducted among professionals in industry, this paper proposes an ontology, OntoDASD, that incorporates relevant factors and dependencies to be considered in task allocation and coordination process in DASD environment. The ontology facilitates team coordination through effective communication and task allocation by defining the concepts to share knowledge and information in an appropriate way. OntoDASD has been properly evaluated and validated by professionals in the field.
MmLwThV framework: A masked face periocular recognition system using thermo-visible fusion

In wake of COVID-19, the world has adapted to a new order. People have started wearing mask on their faces to prevent getting infected. The present face recognition models are no longer proving to be efficient in the current circumstances. This is because, most of the informative part of the face is covered by mask. The periocular recognition therefore holds the key to future of face recognition. However, the periocular region proves to be insufficiently enough to generate highly discriminative features. Also, most of the pre-COVID-19 algorithms fail to work in cases, where the number of training images available is very less. We propose a lightweight periocular recognition framework that uses thermo-visible features and ensemble subspace network classifier to improve upon the existing periocular recognition systems named as Masked Mobile Lightweight Thermo-visible Face Recognition (MmLwThV). The framework successfully improves the accuracy over a single visible modality by mitigating the effect of noise present in the thermo-visible features. The experiments on WHU-IIP dataset and an in-house collected dataset named, CVBL masked dataset, successfully validate the efficacy of our proposed framework. The MmLwFR framework is lightweight and can be easily deployed on mobile phones with a visible and an infrared camera.
A Lamus-Based Flight Data Sharing Model on Consortium Blockchain

Currently, traditional flight data sharing models cannot resist quantum attacks, which poses the risk of data leakage. The research on the flight data sharing model against quantum attack has become one of the research hotspots. Lattice-based cryptography is recognized as an effective way to resist quantum attacks. A flight data sharing model on consortium blockchain is proposed in this paper to resolve data leakage during data sharing. First, a new lattice-based multisignature scheme (Lamus) is proposed, capable of resisting quantum attacks. We prove the security of the proposed Lamus scheme in the random oracle model. Moreover, a flight data sharing model on consortium blockchain is proposed by applying the proposed Lamus scheme to resist quantum attacks. Security and performance analysis show that the model guarantees antiquantum security, and it achieves good performance in terms of storage efficiency and operating efficiency.
On the Probability of Finding Extremes in a Random Set

We consider a sequence Zjj≥1 of i.i.d. d-dimensional random vectors and for every n≥1 consider the sample Sn={Z1,Z2,…,Zn}. We say that Zj is a “leader” in the sample Sn if Zj≥Zk,∀k∈{1,2,…,n}and Zj is an “anti-leader” if Zj≤Zk,∀k∈{1,2,…,n}. After all, the leader and the anti-leader are the naive extremes. Let an be the probability that Sn has a leader, bn be the probability that Sn has an anti-leader and cn be the probability that Sn has both a leader and an anti-leader. One of the aims of the paper is to compute, or, at least to estimate, or if even that is not possible, to estimate the limits of this quantities. Another goal is to find conditions on the distribution F of Zjj≥1 so that the inferior limits of an,bn,cn are positive. We give examples of distributions for which we can compute these probabilities and also examples when we are not able to do that. Then we establish conditions, unfortunately only sufficient when the limits are positive. Doing that we discovered a lot of open questions and we make two annoying conjectures—annoying because they seemed to be obvious but at a second thought we were not able to prove them. It seems that these problems have never been approached in the literature.
Digital Challenge to Education

Technological innovations, especially in the field of digital technologies, undoubtedly occupy a leading place among the factors that have the greatest impact on life and education in the 21st century. Thanks to them, the model of education and management of education at its various levels, the characteristics of students and their attitudes to learning, the educational environment and the learning process (teaching methodology), teachers and the requirements for their competencies are changing. All these changes are a serious challenge to education and its subjects, provoked by digital technologies. The author's aim is to comment on how and to what extent education can meet this challenge.
Estimation of White Blood Cells using Convolutional Neural Network

Normally blood samples contain red blood cells, white blood cells and platelets. White blood cells are also called as leukocytes and they are the cells of immune system. The measure of White Blood Cells is so important for the doctors in diagnosing various diseases like leukemia or tissue damage etc. So, counting of White Blood Cells plays an important role. The manual counting of White Blood Cells in medical laboratories involves a device called Haemocytometer. But this process is extremely monotonous, time consuming, and leads to inaccurate results. In this work, image processing and deep learning mechanisms are used to locate and classify the White Blood Cells based on their categories. The White Blood Cells which are classified are counted and compared with the standard range of the types available in the human blood sample. By comparing the availability of White Blood Cells types, the normal and the abnormal blood samples are predicted accordingly. The dataset of the normal blood sample is obtained from the laboratory in biotechnology department and the datasets used for training in Convolutional Neural Network are attained from the website Leukocyte Images for Segmentation and Classification (LISC). This will increase efficiency and reduce the doctor’s burden as traditional manual counting is dull, tedious, and possibly subjective.
Research on Measures to Improve the Employment Competitiveness of Students Majoring in Power Based on the PDCA Program

Taking a college as an example, this paper expounds on how to solve the problem of low competitiveness of power students in employment based on PDCA program and new energy simulation technology platform. In this paper, the fishbone diagram is firstly used to find out the main reasons for the low employment competitiveness of the students majoring in power in this college. Then, countermeasures are put forward for the main reasons, such as adding waste incineration power generation simulation unit and biomass power generation simulation unit. Finally, after participating in the online National Power generation technical skills competition and vocational skills grade certificate examination, the goal of increasing the number of certificates has been realized, and the way to improve the employment competitiveness of student majoring in power has been found.
Enormous Information Examination using Big Data in a Distributed Environment with Profound Learning of Next Generation Interruption Identification Framework Enhancement

With the developing utilization of data innovation in all life areas, hacking has turned out to be more contrarily powerful than any other time in recent memory. Additionally, with creating advances, assaults numbers are developing exponentially like clockwork and become progressively refined so conventional I.D.S ends up wasteful recognizing them. We accomplish those outcomes by utilizing Networking Chabot, a profound intermittent neural system: Long Short Term Memory (L.S.T.M) [2]over Apache Spark Framework that has a contribution of stream traffic and traffic conglomeration and the yield is a language of two words, typical or strange. The new and proposed blending ideas of the language are preparing, relevant examination, circulated profound adapting, huge information, and oddity discovery of stream investigation. We propose a model that portrays the system dynamic typical conduct from an arrangement of a great many parcels inside their unique circumstance and examines them in close to constant to identify point, aggregate and relevant inconsistencies. The examination shows lower false positive, higher identification rate and better point abnormalities location. With respect to demonstrate of relevant and aggregate oddities identification, we talk about our case and the explanation for our speculation. Be that as it may, the investigation is done on arbitrary little subsets of the dataset as a result of equipment restrictions, so we offer examination and our future vision musings as we wish that full demonstrate will be done in future by other intrigued specialists who have preferable equipment foundation over our own..